From b784537210802faf0b46cb64fccb5661ea6f4847 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 26 Nov 2023 19:37:50 +0800 Subject: [PATCH 01/28] Divide cnn into three projects --- open-machine-learning-jupyter-book/_toc.yml | 2 + .../deep-learning/cnn-deepdream.ipynb | 321 +++++++++++ .../deep-learning/cnn-vgg.ipynb | 465 +++++++++++++++ .../deep-learning/cnn.ipynb | 542 +----------------- 4 files changed, 789 insertions(+), 541 deletions(-) create mode 100644 open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb create mode 100644 open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index dd0d11fe2a..aa14ed02e1 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -90,6 +90,8 @@ parts: chapters: - file: deep-learning/dl-overview - file: deep-learning/cnn + - file: deep-learning/cnn-vgg + - file: deep-learning/cnn-deepdream - file: deep-learning/gan.md - file: deep-learning/rnn.ipynb - file: deep-learning/autoencoder.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb new file mode 100644 index 0000000000..2643f32084 --- /dev/null +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -0,0 +1,321 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "# Install the necessary dependencies\n", + "\n", + "import os\n", + "import sys \n", + "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests\n", + "# Convolutional Neural Networks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "---\n", + "license:\n", + " code: MIT\n", + " content: CC-BY-4.0\n", + "github: https://github.com/ocademy-ai/machine-learning\n", + "venue: By Ocademy\n", + "open_access: true\n", + "bibliography:\n", + " - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deepdream in TensorFlow\n", + "Note: There is no new code in this script. It originates from the TensorFlow tutorial located here. However, this code is modified slightly to run on Python 3. The code is also commented very heavily to explain, line-by-line, what occurs in the deepdream demo.\n", + "\n", + "Here are some potential outputs.\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/06_deepdream_ex.png\n", + "name: Deepdream outputs\n", + ":::" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using TensorFlow for Deep Dream\n", + "\n", + "From: Alexander Mordvintsev\n", + "https://www.tensorflow.org/tutorials/generative/deepdream" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "import numpy as np\n", + "import matplotlib as mpl\n", + "import IPython.display as display\n", + "import PIL.Image" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download an image and read it into a NumPy array.\n", + "def download(url, max_dim=None):\n", + " name = url.split('/')[-1]\n", + " image_path = tf.keras.utils.get_file(name, origin=url)\n", + " img = PIL.Image.open(image_path)\n", + " if max_dim:\n", + " img.thumbnail((max_dim, max_dim))\n", + " return np.array(img)\n", + "\n", + "# Normalize an image\n", + "def deprocess(img):\n", + " img = 255*(img + 1.0)/2.0\n", + " return tf.cast(img, tf.uint8)\n", + "\n", + "# Display an image\n", + "def show(img):\n", + " display.display(PIL.Image.fromarray(np.array(img)))\n", + "\n", + "\n", + "# Downsizing the image makes it easier to work with.\n", + "original_img = download(url, max_dim=500)\n", + "show(original_img)\n", + "display.display(display.HTML('Image cc-by: Von.grzanka'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "base_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Maximize the activations of these layers\n", + "names = ['mixed3', 'mixed5']\n", + "layers = [base_model.get_layer(name).output for name in names]\n", + "\n", + "# Create the feature extraction model\n", + "dream_model = tf.keras.Model(inputs=base_model.input, outputs=layers)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_loss(img, model):\n", + " # Pass forward the image through the model to retrieve the activations.\n", + " # Converts the image into a batch of size 1.\n", + " img_batch = tf.expand_dims(img, axis=0)\n", + " layer_activations = model(img_batch)\n", + " if len(layer_activations) == 1:\n", + " layer_activations = [layer_activations]\n", + "\n", + " losses = []\n", + " for act in layer_activations:\n", + " loss = tf.math.reduce_mean(act)\n", + " losses.append(loss)\n", + "\n", + " return tf.reduce_sum(losses)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class DeepDream(tf.Module):\n", + " def __init__(self, model):\n", + " self.model = model\n", + "\n", + " @tf.function(\n", + " input_signature=(\n", + " tf.TensorSpec(shape=[None,None,3], dtype=tf.float32),\n", + " tf.TensorSpec(shape=[], dtype=tf.int32),\n", + " tf.TensorSpec(shape=[], dtype=tf.float32),)\n", + " )\n", + " def __call__(self, img, steps, step_size):\n", + " print(\"Tracing\")\n", + " loss = tf.constant(0.0)\n", + " for n in tf.range(steps):\n", + " with tf.GradientTape() as tape:\n", + " # This needs gradients relative to `img`\n", + " # `GradientTape` only watches `tf.Variable`s by default\n", + " tape.watch(img)\n", + " loss = calc_loss(img, self.model)\n", + "\n", + " # Calculate the gradient of the loss with respect to the pixels of the input image.\n", + " gradients = tape.gradient(loss, img)\n", + "\n", + " # Normalize the gradients.\n", + " gradients /= tf.math.reduce_std(gradients) + 1e-8 \n", + " \n", + " # In gradient ascent, the \"loss\" is maximized so that the input image increasingly \"excites\" the layers.\n", + " # You can update the image by directly adding the gradients (because they're the same shape!)\n", + " img = img + gradients*step_size\n", + " img = tf.clip_by_value(img, -1, 1)\n", + "\n", + " return loss, img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "deepdream = DeepDream(dream_model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run_deep_dream_simple(img, steps=100, step_size=0.01):\n", + " # Convert from uint8 to the range expected by the model.\n", + " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", + " img = tf.convert_to_tensor(img)\n", + " step_size = tf.convert_to_tensor(step_size)\n", + " steps_remaining = steps\n", + " step = 0\n", + " while steps_remaining:\n", + " if steps_remaining>100:\n", + " run_steps = tf.constant(100)\n", + " else:\n", + " run_steps = tf.constant(steps_remaining)\n", + " steps_remaining -= run_steps\n", + " step += run_steps\n", + "\n", + " loss, img = deepdream(img, run_steps, tf.constant(step_size))\n", + " \n", + " display.clear_output(wait=True)\n", + " show(deprocess(img))\n", + " print (\"Step {}, loss {}\".format(step, loss))\n", + "\n", + "\n", + " result = deprocess(img)\n", + " display.clear_output(wait=True)\n", + " show(result)\n", + "\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dream_img = run_deep_dream_simple(img=original_img, \n", + " steps=100, step_size=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "start = time.time()\n", + "\n", + "OCTAVE_SCALE = 1.30\n", + "\n", + "img = tf.constant(np.array(original_img))\n", + "base_shape = tf.shape(img)[:-1]\n", + "float_base_shape = tf.cast(base_shape, tf.float32)\n", + "\n", + "for n in range(-2, 3):\n", + " new_shape = tf.cast(float_base_shape*(OCTAVE_SCALE**n), tf.int32)\n", + "\n", + " img = tf.image.resize(img, new_shape).numpy()\n", + "\n", + " img = run_deep_dream_simple(img=img, steps=50, step_size=0.01)\n", + "\n", + "display.clear_output(wait=True)\n", + "img = tf.image.resize(img, base_shape)\n", + "img = tf.image.convert_image_dtype(img/255.0, dtype=tf.uint8)\n", + "show(img)\n", + "\n", + "end = time.time()\n", + "end-start" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your turn! 🚀\n", + "\n", + "TBD." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgments\n", + "\n", + "Thanks to [TensorFlow](https://www.tensorflow.org/) for creating the open source project [DeepDream](https://www.tensorflow.org/tutorials/generative/deepdream). It inspires the majority of the content in this chapter.\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb new file mode 100644 index 0000000000..77e0957a37 --- /dev/null +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "# Install the necessary dependencies\n", + "\n", + "import os\n", + "import sys \n", + "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests\n", + "# Convolutional Neural Networks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "---\n", + "license:\n", + " code: MIT\n", + " content: CC-BY-4.0\n", + "github: https://github.com/ocademy-ai/machine-learning\n", + "venue: By Ocademy\n", + "open_access: true\n", + "bibliography:\n", + " - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stylenet / Neural-Style\n", + "\n", + "The purpose of this script is to illustrate how to do stylenet in TensorFlow. We reference the following [paper](https://arxiv.org/abs/1508.06576) for this algorithm.\n", + "\n", + "But there is some prerequisites,\n", + "\n", + "- Download the `VGG-verydeep-19.mat` file.\n", + "- You must download two images, a style image and a content image for the algorithm to blend.\n", + "\n", + "The style image is\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg\n", + "name: Style image starry night\n", + ":::\n", + "\n", + "The context image is below.\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg\n", + "name: Content image book cover\n", + ":::\n", + "\n", + "The final result looks like\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png\n", + "name: stylenet final result\n", + ":::\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use two images, an original image and a style image and try to make the original image in the style of the style image.\n", + "\n", + "Reference paper:https://arxiv.org/abs/1508.06576\n", + "\n", + "Need to download the model 'imagenet-vgg-verydee-19.mat' from: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import scipy.io\n", + "import scipy.misc\n", + "import imageio\n", + "from skimage.transform import resize\n", + "from operator import mul\n", + "from functools import reduce\n", + "from PIL import Image\n", + "import numpy as np\n", + "import requests\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_eager_execution() #This is tensorflow 1.x version code. Some of them are not fit tensorflow 2.x.\n", + "from tensorflow.python.framework import ops\n", + "ops.reset_default_graph()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# URLs\n", + "original_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg'\n", + "style_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg'\n", + "vgg_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/imagenet-vgg-verydeep-19.mat'\n", + "\n", + "# Local directories\n", + "data_dir = 'temp'\n", + "vgg_dir = os.path.join(data_dir, 'VGG')\n", + "if not os.path.exists(vgg_dir):\n", + " os.makedirs(vgg_dir)\n", + "\n", + "# Function to download and save a file\n", + "def download_file(url, directory):\n", + " response = requests.get(url)\n", + " filename = url.split('/')[-1]\n", + " filepath = os.path.join(directory, filename)\n", + " with open(filepath, 'wb') as f:\n", + " f.write(response.content)\n", + " return filepath\n", + "\n", + "# Download images and VGG Network\n", + "original_image_path = download_file(original_image_url, data_dir)\n", + "style_image_path = download_file(style_image_url, data_dir)\n", + "vgg_path = download_file(vgg_url, vgg_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data and set default arguments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load images using PIL and convert to NumPy arrays\n", + "original_image = Image.open(original_image_path)\n", + "style_image = Image.open(style_image_path)\n", + "original_image = np.array(original_image)\n", + "style_image = np.array(style_image)\n", + "\n", + "# Default Arguments\n", + "original_image_weight = 5.0\n", + "style_image_weight = 500.0\n", + "regularization_weight = 100\n", + "learning_rate = 10\n", + "generations = 100\n", + "output_generations = 25\n", + "beta1 = 0.9\n", + "beta2 = 0.999" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Style Transfer Implementation using VGG-19 Network\n", + "\n", + "Now setting VGG-19." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get shape of target and make the style image the same\n", + "target_shape = original_image.shape\n", + "style_image = resize(style_image, target_shape)\n", + "\n", + "# VGG-19 Layer Setup\n", + "# From paper\n", + "vgg_layers = ['conv1_1', 'relu1_1',\n", + " 'conv1_2', 'relu1_2', 'pool1',\n", + " 'conv2_1', 'relu2_1',\n", + " 'conv2_2', 'relu2_2', 'pool2',\n", + " 'conv3_1', 'relu3_1',\n", + " 'conv3_2', 'relu3_2',\n", + " 'conv3_3', 'relu3_3',\n", + " 'conv3_4', 'relu3_4', 'pool3',\n", + " 'conv4_1', 'relu4_1',\n", + " 'conv4_2', 'relu4_2',\n", + " 'conv4_3', 'relu4_3',\n", + " 'conv4_4', 'relu4_4', 'pool4',\n", + " 'conv5_1', 'relu5_1',\n", + " 'conv5_2', 'relu5_2',\n", + " 'conv5_3', 'relu5_3',\n", + " 'conv5_4', 'relu5_4']\n", + "\n", + "\n", + "# Extract weights and matrix means\n", + "def extract_net_info(path_to_params):\n", + " vgg_data = scipy.io.loadmat(path_to_params)\n", + " normalization_matrix = vgg_data['normalization'][0][0][0]\n", + " mat_mean = np.mean(normalization_matrix, axis=(0, 1))\n", + " network_weights = vgg_data['layers'][0]\n", + " return mat_mean, network_weights\n", + " \n", + "\n", + "# Create the VGG-19 Network\n", + "def vgg_network(network_weights, init_image):\n", + " network = {}\n", + " image = init_image\n", + "\n", + " for i, layer in enumerate(vgg_layers):\n", + " if layer[0] == 'c':\n", + " weights, bias = network_weights[i][0][0][0][0]\n", + " weights = np.transpose(weights, (1, 0, 2, 3))\n", + " bias = bias.reshape(-1)\n", + " conv_layer = tf.nn.conv2d(image, tf.constant(weights), (1, 1, 1, 1), 'SAME')\n", + " image = tf.nn.bias_add(conv_layer, bias)\n", + " elif layer[0] == 'r':\n", + " image = tf.nn.relu(image)\n", + " else: # pooling\n", + " image = tf.nn.max_pool(image, (1, 2, 2, 1), (1, 2, 2, 1), 'SAME')\n", + " network[layer] = image\n", + " return network\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define which layers apply to the original or style image and get network parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "original_layers = ['relu4_2', 'relu5_2']\n", + "style_layers = ['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1']\n", + "\n", + "normalization_mean, network_weights = extract_net_info(vgg_path)\n", + "\n", + "shape = (1,) + original_image.shape\n", + "style_shape = (1,) + style_image.shape\n", + "original_features = {}\n", + "style_features = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set style weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "style_weights = {l: 1./(len(style_layers)) for l in style_layers}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computer feature layers with original image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g_original = tf.Graph()\n", + "with g_original.as_default(), tf.Session() as sess1:\n", + " image = tf.placeholder('float', shape=shape)\n", + " vgg_net = vgg_network(network_weights, image)\n", + " original_minus_mean = original_image - normalization_mean\n", + " original_norm = np.array([original_minus_mean])\n", + " for layer in original_layers:\n", + " original_features[layer] = vgg_net[layer].eval(feed_dict={image: original_norm})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get style image network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g_style = tf.Graph()\n", + "with g_style.as_default(), tf.Session() as sess2:\n", + " image = tf.placeholder('float', shape=style_shape)\n", + " vgg_net = vgg_network(network_weights, image)\n", + " style_minus_mean = style_image - normalization_mean\n", + " style_norm = np.array([style_minus_mean])\n", + " for layer in style_layers:\n", + " features = vgg_net[layer].eval(feed_dict={image: style_norm})\n", + " features = np.reshape(features, (-1, features.shape[3]))\n", + " gram = np.matmul(features.T, features) / features.size\n", + " style_features[layer] = gram\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make Combined Image via loss function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Get network parameters\n", + " initial = tf.random_normal(shape) * 0.256\n", + " init_image = tf.Variable(initial)\n", + " vgg_net = vgg_network(network_weights, init_image)\n", + "\n", + " # Loss from Original Image\n", + " original_layers_w = {'relu4_2': 0.5, 'relu5_2': 0.5}\n", + " original_loss = 0\n", + " for o_layer in original_layers:\n", + " temp_original_loss = original_layers_w[o_layer] * original_image_weight *\\\n", + " (2 * tf.nn.l2_loss(vgg_net[o_layer] - original_features[o_layer]))\n", + " original_loss += (temp_original_loss / original_features[o_layer].size)\n", + "\n", + " # Loss from Style Image\n", + " style_loss = 0\n", + " style_losses = []\n", + " for style_layer in style_layers:\n", + " layer = vgg_net[style_layer]\n", + " feats, height, width, channels = [x.value for x in layer.get_shape()]\n", + " size = height * width * channels\n", + " features = tf.reshape(layer, (-1, channels))\n", + " style_gram_matrix = tf.matmul(tf.transpose(features), features) / size\n", + " style_expected = style_features[style_layer]\n", + " style_losses.append(style_weights[style_layer] * 2 *\n", + " tf.nn.l2_loss(style_gram_matrix - style_expected) /\n", + " style_expected.size)\n", + " style_loss += style_image_weight * tf.reduce_sum(style_losses)\n", + "\n", + " # To Smooth the results, we add in total variation loss\n", + " total_var_x = reduce(mul, init_image[:, 1:, :, :].get_shape().as_list(), 1)\n", + " total_var_y = reduce(mul, init_image[:, :, 1:, :].get_shape().as_list(), 1)\n", + " first_term = regularization_weight * 2\n", + " second_term_numerator = tf.nn.l2_loss(init_image[:, 1:, :, :] - init_image[:, :shape[1]-1, :, :])\n", + " second_term = second_term_numerator / total_var_y\n", + " third_term = (tf.nn.l2_loss(init_image[:, :, 1:, :] - init_image[:, :, :shape[2]-1, :]) / total_var_x)\n", + " total_variation_loss = first_term * (second_term + third_term)\n", + "\n", + " # Combined Loss\n", + " loss = original_loss + style_loss + total_variation_loss\n", + "\n", + " # Declare Optimization Algorithm\n", + " optimizer = tf.train.AdamOptimizer(learning_rate, beta1, beta2)\n", + " train_step = optimizer.minimize(loss)\n", + "\n", + " # Initialize variables and start training\n", + " with tf.Session() as sess:\n", + " tf.global_variables_initializer().run()\n", + " for i in range(generations):\n", + "\n", + " train_step.run()\n", + "\n", + " # Print update and save temporary output\n", + " if (i+1) % output_generations == 0:\n", + " print('Generation {} out of {}, loss: {}'.format(i + 1, generations,sess.run(loss)))\n", + "\n", + " image_eval = init_image.eval(session=sess)\n", + " image_eval = image_eval.reshape(shape[1:]) # Make sure form is right\n", + " image_eval += normalization_mean # Add avg\n", + " image_eval = np.clip(image_eval, 0, 255) # Make sure value between 0 and 255\n", + " image_eval = image_eval.astype(np.uint8) # Transform to uint8\n", + "\n", + "# Save image\n", + "output_file = 'temp_output_{}.jpg'.format(i)\n", + "imageio.imwrite(output_file, image_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your turn! 🚀\n", + "\n", + "TBD." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Self study\n", + "\n", + "You can refer to those YouTube videos for further study:\n", + "\n", + "- [Convolutional Neural Networks (CNNs) explained, by deeplizard](https://www.youtube.com/watch?v=YRhxdVk_sIs)\n", + "- [Convolutional Neural Networks Explained (CNN Visualized), by Futurology](https://www.youtube.com/watch?v=pj9-rr1wDhM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgments\n", + "\n", + "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). It inspires the majority of the content in this chapter.\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb index b56a91363c..2b06cc2962 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb @@ -598,546 +598,6 @@ " labels_file.write(\"{}\\n\".format(item))" ] }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Stylenet / Neural-Style\n", - "\n", - "The purpose of this script is to illustrate how to do stylenet in TensorFlow. We reference the following [paper](https://arxiv.org/abs/1508.06576) for this algorithm.\n", - "\n", - "But there is some prerequisites,\n", - "\n", - "- Download the `VGG-verydeep-19.mat` file.\n", - "- You must download two images, a style image and a content image for the algorithm to blend.\n", - "\n", - "The style image is\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg\n", - "name: Style image starry night\n", - ":::\n", - "\n", - "The context image is below.\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg\n", - "name: Content image book cover\n", - ":::\n", - "\n", - "The final result looks like\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png\n", - "name: stylenet final result\n", - ":::\n" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# We use two images, an original image and a style image\n", - "# and try to make the original image in the style of the style image.\n", - "#\n", - "# Reference paper:\n", - "# https://arxiv.org/abs/1508.06576\n", - "#\n", - "# Need to download the model 'imagenet-vgg-verydee-19.mat' from:\n", - "# http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat\n", - "\n", - "import os\n", - "import scipy.io\n", - "import scipy.misc\n", - "import imageio\n", - "from skimage.transform import resize\n", - "from operator import mul\n", - "from functools import reduce\n", - "from PIL import Image\n", - "import numpy as np\n", - "import requests\n", - "import tensorflow.compat.v1 as tf\n", - "tf.disable_eager_execution()\n", - "from tensorflow.python.framework import ops\n", - "ops.reset_default_graph()\n", - "\n", - "# URLs\n", - "original_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg'\n", - "style_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg'\n", - "vgg_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/imagenet-vgg-verydeep-19.mat'\n", - "\n", - "# Local directories\n", - "data_dir = 'temp'\n", - "vgg_dir = os.path.join(data_dir, 'VGG')\n", - "if not os.path.exists(vgg_dir):\n", - " os.makedirs(vgg_dir)\n", - "\n", - "# Function to download and save a file\n", - "def download_file(url, directory):\n", - " response = requests.get(url)\n", - " filename = url.split('/')[-1]\n", - " filepath = os.path.join(directory, filename)\n", - " with open(filepath, 'wb') as f:\n", - " f.write(response.content)\n", - " return filepath\n", - "\n", - "# Download images and VGG Network\n", - "original_image_path = download_file(original_image_url, data_dir)\n", - "style_image_path = download_file(style_image_url, data_dir)\n", - "vgg_path = download_file(vgg_url, vgg_dir)\n", - "\n", - "# Load images using PIL and convert to NumPy arrays\n", - "original_image = Image.open(original_image_path)\n", - "style_image = Image.open(style_image_path)\n", - "original_image = np.array(original_image)\n", - "style_image = np.array(style_image)\n", - "\n", - "# Default Arguments\n", - "original_image_weight = 5.0\n", - "style_image_weight = 500.0\n", - "regularization_weight = 100\n", - "learning_rate = 10\n", - "generations = 100\n", - "output_generations = 25\n", - "beta1 = 0.9\n", - "beta2 = 0.999\n", - "\n", - "# Get shape of target and make the style image the same\n", - "target_shape = original_image.shape\n", - "style_image = resize(style_image, target_shape)\n", - "\n", - "# VGG-19 Layer Setup\n", - "# From paper\n", - "vgg_layers = ['conv1_1', 'relu1_1',\n", - " 'conv1_2', 'relu1_2', 'pool1',\n", - " 'conv2_1', 'relu2_1',\n", - " 'conv2_2', 'relu2_2', 'pool2',\n", - " 'conv3_1', 'relu3_1',\n", - " 'conv3_2', 'relu3_2',\n", - " 'conv3_3', 'relu3_3',\n", - " 'conv3_4', 'relu3_4', 'pool3',\n", - " 'conv4_1', 'relu4_1',\n", - " 'conv4_2', 'relu4_2',\n", - " 'conv4_3', 'relu4_3',\n", - " 'conv4_4', 'relu4_4', 'pool4',\n", - " 'conv5_1', 'relu5_1',\n", - " 'conv5_2', 'relu5_2',\n", - " 'conv5_3', 'relu5_3',\n", - " 'conv5_4', 'relu5_4']\n", - "\n", - "\n", - "# Extract weights and matrix means\n", - "def extract_net_info(path_to_params):\n", - " vgg_data = scipy.io.loadmat(path_to_params)\n", - " normalization_matrix = vgg_data['normalization'][0][0][0]\n", - " mat_mean = np.mean(normalization_matrix, axis=(0, 1))\n", - " network_weights = vgg_data['layers'][0]\n", - " return mat_mean, network_weights\n", - " \n", - "\n", - "# Create the VGG-19 Network\n", - "def vgg_network(network_weights, init_image):\n", - " network = {}\n", - " image = init_image\n", - "\n", - " for i, layer in enumerate(vgg_layers):\n", - " if layer[0] == 'c':\n", - " weights, bias = network_weights[i][0][0][0][0]\n", - " weights = np.transpose(weights, (1, 0, 2, 3))\n", - " bias = bias.reshape(-1)\n", - " conv_layer = tf.nn.conv2d(image, tf.constant(weights), (1, 1, 1, 1), 'SAME')\n", - " image = tf.nn.bias_add(conv_layer, bias)\n", - " elif layer[0] == 'r':\n", - " image = tf.nn.relu(image)\n", - " else: # pooling\n", - " image = tf.nn.max_pool(image, (1, 2, 2, 1), (1, 2, 2, 1), 'SAME')\n", - " network[layer] = image\n", - " return network\n", - "\n", - "# Here we define which layers apply to the original or style image\n", - "original_layers = ['relu4_2', 'relu5_2']\n", - "style_layers = ['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1']\n", - "\n", - "# Get network parameters\n", - "normalization_mean, network_weights = extract_net_info(vgg_path)\n", - "\n", - "shape = (1,) + original_image.shape\n", - "style_shape = (1,) + style_image.shape\n", - "original_features = {}\n", - "style_features = {}\n", - "\n", - "# Set style weights\n", - "style_weights = {l: 1./(len(style_layers)) for l in style_layers}\n", - "\n", - "# Computer feature layers with original image\n", - "g_original = tf.Graph()\n", - "with g_original.as_default(), tf.Session() as sess1:\n", - " image = tf.placeholder('float', shape=shape)\n", - " vgg_net = vgg_network(network_weights, image)\n", - " original_minus_mean = original_image - normalization_mean\n", - " original_norm = np.array([original_minus_mean])\n", - " for layer in original_layers:\n", - " original_features[layer] = vgg_net[layer].eval(feed_dict={image: original_norm})\n", - "\n", - "# Get style image network\n", - "g_style = tf.Graph()\n", - "with g_style.as_default(), tf.Session() as sess2:\n", - " image = tf.placeholder('float', shape=style_shape)\n", - " vgg_net = vgg_network(network_weights, image)\n", - " style_minus_mean = style_image - normalization_mean\n", - " style_norm = np.array([style_minus_mean])\n", - " for layer in style_layers:\n", - " features = vgg_net[layer].eval(feed_dict={image: style_norm})\n", - " features = np.reshape(features, (-1, features.shape[3]))\n", - " gram = np.matmul(features.T, features) / features.size\n", - " style_features[layer] = gram\n", - "\n", - "# Make Combined Image via loss function\n", - "with tf.Graph().as_default():\n", - " # Get network parameters\n", - " initial = tf.random_normal(shape) * 0.256\n", - " init_image = tf.Variable(initial)\n", - " vgg_net = vgg_network(network_weights, init_image)\n", - "\n", - " # Loss from Original Image\n", - " original_layers_w = {'relu4_2': 0.5, 'relu5_2': 0.5}\n", - " original_loss = 0\n", - " for o_layer in original_layers:\n", - " temp_original_loss = original_layers_w[o_layer] * original_image_weight *\\\n", - " (2 * tf.nn.l2_loss(vgg_net[o_layer] - original_features[o_layer]))\n", - " original_loss += (temp_original_loss / original_features[o_layer].size)\n", - "\n", - " # Loss from Style Image\n", - " style_loss = 0\n", - " style_losses = []\n", - " for style_layer in style_layers:\n", - " layer = vgg_net[style_layer]\n", - " feats, height, width, channels = [x.value for x in layer.get_shape()]\n", - " size = height * width * channels\n", - " features = tf.reshape(layer, (-1, channels))\n", - " style_gram_matrix = tf.matmul(tf.transpose(features), features) / size\n", - " style_expected = style_features[style_layer]\n", - " style_losses.append(style_weights[style_layer] * 2 *\n", - " tf.nn.l2_loss(style_gram_matrix - style_expected) /\n", - " style_expected.size)\n", - " style_loss += style_image_weight * tf.reduce_sum(style_losses)\n", - "\n", - " # To Smooth the results, we add in total variation loss\n", - " total_var_x = reduce(mul, init_image[:, 1:, :, :].get_shape().as_list(), 1)\n", - " total_var_y = reduce(mul, init_image[:, :, 1:, :].get_shape().as_list(), 1)\n", - " first_term = regularization_weight * 2\n", - " second_term_numerator = tf.nn.l2_loss(init_image[:, 1:, :, :] - init_image[:, :shape[1]-1, :, :])\n", - " second_term = second_term_numerator / total_var_y\n", - " third_term = (tf.nn.l2_loss(init_image[:, :, 1:, :] - init_image[:, :, :shape[2]-1, :]) / total_var_x)\n", - " total_variation_loss = first_term * (second_term + third_term)\n", - "\n", - " # Combined Loss\n", - " loss = original_loss + style_loss + total_variation_loss\n", - "\n", - " # Declare Optimization Algorithm\n", - " optimizer = tf.train.AdamOptimizer(learning_rate, beta1, beta2)\n", - " train_step = optimizer.minimize(loss)\n", - "\n", - " # Initialize variables and start training\n", - " with tf.Session() as sess:\n", - " tf.global_variables_initializer().run()\n", - " for i in range(generations):\n", - "\n", - " train_step.run()\n", - "\n", - " # Print update and save temporary output\n", - " if (i+1) % output_generations == 0:\n", - " print('Generation {} out of {}, loss: {}'.format(i + 1, generations,sess.run(loss)))\n", - "\n", - " image_eval = init_image.eval(session=sess)\n", - " image_eval = image_eval.reshape(shape[1:]) # 确保形状正确\n", - " image_eval += normalization_mean # 加上均值\n", - " image_eval = np.clip(image_eval, 0, 255) # 确保值在0到255之间\n", - " image_eval = image_eval.astype(np.uint8) # 转换为uint8\n", - "\n", - "# 保存图像\n", - "output_file = 'temp_output_{}.jpg'.format(i)\n", - "imageio.imwrite(output_file, image_eval)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deepdream in TensorFlow\n", - "Note: There is no new code in this script. It originates from the TensorFlow tutorial located here. However, this code is modified slightly to run on Python 3. The code is also commented very heavily to explain, line-by-line, what occurs in the deepdream demo.\n", - "\n", - "Here are some potential outputs.\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/06_deepdream_ex.png\n", - "name: Deepdream outputs\n", - ":::" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Code" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Using TensorFlow for Deep Dream\n", - "#---------------------------------------\n", - "# From: Alexander Mordvintsev\n", - "# --https://www.tensorflow.org/tutorials/generative/deepdream\n", - "#\n", - "# And as this code use Tensorflow 2.x, you may need to restart the kernel to run successfully." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import tensorflow as tf" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "import matplotlib as mpl\n", - "\n", - "import IPython.display as display\n", - "import PIL.Image" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "url = 'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Download an image and read it into a NumPy array.\n", - "def download(url, max_dim=None):\n", - " name = url.split('/')[-1]\n", - " image_path = tf.keras.utils.get_file(name, origin=url)\n", - " img = PIL.Image.open(image_path)\n", - " if max_dim:\n", - " img.thumbnail((max_dim, max_dim))\n", - " return np.array(img)\n", - "\n", - "# Normalize an image\n", - "def deprocess(img):\n", - " img = 255*(img + 1.0)/2.0\n", - " return tf.cast(img, tf.uint8)\n", - "\n", - "# Display an image\n", - "def show(img):\n", - " display.display(PIL.Image.fromarray(np.array(img)))\n", - "\n", - "\n", - "# Downsizing the image makes it easier to work with.\n", - "original_img = download(url, max_dim=500)\n", - "show(original_img)\n", - "display.display(display.HTML('Image cc-by: Von.grzanka'))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "base_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Maximize the activations of these layers\n", - "names = ['mixed3', 'mixed5']\n", - "layers = [base_model.get_layer(name).output for name in names]\n", - "\n", - "# Create the feature extraction model\n", - "dream_model = tf.keras.Model(inputs=base_model.input, outputs=layers)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_loss(img, model):\n", - " # Pass forward the image through the model to retrieve the activations.\n", - " # Converts the image into a batch of size 1.\n", - " img_batch = tf.expand_dims(img, axis=0)\n", - " layer_activations = model(img_batch)\n", - " if len(layer_activations) == 1:\n", - " layer_activations = [layer_activations]\n", - "\n", - " losses = []\n", - " for act in layer_activations:\n", - " loss = tf.math.reduce_mean(act)\n", - " losses.append(loss)\n", - "\n", - " return tf.reduce_sum(losses)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class DeepDream(tf.Module):\n", - " def __init__(self, model):\n", - " self.model = model\n", - "\n", - " @tf.function(\n", - " input_signature=(\n", - " tf.TensorSpec(shape=[None,None,3], dtype=tf.float32),\n", - " tf.TensorSpec(shape=[], dtype=tf.int32),\n", - " tf.TensorSpec(shape=[], dtype=tf.float32),)\n", - " )\n", - " def __call__(self, img, steps, step_size):\n", - " print(\"Tracing\")\n", - " loss = tf.constant(0.0)\n", - " for n in tf.range(steps):\n", - " with tf.GradientTape() as tape:\n", - " # This needs gradients relative to `img`\n", - " # `GradientTape` only watches `tf.Variable`s by default\n", - " tape.watch(img)\n", - " loss = calc_loss(img, self.model)\n", - "\n", - " # Calculate the gradient of the loss with respect to the pixels of the input image.\n", - " gradients = tape.gradient(loss, img)\n", - "\n", - " # Normalize the gradients.\n", - " gradients /= tf.math.reduce_std(gradients) + 1e-8 \n", - " \n", - " # In gradient ascent, the \"loss\" is maximized so that the input image increasingly \"excites\" the layers.\n", - " # You can update the image by directly adding the gradients (because they're the same shape!)\n", - " img = img + gradients*step_size\n", - " img = tf.clip_by_value(img, -1, 1)\n", - "\n", - " return loss, img" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deepdream = DeepDream(dream_model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def run_deep_dream_simple(img, steps=100, step_size=0.01):\n", - " # Convert from uint8 to the range expected by the model.\n", - " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", - " img = tf.convert_to_tensor(img)\n", - " step_size = tf.convert_to_tensor(step_size)\n", - " steps_remaining = steps\n", - " step = 0\n", - " while steps_remaining:\n", - " if steps_remaining>100:\n", - " run_steps = tf.constant(100)\n", - " else:\n", - " run_steps = tf.constant(steps_remaining)\n", - " steps_remaining -= run_steps\n", - " step += run_steps\n", - "\n", - " loss, img = deepdream(img, run_steps, tf.constant(step_size))\n", - " \n", - " display.clear_output(wait=True)\n", - " show(deprocess(img))\n", - " print (\"Step {}, loss {}\".format(step, loss))\n", - "\n", - "\n", - " result = deprocess(img)\n", - " display.clear_output(wait=True)\n", - " show(result)\n", - "\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dream_img = run_deep_dream_simple(img=original_img, \n", - " steps=100, step_size=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "start = time.time()\n", - "\n", - "OCTAVE_SCALE = 1.30\n", - "\n", - "img = tf.constant(np.array(original_img))\n", - "base_shape = tf.shape(img)[:-1]\n", - "float_base_shape = tf.cast(base_shape, tf.float32)\n", - "\n", - "for n in range(-2, 3):\n", - " new_shape = tf.cast(float_base_shape*(OCTAVE_SCALE**n), tf.int32)\n", - "\n", - " img = tf.image.resize(img, new_shape).numpy()\n", - "\n", - " img = run_deep_dream_simple(img=img, steps=50, step_size=0.01)\n", - "\n", - "display.clear_output(wait=True)\n", - "img = tf.image.resize(img, base_shape)\n", - "img = tf.image.convert_image_dtype(img/255.0, dtype=tf.uint8)\n", - "show(img)\n", - "\n", - "end = time.time()\n", - "end-start" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -1182,7 +642,7 @@ "source": [ "## Acknowledgments\n", "\n", - "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). And thanks to [TensorFlow](https://www.tensorflow.org/) for creating the open source project [DeepDream](https://www.tensorflow.org/tutorials/generative/deepdream) It inspires the majority of the content in this chapter.\n" + "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). It inspires the majority of the content in this chapter.\n" ] } ], From 7467ba99db5d0fb4ea77110dd12c480f5ef720e2 Mon Sep 17 00:00:00 2001 From: Xu Senbo <1170676717@qq.com> Date: Sun, 26 Nov 2023 22:51:29 +0800 Subject: [PATCH 02/28] Try fix error by add package --- .../deep-learning/cnn-deepdream.ipynb | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb index 2643f32084..10756a029f 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -14,7 +14,7 @@ "\n", "import os\n", "import sys \n", - "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests\n", + "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests pillow\n", "# Convolutional Neural Networks" ] }, @@ -312,8 +312,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "open-machine-learning-jupyter-book", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.9.13" } }, "nbformat": 4, From 4e45b68a9ac71b0cbb1287725906a4ee8be09d1d Mon Sep 17 00:00:00 2001 From: Xu Senbo <1170676717@qq.com> Date: Sun, 26 Nov 2023 23:03:51 +0800 Subject: [PATCH 03/28] Try fix error by add package --- .../deep-learning/cnn-deepdream.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb index 10756a029f..7517debd24 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -42,7 +42,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Deepdream in TensorFlow\n", + "# Deepdream in TensorFlow\n", "Note: There is no new code in this script. It originates from the TensorFlow tutorial located here. However, this code is modified slightly to run on Python 3. The code is also commented very heavily to explain, line-by-line, what occurs in the deepdream demo.\n", "\n", "Here are some potential outputs.\n", @@ -56,7 +56,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Code" + "## Code" ] }, { From efe8b6984092e72b6e5fe50562bbb9eaf4431614 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Mon, 27 Nov 2023 08:05:57 +0800 Subject: [PATCH 04/28] Try fix error --- open-machine-learning-jupyter-book/_toc.yml | 2 +- .../deep-learning/cnn-deepdream.ipynb | 147 ------ .../deep-learning/cnn-vgg.ipynb | 465 ------------------ 3 files changed, 1 insertion(+), 613 deletions(-) delete mode 100644 open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index aa14ed02e1..5b88805923 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -90,7 +90,7 @@ parts: chapters: - file: deep-learning/dl-overview - file: deep-learning/cnn - - file: deep-learning/cnn-vgg + # - file: deep-learning/cnn-vgg - file: deep-learning/cnn-deepdream - file: deep-learning/gan.md - file: deep-learning/rnn.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb index 7517debd24..6fabb42de9 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -145,153 +145,6 @@ "dream_model = tf.keras.Model(inputs=base_model.input, outputs=layers)" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def calc_loss(img, model):\n", - " # Pass forward the image through the model to retrieve the activations.\n", - " # Converts the image into a batch of size 1.\n", - " img_batch = tf.expand_dims(img, axis=0)\n", - " layer_activations = model(img_batch)\n", - " if len(layer_activations) == 1:\n", - " layer_activations = [layer_activations]\n", - "\n", - " losses = []\n", - " for act in layer_activations:\n", - " loss = tf.math.reduce_mean(act)\n", - " losses.append(loss)\n", - "\n", - " return tf.reduce_sum(losses)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "class DeepDream(tf.Module):\n", - " def __init__(self, model):\n", - " self.model = model\n", - "\n", - " @tf.function(\n", - " input_signature=(\n", - " tf.TensorSpec(shape=[None,None,3], dtype=tf.float32),\n", - " tf.TensorSpec(shape=[], dtype=tf.int32),\n", - " tf.TensorSpec(shape=[], dtype=tf.float32),)\n", - " )\n", - " def __call__(self, img, steps, step_size):\n", - " print(\"Tracing\")\n", - " loss = tf.constant(0.0)\n", - " for n in tf.range(steps):\n", - " with tf.GradientTape() as tape:\n", - " # This needs gradients relative to `img`\n", - " # `GradientTape` only watches `tf.Variable`s by default\n", - " tape.watch(img)\n", - " loss = calc_loss(img, self.model)\n", - "\n", - " # Calculate the gradient of the loss with respect to the pixels of the input image.\n", - " gradients = tape.gradient(loss, img)\n", - "\n", - " # Normalize the gradients.\n", - " gradients /= tf.math.reduce_std(gradients) + 1e-8 \n", - " \n", - " # In gradient ascent, the \"loss\" is maximized so that the input image increasingly \"excites\" the layers.\n", - " # You can update the image by directly adding the gradients (because they're the same shape!)\n", - " img = img + gradients*step_size\n", - " img = tf.clip_by_value(img, -1, 1)\n", - "\n", - " return loss, img" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "deepdream = DeepDream(dream_model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def run_deep_dream_simple(img, steps=100, step_size=0.01):\n", - " # Convert from uint8 to the range expected by the model.\n", - " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", - " img = tf.convert_to_tensor(img)\n", - " step_size = tf.convert_to_tensor(step_size)\n", - " steps_remaining = steps\n", - " step = 0\n", - " while steps_remaining:\n", - " if steps_remaining>100:\n", - " run_steps = tf.constant(100)\n", - " else:\n", - " run_steps = tf.constant(steps_remaining)\n", - " steps_remaining -= run_steps\n", - " step += run_steps\n", - "\n", - " loss, img = deepdream(img, run_steps, tf.constant(step_size))\n", - " \n", - " display.clear_output(wait=True)\n", - " show(deprocess(img))\n", - " print (\"Step {}, loss {}\".format(step, loss))\n", - "\n", - "\n", - " result = deprocess(img)\n", - " display.clear_output(wait=True)\n", - " show(result)\n", - "\n", - " return result" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dream_img = run_deep_dream_simple(img=original_img, \n", - " steps=100, step_size=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import time\n", - "start = time.time()\n", - "\n", - "OCTAVE_SCALE = 1.30\n", - "\n", - "img = tf.constant(np.array(original_img))\n", - "base_shape = tf.shape(img)[:-1]\n", - "float_base_shape = tf.cast(base_shape, tf.float32)\n", - "\n", - "for n in range(-2, 3):\n", - " new_shape = tf.cast(float_base_shape*(OCTAVE_SCALE**n), tf.int32)\n", - "\n", - " img = tf.image.resize(img, new_shape).numpy()\n", - "\n", - " img = run_deep_dream_simple(img=img, steps=50, step_size=0.01)\n", - "\n", - "display.clear_output(wait=True)\n", - "img = tf.image.resize(img, base_shape)\n", - "img = tf.image.convert_image_dtype(img/255.0, dtype=tf.uint8)\n", - "show(img)\n", - "\n", - "end = time.time()\n", - "end-start" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb deleted file mode 100644 index 77e0957a37..0000000000 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb +++ /dev/null @@ -1,465 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [ - "hide-cell" - ] - }, - "outputs": [], - "source": [ - "# Install the necessary dependencies\n", - "\n", - "import os\n", - "import sys \n", - "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests\n", - "# Convolutional Neural Networks" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [ - "remove-cell" - ] - }, - "source": [ - "---\n", - "license:\n", - " code: MIT\n", - " content: CC-BY-4.0\n", - "github: https://github.com/ocademy-ai/machine-learning\n", - "venue: By Ocademy\n", - "open_access: true\n", - "bibliography:\n", - " - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n", - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Stylenet / Neural-Style\n", - "\n", - "The purpose of this script is to illustrate how to do stylenet in TensorFlow. We reference the following [paper](https://arxiv.org/abs/1508.06576) for this algorithm.\n", - "\n", - "But there is some prerequisites,\n", - "\n", - "- Download the `VGG-verydeep-19.mat` file.\n", - "- You must download two images, a style image and a content image for the algorithm to blend.\n", - "\n", - "The style image is\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg\n", - "name: Style image starry night\n", - ":::\n", - "\n", - "The context image is below.\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg\n", - "name: Content image book cover\n", - ":::\n", - "\n", - "The final result looks like\n", - "\n", - ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png\n", - "name: stylenet final result\n", - ":::\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We use two images, an original image and a style image and try to make the original image in the style of the style image.\n", - "\n", - "Reference paper:https://arxiv.org/abs/1508.06576\n", - "\n", - "Need to download the model 'imagenet-vgg-verydee-19.mat' from: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Code" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import scipy.io\n", - "import scipy.misc\n", - "import imageio\n", - "from skimage.transform import resize\n", - "from operator import mul\n", - "from functools import reduce\n", - "from PIL import Image\n", - "import numpy as np\n", - "import requests\n", - "import tensorflow.compat.v1 as tf\n", - "tf.disable_eager_execution() #This is tensorflow 1.x version code. Some of them are not fit tensorflow 2.x.\n", - "from tensorflow.python.framework import ops\n", - "ops.reset_default_graph()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# URLs\n", - "original_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg'\n", - "style_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg'\n", - "vgg_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/imagenet-vgg-verydeep-19.mat'\n", - "\n", - "# Local directories\n", - "data_dir = 'temp'\n", - "vgg_dir = os.path.join(data_dir, 'VGG')\n", - "if not os.path.exists(vgg_dir):\n", - " os.makedirs(vgg_dir)\n", - "\n", - "# Function to download and save a file\n", - "def download_file(url, directory):\n", - " response = requests.get(url)\n", - " filename = url.split('/')[-1]\n", - " filepath = os.path.join(directory, filename)\n", - " with open(filepath, 'wb') as f:\n", - " f.write(response.content)\n", - " return filepath\n", - "\n", - "# Download images and VGG Network\n", - "original_image_path = download_file(original_image_url, data_dir)\n", - "style_image_path = download_file(style_image_url, data_dir)\n", - "vgg_path = download_file(vgg_url, vgg_dir)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load data and set default arguments" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load images using PIL and convert to NumPy arrays\n", - "original_image = Image.open(original_image_path)\n", - "style_image = Image.open(style_image_path)\n", - "original_image = np.array(original_image)\n", - "style_image = np.array(style_image)\n", - "\n", - "# Default Arguments\n", - "original_image_weight = 5.0\n", - "style_image_weight = 500.0\n", - "regularization_weight = 100\n", - "learning_rate = 10\n", - "generations = 100\n", - "output_generations = 25\n", - "beta1 = 0.9\n", - "beta2 = 0.999" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Style Transfer Implementation using VGG-19 Network\n", - "\n", - "Now setting VGG-19." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Get shape of target and make the style image the same\n", - "target_shape = original_image.shape\n", - "style_image = resize(style_image, target_shape)\n", - "\n", - "# VGG-19 Layer Setup\n", - "# From paper\n", - "vgg_layers = ['conv1_1', 'relu1_1',\n", - " 'conv1_2', 'relu1_2', 'pool1',\n", - " 'conv2_1', 'relu2_1',\n", - " 'conv2_2', 'relu2_2', 'pool2',\n", - " 'conv3_1', 'relu3_1',\n", - " 'conv3_2', 'relu3_2',\n", - " 'conv3_3', 'relu3_3',\n", - " 'conv3_4', 'relu3_4', 'pool3',\n", - " 'conv4_1', 'relu4_1',\n", - " 'conv4_2', 'relu4_2',\n", - " 'conv4_3', 'relu4_3',\n", - " 'conv4_4', 'relu4_4', 'pool4',\n", - " 'conv5_1', 'relu5_1',\n", - " 'conv5_2', 'relu5_2',\n", - " 'conv5_3', 'relu5_3',\n", - " 'conv5_4', 'relu5_4']\n", - "\n", - "\n", - "# Extract weights and matrix means\n", - "def extract_net_info(path_to_params):\n", - " vgg_data = scipy.io.loadmat(path_to_params)\n", - " normalization_matrix = vgg_data['normalization'][0][0][0]\n", - " mat_mean = np.mean(normalization_matrix, axis=(0, 1))\n", - " network_weights = vgg_data['layers'][0]\n", - " return mat_mean, network_weights\n", - " \n", - "\n", - "# Create the VGG-19 Network\n", - "def vgg_network(network_weights, init_image):\n", - " network = {}\n", - " image = init_image\n", - "\n", - " for i, layer in enumerate(vgg_layers):\n", - " if layer[0] == 'c':\n", - " weights, bias = network_weights[i][0][0][0][0]\n", - " weights = np.transpose(weights, (1, 0, 2, 3))\n", - " bias = bias.reshape(-1)\n", - " conv_layer = tf.nn.conv2d(image, tf.constant(weights), (1, 1, 1, 1), 'SAME')\n", - " image = tf.nn.bias_add(conv_layer, bias)\n", - " elif layer[0] == 'r':\n", - " image = tf.nn.relu(image)\n", - " else: # pooling\n", - " image = tf.nn.max_pool(image, (1, 2, 2, 1), (1, 2, 2, 1), 'SAME')\n", - " network[layer] = image\n", - " return network\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here we define which layers apply to the original or style image and get network parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "original_layers = ['relu4_2', 'relu5_2']\n", - "style_layers = ['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1']\n", - "\n", - "normalization_mean, network_weights = extract_net_info(vgg_path)\n", - "\n", - "shape = (1,) + original_image.shape\n", - "style_shape = (1,) + style_image.shape\n", - "original_features = {}\n", - "style_features = {}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Set style weights." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "style_weights = {l: 1./(len(style_layers)) for l in style_layers}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Computer feature layers with original image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "g_original = tf.Graph()\n", - "with g_original.as_default(), tf.Session() as sess1:\n", - " image = tf.placeholder('float', shape=shape)\n", - " vgg_net = vgg_network(network_weights, image)\n", - " original_minus_mean = original_image - normalization_mean\n", - " original_norm = np.array([original_minus_mean])\n", - " for layer in original_layers:\n", - " original_features[layer] = vgg_net[layer].eval(feed_dict={image: original_norm})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get style image network." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "g_style = tf.Graph()\n", - "with g_style.as_default(), tf.Session() as sess2:\n", - " image = tf.placeholder('float', shape=style_shape)\n", - " vgg_net = vgg_network(network_weights, image)\n", - " style_minus_mean = style_image - normalization_mean\n", - " style_norm = np.array([style_minus_mean])\n", - " for layer in style_layers:\n", - " features = vgg_net[layer].eval(feed_dict={image: style_norm})\n", - " features = np.reshape(features, (-1, features.shape[3]))\n", - " gram = np.matmul(features.T, features) / features.size\n", - " style_features[layer] = gram\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Make Combined Image via loss function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with tf.Graph().as_default():\n", - " # Get network parameters\n", - " initial = tf.random_normal(shape) * 0.256\n", - " init_image = tf.Variable(initial)\n", - " vgg_net = vgg_network(network_weights, init_image)\n", - "\n", - " # Loss from Original Image\n", - " original_layers_w = {'relu4_2': 0.5, 'relu5_2': 0.5}\n", - " original_loss = 0\n", - " for o_layer in original_layers:\n", - " temp_original_loss = original_layers_w[o_layer] * original_image_weight *\\\n", - " (2 * tf.nn.l2_loss(vgg_net[o_layer] - original_features[o_layer]))\n", - " original_loss += (temp_original_loss / original_features[o_layer].size)\n", - "\n", - " # Loss from Style Image\n", - " style_loss = 0\n", - " style_losses = []\n", - " for style_layer in style_layers:\n", - " layer = vgg_net[style_layer]\n", - " feats, height, width, channels = [x.value for x in layer.get_shape()]\n", - " size = height * width * channels\n", - " features = tf.reshape(layer, (-1, channels))\n", - " style_gram_matrix = tf.matmul(tf.transpose(features), features) / size\n", - " style_expected = style_features[style_layer]\n", - " style_losses.append(style_weights[style_layer] * 2 *\n", - " tf.nn.l2_loss(style_gram_matrix - style_expected) /\n", - " style_expected.size)\n", - " style_loss += style_image_weight * tf.reduce_sum(style_losses)\n", - "\n", - " # To Smooth the results, we add in total variation loss\n", - " total_var_x = reduce(mul, init_image[:, 1:, :, :].get_shape().as_list(), 1)\n", - " total_var_y = reduce(mul, init_image[:, :, 1:, :].get_shape().as_list(), 1)\n", - " first_term = regularization_weight * 2\n", - " second_term_numerator = tf.nn.l2_loss(init_image[:, 1:, :, :] - init_image[:, :shape[1]-1, :, :])\n", - " second_term = second_term_numerator / total_var_y\n", - " third_term = (tf.nn.l2_loss(init_image[:, :, 1:, :] - init_image[:, :, :shape[2]-1, :]) / total_var_x)\n", - " total_variation_loss = first_term * (second_term + third_term)\n", - "\n", - " # Combined Loss\n", - " loss = original_loss + style_loss + total_variation_loss\n", - "\n", - " # Declare Optimization Algorithm\n", - " optimizer = tf.train.AdamOptimizer(learning_rate, beta1, beta2)\n", - " train_step = optimizer.minimize(loss)\n", - "\n", - " # Initialize variables and start training\n", - " with tf.Session() as sess:\n", - " tf.global_variables_initializer().run()\n", - " for i in range(generations):\n", - "\n", - " train_step.run()\n", - "\n", - " # Print update and save temporary output\n", - " if (i+1) % output_generations == 0:\n", - " print('Generation {} out of {}, loss: {}'.format(i + 1, generations,sess.run(loss)))\n", - "\n", - " image_eval = init_image.eval(session=sess)\n", - " image_eval = image_eval.reshape(shape[1:]) # Make sure form is right\n", - " image_eval += normalization_mean # Add avg\n", - " image_eval = np.clip(image_eval, 0, 255) # Make sure value between 0 and 255\n", - " image_eval = image_eval.astype(np.uint8) # Transform to uint8\n", - "\n", - "# Save image\n", - "output_file = 'temp_output_{}.jpg'.format(i)\n", - "imageio.imwrite(output_file, image_eval)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your turn! 🚀\n", - "\n", - "TBD." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Self study\n", - "\n", - "You can refer to those YouTube videos for further study:\n", - "\n", - "- [Convolutional Neural Networks (CNNs) explained, by deeplizard](https://www.youtube.com/watch?v=YRhxdVk_sIs)\n", - "- [Convolutional Neural Networks Explained (CNN Visualized), by Futurology](https://www.youtube.com/watch?v=pj9-rr1wDhM)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Acknowledgments\n", - "\n", - "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). It inspires the majority of the content in this chapter.\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From 8c4d39b044fe8b0b0f894d8a504e3c51372b997e Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Mon, 27 Nov 2023 08:20:13 +0800 Subject: [PATCH 05/28] Add three code cell --- .../deep-learning/cnn-deepdream.ipynb | 71 +++++++++++++++++++ 1 file changed, 71 insertions(+) diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb index 6fabb42de9..7efe1fa14b 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -145,6 +145,77 @@ "dream_model = tf.keras.Model(inputs=base_model.input, outputs=layers)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def calc_loss(img, model):\n", + " # Pass forward the image through the model to retrieve the activations.\n", + " # Converts the image into a batch of size 1.\n", + " img_batch = tf.expand_dims(img, axis=0)\n", + " layer_activations = model(img_batch)\n", + " if len(layer_activations) == 1:\n", + " layer_activations = [layer_activations]\n", + "\n", + " losses = []\n", + " for act in layer_activations:\n", + " loss = tf.math.reduce_mean(act)\n", + " losses.append(loss)\n", + "\n", + " return tf.reduce_sum(losses)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "class DeepDream(tf.Module):\n", + " def __init__(self, model):\n", + " self.model = model\n", + "\n", + " @tf.function(\n", + " input_signature=(\n", + " tf.TensorSpec(shape=[None,None,3], dtype=tf.float32),\n", + " tf.TensorSpec(shape=[], dtype=tf.int32),\n", + " tf.TensorSpec(shape=[], dtype=tf.float32),)\n", + " )\n", + " def __call__(self, img, steps, step_size):\n", + " print(\"Tracing\")\n", + " loss = tf.constant(0.0)\n", + " for n in tf.range(steps):\n", + " with tf.GradientTape() as tape:\n", + " # This needs gradients relative to `img`\n", + " # `GradientTape` only watches `tf.Variable`s by default\n", + " tape.watch(img)\n", + " loss = calc_loss(img, self.model)\n", + "\n", + " # Calculate the gradient of the loss with respect to the pixels of the input image.\n", + " gradients = tape.gradient(loss, img)\n", + "\n", + " # Normalize the gradients.\n", + " gradients /= tf.math.reduce_std(gradients) + 1e-8 \n", + " \n", + " # In gradient ascent, the \"loss\" is maximized so that the input image increasingly \"excites\" the layers.\n", + " # You can update the image by directly adding the gradients (because they're the same shape!)\n", + " img = img + gradients*step_size\n", + " img = tf.clip_by_value(img, -1, 1)\n", + "\n", + " return loss, img" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "deepdream = DeepDream(dream_model)" + ] + }, { "cell_type": "markdown", "metadata": {}, From 5bdfbc3eea30e792aaf17872680552afc1919010 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Mon, 27 Nov 2023 09:27:06 +0800 Subject: [PATCH 06/28] Try fix error --- open-machine-learning-jupyter-book/_toc.yml | 2 +- .../deep-learning/cnn-deepdream.ipynb | 91 +++- .../deep-learning/cnn-vgg.ipynb | 479 ++++++++++++++++++ .../deep-learning/cnn.ipynb | 2 +- 4 files changed, 568 insertions(+), 6 deletions(-) create mode 100644 open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index 5b88805923..aa14ed02e1 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -90,7 +90,7 @@ parts: chapters: - file: deep-learning/dl-overview - file: deep-learning/cnn - # - file: deep-learning/cnn-vgg + - file: deep-learning/cnn-vgg - file: deep-learning/cnn-deepdream - file: deep-learning/gan.md - file: deep-learning/rnn.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb index 7efe1fa14b..9cfeb8a4bd 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "tags": [ "hide-cell" @@ -14,8 +14,7 @@ "\n", "import os\n", "import sys \n", - "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests pillow\n", - "# Convolutional Neural Networks" + "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests pillow" ] }, { @@ -216,6 +215,82 @@ "deepdream = DeepDream(dream_model)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def run_deep_dream_simple(img, steps=100, step_size=0.01):\n", + " # Convert from uint8 to the range expected by the model.\n", + " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", + " img = tf.convert_to_tensor(img)\n", + " step_size = tf.convert_to_tensor(step_size)\n", + " steps_remaining = steps\n", + " step = 0\n", + " while steps_remaining:\n", + " if steps_remaining>100:\n", + " run_steps = tf.constant(100)\n", + " else:\n", + " run_steps = tf.constant(steps_remaining)\n", + " steps_remaining -= run_steps\n", + " step += run_steps\n", + "\n", + " loss, img = deepdream(img, run_steps, tf.constant(step_size))\n", + " \n", + " display.clear_output(wait=True)\n", + " show(deprocess(img))\n", + " print (\"Step {}, loss {}\".format(step, loss))\n", + "\n", + "\n", + " result = deprocess(img)\n", + " display.clear_output(wait=True)\n", + " show(result)\n", + "\n", + " return result" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dream_img = run_deep_dream_simple(img=original_img, \n", + " steps=100, step_size=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import time\n", + "start = time.time()\n", + "\n", + "OCTAVE_SCALE = 1.30\n", + "\n", + "img = tf.constant(np.array(original_img))\n", + "base_shape = tf.shape(img)[:-1]\n", + "float_base_shape = tf.cast(base_shape, tf.float32)\n", + "\n", + "for n in range(-2, 3):\n", + " new_shape = tf.cast(float_base_shape*(OCTAVE_SCALE**n), tf.int32)\n", + "\n", + " img = tf.image.resize(img, new_shape).numpy()\n", + "\n", + " img = run_deep_dream_simple(img=img, steps=50, step_size=0.01)\n", + "\n", + "display.clear_output(wait=True)\n", + "img = tf.image.resize(img, base_shape)\n", + "img = tf.image.convert_image_dtype(img/255.0, dtype=tf.uint8)\n", + "show(img)\n", + "\n", + "end = time.time()\n", + "end-start" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -242,8 +317,16 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "3.9.13" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" } }, "nbformat": 4, diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb new file mode 100644 index 0000000000..dca2bd6c62 --- /dev/null +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb @@ -0,0 +1,479 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [ + "hide-cell" + ] + }, + "outputs": [], + "source": [ + "# Install the necessary dependencies\n", + "\n", + "import os\n", + "import sys \n", + "!{sys.executable} -m pip install --quiet pandas scikit-learn numpy matplotlib jupyterlab_myst ipython imageio scikit-image requests\n", + "# Convolutional Neural Networks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "source": [ + "---\n", + "license:\n", + " code: MIT\n", + " content: CC-BY-4.0\n", + "github: https://github.com/ocademy-ai/machine-learning\n", + "venue: By Ocademy\n", + "open_access: true\n", + "bibliography:\n", + " - https://raw.githubusercontent.com/ocademy-ai/machine-learning/main/open-machine-learning-jupyter-book/references.bib\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Stylenet / Neural-Style\n", + "\n", + "The purpose of this script is to illustrate how to do stylenet in TensorFlow. We reference the following [paper](https://arxiv.org/abs/1508.06576) for this algorithm.\n", + "\n", + "But there is some prerequisites,\n", + "\n", + "- Download the `VGG-verydeep-19.mat` file.\n", + "- You must download two images, a style image and a content image for the algorithm to blend.\n", + "\n", + "The style image is\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg\n", + "name: Style image starry night\n", + ":::\n", + "\n", + "The context image is below.\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg\n", + "name: Content image book cover\n", + ":::\n", + "\n", + "The final result looks like\n", + "\n", + ":::{figure} https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/05_stylenet_ex.png\n", + "name: stylenet final result\n", + ":::\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use two images, an original image and a style image and try to make the original image in the style of the style image.\n", + "\n", + "Reference paper:https://arxiv.org/abs/1508.06576\n", + "\n", + "Need to download the model 'imagenet-vgg-verydee-19.mat' from: http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import scipy.io\n", + "import scipy.misc\n", + "import imageio\n", + "from skimage.transform import resize\n", + "from operator import mul\n", + "from functools import reduce\n", + "from PIL import Image\n", + "import numpy as np\n", + "import requests\n", + "import tensorflow.compat.v1 as tf\n", + "tf.disable_eager_execution() #This is tensorflow 1.x version code. Some of them are not fit tensorflow 2.x.\n", + "from tensorflow.python.framework import ops\n", + "ops.reset_default_graph()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Download data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# URLs\n", + "original_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/book_cover.jpg'\n", + "style_image_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/images/deep-learning/CNN/starry_night.jpg'\n", + "vgg_url = 'https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/deep-learning/cnn/imagenet-vgg-verydeep-19.mat'\n", + "\n", + "# Local directories\n", + "data_dir = 'temp'\n", + "vgg_dir = os.path.join(data_dir, 'VGG')\n", + "if not os.path.exists(vgg_dir):\n", + " os.makedirs(vgg_dir)\n", + "\n", + "# Function to download and save a file\n", + "def download_file(url, directory):\n", + " response = requests.get(url)\n", + " filename = url.split('/')[-1]\n", + " filepath = os.path.join(directory, filename)\n", + " with open(filepath, 'wb') as f:\n", + " f.write(response.content)\n", + " return filepath\n", + "\n", + "# Download images and VGG Network\n", + "original_image_path = download_file(original_image_url, data_dir)\n", + "style_image_path = download_file(style_image_url, data_dir)\n", + "vgg_path = download_file(vgg_url, vgg_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data and set default arguments" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Load images using PIL and convert to NumPy arrays\n", + "original_image = Image.open(original_image_path)\n", + "style_image = Image.open(style_image_path)\n", + "original_image = np.array(original_image)\n", + "style_image = np.array(style_image)\n", + "\n", + "# Default Arguments\n", + "original_image_weight = 5.0\n", + "style_image_weight = 500.0\n", + "regularization_weight = 100\n", + "learning_rate = 10\n", + "generations = 100\n", + "output_generations = 25\n", + "beta1 = 0.9\n", + "beta2 = 0.999" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Style Transfer Implementation using VGG-19 Network\n", + "\n", + "Now setting VGG-19." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Get shape of target and make the style image the same\n", + "target_shape = original_image.shape\n", + "style_image = resize(style_image, target_shape)\n", + "\n", + "# VGG-19 Layer Setup\n", + "# From paper\n", + "vgg_layers = ['conv1_1', 'relu1_1',\n", + " 'conv1_2', 'relu1_2', 'pool1',\n", + " 'conv2_1', 'relu2_1',\n", + " 'conv2_2', 'relu2_2', 'pool2',\n", + " 'conv3_1', 'relu3_1',\n", + " 'conv3_2', 'relu3_2',\n", + " 'conv3_3', 'relu3_3',\n", + " 'conv3_4', 'relu3_4', 'pool3',\n", + " 'conv4_1', 'relu4_1',\n", + " 'conv4_2', 'relu4_2',\n", + " 'conv4_3', 'relu4_3',\n", + " 'conv4_4', 'relu4_4', 'pool4',\n", + " 'conv5_1', 'relu5_1',\n", + " 'conv5_2', 'relu5_2',\n", + " 'conv5_3', 'relu5_3',\n", + " 'conv5_4', 'relu5_4']\n", + "\n", + "\n", + "# Extract weights and matrix means\n", + "def extract_net_info(path_to_params):\n", + " vgg_data = scipy.io.loadmat(path_to_params)\n", + " normalization_matrix = vgg_data['normalization'][0][0][0]\n", + " mat_mean = np.mean(normalization_matrix, axis=(0, 1))\n", + " network_weights = vgg_data['layers'][0]\n", + " return mat_mean, network_weights\n", + " \n", + "\n", + "# Create the VGG-19 Network\n", + "def vgg_network(network_weights, init_image):\n", + " network = {}\n", + " image = init_image\n", + "\n", + " for i, layer in enumerate(vgg_layers):\n", + " if layer[0] == 'c':\n", + " weights, bias = network_weights[i][0][0][0][0]\n", + " weights = np.transpose(weights, (1, 0, 2, 3))\n", + " bias = bias.reshape(-1)\n", + " conv_layer = tf.nn.conv2d(image, tf.constant(weights), (1, 1, 1, 1), 'SAME')\n", + " image = tf.nn.bias_add(conv_layer, bias)\n", + " elif layer[0] == 'r':\n", + " image = tf.nn.relu(image)\n", + " else: # pooling\n", + " image = tf.nn.max_pool(image, (1, 2, 2, 1), (1, 2, 2, 1), 'SAME')\n", + " network[layer] = image\n", + " return network\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we define which layers apply to the original or style image and get network parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "original_layers = ['relu4_2', 'relu5_2']\n", + "style_layers = ['relu1_1', 'relu2_1', 'relu3_1', 'relu4_1', 'relu5_1']\n", + "\n", + "normalization_mean, network_weights = extract_net_info(vgg_path)\n", + "\n", + "shape = (1,) + original_image.shape\n", + "style_shape = (1,) + style_image.shape\n", + "original_features = {}\n", + "style_features = {}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set style weights." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "style_weights = {l: 1./(len(style_layers)) for l in style_layers}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Computer feature layers with original image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g_original = tf.Graph()\n", + "with g_original.as_default(), tf.Session() as sess1:\n", + " image = tf.placeholder('float', shape=shape)\n", + " vgg_net = vgg_network(network_weights, image)\n", + " original_minus_mean = original_image - normalization_mean\n", + " original_norm = np.array([original_minus_mean])\n", + " for layer in original_layers:\n", + " original_features[layer] = vgg_net[layer].eval(feed_dict={image: original_norm})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get style image network." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "g_style = tf.Graph()\n", + "with g_style.as_default(), tf.Session() as sess2:\n", + " image = tf.placeholder('float', shape=style_shape)\n", + " vgg_net = vgg_network(network_weights, image)\n", + " style_minus_mean = style_image - normalization_mean\n", + " style_norm = np.array([style_minus_mean])\n", + " for layer in style_layers:\n", + " features = vgg_net[layer].eval(feed_dict={image: style_norm})\n", + " features = np.reshape(features, (-1, features.shape[3]))\n", + " gram = np.matmul(features.T, features) / features.size\n", + " style_features[layer] = gram\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make Combined Image via loss function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.Graph().as_default():\n", + " # Get network parameters\n", + " initial = tf.random_normal(shape) * 0.256\n", + " init_image = tf.Variable(initial)\n", + " vgg_net = vgg_network(network_weights, init_image)\n", + "\n", + " # Loss from Original Image\n", + " original_layers_w = {'relu4_2': 0.5, 'relu5_2': 0.5}\n", + " original_loss = 0\n", + " for o_layer in original_layers:\n", + " temp_original_loss = original_layers_w[o_layer] * original_image_weight *\\\n", + " (2 * tf.nn.l2_loss(vgg_net[o_layer] - original_features[o_layer]))\n", + " original_loss += (temp_original_loss / original_features[o_layer].size)\n", + "\n", + " # Loss from Style Image\n", + " style_loss = 0\n", + " style_losses = []\n", + " for style_layer in style_layers:\n", + " layer = vgg_net[style_layer]\n", + " feats, height, width, channels = [x.value for x in layer.get_shape()]\n", + " size = height * width * channels\n", + " features = tf.reshape(layer, (-1, channels))\n", + " style_gram_matrix = tf.matmul(tf.transpose(features), features) / size\n", + " style_expected = style_features[style_layer]\n", + " style_losses.append(style_weights[style_layer] * 2 *\n", + " tf.nn.l2_loss(style_gram_matrix - style_expected) /\n", + " style_expected.size)\n", + " style_loss += style_image_weight * tf.reduce_sum(style_losses)\n", + "\n", + " # To Smooth the results, we add in total variation loss\n", + " total_var_x = reduce(mul, init_image[:, 1:, :, :].get_shape().as_list(), 1)\n", + " total_var_y = reduce(mul, init_image[:, :, 1:, :].get_shape().as_list(), 1)\n", + " first_term = regularization_weight * 2\n", + " second_term_numerator = tf.nn.l2_loss(init_image[:, 1:, :, :] - init_image[:, :shape[1]-1, :, :])\n", + " second_term = second_term_numerator / total_var_y\n", + " third_term = (tf.nn.l2_loss(init_image[:, :, 1:, :] - init_image[:, :, :shape[2]-1, :]) / total_var_x)\n", + " total_variation_loss = first_term * (second_term + third_term)\n", + "\n", + " # Combined Loss\n", + " loss = original_loss + style_loss + total_variation_loss\n", + "\n", + " # Declare Optimization Algorithm\n", + " optimizer = tf.train.AdamOptimizer(learning_rate, beta1, beta2)\n", + " train_step = optimizer.minimize(loss)\n", + "\n", + " # Initialize variables and start training\n", + " with tf.Session() as sess:\n", + " tf.global_variables_initializer().run()\n", + " for i in range(generations):\n", + "\n", + " train_step.run()\n", + "\n", + " # Print update and save temporary output\n", + " if (i+1) % output_generations == 0:\n", + " print('Generation {} out of {}, loss: {}'.format(i + 1, generations,sess.run(loss)))\n", + "\n", + " image_eval = init_image.eval(session=sess)\n", + " image_eval = image_eval.reshape(shape[1:]) # Make sure form is right\n", + " image_eval += normalization_mean # Add avg\n", + " image_eval = np.clip(image_eval, 0, 255) # Make sure value between 0 and 255\n", + " image_eval = image_eval.astype(np.uint8) # Transform to uint8\n", + "\n", + "# Save image\n", + "output_file = 'temp_output_{}.jpg'.format(i)\n", + "imageio.imwrite(output_file, image_eval)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Your turn! 🚀\n", + "\n", + "TBD." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Self study\n", + "\n", + "You can refer to those YouTube videos for further study:\n", + "\n", + "- [Convolutional Neural Networks (CNNs) explained, by deeplizard](https://www.youtube.com/watch?v=YRhxdVk_sIs)\n", + "- [Convolutional Neural Networks Explained (CNN Visualized), by Futurology](https://www.youtube.com/watch?v=pj9-rr1wDhM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Acknowledgments\n", + "\n", + "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). It inspires the majority of the content in this chapter.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "open-machine-learning-jupyter-book", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb index 2b06cc2962..7a7520e6c7 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb @@ -354,7 +354,7 @@ "batch_size = 128\n", "data_dir = 'temp'\n", "output_every = 50\n", - "generations = 200\n", + "generations = 20000\n", "eval_every = 500\n", "image_height = 32\n", "image_width = 32\n", From 111625adb9be7e8ba43f773cb8f724370443c539 Mon Sep 17 00:00:00 2001 From: fuqiongying <3047530642@qq.com> Date: Thu, 30 Nov 2023 23:22:51 +0800 Subject: [PATCH 07/28] Updated the path for loading cleaned_cuisines --- .../build-classification-model.ipynb | 432 +++++++++++++++++- 1 file changed, 424 insertions(+), 8 deletions(-) diff --git a/open-machine-learning-jupyter-book/assignments/ml-fundamentals/build-classification-model.ipynb b/open-machine-learning-jupyter-book/assignments/ml-fundamentals/build-classification-model.ipynb index 937b1a7678..4799333550 100644 --- a/open-machine-learning-jupyter-book/assignments/ml-fundamentals/build-classification-model.ipynb +++ b/open-machine-learning-jupyter-book/assignments/ml-fundamentals/build-classification-model.ipynb @@ -9,20 +9,236 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " almond angelica anise anise_seed apple apple_brandy apricot \\\n", + "0 0 0 0 0 0 0 0 \n", + "1 1 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 0 \n", + "\n", + " armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n", + "0 0 0 0 ... 0 0 0 \n", + "1 0 0 0 ... 0 0 0 \n", + "2 0 0 0 ... 0 0 0 \n", + "3 0 0 0 ... 0 0 0 \n", + "4 0 0 0 ... 0 0 0 \n", + "\n", + " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", + "0 0 0 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 1 0 \n", + "\n", + "[5 rows x 380 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_feature_df.head()" @@ -67,7 +483,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.0" + "version": "3.9.18" }, "metadata": { "interpreter": { From 4fb34d032e25339fb65ddc55893caa34d60d236c Mon Sep 17 00:00:00 2001 From: fuqiongying <3047530642@qq.com> Date: Thu, 30 Nov 2023 23:28:46 +0800 Subject: [PATCH 08/28] update gitignore --- .gitignore | 2 -- 1 file changed, 2 deletions(-) diff --git a/.gitignore b/.gitignore index c521fcc3c0..40bab5087b 100644 --- a/.gitignore +++ b/.gitignore @@ -14,8 +14,6 @@ __pycache__ /venv/ *.py[cod] *.DS_Store -open-machine-learning-jupyter-book/assets/pickle/ufo-model.pkl -open-machine-learning-jupyter-book/ml-fundamentals/classification/cleaned_cuisines.csv open-machine-learning-jupyter-book/assignments/environment.yml open-machine-learning-jupyter-book/assignments/README.md open-machine-learning-jupyter-book/assignments/prerequisites/* From 10e937d1a21fb946e03c64d2ebddb049954bb1c6 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Fri, 1 Dec 2023 10:48:40 +0800 Subject: [PATCH 09/28] Test of Intro-to-clas --- open-machine-learning-jupyter-book/_config.yml | 1 - .../classification/introduction-to-classification.ipynb | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/open-machine-learning-jupyter-book/_config.yml b/open-machine-learning-jupyter-book/_config.yml index 5f4b5be761..7457d0cad0 100644 --- a/open-machine-learning-jupyter-book/_config.yml +++ b/open-machine-learning-jupyter-book/_config.yml @@ -17,7 +17,6 @@ execute: - 'slides/*' - 'slides/**/*' - 'data-science/working-with-data/pandas/*' - - 'ml-fundamentals/classification/introduction-to-classification.ipynb' - 'ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.md' - 'ml-fundamentals/regression/managing-data.ipynb' - 'ml-fundamentals/regression/loss-function.ipynb' diff --git a/open-machine-learning-jupyter-book/ml-fundamentals/classification/introduction-to-classification.ipynb b/open-machine-learning-jupyter-book/ml-fundamentals/classification/introduction-to-classification.ipynb index 38c2f20623..90e3bfe329 100644 --- a/open-machine-learning-jupyter-book/ml-fundamentals/classification/introduction-to-classification.ipynb +++ b/open-machine-learning-jupyter-book/ml-fundamentals/classification/introduction-to-classification.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "848fcc94-3480-439c-b565-b8dc6072268a", "metadata": { "editable": true, From 02dd3276d9129c331fb8c75908745b68b62b8085 Mon Sep 17 00:00:00 2001 From: HenryCheval <875542931@qq.com> Date: Fri, 1 Dec 2023 20:58:14 +0800 Subject: [PATCH 10/28] ultimate reference fixed --- awesome/database/data.db | Bin 2564096 -> 2564096 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/awesome/database/data.db b/awesome/database/data.db index 4c081317490ac35a339025a197b86da9e92a528e..824e690c20d60788c093afaed1225ea170da2bad 100644 GIT binary patch delta 755 zcmZwE-%C?b902fhe{Jq=)0X$TA7`6)Z7LGObPqj5+0q9IGDJtH6?ch{lv-v}QT_XLt~JgwSJ1 z%=3?2$z!Y>(>N_YauOlAsKV((dJqW_cs(hH$kIGt%sNCx5yMJ*v`2||^mkqBRG{%4 z-)zc9!M?=Th}{<}cd{U=Tcup^uyrTWa-QDkS>oH_RkzKP+?q2}1(bynDJx~8>{KCD zMD3v*)LyEXlE~Ja>|<@_X~eO9RN&*^v&1nIv@;nc%QM9PM%MoIbrYTu`G)hJDoHV| zX<(#kw=J*)A_9DzGKa`IkyjW~U2{#Ox@Y1{Nhx?2 z2fx|bLBt_ImRsyFf`^XmD1jOLo$=6FnquL**9)asxMR2qjn(GII2tA6PqD@`3VcXm zC!9;+Qus3=ir`J*FlZ_4%O4$q--|^uWK!4#)-(=6M;b@q!2!1nb!qH_i8KzdN*J0< zZV&ioacw?-c@;;PN^*J@zbqHq5Na#jZ+R}1(aMfHM;k0stoDM2Q8JpZCV4p^2%yv# zhsfk7{FCJsy5RN(Zq4u3H?Zo;#d1G=qPMS8X}Q|p+t(GpctxqsKd5eEubckqe{*V} zcUis!zc#TX-5In=v6$u_8`zz_c8bJT@edAWq>9`?8rSey@?{MVnsfu}hF3Ryy5ZN2 YQr%E=BcL1mbR(!6A>Al@VU!>J3s>y|M*si- delta 746 zcmajcUr19?9Ki8=ckgDKuDRT8&f~VT{FlU>W*8VXMGB$@l^aBs-rII`>biH_-60C1 z%XY`Y$O=3U1E@Nbuh8+(4MB`?(hp)LDeL+NNSxAUTnUdtmlpw}p2qhCpki~IvtRpJjxOO5* z@>}jiZflbx4iZkHo^WYfn+?am{(UG4Q|39tEQKG~OL+|!z&F}?XKueE;*Tx_sKp>> zYje7(e8|^r#CKcjnzeV2!4(#xBH-|MyVb{#Fzi|bJ#LF14h^Qou?a~MNl+MxDM>z) zkf!`yon83H8ykmzt$}(2nSZ(lPGMUSG~xT5E)LHWftx88{P<`QbYoluzPJej6q3+N*uw+z*!g)qVaJN!Snw<30^`;DK)N0 z@^pu`umfgIj=ab0hy*7yfXemn-Ia4|;s;R6;Dv2)8SC3%q>4O6NRkoGSghVgMpbvKx<}Q$st#4%r|O4Py-C%ZRlVhz-rDyYMsE~I From a15a687941e5a9f15d5570bf0d3b7d6d16936584 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Fri, 1 Dec 2023 21:14:03 +0800 Subject: [PATCH 11/28] Test --- open-machine-learning-jupyter-book/_config.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/open-machine-learning-jupyter-book/_config.yml b/open-machine-learning-jupyter-book/_config.yml index 63159583c1..b7d61a908e 100644 --- a/open-machine-learning-jupyter-book/_config.yml +++ b/open-machine-learning-jupyter-book/_config.yml @@ -17,9 +17,9 @@ execute: - 'slides/*' - 'slides/**/*' - 'data-science/working-with-data/pandas/*' - - 'ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb' - - 'ml-fundamentals/regression/managing-data.ipynb' - - 'ml-fundamentals/regression/loss-function.ipynb' + # - 'ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb' + # - 'ml-fundamentals/regression/managing-data.ipynb' + # - 'ml-fundamentals/regression/loss-function.ipynb' - 'ml-advanced/ensemble-learning/random-forest.ipynb' - 'ml-advanced/clustering/introduction-to-clustering.ipynb' - 'ml-advanced/clustering/k-means-clustering.ipynb' From b7179f8ad6f6f3b18ab353e3f3dea638fa530161 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Fri, 1 Dec 2023 21:48:13 +0800 Subject: [PATCH 12/28] Test --- open-machine-learning-jupyter-book/_config.yml | 3 --- 1 file changed, 3 deletions(-) diff --git a/open-machine-learning-jupyter-book/_config.yml b/open-machine-learning-jupyter-book/_config.yml index b7d61a908e..afc1dc7082 100644 --- a/open-machine-learning-jupyter-book/_config.yml +++ b/open-machine-learning-jupyter-book/_config.yml @@ -17,9 +17,6 @@ execute: - 'slides/*' - 'slides/**/*' - 'data-science/working-with-data/pandas/*' - # - 'ml-fundamentals/build-a-web-app-to-use-a-machine-learning-model.ipynb' - # - 'ml-fundamentals/regression/managing-data.ipynb' - # - 'ml-fundamentals/regression/loss-function.ipynb' - 'ml-advanced/ensemble-learning/random-forest.ipynb' - 'ml-advanced/clustering/introduction-to-clustering.ipynb' - 'ml-advanced/clustering/k-means-clustering.ipynb' From 8beaddd2daf6658caafaf7c46d7049126457db15 Mon Sep 17 00:00:00 2001 From: fuqiongying <3047530642@qq.com> Date: Fri, 1 Dec 2023 22:30:55 +0800 Subject: [PATCH 13/28] Update the link to the assignment path --- .../prerequisites/python-programming-advanced.md | 2 +- .../prerequisites/python-programming-basics.ipynb | 2 +- .../prerequisites/python-programming-introduction.ipynb | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/open-machine-learning-jupyter-book/prerequisites/python-programming-advanced.md b/open-machine-learning-jupyter-book/prerequisites/python-programming-advanced.md index e29cb3c12a..460d657229 100644 --- a/open-machine-learning-jupyter-book/prerequisites/python-programming-advanced.md +++ b/open-machine-learning-jupyter-book/prerequisites/python-programming-advanced.md @@ -1625,7 +1625,7 @@ assert custom_exception_is_caught ## Your turn! 🚀 -Practice the Python programming basics by following this [assignment](../assignments/prerequisites/python-programming-advanced.ipynb). +Practice the Python programming basics by following this [assignment](../../assignments/prerequisites/python-programming-advanced.ipynb). ## Self study diff --git a/open-machine-learning-jupyter-book/prerequisites/python-programming-basics.ipynb b/open-machine-learning-jupyter-book/prerequisites/python-programming-basics.ipynb index fd40f3b1c0..e174ca9b72 100644 --- a/open-machine-learning-jupyter-book/prerequisites/python-programming-basics.ipynb +++ b/open-machine-learning-jupyter-book/prerequisites/python-programming-basics.ipynb @@ -4703,7 +4703,7 @@ "source": [ "## Your turn! 🚀\n", "\n", - "Practice the Python programming basics by following this [assignment](../assignments/prerequisites/python-programming-basics.ipynb).\n", + "Practice the Python programming basics by following this [assignment](../../assignments/prerequisites/python-programming-basics.ipynb).\n", "\n", "## Self study\n", "\n", diff --git a/open-machine-learning-jupyter-book/prerequisites/python-programming-introduction.ipynb b/open-machine-learning-jupyter-book/prerequisites/python-programming-introduction.ipynb index ba854f2758..0b321110f7 100644 --- a/open-machine-learning-jupyter-book/prerequisites/python-programming-introduction.ipynb +++ b/open-machine-learning-jupyter-book/prerequisites/python-programming-introduction.ipynb @@ -845,7 +845,7 @@ "\n", "## Your turn! 🚀\n", "\n", - "Try to [write some simple Python code](../assignments/prerequisites/python-programming-introduction.ipynb) through Python shell, Python file, and Jupyter Notebook.\n", + "Try to [write some simple Python code](../../assignments/prerequisites/python-programming-introduction.ipynb) through Python shell, Python file, and Jupyter Notebook.\n", "\n", "## Acknowledgments\n", "\n", From 2bd024bbbc0ed57d8b3e8426e53b178ef6ffe777 Mon Sep 17 00:00:00 2001 From: fuqiongying <3047530642@qq.com> Date: Fri, 1 Dec 2023 22:32:06 +0800 Subject: [PATCH 14/28] update gitignore --- .gitignore | 3 --- 1 file changed, 3 deletions(-) diff --git a/.gitignore b/.gitignore index 40bab5087b..b377102527 100644 --- a/.gitignore +++ b/.gitignore @@ -14,9 +14,6 @@ __pycache__ /venv/ *.py[cod] *.DS_Store -open-machine-learning-jupyter-book/assignments/environment.yml -open-machine-learning-jupyter-book/assignments/README.md -open-machine-learning-jupyter-book/assignments/prerequisites/* */tmp/* */tmp/** */**/node_modules/* From 1bef18d1bd04ca6e75b66887c43ca1a92512d070 Mon Sep 17 00:00:00 2001 From: fuqiongying <3047530642@qq.com> Date: Sat, 2 Dec 2023 01:10:16 +0800 Subject: [PATCH 15/28] Resolve run errors in the pandas folder --- .../pandas/advanced-pandas-techniques.ipynb | 3578 ++++++++++++- .../pandas/data-selection.ipynb | 806 ++- .../introduction-and-data-structures.ipynb | 4712 ++++++++++++++++- 3 files changed, 8307 insertions(+), 789 deletions(-) diff --git a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb index 11c7e9fe48..d88b627c00 100644 --- a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb +++ b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "b08dcc94", "metadata": { "attributes": { @@ -91,7 +91,22 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 a\n", + "1 b\n", + "0 c\n", + "1 d\n", + "dtype: object" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s1 = pd.Series(['a', 'b'])\n", "s2 = pd.Series(['c', 'd'])\n", @@ -108,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "32049abb", "metadata": { "attributes": { @@ -118,8 +133,25 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 a\n", + "1 b\n", + "2 c\n", + "3 d\n", + "dtype: object" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ + "# Remove # and run to see the ValueError raised by verify_integrity=True\n", + "\n", "pd.concat([s1, s2], ignore_index=True)" ] }, @@ -133,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "d5b95507", "metadata": { "attributes": { @@ -143,7 +175,22 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "s1 0 a\n", + " 1 b\n", + "s2 0 c\n", + " 1 d\n", + "dtype: object" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.concat([s1, s2], keys=['s1', 's2'])" ] @@ -158,7 +205,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "6d54830d", "metadata": { "attributes": { @@ -168,7 +215,23 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Series name Row ID\n", + "s1 0 a\n", + " 1 b\n", + "s2 0 c\n", + " 1 d\n", + "dtype: object" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.concat([s1, s2], keys=['s1', 's2'],\n", " names=['Series name', 'Row ID'])" @@ -184,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "fec72294", "metadata": { "attributes": { @@ -194,7 +257,58 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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0c3cat
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" + ], + "text/plain": [ + " letter number animal\n", + "0 c 3 cat\n", + "1 d 4 dog" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df3 = pd.DataFrame([['c', 3, 'cat'], ['d', 4, 'dog']],\n", " columns=['letter', 'number', 'animal'])\n", @@ -266,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "9def1cdd", "metadata": { "attributes": { @@ -276,7 +558,75 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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1b2monkeygeorge
\n", + "
" + ], + "text/plain": [ + " letter number animal name\n", + "0 a 1 bird polly\n", + "1 b 2 monkey george" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df4 = pd.DataFrame([['bird', 'polly'], ['monkey', 'george']],\n", " columns=['animal', 'name'])\n", @@ -343,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "45bea28a", "metadata": { "attributes": { @@ -353,7 +823,50 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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0
a1
\n", + "
" + ], + "text/plain": [ + " 0\n", + "a 1" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df5 = pd.DataFrame([1], index=['a'])\n", "df5" @@ -361,7 +874,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "db871526", "metadata": { "attributes": { @@ -371,7 +884,50 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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0
a2
\n", + "
" + ], + "text/plain": [ + " 0\n", + "a 2" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df6 = pd.DataFrame([2], index=['a'])\n", "df6" @@ -379,7 +935,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "1ab6b3b0", "metadata": { "attributes": { @@ -394,7 +950,9 @@ }, "outputs": [], "source": [ - "pd.concat([df5, df6], verify_integrity=True)" + "# Remove # and run to see the ValueError raised by verify_integrity=True\n", + "\n", + "# pd.concat([df5, df6], verify_integrity=True)" ] }, { @@ -407,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "007c1ed6", "metadata": { "attributes": { @@ -417,7 +975,52 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ab
012
\n", + "
" + ], + "text/plain": [ + " a b\n", + "0 1 2" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df7 = pd.DataFrame({'a': 1, 'b': 2}, index=[0])\n", "df7" @@ -425,7 +1028,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "9dbaddff", "metadata": { "attributes": { @@ -435,7 +1038,20 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 3\n", + "b 4\n", + "dtype: int64" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "new_row = pd.Series({'a': 3, 'b': 4})\n", "new_row" @@ -443,7 +1059,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "ad2d1313", "metadata": { "attributes": { @@ -453,7 +1069,58 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ab
012
134
\n", + "
" + ], + "text/plain": [ + " a b\n", + "0 1 2\n", + "1 3 4" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.concat([df7, new_row.to_frame().T], ignore_index=True)" ] @@ -501,7 +1168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "e223179b", "metadata": {}, "outputs": [], @@ -522,24 +1189,138 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "e22da8fc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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lkeyvalue_leftrkeyvalue_right
0foo1foo5
1foo1foo8
2foo5foo5
3foo5foo8
4bar2bar6
5baz3baz7
\n", + "
" + ], + "text/plain": [ + " lkey value_left rkey value_right\n", + "0 foo 1 foo 5\n", + "1 foo 1 foo 8\n", + "2 foo 5 foo 5\n", + "3 foo 5 foo 8\n", + "4 bar 2 bar 6\n", + "5 baz 3 baz 7" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=('_left', '_right'))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "6147bab8-4644-4a23-ba71-205573a1c3f9", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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\n", + "

Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -574,7 +1355,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "id": "3dea68f6", "metadata": { "attributes": { @@ -589,7 +1370,9 @@ }, "outputs": [], "source": [ - "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))" + "# Remove # and run to see the exception raised caused by overlapping columns in the DataFrames\n", + "\n", + "# df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))" ] }, { @@ -602,7 +1385,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "id": "1026fc27", "metadata": {}, "outputs": [], @@ -613,24 +1396,95 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "id": "b4379cb1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
abc
0foo13
\n", + "
" + ], + "text/plain": [ + " a b c\n", + "0 foo 1 3" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1.merge(df2, how='inner', on='a')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "90916930-6a8e-40e3-871e-d0043aae93d8", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "

Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -655,24 +1509,101 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "2a8bb3d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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abc
0foo13.0
1bar2NaN
\n", + "
" + ], + "text/plain": [ + " a b c\n", + "0 foo 1 3.0\n", + "1 bar 2 NaN" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1.merge(df2, how='left', on='a')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "467da7f9-a710-442e-9fcf-afb4990ea3b0", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
\n", + "
\n", + "

Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -696,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "id": "8951b7b9", "metadata": {}, "outputs": [], @@ -707,24 +1638,114 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "id": "93051401", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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leftright
0foo7
1foo8
2bar7
3bar8
\n", + "
" + ], + "text/plain": [ + " left right\n", + "0 foo 7\n", + "1 foo 8\n", + "2 bar 7\n", + "3 bar 8" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df1.merge(df2, how='cross')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "id": "bc243059-83f7-485c-bcd0-453d611c3d1f", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -767,7 +1788,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "5ad178d6", "metadata": { "attributes": { @@ -785,7 +1806,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "id": "ff1aa936", "metadata": { "attributes": { @@ -811,24 +1832,140 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "a2517b83", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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key_callerAkey_otherB
0K0A0K0B0
1K1A1K1B1
2K2A2K2B2
3K3A3NaNNaN
4K4A4NaNNaN
5K5A5NaNNaN
\n", + "
" + ], + "text/plain": [ + " key_caller A key_other B\n", + "0 K0 A0 K0 B0\n", + "1 K1 A1 K1 B1\n", + "2 K2 A2 K2 B2\n", + "3 K3 A3 NaN NaN\n", + "4 K4 A4 NaN NaN\n", + "5 K5 A5 NaN NaN" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.join(other, lsuffix='_caller', rsuffix='_other')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "81738ab5-bc94-4264-bb43-8c64c041c332", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -865,24 +2002,132 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "91c6f0f0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
AB
key
K0A0B0
K1A1B1
K2A2B2
K3A3NaN
K4A4NaN
K5A5NaN
\n", + "
" + ], + "text/plain": [ + " A B\n", + "key \n", + "K0 A0 B0\n", + "K1 A1 B1\n", + "K2 A2 B2\n", + "K3 A3 NaN\n", + "K4 A4 NaN\n", + "K5 A5 NaN" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.set_index('key').join(other.set_index('key'))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "id": "f942120e-c151-473d-aa0a-3ed6b0679204", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "\n", + "\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -919,7 +2164,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "id": "d8fbb1f7", "metadata": { "attributes": { @@ -929,7 +2174,89 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
keyAB
0K0A0B0
1K1A1B1
2K2A2B2
3K3A3NaN
4K4A4NaN
5K5A5NaN
\n", + "
" + ], + "text/plain": [ + " key A B\n", + "0 K0 A0 B0\n", + "1 K1 A1 B1\n", + "2 K2 A2 B2\n", + "3 K3 A3 NaN\n", + "4 K4 A4 NaN\n", + "5 K5 A5 NaN" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.join(other.set_index('key'), on='key')" ] @@ -944,7 +2271,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "id": "b4d1eb0d", "metadata": { "attributes": { @@ -954,7 +2281,82 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
keyA
0K0A0
1K1A1
2K1A2
3K3A3
4K0A4
5K1A5
\n", + "
" + ], + "text/plain": [ + " key A\n", + "0 K0 A0\n", + "1 K1 A1\n", + "2 K1 A2\n", + "3 K3 A3\n", + "4 K0 A4\n", + "5 K1 A5" + ] + }, + "execution_count": 75, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.DataFrame({'key': ['K0', 'K1', 'K1', 'K3', 'K0', 'K1'],\n", " 'A': ['A0', 'A1', 'A2', 'A3', 'A4', 'A5']})\n", @@ -963,7 +2365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "id": "7f6bc83d", "metadata": { "attributes": { @@ -973,7 +2375,89 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
keyAB
0K0A0B0
1K1A1B1
2K1A2B1
3K3A3NaN
4K0A4B0
5K1A5B1
\n", + "
" + ], + "text/plain": [ + " key A B\n", + "0 K0 A0 B0\n", + "1 K1 A1 B1\n", + "2 K1 A2 B1\n", + "3 K3 A3 NaN\n", + "4 K0 A4 B0\n", + "5 K1 A5 B1" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.join(other.set_index('key'), on='key', validate='m:1')" ] @@ -994,10 +2478,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "id": "38adb2b7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
Max Speed
Animal
Falcon375.0
Parrot25.0
\n", + "
" + ], + "text/plain": [ + " Max Speed\n", + "Animal \n", + "Falcon 375.0\n", + "Parrot 25.0" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\n", " 'Parrot', 'Parrot'],\n", @@ -1008,14 +2545,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "id": "917ba231-1ee4-4f2c-bcb9-4262d7eba119", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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Max Speed
Animal
Falcon370.0
Parrot25.0
\n", + "
" + ], + "text/plain": [ + " Max Speed\n", + "Animal \n", + "Falcon 370.0\n", + "Parrot 25.0" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "arrays = [['Falcon', 'Falcon', 'Parrot', 'Parrot'],\n", " ['Captive', 'Wild', 'Captive', 'Wild']]\n", @@ -1068,14 +2684,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "id": "8d6ff678-1c1e-4629-9e06-1874511ecdf0", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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Max Speed
Type
Captive210.0
Wild185.0
\n", + "
" + ], + "text/plain": [ + " Max Speed\n", + "Type \n", + "Captive 210.0\n", + "Wild 185.0" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.groupby(level=\"Type\").mean()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "id": "31f4c668-6a8b-4dba-a6db-29673e7fbdba", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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ac
b
1.023
2.025
\n", + "
" + ], + "text/plain": [ + " a c\n", + "b \n", + "1.0 2 3\n", + "2.0 2 5" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]\n", "df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\n", @@ -1165,14 +2941,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "id": "47261c15-1d74-4a39-a7bb-073f6835cbf8", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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ac
b
1.023
2.025
NaN14
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" + ], + "text/plain": [ + " a c\n", + "b \n", + "1.0 2 3\n", + "2.0 2 5\n", + "NaN 1 4" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.groupby(by=[\"b\"], dropna=False).sum()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "id": "17c93213-8bcf-4ac8-a30d-09df48b9ca71", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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bc
a
a13.013.0
b12.3123.0
\n", + "
" + ], + "text/plain": [ + " b c\n", + "a \n", + "a 13.0 13.0\n", + "b 12.3 123.0" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "l = [[\"a\", 12, 12], [None, 12.3, 33.], [\"b\", 12.3, 123], [\"a\", 1, 1]]\n", "df = pd.DataFrame(l, columns=[\"a\", \"b\", \"c\"])\n", @@ -1250,14 +3193,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "id": "ba2d22de-ed75-4d52-a6d8-badf4791429f", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
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bc
a
a13.013.0
b12.3123.0
NaN12.333.0
\n", + "
" + ], + "text/plain": [ + " b c\n", + "a \n", + "a 13.0 13.0\n", + "b 12.3 123.0\n", + "NaN 12.3 33.0" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.groupby(by=\"a\", dropna=False).sum()" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "70cc2217-577e-4b8c-8fc2-ce02f036622b", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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AnimalMax Speed
Animal
Falcon0Falcon380.0
1Falcon370.0
Parrot2Parrot24.0
3Parrot26.0
\n", + "
" + ], + "text/plain": [ + " Animal Max Speed\n", + "Animal \n", + "Falcon 0 Falcon 380.0\n", + " 1 Falcon 370.0\n", + "Parrot 2 Parrot 24.0\n", + " 3 Parrot 26.0" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.DataFrame({'Animal': ['Falcon', 'Falcon',\n", " 'Parrot', 'Parrot'],\n", @@ -1354,7 +3482,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "67e4668e", "metadata": { "attributes": { @@ -1364,7 +3492,70 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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AnimalMax Speed
0Falcon380.0
1Falcon370.0
2Parrot24.0
3Parrot26.0
\n", + "
" + ], + "text/plain": [ + " Animal Max Speed\n", + "0 Falcon 380.0\n", + "1 Falcon 370.0\n", + "2 Parrot 24.0\n", + "3 Parrot 26.0" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.groupby(\"Animal\", group_keys=False).apply(lambda x: x)" ] @@ -1383,7 +3574,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "id": "c8e1b317", "metadata": { "attributes": { @@ -1393,7 +3584,130 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ABCDE
0fooonesmall12
1fooonelarge24
2fooonelarge25
3footwosmall35
4footwosmall36
5baronelarge46
6baronesmall58
7bartwosmall69
8bartwolarge79
\n", + "
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Clargesmall
AB
barone4.05.0
two7.06.0
fooone4.01.0
twoNaN6.0
\n", + "
" + ], + "text/plain": [ + "C large small\n", + "A B \n", + "bar one 4.0 5.0\n", + " two 7.0 6.0\n", + "foo one 4.0 1.0\n", + " two NaN 6.0" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "table = pd.pivot_table(df, values='D', index=['A', 'B'],\n", " columns=['C'], aggfunc=np.sum)\n", @@ -1444,7 +3831,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "id": "6cfd03f9", "metadata": { "attributes": { @@ -1454,7 +3841,80 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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Clargesmall
AB
barone45
two76
fooone41
two06
\n", + "
" + ], + "text/plain": [ + "C large small\n", + "A B \n", + "bar one 4 5\n", + " two 7 6\n", + "foo one 4 1\n", + " two 0 6" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "table = pd.pivot_table(df, values='D', index=['A', 'B'],\n", " columns=['C'], aggfunc=np.sum, fill_value=0)\n", @@ -1471,7 +3931,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "id": "900dc876", "metadata": { "attributes": { @@ -1481,7 +3941,80 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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DE
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barlarge5.5000007.500000
small5.5000008.500000
foolarge2.0000004.500000
small2.3333334.333333
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DE
meanmaxmeanmin
AC
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small5.50000098.5000008
foolarge2.00000054.5000004
small2.33333364.3333332
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" + ], + "text/plain": [ + " A B C C\n", + "0 1 10 10" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df[df.B == df['C C']]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "6ec1ded1-6f8a-46ca-b304-25621fe08677", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
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\n", + "
\n", + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -1872,9 +5172,9 @@ ], "metadata": { "kernelspec": { - "display_name": "EnvName", + "display_name": "py39", "language": "python", - "name": "envname" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb index c09140066a..a12db150ef 100644 --- a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb +++ b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "f1931205-8c05-40ca-b266-c0f14e26cff3", "metadata": { "tags": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 73, "id": "19faf0a0", "metadata": { "attributes": { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 74, "id": "5cd6165e", "metadata": { "attributes": { @@ -131,30 +131,11 @@ "raises-exception" ] }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "cannot do slice indexing on DatetimeIndex with these indexers [2] of type int", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[3], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#:tags: [\"raises-exception\"]\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dfl\u001b[38;5;241m.\u001b[39mloc[\u001b[38;5;241m2\u001b[39m:\u001b[38;5;241m3\u001b[39m]\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1290\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m 1289\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[1;32m-> 1290\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_slice_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m 1291\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n\u001b[0;32m 1292\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getbool_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1324\u001b[0m, in \u001b[0;36m_LocIndexer._get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m 1321\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39mcopy(deep\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m 1323\u001b[0m labels \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis(axis)\n\u001b[1;32m-> 1324\u001b[0m indexer \u001b[38;5;241m=\u001b[39m labels\u001b[38;5;241m.\u001b[39mslice_indexer(slice_obj\u001b[38;5;241m.\u001b[39mstart, slice_obj\u001b[38;5;241m.\u001b[39mstop, slice_obj\u001b[38;5;241m.\u001b[39mstep)\n\u001b[0;32m 1326\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(indexer, \u001b[38;5;28mslice\u001b[39m):\n\u001b[0;32m 1327\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_slice(indexer, axis\u001b[38;5;241m=\u001b[39maxis)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\datetimes.py:809\u001b[0m, in \u001b[0;36mDatetimeIndex.slice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m 801\u001b[0m \u001b[38;5;66;03m# GH#33146 if start and end are combinations of str and None and Index is not\u001b[39;00m\n\u001b[0;32m 802\u001b[0m \u001b[38;5;66;03m# monotonic, we can not use Index.slice_indexer because it does not honor the\u001b[39;00m\n\u001b[0;32m 803\u001b[0m \u001b[38;5;66;03m# actual elements, is only searching for start and end\u001b[39;00m\n\u001b[0;32m 804\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[0;32m 805\u001b[0m check_str_or_none(start)\n\u001b[0;32m 806\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m check_str_or_none(end)\n\u001b[0;32m 807\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_monotonic_increasing\n\u001b[0;32m 808\u001b[0m ):\n\u001b[1;32m--> 809\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Index\u001b[38;5;241m.\u001b[39mslice_indexer(\u001b[38;5;28mself\u001b[39m, start, end, step, kind\u001b[38;5;241m=\u001b[39mkind)\n\u001b[0;32m 811\u001b[0m mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m 812\u001b[0m deprecation_mask \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(\u001b[38;5;28;01mTrue\u001b[39;00m)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6559\u001b[0m, in \u001b[0;36mIndex.slice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m 6516\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 6517\u001b[0m \u001b[38;5;124;03mCompute the slice indexer for input labels and step.\u001b[39;00m\n\u001b[0;32m 6518\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 6555\u001b[0m \u001b[38;5;124;03mslice(1, 3, None)\u001b[39;00m\n\u001b[0;32m 6556\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 6557\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecated_arg(kind, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice_indexer\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 6559\u001b[0m start_slice, end_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mslice_locs(start, end, step\u001b[38;5;241m=\u001b[39mstep)\n\u001b[0;32m 6561\u001b[0m \u001b[38;5;66;03m# return a slice\u001b[39;00m\n\u001b[0;32m 6562\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_scalar(start_slice):\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6767\u001b[0m, in \u001b[0;36mIndex.slice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m 6765\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 6766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 6767\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_slice_bound(start, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6768\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m start_slice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 6769\u001b[0m start_slice \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6676\u001b[0m, in \u001b[0;36mIndex.get_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m 6672\u001b[0m original_label \u001b[38;5;241m=\u001b[39m label\n\u001b[0;32m 6674\u001b[0m \u001b[38;5;66;03m# For datetime indices label may be a string that has to be converted\u001b[39;00m\n\u001b[0;32m 6675\u001b[0m \u001b[38;5;66;03m# to datetime boundary according to its resolution.\u001b[39;00m\n\u001b[1;32m-> 6676\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_cast_slice_bound(label, side)\n\u001b[0;32m 6678\u001b[0m \u001b[38;5;66;03m# we need to look up the label\u001b[39;00m\n\u001b[0;32m 6679\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\datetimes.py:767\u001b[0m, in \u001b[0;36mDatetimeIndex._maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m 762\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, date) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, datetime):\n\u001b[0;32m 763\u001b[0m \u001b[38;5;66;03m# Pandas supports slicing with dates, treated as datetimes at midnight.\u001b[39;00m\n\u001b[0;32m 764\u001b[0m \u001b[38;5;66;03m# https://github.com/pandas-dev/pandas/issues/31501\u001b[39;00m\n\u001b[0;32m 765\u001b[0m label \u001b[38;5;241m=\u001b[39m Timestamp(label)\u001b[38;5;241m.\u001b[39mto_pydatetime()\n\u001b[1;32m--> 767\u001b[0m label \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m_maybe_cast_slice_bound(label, side, kind\u001b[38;5;241m=\u001b[39mkind)\n\u001b[0;32m 768\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_deprecate_mismatched_indexing(label)\n\u001b[0;32m 769\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_cast_for_get_loc(label)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexes\\datetimelike.py:320\u001b[0m, in \u001b[0;36mDatetimeIndexOpsMixin._maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m 318\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m lower \u001b[38;5;28;01mif\u001b[39;00m side \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mleft\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m upper\n\u001b[0;32m 319\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data\u001b[38;5;241m.\u001b[39m_recognized_scalars):\n\u001b[1;32m--> 320\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalid_indexer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslice\u001b[39m\u001b[38;5;124m\"\u001b[39m, label)\n\u001b[0;32m 322\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m label\n", - "\u001b[1;31mTypeError\u001b[0m: cannot do slice indexing on DatetimeIndex with these indexers [2] of type int" - ] - } - ], + "outputs": [], "source": [ - "dfl.loc[2:3]" + "# Remove # and run to see the TypeError\n", + "\n", + "# dfl.loc[2:3]" ] }, { @@ -174,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 75, "id": "3f5fb2f0", "metadata": {}, "outputs": [ @@ -208,24 +189,24 @@ " \n", " \n", " 2013-01-02\n", - " -0.529216\n", - " 1.223634\n", - " -0.783708\n", - " -1.209286\n", + " 1.481584\n", + " -1.691289\n", + " -0.086724\n", + " -0.393754\n", " \n", " \n", " 2013-01-03\n", - " -1.570743\n", - " -0.316004\n", - " -1.132640\n", - " -0.464328\n", + " 0.476774\n", + " 0.605450\n", + " -0.091083\n", + " -1.410096\n", " \n", " \n", " 2013-01-04\n", - " 1.390855\n", - " -0.319271\n", - " -1.093100\n", - " -1.090622\n", + " 0.035828\n", + " -0.095133\n", + " 1.377407\n", + " 0.495220\n", " \n", " \n", "\n", @@ -233,12 +214,12 @@ ], "text/plain": [ " A B C D\n", - "2013-01-02 -0.529216 1.223634 -0.783708 -1.209286\n", - "2013-01-03 -1.570743 -0.316004 -1.132640 -0.464328\n", - "2013-01-04 1.390855 -0.319271 -1.093100 -1.090622" + "2013-01-02 1.481584 -1.691289 -0.086724 -0.393754\n", + "2013-01-03 0.476774 0.605450 -0.091083 -1.410096\n", + "2013-01-04 0.035828 -0.095133 1.377407 0.495220" ] }, - "execution_count": 4, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -249,7 +230,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 76, "id": "abe5968b-ffe5-4302-9918-81a1d97ed568", "metadata": { "tags": [ @@ -344,21 +325,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 77, "id": "8a174f11", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "c 0.697303\n", - "d -1.412259\n", - "e 0.104600\n", - "f 1.718896\n", + "c 0.152695\n", + "d -0.615396\n", + "e 0.203773\n", + "f 1.487611\n", "dtype: float64" ] }, - "execution_count": 6, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -371,7 +352,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 78, "id": "b276bd82-797f-4eb6-8886-51153d771bb0", "metadata": { "tags": [ @@ -431,17 +412,17 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 79, "id": "11e56acc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-0.3374040853531507" + "1.8417073794042274" ] }, - "execution_count": 8, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -452,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 80, "id": "74a7ae51-b334-4d5f-b9a2-e2080958663f", "metadata": { "tags": [ @@ -520,15 +501,15 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 81, "id": "8fe78c41", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "a -0.985634\n", - "b -0.337404\n", + "a -0.221293\n", + "b 1.841707\n", "c 0.000000\n", "d 0.000000\n", "e 0.000000\n", @@ -536,7 +517,7 @@ "dtype: float64" ] }, - "execution_count": 10, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 82, "id": "e32f82e4-6b3e-48a7-ab56-c6ea820274e5", "metadata": { "tags": [ @@ -604,7 +585,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 83, "id": "cfb25d9f", "metadata": {}, "outputs": [ @@ -638,24 +619,24 @@ " \n", " \n", " a\n", - " 1.401532\n", - " -1.744216\n", - " -0.212177\n", - " -1.295240\n", + " -0.829219\n", + " 1.185075\n", + " 0.093787\n", + " -0.442140\n", " \n", " \n", " b\n", - " 0.965335\n", - " -1.586035\n", - " -2.275384\n", - " 0.615352\n", + " -0.473605\n", + " -0.317633\n", + " -0.047595\n", + " -1.409355\n", " \n", " \n", " d\n", - " -0.131692\n", - " -0.910665\n", - " -1.286641\n", - " 0.340830\n", + " -0.721064\n", + " 1.436217\n", + " -2.073527\n", + " 0.452794\n", " \n", " \n", "\n", @@ -663,12 +644,12 @@ ], "text/plain": [ " A B C D\n", - "a 1.401532 -1.744216 -0.212177 -1.295240\n", - "b 0.965335 -1.586035 -2.275384 0.615352\n", - "d -0.131692 -0.910665 -1.286641 0.340830" + "a -0.829219 1.185075 0.093787 -0.442140\n", + "b -0.473605 -0.317633 -0.047595 -1.409355\n", + "d -0.721064 1.436217 -2.073527 0.452794" ] }, - "execution_count": 12, + "execution_count": 83, "metadata": {}, "output_type": "execute_result" } @@ -683,7 +664,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 84, "id": "de1a7123-2c8e-4910-b435-cdd489baff5b", "metadata": { "tags": [ @@ -749,7 +730,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 85, "id": "2934e9e8", "metadata": {}, "outputs": [ @@ -782,21 +763,21 @@ " \n", " \n", " d\n", - " -0.131692\n", - " -0.910665\n", - " -1.286641\n", + " -0.721064\n", + " 1.436217\n", + " -2.073527\n", " \n", " \n", " e\n", - " 0.238683\n", - " -0.169771\n", - " 1.322003\n", + " 0.400573\n", + " 1.644355\n", + " -0.021278\n", " \n", " \n", " f\n", - " 1.511896\n", - " -0.247248\n", - " 3.169958\n", + " -0.282458\n", + " -0.657392\n", + " -0.091122\n", " \n", " \n", "\n", @@ -804,12 +785,12 @@ ], "text/plain": [ " A B C\n", - "d -0.131692 -0.910665 -1.286641\n", - "e 0.238683 -0.169771 1.322003\n", - "f 1.511896 -0.247248 3.169958" + "d -0.721064 1.436217 -2.073527\n", + "e 0.400573 1.644355 -0.021278\n", + "f -0.282458 -0.657392 -0.091122" ] }, - "execution_count": 14, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -828,21 +809,21 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 86, "id": "ccbffe12", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "A 1.401532\n", - "B -1.744216\n", - "C -0.212177\n", - "D -1.295240\n", + "A -0.829219\n", + "B 1.185075\n", + "C 0.093787\n", + "D -0.442140\n", "Name: a, dtype: float64" ] }, - "execution_count": 15, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -853,7 +834,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 87, "id": "c9570d12-8020-4328-94e8-91266619e666", "metadata": { "tags": [ @@ -919,21 +900,21 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 88, "id": "e60fdddf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "A True\n", - "B False\n", - "C False\n", + "A False\n", + "B True\n", + "C True\n", "D False\n", "Name: a, dtype: bool" ] }, - "execution_count": 17, + "execution_count": 88, "metadata": {}, "output_type": "execute_result" } @@ -944,7 +925,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 89, "id": "4a9f2648-9f92-4077-a7ec-00836c2f28fd", "metadata": { "tags": [ @@ -1000,7 +981,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 90, "id": "d6226934", "metadata": {}, "outputs": [ @@ -1025,49 +1006,56 @@ " \n", " \n", " \n", - " A\n", + " B\n", + " C\n", " \n", " \n", " \n", " \n", " a\n", - " 1.401532\n", + " 1.185075\n", + " 0.093787\n", " \n", " \n", " b\n", - " 0.965335\n", + " -0.317633\n", + " -0.047595\n", " \n", " \n", " c\n", - " -0.097299\n", + " 0.205571\n", + " -1.191746\n", " \n", " \n", " d\n", - " -0.131692\n", + " 1.436217\n", + " -2.073527\n", " \n", " \n", " e\n", - " 0.238683\n", + " 1.644355\n", + " -0.021278\n", " \n", " \n", " f\n", - " 1.511896\n", + " -0.657392\n", + " -0.091122\n", " \n", " \n", "\n", "" ], "text/plain": [ - " A\n", - "a 1.401532\n", - "b 0.965335\n", - "c -0.097299\n", - "d -0.131692\n", - "e 0.238683\n", - "f 1.511896" + " B C\n", + "a 1.185075 0.093787\n", + "b -0.317633 -0.047595\n", + "c 0.205571 -1.191746\n", + "d 1.436217 -2.073527\n", + "e 1.644355 -0.021278\n", + "f -0.657392 -0.091122" ] }, - "execution_count": 19, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } @@ -1078,7 +1066,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 91, "id": "f8ae65cd-dbea-4f40-a464-7b07554b9b11", "metadata": { "tags": [ @@ -1146,7 +1134,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 92, "id": "0ca93c29", "metadata": { "attributes": { @@ -1165,7 +1153,7 @@ "Length: 6, dtype: boolean" ] }, - "execution_count": 21, + "execution_count": 92, "metadata": {}, "output_type": "execute_result" } @@ -1177,7 +1165,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 93, "id": "fd577bd5", "metadata": {}, "outputs": [ @@ -1211,17 +1199,17 @@ " \n", " \n", " a\n", - " 1.401532\n", - " -1.744216\n", - " -0.212177\n", - " -1.295240\n", + " -0.829219\n", + " 1.185075\n", + " 0.093787\n", + " -0.442140\n", " \n", " \n", " c\n", - " -0.097299\n", - " -0.834496\n", - " 0.188575\n", - " -0.271869\n", + " 1.195465\n", + " 0.205571\n", + " -1.191746\n", + " -0.836474\n", " \n", " \n", "\n", @@ -1229,11 +1217,11 @@ ], "text/plain": [ " A B C D\n", - "a 1.401532 -1.744216 -0.212177 -1.295240\n", - "c -0.097299 -0.834496 0.188575 -0.271869" + "a -0.829219 1.185075 0.093787 -0.442140\n", + "c 1.195465 0.205571 -1.191746 -0.836474" ] }, - "execution_count": 22, + "execution_count": 93, "metadata": {}, "output_type": "execute_result" } @@ -1244,7 +1232,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 94, "id": "4f1b5f67-5c56-4e47-8953-4d6383f283e1", "metadata": { "tags": [ @@ -1312,17 +1300,17 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 95, "id": "7e425a66", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.4015323563287203" + "-0.8292186214151204" ] }, - "execution_count": 24, + "execution_count": 95, "metadata": {}, "output_type": "execute_result" } @@ -1333,7 +1321,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 96, "id": "50e88f3d-07f0-443d-994c-d7fb36c4dc7a", "metadata": { "tags": [ @@ -1405,7 +1393,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 97, "id": "2bd13eab", "metadata": {}, "outputs": [ @@ -1418,7 +1406,7 @@ "dtype: object" ] }, - "execution_count": 26, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -1430,7 +1418,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 98, "id": "63081450-8216-403c-8b53-04b2cc18e442", "metadata": { "tags": [ @@ -1502,7 +1490,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 99, "id": "a08caf62", "metadata": {}, "outputs": [ @@ -1517,7 +1505,7 @@ "dtype: object" ] }, - "execution_count": 28, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -1528,7 +1516,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 100, "id": "7d665bb1-9bd1-4826-9a0f-f13496d64549", "metadata": { "tags": [ @@ -1590,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 101, "id": "a5f5d2ba", "metadata": {}, "outputs": [ @@ -1604,7 +1592,7 @@ "dtype: object" ] }, - "execution_count": 30, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -1615,7 +1603,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 102, "id": "81114a6f-4511-4f2e-990b-c7edd5e4cf86", "metadata": { "tags": [ @@ -1685,7 +1673,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 103, "id": "318b8e37", "metadata": {}, "outputs": [ @@ -1698,7 +1686,7 @@ "dtype: object" ] }, - "execution_count": 32, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -1710,7 +1698,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 104, "id": "537dd0b6-b4fc-468b-88a4-5d828eba5ed8", "metadata": { "tags": [ @@ -1816,20 +1804,20 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 105, "id": "e7b93cb1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 0.531403\n", - "2 1.164702\n", - "4 -0.384782\n", + "0 -0.124201\n", + "2 1.294954\n", + "4 -0.793453\n", "dtype: float64" ] }, - "execution_count": 34, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -1842,7 +1830,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 106, "id": "24d4de8c-5c42-484b-89d7-e21ebb0ba7c3", "metadata": { "tags": [ @@ -1898,17 +1886,17 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 107, "id": "fe63cdf3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.21764232439885461" + "-0.9449480012698949" ] }, - "execution_count": 36, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -1919,7 +1907,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 108, "id": "ed15834b-fd14-4000-bbdb-0eb86a214984", "metadata": { "tags": [ @@ -1983,7 +1971,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 109, "id": "9c4e8129", "metadata": {}, "outputs": [ @@ -1993,12 +1981,12 @@ "0 0.000000\n", "2 0.000000\n", "4 0.000000\n", - "6 0.217642\n", - "8 1.458410\n", + "6 -0.944948\n", + "8 0.385288\n", "dtype: float64" ] }, - "execution_count": 38, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -2010,7 +1998,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 110, "id": "5b793d9f-5ddb-4121-8218-8a5eda713eab", "metadata": { "tags": [ @@ -2074,7 +2062,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 111, "id": "3d55d682", "metadata": {}, "outputs": [ @@ -2108,24 +2096,24 @@ " \n", " \n", " 0\n", - " -1.194412\n", - " -0.126540\n", - " 0.496297\n", - " -0.194096\n", + " 0.176708\n", + " -0.734049\n", + " -0.874521\n", + " 0.013537\n", " \n", " \n", " 2\n", - " -1.116951\n", - " 1.041856\n", - " -0.662633\n", - " 0.493678\n", + " 1.809582\n", + " 0.802905\n", + " -0.563674\n", + " -0.466175\n", " \n", " \n", " 4\n", - " 1.497028\n", - " -0.260497\n", - " 0.697729\n", - " -1.092215\n", + " 0.813012\n", + " -0.131666\n", + " 1.373226\n", + " -0.568180\n", " \n", " \n", "\n", @@ -2133,12 +2121,12 @@ ], "text/plain": [ " 0 2 4 6\n", - "0 -1.194412 -0.126540 0.496297 -0.194096\n", - "2 -1.116951 1.041856 -0.662633 0.493678\n", - "4 1.497028 -0.260497 0.697729 -1.092215" + "0 0.176708 -0.734049 -0.874521 0.013537\n", + "2 1.809582 0.802905 -0.563674 -0.466175\n", + "4 0.813012 -0.131666 1.373226 -0.568180" ] }, - "execution_count": 40, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -2153,7 +2141,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 112, "id": "172e44bf-8faf-42a1-b9a7-3adab79b97d1", "metadata": { "tags": [ @@ -2207,7 +2195,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 113, "id": "b5427ec6", "metadata": {}, "outputs": [ @@ -2239,23 +2227,23 @@ " \n", " \n", " 2\n", - " -0.662633\n", - " 0.493678\n", + " -0.563674\n", + " -0.466175\n", " \n", " \n", " 4\n", - " 0.697729\n", - " -1.092215\n", + " 1.373226\n", + " -0.568180\n", " \n", " \n", " 6\n", - " 0.715370\n", - " 1.528302\n", + " -0.467455\n", + " 1.028096\n", " \n", " \n", " 8\n", - " -0.548232\n", - " 0.081242\n", + " 0.156377\n", + " -0.368254\n", " \n", " \n", "\n", @@ -2263,13 +2251,13 @@ ], "text/plain": [ " 4 6\n", - "2 -0.662633 0.493678\n", - "4 0.697729 -1.092215\n", - "6 0.715370 1.528302\n", - "8 -0.548232 0.081242" + "2 -0.563674 -0.466175\n", + "4 1.373226 -0.568180\n", + "6 -0.467455 1.028096\n", + "8 0.156377 -0.368254" ] }, - "execution_count": 42, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -2288,7 +2276,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 114, "id": "d86dd6d1", "metadata": {}, "outputs": [ @@ -2320,18 +2308,18 @@ " \n", " \n", " 2\n", - " 1.041856\n", - " 0.493678\n", + " 0.802905\n", + " -0.466175\n", " \n", " \n", " 6\n", - " -0.177517\n", - " 1.528302\n", + " -1.760254\n", + " 1.028096\n", " \n", " \n", " 10\n", - " 0.666088\n", - " -0.595855\n", + " -1.020584\n", + " 1.987550\n", " \n", " \n", "\n", @@ -2339,12 +2327,12 @@ ], "text/plain": [ " 2 6\n", - "2 1.041856 0.493678\n", - "6 -0.177517 1.528302\n", - "10 0.666088 -0.595855" + "2 0.802905 -0.466175\n", + "6 -1.760254 1.028096\n", + "10 -1.020584 1.987550" ] }, - "execution_count": 43, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -2355,7 +2343,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 115, "id": "a5e2a6ba-671b-4aab-b63d-5ab4ee92501f", "metadata": { "tags": [ @@ -2409,7 +2397,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 116, "id": "8528cc39", "metadata": {}, "outputs": [ @@ -2443,17 +2431,17 @@ " \n", " \n", " 2\n", - " -1.116951\n", - " 1.041856\n", - " -0.662633\n", - " 0.493678\n", + " 1.809582\n", + " 0.802905\n", + " -0.563674\n", + " -0.466175\n", " \n", " \n", " 4\n", - " 1.497028\n", - " -0.260497\n", - " 0.697729\n", - " -1.092215\n", + " 0.813012\n", + " -0.131666\n", + " 1.373226\n", + " -0.568180\n", " \n", " \n", "\n", @@ -2461,11 +2449,11 @@ ], "text/plain": [ " 0 2 4 6\n", - "2 -1.116951 1.041856 -0.662633 0.493678\n", - "4 1.497028 -0.260497 0.697729 -1.092215" + "2 1.809582 0.802905 -0.563674 -0.466175\n", + "4 0.813012 -0.131666 1.373226 -0.568180" ] }, - "execution_count": 45, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -2476,7 +2464,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 117, "id": "178d6f69-464f-464e-ad45-fac857b9a370", "metadata": { "tags": [ @@ -2536,7 +2524,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 118, "id": "f9288433", "metadata": {}, "outputs": [ @@ -2568,33 +2556,33 @@ " \n", " \n", " 0\n", - " -0.126540\n", - " 0.496297\n", + " -0.734049\n", + " -0.874521\n", " \n", " \n", " 2\n", - " 1.041856\n", - " -0.662633\n", + " 0.802905\n", + " -0.563674\n", " \n", " \n", " 4\n", - " -0.260497\n", - " 0.697729\n", + " -0.131666\n", + " 1.373226\n", " \n", " \n", " 6\n", - " -0.177517\n", - " 0.715370\n", + " -1.760254\n", + " -0.467455\n", " \n", " \n", " 8\n", - " -0.980550\n", - " -0.548232\n", + " -1.629683\n", + " 0.156377\n", " \n", " \n", " 10\n", - " 0.666088\n", - " 0.114509\n", + " -1.020584\n", + " -0.194566\n", " \n", " \n", "\n", @@ -2602,15 +2590,15 @@ ], "text/plain": [ " 2 4\n", - "0 -0.126540 0.496297\n", - "2 1.041856 -0.662633\n", - "4 -0.260497 0.697729\n", - "6 -0.177517 0.715370\n", - "8 -0.980550 -0.548232\n", - "10 0.666088 0.114509" + "0 -0.734049 -0.874521\n", + "2 0.802905 -0.563674\n", + "4 -0.131666 1.373226\n", + "6 -1.760254 -0.467455\n", + "8 -1.629683 0.156377\n", + "10 -1.020584 -0.194566" ] }, - "execution_count": 47, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -2621,7 +2609,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 119, "id": "71859ce4-7ad5-4bea-9df2-f5929c0c2470", "metadata": { "tags": [ @@ -2679,17 +2667,17 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 120, "id": "eb3f25f3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1.0418559735628448" + "0.8029050594558378" ] }, - "execution_count": 49, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -2700,7 +2688,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 121, "id": "5dad7d1a-0bf5-40d8-a4ef-2c3e573ae6fc", "metadata": { "tags": [ @@ -2767,21 +2755,21 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 122, "id": "cc95030f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0 -1.116951\n", - "2 1.041856\n", - "4 -0.662633\n", - "6 0.493678\n", + "0 1.809582\n", + "2 0.802905\n", + "4 -0.563674\n", + "6 -0.466175\n", "Name: 2, dtype: float64" ] }, - "execution_count": 51, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -2792,7 +2780,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 123, "id": "bfa6df43-353d-4ba4-94a0-e65c9a659468", "metadata": { "tags": [ @@ -2858,7 +2846,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 124, "id": "0c635e2f", "metadata": { "attributes": { @@ -2875,7 +2863,7 @@ "['a', 'b', 'c', 'd', 'e', 'f']" ] }, - "execution_count": 53, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -2887,7 +2875,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 125, "id": "bae9b708", "metadata": { "attributes": { @@ -2904,7 +2892,7 @@ "['e', 'f']" ] }, - "execution_count": 54, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -2915,7 +2903,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 126, "id": "ccb95b2c", "metadata": { "attributes": { @@ -2932,7 +2920,7 @@ "[]" ] }, - "execution_count": 55, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -2943,7 +2931,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 127, "id": "fcaaeb73", "metadata": { "attributes": { @@ -2966,7 +2954,7 @@ "dtype: object" ] }, - "execution_count": 56, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -2978,7 +2966,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 128, "id": "19e7f165", "metadata": {}, "outputs": [ @@ -2990,7 +2978,7 @@ "dtype: object" ] }, - "execution_count": 57, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -3001,7 +2989,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 129, "id": "3b612356-7774-472e-849e-0f3dc267b578", "metadata": { "tags": [ @@ -3057,7 +3045,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 130, "id": "2a25cc5c", "metadata": { "attributes": { @@ -3074,7 +3062,7 @@ "Series([], dtype: object)" ] }, - "execution_count": 59, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -3093,7 +3081,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 131, "id": "f9024d15", "metadata": { "attributes": { @@ -3111,7 +3099,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 132, "id": "5837f585", "metadata": {}, "outputs": [ @@ -3164,7 +3152,7 @@ "Index: [0, 1, 2, 3, 4]" ] }, - "execution_count": 61, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -3175,7 +3163,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 133, "id": "4b81ac82-5d47-4410-90b9-040f0dac662b", "metadata": { "tags": [ @@ -3229,7 +3217,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 134, "id": "d0e19553", "metadata": {}, "outputs": [ @@ -3260,23 +3248,23 @@ " \n", " \n", " 0\n", - " 0.700611\n", + " -0.491934\n", " \n", " \n", " 1\n", - " -0.358047\n", + " -0.758957\n", " \n", " \n", " 2\n", - " 0.620409\n", + " 1.793034\n", " \n", " \n", " 3\n", - " 0.953488\n", + " -0.330006\n", " \n", " \n", " 4\n", - " -2.263445\n", + " 1.362746\n", " \n", " \n", "\n", @@ -3284,14 +3272,14 @@ ], "text/plain": [ " B\n", - "0 0.700611\n", - "1 -0.358047\n", - "2 0.620409\n", - "3 0.953488\n", - "4 -2.263445" + "0 -0.491934\n", + "1 -0.758957\n", + "2 1.793034\n", + "3 -0.330006\n", + "4 1.362746" ] }, - "execution_count": 63, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -3302,7 +3290,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 135, "id": "39dab713-a3f6-4189-bad9-cba564f56951", "metadata": { "tags": [ @@ -3358,7 +3346,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 136, "id": "f91ab868", "metadata": {}, "outputs": [ @@ -3390,8 +3378,8 @@ " \n", " \n", " 4\n", - " 0.374726\n", - " -2.263445\n", + " 0.238833\n", + " 1.362746\n", " \n", " \n", "\n", @@ -3399,10 +3387,10 @@ ], "text/plain": [ " A B\n", - "4 0.374726 -2.263445" + "4 0.238833 1.362746" ] }, - "execution_count": 65, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -3413,7 +3401,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 137, "id": "220aa5af-5003-45e9-87cf-c4f5d0ac6d93", "metadata": { "tags": [ @@ -3478,7 +3466,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 138, "id": "f3496be2", "metadata": { "attributes": { @@ -3491,37 +3479,16 @@ "raises-exception" ] }, - "outputs": [ - { - "ename": "IndexError", - "evalue": "positional indexers are out-of-bounds", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1587\u001b[0m, in \u001b[0;36m_iLocIndexer._get_list_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1586\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_take_with_is_copy(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 1589\u001b[0m \u001b[38;5;66;03m# re-raise with different error message\u001b[39;00m\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\generic.py:3902\u001b[0m, in \u001b[0;36mNDFrame._take_with_is_copy\u001b[1;34m(self, indices, axis)\u001b[0m\n\u001b[0;32m 3895\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 3896\u001b[0m \u001b[38;5;124;03mInternal version of the `take` method that sets the `_is_copy`\u001b[39;00m\n\u001b[0;32m 3897\u001b[0m \u001b[38;5;124;03mattribute to keep track of the parent dataframe (using in indexing\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 3900\u001b[0m \u001b[38;5;124;03mSee the docstring of `take` for full explanation of the parameters.\u001b[39;00m\n\u001b[0;32m 3901\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m-> 3902\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_take(indices\u001b[38;5;241m=\u001b[39mindices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m 3903\u001b[0m \u001b[38;5;66;03m# Maybe set copy if we didn't actually change the index.\u001b[39;00m\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\generic.py:3886\u001b[0m, in \u001b[0;36mNDFrame._take\u001b[1;34m(self, indices, axis, convert_indices)\u001b[0m\n\u001b[0;32m 3884\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_consolidate_inplace()\n\u001b[1;32m-> 3886\u001b[0m new_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mgr\u001b[38;5;241m.\u001b[39mtake(\n\u001b[0;32m 3887\u001b[0m indices,\n\u001b[0;32m 3888\u001b[0m axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_block_manager_axis(axis),\n\u001b[0;32m 3889\u001b[0m verify\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 3890\u001b[0m convert_indices\u001b[38;5;241m=\u001b[39mconvert_indices,\n\u001b[0;32m 3891\u001b[0m )\n\u001b[0;32m 3892\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_constructor(new_data)\u001b[38;5;241m.\u001b[39m__finalize__(\u001b[38;5;28mself\u001b[39m, method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtake\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:975\u001b[0m, in \u001b[0;36mBaseBlockManager.take\u001b[1;34m(self, indexer, axis, verify, convert_indices)\u001b[0m\n\u001b[0;32m 974\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convert_indices:\n\u001b[1;32m--> 975\u001b[0m indexer \u001b[38;5;241m=\u001b[39m maybe_convert_indices(indexer, n, verify\u001b[38;5;241m=\u001b[39mverify)\n\u001b[0;32m 977\u001b[0m new_labels \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis]\u001b[38;5;241m.\u001b[39mtake(indexer)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexers\\utils.py:286\u001b[0m, in \u001b[0;36mmaybe_convert_indices\u001b[1;34m(indices, n, verify)\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m--> 286\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mindices are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 287\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m indices\n", - "\u001b[1;31mIndexError\u001b[0m: indices are out-of-bounds", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[67], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#:tags: [\"raises-exception\"]\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dfl\u001b[38;5;241m.\u001b[39miloc[[\u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m, \u001b[38;5;241m6\u001b[39m]]\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1073\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 1072\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[1;32m-> 1073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_axis(maybe_callable, axis\u001b[38;5;241m=\u001b[39maxis)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1616\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1614\u001b[0m \u001b[38;5;66;03m# a list of integers\u001b[39;00m\n\u001b[0;32m 1615\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_list_like_indexer(key):\n\u001b[1;32m-> 1616\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_list_axis(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m 1618\u001b[0m \u001b[38;5;66;03m# a single integer\u001b[39;00m\n\u001b[0;32m 1619\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1620\u001b[0m key \u001b[38;5;241m=\u001b[39m item_from_zerodim(key)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1590\u001b[0m, in \u001b[0;36m_iLocIndexer._get_list_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1587\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_take_with_is_copy(key, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 1589\u001b[0m \u001b[38;5;66;03m# re-raise with different error message\u001b[39;00m\n\u001b[1;32m-> 1590\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositional indexers are out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n", - "\u001b[1;31mIndexError\u001b[0m: positional indexers are out-of-bounds" - ] - } - ], + "outputs": [], "source": [ - "dfl.iloc[[4, 5, 6]]" + "# Remove # and run to see the IndexError\n", + "\n", + "# dfl.iloc[[4, 5, 6]]" ] }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 139, "id": "7b081f89", "metadata": { "attributes": { @@ -3534,26 +3501,11 @@ "raises-exception" ] }, - "outputs": [ - { - "ename": "IndexError", - "evalue": "single positional indexer is out-of-bounds", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[68], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[38;5;66;03m#:tags: [\"raises-exception\"]\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dfl\u001b[38;5;241m.\u001b[39miloc[:, \u001b[38;5;241m4\u001b[39m]\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1067\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1065\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_scalar_access(key):\n\u001b[0;32m 1066\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_value(\u001b[38;5;241m*\u001b[39mkey, takeable\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_takeable)\n\u001b[1;32m-> 1067\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_tuple(key)\n\u001b[0;32m 1068\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1069\u001b[0m \u001b[38;5;66;03m# we by definition only have the 0th axis\u001b[39;00m\n\u001b[0;32m 1070\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1563\u001b[0m, in \u001b[0;36m_iLocIndexer._getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_getitem_tuple\u001b[39m(\u001b[38;5;28mself\u001b[39m, tup: \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m-> 1563\u001b[0m tup \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_tuple_indexer(tup)\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(IndexingError):\n\u001b[0;32m 1565\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_lowerdim(tup)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:873\u001b[0m, in \u001b[0;36m_LocationIndexer._validate_tuple_indexer\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 871\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(key):\n\u001b[0;32m 872\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 873\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(k, i)\n\u001b[0;32m 874\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m 875\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m 876\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLocation based indexing can only have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 877\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_valid_types\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] types\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m 878\u001b[0m ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1466\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1464\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m 1465\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_integer(key):\n\u001b[1;32m-> 1466\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_integer(key, axis)\n\u001b[0;32m 1467\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m 1468\u001b[0m \u001b[38;5;66;03m# a tuple should already have been caught by this point\u001b[39;00m\n\u001b[0;32m 1469\u001b[0m \u001b[38;5;66;03m# so don't treat a tuple as a valid indexer\u001b[39;00m\n\u001b[0;32m 1470\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m IndexingError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mToo many indexers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[1;32mF:\\anaconda\\Lib\\site-packages\\pandas\\core\\indexing.py:1557\u001b[0m, in \u001b[0;36m_iLocIndexer._validate_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1555\u001b[0m len_axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39m_get_axis(axis))\n\u001b[0;32m 1556\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m len_axis \u001b[38;5;129;01mor\u001b[39;00m key \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39mlen_axis:\n\u001b[1;32m-> 1557\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msingle positional indexer is out-of-bounds\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds" - ] - } - ], + "outputs": [], "source": [ - "dfl.iloc[:, 4]" + "# Remove # and run to see the IndexError\n", + "\n", + "# dfl.iloc[:, 4]" ] }, { @@ -3568,7 +3520,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 140, "id": "72420538", "metadata": {}, "outputs": [ @@ -3601,25 +3553,25 @@ " \n", " \n", " \n", - " d\n", - " 0.852422\n", - " -0.452728\n", - " -0.384272\n", - " 0.370443\n", + " b\n", + " 0.206097\n", + " 0.325348\n", + " -0.811762\n", + " 0.696057\n", " \n", " \n", - " e\n", - " 0.263762\n", - " -0.053398\n", - " 0.135893\n", - " 0.618338\n", + " c\n", + " 1.369032\n", + " 1.861469\n", + " 0.355490\n", + " 0.416873\n", " \n", " \n", - " f\n", - " 0.857898\n", - " 0.456715\n", - " 0.556336\n", - " -1.022002\n", + " d\n", + " 0.028375\n", + " 0.855487\n", + " 0.998617\n", + " -1.899382\n", " \n", " \n", "\n", @@ -3627,12 +3579,12 @@ ], "text/plain": [ " A B C D\n", - "d 0.852422 -0.452728 -0.384272 0.370443\n", - "e 0.263762 -0.053398 0.135893 0.618338\n", - "f 0.857898 0.456715 0.556336 -1.022002" + "b 0.206097 0.325348 -0.811762 0.696057\n", + "c 1.369032 1.861469 0.355490 0.416873\n", + "d 0.028375 0.855487 0.998617 -1.899382" ] }, - "execution_count": 69, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -3647,7 +3599,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 141, "id": "7206088f-3aa5-4392-9982-cadec553e616", "metadata": { "tags": [ @@ -3701,7 +3653,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 142, "id": "ab18a18f", "metadata": {}, "outputs": [ @@ -3733,33 +3685,33 @@ " \n", " \n", " a\n", - " -1.863049\n", - " 1.979038\n", + " -0.889154\n", + " -0.228248\n", " \n", " \n", " b\n", - " -0.336175\n", - " -1.192626\n", + " 0.206097\n", + " 0.325348\n", " \n", " \n", " c\n", - " -0.317314\n", - " 2.267986\n", + " 1.369032\n", + " 1.861469\n", " \n", " \n", " d\n", - " 0.852422\n", - " -0.452728\n", + " 0.028375\n", + " 0.855487\n", " \n", " \n", " e\n", - " 0.263762\n", - " -0.053398\n", + " -0.344703\n", + " 1.783202\n", " \n", " \n", " f\n", - " 0.857898\n", - " 0.456715\n", + " -0.660587\n", + " 0.034734\n", " \n", " \n", "\n", @@ -3767,15 +3719,15 @@ ], "text/plain": [ " A B\n", - "a -1.863049 1.979038\n", - "b -0.336175 -1.192626\n", - "c -0.317314 2.267986\n", - "d 0.852422 -0.452728\n", - "e 0.263762 -0.053398\n", - "f 0.857898 0.456715" + "a -0.889154 -0.228248\n", + "b 0.206097 0.325348\n", + "c 1.369032 1.861469\n", + "d 0.028375 0.855487\n", + "e -0.344703 1.783202\n", + "f -0.660587 0.034734" ] }, - "execution_count": 71, + "execution_count": 142, "metadata": {}, "output_type": "execute_result" } @@ -3786,7 +3738,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 143, "id": "2166496e-975d-4539-a3b6-54cedd012e73", "metadata": { "tags": [ @@ -3846,7 +3798,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 144, "id": "aeb4a77e", "metadata": {}, "outputs": [ @@ -3878,33 +3830,33 @@ " \n", " \n", " a\n", - " -1.863049\n", - " 1.979038\n", + " -0.889154\n", + " -0.228248\n", " \n", " \n", " b\n", - " -0.336175\n", - " -1.192626\n", + " 0.206097\n", + " 0.325348\n", " \n", " \n", " c\n", - " -0.317314\n", - " 2.267986\n", + " 1.369032\n", + " 1.861469\n", " \n", " \n", " d\n", - " 0.852422\n", - " -0.452728\n", + " 0.028375\n", + " 0.855487\n", " \n", " \n", " e\n", - " 0.263762\n", - " -0.053398\n", + " -0.344703\n", + " 1.783202\n", " \n", " \n", " f\n", - " 0.857898\n", - " 0.456715\n", + " -0.660587\n", + " 0.034734\n", " \n", " \n", "\n", @@ -3912,15 +3864,15 @@ ], "text/plain": [ " A B\n", - "a -1.863049 1.979038\n", - "b -0.336175 -1.192626\n", - "c -0.317314 2.267986\n", - "d 0.852422 -0.452728\n", - "e 0.263762 -0.053398\n", - "f 0.857898 0.456715" + "a -0.889154 -0.228248\n", + "b 0.206097 0.325348\n", + "c 1.369032 1.861469\n", + "d 0.028375 0.855487\n", + "e -0.344703 1.783202\n", + "f -0.660587 0.034734" ] }, - "execution_count": 73, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -3931,7 +3883,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 145, "id": "e8fe3be5-15de-4036-ab8a-d6483abf265f", "metadata": { "tags": [ @@ -3989,23 +3941,23 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 146, "id": "ec331b54", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "a -1.863049\n", - "b -0.336175\n", - "c -0.317314\n", - "d 0.852422\n", - "e 0.263762\n", - "f 0.857898\n", + "a -0.889154\n", + "b 0.206097\n", + "c 1.369032\n", + "d 0.028375\n", + "e -0.344703\n", + "f -0.660587\n", "Name: A, dtype: float64" ] }, - "execution_count": 75, + "execution_count": 146, "metadata": {}, "output_type": "execute_result" } @@ -4016,7 +3968,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 147, "id": "31840764-a775-4e5f-8023-6c4762005ff6", "metadata": { "tags": [ @@ -4081,20 +4033,20 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 148, "id": "d4e60491", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "d 0.852422\n", - "e 0.263762\n", - "f 0.857898\n", + "b 0.206097\n", + "c 1.369032\n", + "d 0.028375\n", "Name: A, dtype: float64" ] }, - "execution_count": 77, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -4105,7 +4057,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 149, "id": "1d7a46f1-98ce-4d87-924a-288812c6b4ed", "metadata": { "tags": [ @@ -4172,7 +4124,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 150, "id": "978312bb", "metadata": {}, "outputs": [ @@ -4184,7 +4136,7 @@ "Name: A, dtype: int64" ] }, - "execution_count": 79, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -4199,7 +4151,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 151, "id": "a8844d1c-fdc5-4c85-923c-092ac6367692", "metadata": { "tags": [ @@ -4264,7 +4216,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 152, "id": "2e7e25d2", "metadata": {}, "outputs": [ @@ -4276,7 +4228,7 @@ "Name: A, dtype: int64" ] }, - "execution_count": 81, + "execution_count": 152, "metadata": {}, "output_type": "execute_result" } @@ -4287,7 +4239,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 153, "id": "48f7feb0-9334-441f-893a-42815523e739", "metadata": { "tags": [ @@ -4356,7 +4308,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 154, "id": "7c0b22e6", "metadata": {}, "outputs": [ @@ -4406,7 +4358,7 @@ "c 3 6" ] }, - "execution_count": 83, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -4417,7 +4369,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 155, "id": "c0924629-67d8-43b6-a435-d91bb8bf6408", "metadata": { "tags": [ @@ -4500,7 +4452,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.9.18" } }, "nbformat": 4, diff --git a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb index cbf4637a06..540d02ee10 100644 --- a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb +++ b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "646c8580", "metadata": { "attributes": { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "2d2455c1", "metadata": { "attributes": { @@ -144,14 +144,30 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.389808\n", + "b 1.166912\n", + "c 1.083422\n", + "d -0.751227\n", + "e -0.930881\n", + "dtype: float64" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "20f33329", "metadata": { "attributes": { @@ -161,14 +177,25 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s.index" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5376f720", "metadata": { "attributes": { @@ -178,7 +205,23 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0 0.532715\n", + "1 0.890063\n", + "2 -1.069293\n", + "3 1.279518\n", + "4 0.599430\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.Series(np.random.randn(5))" ] @@ -198,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "e8095575", "metadata": { "attributes": { @@ -215,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "ba462934", "metadata": { "attributes": { @@ -225,7 +268,21 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "b 1\n", + "a 0\n", + "c 2\n", + "dtype: int64" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.Series(d)" ] @@ -240,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "03488418", "metadata": { "attributes": { @@ -257,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c35e968c", "metadata": { "attributes": { @@ -267,14 +324,28 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.0\n", + "b 1.0\n", + "c 2.0\n", + "dtype: float64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.Series(d)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "95eafc4d", "metadata": { "attributes": { @@ -284,7 +355,22 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "b 1.0\n", + "c 2.0\n", + "d NaN\n", + "a 0.0\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.Series(d, index=[\"b\", \"c\", \"d\", \"a\"])" ] @@ -305,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "6f744115", "metadata": { "attributes": { @@ -315,7 +401,23 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 5.0\n", + "b 5.0\n", + "c 5.0\n", + "d 5.0\n", + "e 5.0\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.Series(5.0, index=[\"a\", \"b\", \"c\", \"d\", \"e\"])" ] @@ -332,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "2ca453e9", "metadata": { "attributes": { @@ -342,14 +444,25 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.3898080889883227" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s[0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "4cf8e176", "metadata": { "attributes": { @@ -359,14 +472,28 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.389808\n", + "b 1.166912\n", + "c 1.083422\n", + "dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s[:3]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "1bab7730", "metadata": { "attributes": { @@ -376,14 +503,27 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "b 1.166912\n", + "c 1.083422\n", + "dtype: float64" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s[s > s.median()]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "b5e98d89", "metadata": { "attributes": { @@ -393,14 +533,28 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "e -0.930881\n", + "d -0.751227\n", + "b 1.166912\n", + "dtype: float64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s[[4, 3, 1]]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "c98a7190", "metadata": { "attributes": { @@ -410,7 +564,23 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 1.476697\n", + "b 3.212059\n", + "c 2.954773\n", + "d 0.471787\n", + "e 0.394206\n", + "dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.exp(s)" ] @@ -425,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "b0298996", "metadata": { "attributes": { @@ -435,7 +605,18 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('float64')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s.dtype" ] @@ -450,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "1989c3a9", "metadata": { "attributes": { @@ -460,7 +641,21 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "[ 0.3898080889883227, 1.1669122983173188, 1.083421954866096,\n", + " -0.7512272141904102, -0.9308814331814397]\n", + "Length: 5, dtype: float64" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s.array" ] @@ -475,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "1cc04172", "metadata": { "attributes": { @@ -485,7 +680,18 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.38980809, 1.1669123 , 1.08342195, -0.75122721, -0.93088143])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s.to_numpy()" ] @@ -504,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "bcfe90c9", "metadata": { "attributes": { @@ -514,14 +720,25 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.3898080889883227" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s[\"a\"]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "00c68766", "metadata": { "attributes": { @@ -538,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "74f58473", "metadata": { "attributes": { @@ -548,14 +765,30 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.389808\n", + "b 1.166912\n", + "c 1.083422\n", + "d -0.751227\n", + "e 12.000000\n", + "dtype: float64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "2f822110", "metadata": { "attributes": { @@ -565,14 +798,25 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"e\" in s" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "164dcf61", "metadata": { "attributes": { @@ -582,7 +826,18 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "\"f\" in s" ] @@ -597,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "40a23c62-9c88-4a6e-9316-60317abe7859", "metadata": { "attributes": { @@ -612,7 +867,9 @@ }, "outputs": [], "source": [ - "s[\"f\"]" + "# Remove # and run to see the exception\n", + "\n", + "# s[\"f\"]" ] }, { @@ -625,7 +882,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "ad2a67c6", "metadata": { "attributes": { @@ -642,7 +899,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "13c1c13b", "metadata": { "attributes": { @@ -652,7 +909,18 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "nan" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s.get(\"f\", np.nan)" ] @@ -671,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "35540134", "metadata": { "attributes": { @@ -681,14 +949,30 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "a 0.779616\n", + "b 2.333825\n", + "c 2.166844\n", + "d -1.502454\n", + "e 24.000000\n", + "dtype: float64" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "s + s" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "aea7c1dc", "metadata": { "attributes": { @@ -698,14 +982,30 @@ "id": "" } }, - 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Let's visualize it! 🎥

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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "(\n", " iris.query(\"SepalLength > 5\")\n", @@ -1941,7 +4019,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "id": "60b7e3c7", "metadata": { "attributes": { @@ -1958,7 +4036,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "id": "4c821875", "metadata": { "attributes": { @@ -1968,7 +4046,72 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ABCD
01456
12579
236912
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "0 1 4 5 6\n", + "1 2 5 7 9\n", + "2 3 6 9 12" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfa.assign(C=lambda x: x[\"A\"] + x[\"B\"], D=lambda x: x[\"A\"] + x[\"C\"])" ] @@ -1997,24 +4140,66 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "id": "82154750", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "one 2.0\n", + "bar 2.0\n", + "flag False\n", + "foo bar\n", + "one_trunc 2.0\n", + "Name: b, dtype: object" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.loc[\"b\"]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "id": "743d6893-bbf3-4fbf-a158-a3aaae040b39", "metadata": { "tags": [ "hide-cell" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n", + "
\n", + "

Let's visualize it! 🎥

\n", + "
\n", + " \n", + "
\n", + "
\n", + "
\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "\n", @@ -2041,7 +4226,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "id": "2fae006c", "metadata": { "attributes": { @@ -2051,7 +4236,23 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "one 3.0\n", + "bar 3.0\n", + "flag True\n", + "foo bar\n", + "one_trunc NaN\n", + "Name: c, dtype: object" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.iloc[2]" ] @@ -2068,7 +4269,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "id": "a3e29475", "metadata": { "attributes": { @@ -2085,7 +4286,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "id": "c4634479", "metadata": { "attributes": { @@ -2102,7 +4303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "id": "09eb77aa", "metadata": { "attributes": { @@ -2112,7 +4313,128 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ABCD
0-2.948481-0.423878-0.607636NaN
1-1.9201052.5419981.177187NaN
20.810385-1.4583961.420052NaN
31.405297-0.623525-0.060302NaN
4-0.3601740.887550-0.152828NaN
53.116284-0.400383-1.851961NaN
6-0.0106161.174856-1.748294NaN
7NaNNaNNaNNaN
8NaNNaNNaNNaN
9NaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " A B C D\n", + "0 -2.948481 -0.423878 -0.607636 NaN\n", + "1 -1.920105 2.541998 1.177187 NaN\n", + "2 0.810385 -1.458396 1.420052 NaN\n", + "3 1.405297 -0.623525 -0.060302 NaN\n", + "4 -0.360174 0.887550 -0.152828 NaN\n", + "5 3.116284 -0.400383 -1.851961 NaN\n", + "6 -0.010616 1.174856 -1.748294 NaN\n", + "7 NaN NaN NaN NaN\n", + "8 NaN NaN NaN NaN\n", + "9 NaN NaN NaN NaN" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df + df2" ] @@ -2127,7 +4449,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "id": "c2a8adda", "metadata": { "attributes": { @@ -2137,7 +4459,128 @@ "id": "" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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ABCD
00.0000000.0000000.0000000.000000
1-0.0931222.3674710.5094430.995821
22.251086-1.8233860.955111-0.083409
32.3515560.3369140.3138880.777614
41.492720-0.1461241.6607332.685753
53.5950880.013952-1.699945-1.258807
62.5983650.038556-1.1473021.108468
72.161150-1.1105381.713914-0.157944
83.347460-0.7440860.674301-0.147782
92.7158352.4546290.6989741.240166
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ABCD
0-6.5618491.475187-1.293874-3.191283
1-7.02745713.3125421.2533401.787824
24.693579-7.6417413.481679-3.608329
35.1959293.1597560.2755660.696787
40.9017490.7445677.00979310.237485
511.4135891.544948-9.793598-9.485320
66.4299771.667968-7.0303852.351056
74.243902-4.0775047.275695-3.981001
810.175453-2.2452432.077633-3.930195
97.01732413.7483312.2009983.009549
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ABCD
0-0.583986-9.527211-1.517969-0.963153
1-0.5538660.441987-6.696488-23.565357
21.856267-0.5185793.374549-0.891531
31.5644914.311251-2.899503-3.836671
4-4.552692-3.9826880.9980450.606981
50.531147-10.987746-0.423959-0.435338
61.128674-15.058810-0.55368614.242736
72.228261-0.8227060.947742-0.835980
80.611587-1.17778964.405384-0.843143
90.9965470.42559224.8759224.952705
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ABCD
08.5978590.0001211.883423e-011.162034
110.62629126.2035944.972921e-040.000003
20.08422513.8274397.711465e-031.582902
30.1669200.0028951.414835e-020.004615
40.0023280.0039751.007858e+007.367132
512.5643760.0000693.095318e+0127.841482
60.6162060.0000191.064009e+010.000024
70.0405642.1828351.239478e+002.047461
87.1477160.5196735.811809e-081.978773
91.01393130.4806852.611459e-060.001662
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01234
A-1.712370-1.8054910.5387160.639186-0.219650
B-0.1049632.262508-1.9283480.231951-0.251087
C-0.658775-0.1493320.296336-0.3448871.001959
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ABCD
2000-01-01-1.003075-1.0689151.2700931.131469
2000-01-020.508569-0.324633-2.092349-0.550827
2000-01-030.762823-0.897289-2.043889-1.096294
2000-01-040.5224462.1526131.6390170.314416
2000-01-05-0.3028380.9159040.803904-1.231580
2000-01-06-0.834977-0.8005500.390671-0.679977
2000-01-07-1.556795-0.502958-1.2396710.730893
2000-01-08-0.4240521.0550650.9000783.551748
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ABCD
2000-01-01-1.003075-1.0689151.2700931.131469
2000-01-020.508569-0.324633-2.092349-0.550827
2000-01-030.762823-0.897289-2.043889-1.096294
2000-01-040.5224462.1526131.6390170.314416
2000-01-05-0.3028380.9159040.803904-1.231580
2000-01-06-0.834977-0.8005500.390671-0.679977
2000-01-07-1.556795-0.502958-1.2396710.730893
2000-01-08-0.4240521.0550650.9000783.551748
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ABCD
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NameScientificNameCategoryOrderFamilyGenusConservationStatusMinLengthMaxLengthMinBodyMassMaxBodyMassMinWingspanMaxWingspan
0Black-bellied whistling-duckDendrocygna autumnalisDucks/Geese/WaterfowlAnseriformesAnatidaeDendrocygnaLC47.056.0652.01020.076.094.0
1Fulvous whistling-duckDendrocygna bicolorDucks/Geese/WaterfowlAnseriformesAnatidaeDendrocygnaLC45.053.0712.01050.085.093.0
2Snow gooseAnser caerulescensDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC64.079.02050.04050.0135.0165.0
3Ross's gooseAnser rossiiDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC57.364.01066.01567.0113.0116.0
4Greater white-fronted gooseAnser albifronsDucks/Geese/WaterfowlAnseriformesAnatidaeAnserLC64.081.01930.03310.0130.0165.0
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" + ], + "text/plain": [ + " Name ScientificName \\\n", + "0 Black-bellied whistling-duck Dendrocygna autumnalis \n", + "1 Fulvous whistling-duck Dendrocygna bicolor \n", + "2 Snow goose Anser caerulescens \n", + "3 Ross's goose Anser rossii \n", + "4 Greater white-fronted goose Anser albifrons \n", + "\n", + " Category Order Family Genus \\\n", + "0 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n", + "1 Ducks/Geese/Waterfowl Anseriformes Anatidae Dendrocygna \n", + "2 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "3 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "4 Ducks/Geese/Waterfowl Anseriformes Anatidae Anser \n", + "\n", + " ConservationStatus MinLength MaxLength MinBodyMass MaxBodyMass \\\n", + "0 LC 47.0 56.0 652.0 1020.0 \n", + "1 LC 45.0 53.0 712.0 1050.0 \n", + "2 LC 64.0 79.0 2050.0 4050.0 \n", + "3 LC 57.3 64.0 1066.0 1567.0 \n", + "4 LC 64.0 81.0 1930.0 3310.0 \n", + "\n", + " MinWingspan MaxWingspan \n", + "0 76.0 94.0 \n", + "1 85.0 93.0 \n", + "2 135.0 165.0 \n", + "3 113.0 116.0 \n", + "4 130.0 165.0 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "birds = pd.read_csv('https://static-1300131294.cos.ap-shanghai.myqcloud.com/data/birds.csv')\n", @@ -90,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "d6676798", "metadata": { "attributes": { @@ -104,7 +271,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "birds.plot(kind='scatter', x='MaxLength', y='Order', figsize=(12, 8))\n", "\n", @@ -129,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d9aff5f1", "metadata": { "attributes": { @@ -143,7 +321,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "birds['MaxBodyMass'].plot(kind='hist', bins=10, figsize=(12, 12))\n", "plt.show()" @@ -159,7 +348,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "6665b3c9", "metadata": { "attributes": { @@ -173,7 +362,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)] \n", "filteredBirds['MaxBodyMass'].plot(kind='hist', bins=40, figsize=(12, 12))\n", @@ -239,7 +450,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "b1ade936", "metadata": { "attributes": { @@ -253,7 +464,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x = filteredBirds['MaxBodyMass']\n", "y = filteredBirds['MaxLength']\n", @@ -311,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "f4ead484", "metadata": { "attributes": { @@ -325,7 +547,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan']\n", "x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan']\n", @@ -365,7 +598,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "46cc4d6f", "metadata": { "attributes": { @@ -379,7 +612,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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bE+TpjN6ezmg0CPglq0TscoiI2oVhiEhEWYXGzRaDvaw7DAEtV5UREVkThiEiEf3eM2S984WaNQ+V7cksgsHAJfZEZD0sIgwtX74cQUFBUKlUiIqKwqFDh67bfuPGjQgJCYFKpUJoaCi2b9/e4vlp06ZBIpG0eIwfP74zPwJRh2QVGXuG+tlAGBoe1B0uSjlKKutxIr9c7HKIiNpM9DC0YcMGzJ49GwsWLEBqairCwsIQFxeHoqLWu9r379+PyZMn46mnnkJaWhri4+MRHx+PkydPtmg3fvx4FBQUmB5fffVVV3wcojYrq65HcfNKMhsYJlPIpYjp6wmAQ2VEZF1ED0Pvv/8+/vSnP2H69OkYOHAgVq5cCScnJ3z66aettl+6dCnGjx+PV199FQMGDMBbb72FYcOGYdmyZS3aKZVKaDQa06Nbt25d8XGI2ux003whP3dHOFvxSrIrNQ+V7c5kGCIi6yFqGKqvr0dKSgpiY2NN16RSKWJjY5GcnNzqa5KTk1u0B4C4uLir2u/ZswdeXl7o378/ZsyYgUuXLl2zjrq6Ouh0uhYPos5mKyvJrnRbSA8AwPEL5SiqqBW5GiKithE1DJWUlECv18Pb27vFdW9vb2i12lZfo9Vqb9h+/Pjx+Pzzz5GUlITFixdj7969uOuuu6DX61t9z0WLFkGtVpse/v7+N/nJiG4sqykM2cJ8oWZeriqE+qkBAHsyi0WuhoiobUQfJusMjzzyCCZOnIjQ0FDEx8dj69atOHz4MPbs2dNq+7lz56K8vNz0yMvL69qCyS6dtqFl9VdqXmLPozmIyFqIGoY8PT0hk8lQWFjY4nphYSE0Gk2rr9FoNO1qDwC9e/eGp6cnzpw50+rzSqUSbm5uLR5Enc2WVpJdqXne0L6sEtQ3GkSuhojoxkQNQwqFAhEREUhKSjJdMxgMSEpKQnR0dKuviY6ObtEeABITE6/ZHgAuXLiAS5cuwcfHxzyFE92ky1X1KKk0riTra2M9Q0P81PB0UaCyrhFHckrFLoeI6IZEHyabPXs2Vq9ejbVr1yI9PR0zZsxAVVUVpk+fDgCYMmUK5s6da2o/a9Ys7NixA0uWLEFGRgYWLlyII0eOYObMmQCAyspKvPrqqzhw4ADOnz+PpKQkTJo0CX379kVcXJwon5Hoj5onT9vSSrJmUqkEY/pxqIyIrIfoYSghIQHvvfce5s+fj/DwcBw9ehQ7duwwTZLOzc1FQUGBqf2oUaOwbt06rFq1CmFhYfjmm2+wZcsWDB48GAAgk8lw/PhxTJw4Ef369cNTTz2FiIgI7Nu3D0qlUpTPSPRHp01DZLbVK9Tsdh7NQURWRCIIAvfN/wOdTge1Wo3y8nLOH6JOMf/bk/g8OQfP3Nobc+8eIHY5ZqerbcCwvyei0SDg51fHIsDDSeySiMgOdPT7W/SeISJ7ZEtnkrXGTeWAyCDjRqc/ZRTeoDURkbgYhohEcMbGh8mA34fKkjhURkQWjmGIqIuVVtWjpLIegO2tJLtS7ADjvL8DZy9BV9sgcjVERNfGMETUxZqHyHp2c4STwrZWkl2pdw8X9OnhjAa9wN2oiciiMQwRdTFbPIbjWu4YaNwMNfEU5w0RkeViGCLqYqZjOGx4vlCzOwYah8r2ZBRxN2oislgMQ0RdrHmYrJ+X7fcMDfV3h6eLEhV1jTh47pLY5RARtYphiKiL2eqZZK2RSiWIHWBcVcahMiKyVAxDRF3oUmUdSquMK8n6eDmLXE3XaB4q23WqENzjlYgsEcMQURdqni/k3922V5JdaXRfTzg6yHCxvBa/XdSJXQ4R0VUYhoi60JnipsnTdjBfqJnKQYZb+3kCAHZyqIyILBDDEFEXym6aL2TLmy22hkvsiciSMQwRdaHspp6hPj3sY75Qs9tDvCCVAOkFOuSVVotdDhFRCwxDRF3obHEVAKBPD/vqGerurEBkUHcAwK509g4RkWVhGCLqIlV1jcgvqwFgf2EIAO5sWlXGoTIisjQMQ0Rd5FyJsVeou7MC3ZwVIlfT9ZqX2B88V4ryah7cSkSWg2GIqIvY63yhZoEezujv7Qq9QUBSBnuHiMhyMAwRdRF7XUl2pbjBxlVlP5zUilwJEdHvGIaIuki2nU6evtJdTWHo59PFqKprFLkaIiIjhiGiLvL7MJn9hqEQjSuCPJxQ12jA7swiscshIgLAMETUJfQGAWdL2DMkkUgwfrAPAGAHh8qIyEIwDBF1gQuXq1HfaIBCLoVfN0exyxHV+Kahst0ZRaht0ItcDRERwxBRl2geIuvt6QyZVCJyNeIK66mGr1qFqno99mWViF0OERHDEFFXyC5qGiKz45VkzSQSyRWrygpEroaIiGGIqEtw8nRLdzXNG9p1qhANeoPI1RCRvWMYIuoC9r7h4h9FBHaDp4sCutpGJGdfErscIrJzDENEXYB7DLUkk0pw5yBuwEhEloFhiKiTlVbVo7SqHgDQmz1DJs0bMCae0kJvEESuhojsGcMQUSdrHiLzc3eEk0IucjWWY2RvD6gdHVBSWY8j50vFLoeI7BjDEFEnaz6TjCvJWnKQSRE7wHiSPYfKiEhMDENEnYyTp6/t7tDfl9gbOFRGRCJhGCLqZJw8fW0xwZ5wVclRqKvDYQ6VEZFIGIaIOhn3GLo2pVyGuKZVZdtOcANGIhIHwxBRJ6pt0COvtBoA0MeLw2StuWeIcQPG7Se4qoyIxMEwRNSJzl+qgkEAXFVy9HBRil2ORRrd1xPdnBxQUlmHg2e5ASMRdT2GIaJO1HwmWV8vF0gk9n1A67U4yKSmk+y/P86hMiLqegxDRJ2I84Xa5p4hvgCAHScLeFYZEXU5hiGiTsQw1DZRvbrDw1mBy9UN2M+zyoioizEMEXUi7jHUNnKZFHc17Tm09dhFkashInvDMETUSQwGwTRniLtP31jzUNmPv2lR38ihMiLqOgxDRJ2kQFeLmgY95FIJAro7iV2OxRse1B1erkroahuxL6tY7HKIyI4wDBF1kuYzyYI8neEg439qNyKTSnB3qHHPoa1cVUZEXYh/QxN1Es4Xar97w4xhKPFUIWob9CJXQ0T2gmGIqJNwJVn7DfXvBl+1CpV1jdiTyaEyIuoaFhGGli9fjqCgIKhUKkRFReHQoUPXbb9x40aEhIRApVIhNDQU27dvv2bbZ599FhKJBB988IGZqya6PtPkaYahNpNKJZjQdDwHzyojoq4iehjasGEDZs+ejQULFiA1NRVhYWGIi4tDUVFRq+3379+PyZMn46mnnkJaWhri4+MRHx+PkydPXtV28+bNOHDgAHx9fTv7YxBd5UxzzxBXkrVL86qypPRC1NRzqIyIOp/oYej999/Hn/70J0yfPh0DBw7EypUr4eTkhE8//bTV9kuXLsX48ePx6quvYsCAAXjrrbcwbNgwLFu2rEW7/Px8vPDCC/jyyy/h4ODQFR+FyKS8pgHFFXUAgN6cM9QuQ3qqEdDdCdX1evyU0fo/ioiIzEnUMFRfX4+UlBTExsaarkmlUsTGxiI5ObnV1yQnJ7doDwBxcXEt2hsMBjzxxBN49dVXMWjQoBvWUVdXB51O1+JBdDPONvUKebsp4aZiGG8PieT3obKtx7kBIxF1PlHDUElJCfR6Pby9vVtc9/b2hlarbfU1Wq32hu0XL14MuVyOF198sU11LFq0CGq12vTw9/dv5ychaim7mPOFbsY9TWHop4wiVNY1ilwNEdk60YfJzC0lJQVLly7FmjVr2nxK+Ny5c1FeXm565OXldXKVZOu4kuzmDPRxQ29PZ9Q1GpCUXih2OURk40QNQ56enpDJZCgsbPmXXWFhITQaTauv0Wg0122/b98+FBUVISAgAHK5HHK5HDk5OfjLX/6CoKCgVt9TqVTCzc2txYPoZjRvuMg9hjpGIpGYeoe+O8qhMiLqXKKGIYVCgYiICCQlJZmuGQwGJCUlITo6utXXREdHt2gPAImJiab2TzzxBI4fP46jR4+aHr6+vnj11Vfx448/dt6HIboCV5LdvInhfgCAvaeLUVpVL3I1RGTL5GIXMHv2bEydOhWRkZEYMWIEPvjgA1RVVWH69OkAgClTpsDPzw+LFi0CAMyaNQtjxozBkiVLMGHCBKxfvx5HjhzBqlWrAAAeHh7w8PBo8TMcHByg0WjQv3//rv1wZJca9AbkXqoGAPRlGOqwvl4uGOznhpP5Omw/UYDHRwaKXRIR2SjRw1BCQgKKi4sxf/58aLVahIeHY8eOHaZJ0rm5uZBKf+/AGjVqFNatW4d58+bh9ddfR3BwMLZs2YLBgweL9RGIWsi5VI1GgwAnhQwaN5XY5Vi1SWF+OJmvw7dH8xmGiKjTSARBEMQuwtLodDqo1WqUl5dz/hC1246TWjz73xSE+qnx/QsxYpdj1bTltYh+OwmCAPzy2lj07OYkdklEZME6+v1tc6vJiMTWvJKMQ2Q3T6NWYWQv47D3d8c4kZqIOgfDEJGZnSliGDKn+KHG4zm4qoyIOgvDEJGZnSniHkPmNH6wDxQyKTK0FcjQcnd4IjI/hiEiMxIE4YphMu4xZA5qRweMDekBAPiWvUNE1AkYhojMqKC8FtX1esilEgR6MAyZy6SmPYe+O3oRBgPXfBCReTEMEZlR8xBZoIcTHGT8z8tcbg/xgqtSjvyyGhzJuSx2OURkYzr0t/XZs2fNXQeRTeDk6c6hcpAhbrDxyJ1vj+aLXA0R2ZoOhaG+ffti7Nix+O9//4va2lpz10Rktc5wWX2niW8aKtt2ogD1jQaRqyEiW9KhMJSamoohQ4Zg9uzZ0Gg0eOaZZ3Do0CFz10ZkdbLZM9Rpovt4oIerEmXVDdiXVSx2OURkQzoUhsLDw7F06VJcvHgRn376KQoKChATE4PBgwfj/fffR3Ex/6Ii+9S8kozL6s1PJpXg3iHGPYe2cFUZEZnRTc3wlMvluP/++7Fx40YsXrwYZ86cwSuvvAJ/f39MmTIFBQUF5qqTyOKVVdejpNJ4ujrDUOeYFG4MQ4mntKiqaxS5GiKyFTcVho4cOYLnnnsOPj4+eP/99/HKK68gOzsbiYmJuHjxIiZNmmSuOoksXvPkaV+1Cs5K0c9AtklDeqrRy9MZtQ0G7DylFbscIrIRHQpD77//PkJDQzFq1ChcvHgRn3/+OXJycvCPf/wDvXr1wi233II1a9YgNTXV3PUSWSzTztOcL9RpJBIJJoYZe4e4ASMRmUuHwtCKFSvw6KOPIicnB1u2bME999wDqbTlW3l5eeGTTz4xS5FE1oDL6rtG81DZvqwSlFTWiVwNEdmCDoWhxMREvPbaa/Dx8WlxXRAE5ObmAgAUCgWmTp168xUSWQlOnu4avXu4YEhPNfQGAVt5kj0RmUGHwlCfPn1QUlJy1fXS0lL06tXrposiskbcY6jr3DfUuOfQ5jRuwEhEN69DYUgQWj8bqLKyEiqV6qYKIrJGtQ16XLhcA4BhqCvcG+YLuVSCYxfKcaaoQuxyiMjKtWvJy+zZswEYJzHOnz8fTk5Opuf0ej0OHjyI8PBwsxZIZA2yiyshCIC7kwM8nBVil2PzPF2UuK1/D+xKL8Km1Hz8dXyI2CURkRVrVxhKS0sDYOwZOnHiBBSK3//SVygUCAsLwyuvvGLeComsgGnydA8XSCQSkauxD/cN7Yld6UXYkpaPV+7sD6mU952IOqZdYWj37t0AgOnTp2Pp0qVwc3PrlKKIrE12cRUADpF1pXEDvOCqkuNieS0OnLuEUX08xS6JiKxUh+YMffbZZwxCRFdoPpOMK8m6jspBhnuajufYlMqJ1ETUcW3uGbr//vuxZs0auLm54f77779u202bNt10YUTWhHsMieOBYX746lAufjhRgL9PGgQnBXf+JqL2a/PfHGq12jQXQq1Wd1pBRNamUW/AuRIOk4khIrAbAro7Ibe0Gjt/K0R805J7IqL2aHMY+uyzz1r9NZG9y7tcg3q9ASoHKfzcHcUux65IJBLcN9QPS5OysCktn2GIiDqkQ3OGampqUF1dbfp9Tk4OPvjgA+zcudNshRFZi+b5Qr09XbiiSQT3DzMGoF+yilGoqxW5GiKyRh0KQ5MmTcLnn38OACgrK8OIESOwZMkSTJo0CStWrDBrgUSWjjtPiyvQwxkRgd1gEIBvj3IiNRG1X4fCUGpqKm655RYAwDfffAONRoOcnBx8/vnn+PDDD81aIJGlO8OVZKJr7h36X0r+NXfIJyK6lg6Foerqari6ugIAdu7cifvvvx9SqRQjR45ETk6OWQsksnRZhcbjIPp5MwyJ5Z5QXyhkUmQWVuBUgU7scojIynQoDPXt2xdbtmxBXl4efvzxR9x5550AgKKiIu4/RHbFYBBwutDYM9RP4ypyNfZL7eSA2IFeAIDN3HOIiNqpQ2Fo/vz5eOWVVxAUFISoqChER0cDMPYSDR061KwFElmyC5drUNOgh0ImRWB3pxu/gDrNfUN7AgC2HL2IRr1B5GqIyJp0aIeyBx98EDExMSgoKEBYWJjp+rhx43DfffeZrTgiS3e6aYisj5cL5LIO/duCzGRMvx7o7qxASWUd9p0pwdj+XmKXRERWosN/e2s0GgwdOhRS6e9vMWLECISE8PRosh+ZTWGoP+cLiU4hl2JiGI/nIKL261DPUFVVFd5++20kJSWhqKgIBkPLLumzZ8+apTgiS9fcMxTszflCluD+YX5Ys/88dv6mha62AW4qB7FLIiIr0KEw9PTTT2Pv3r144okn4OPjYzqmg8jeNE+e7s8wZBFC/dTo6+WCM0WV2Ha8AJNHBIhdEhFZgQ6FoR9++AHbtm3D6NGjzV0PkdVo1BtMu0/350oyiyCRSPBQRE8s+iEDXx/JYxgiojbp0Jyhbt26oXv37uauhciqnL9UjXq9AY4OMp5JZkHuG+YHmVSCtNwynCmqELscIrICHQpDb731FubPn9/ifDIie3P6is0WeSaZ5fByVZlWkm08ckHkaojIGnRomGzJkiXIzs6Gt7c3goKC4ODQcpJiamqqWYojsmS/hyEOkVmahyJ7Yld6If6Xmo9X4vrDgdseENF1dCgMxcfHm7kMIuvDMGS5bg/xgqeLcc+hvZnFiB3oLXZJRGTBOhSGFixYYO46iKxOprYpDHHytMVxkElx31A/rN53DhtT8hiGiOi6Otx3XFZWho8//hhz585FaWkpAOPwWH4+Nzsj21fXqMf5S8Y5c1xWb5keivQHACSlF6Gksk7kaojIknUoDB0/fhz9+vXD4sWL8d5776GsrAwAsGnTJsydO9ec9RFZpLPFVdAbBLiq5PB2U4pdDrWin7crwvzd0WgQsCWN/0gjomvrUBiaPXs2pk2bhqysLKhUKtP1u+++Gz///LPZiiOyVKdNx3C4ctNRC/ZQhPHw1q+P5EEQBJGrISJL1aEwdPjwYTzzzDNXXffz84NWq73poogsHecLWYd7w3yhlEtxurASxy+Ui10OEVmoDoUhpVIJnU531fXTp0+jR48e7X6/5cuXIygoCCqVClFRUTh06NB122/cuBEhISFQqVQIDQ3F9u3bWzy/cOFChISEwNnZGd26dUNsbCwOHjzY7rqIrqX5GI5+Xjyg1ZKpHR1w12ANAGPvEBFRazoUhiZOnIi///3vaGhoAGDcAj83NxevvfYaHnjggXa914YNGzB79mwsWLAAqampCAsLQ1xcHIqKilptv3//fkyePBlPPfUU0tLSEB8fj/j4eJw8edLUpl+/fli2bBlOnDiBX375BUFBQbjzzjtRXFzckY9LdBXTsnr2DFm8h5smUn979CKq6xtFroaILJFE6MBAenl5OR588EEcPnwYlZWV8PX1hVarRXR0NLZv3w5nZ+c2v1dUVBSGDx+OZcuWAQAMBgP8/f3xwgsvYM6cOVe1T0hIQFVVFbZu3Wq6NnLkSISHh2PlypWt/gydTge1Wo1du3Zh3LhxN6ypuX15eTnc3Nza/FnIPlTXN2Lg/B8BACnzYuHhwgnUlsxgEHD7kj04f6ka7zwwBA8P9xe7JCLqJB39/u5Qz5BarUZiYiK2bduGDz/8EDNnzsT27duxd+/edgWh+vp6pKSkIDY29veCpFLExsYiOTm51dckJye3aA8AcXFx12xfX1+PVatWQa1WIywsrNU2dXV10Ol0LR5E13Km6XBWD2cFg5AVkEoleKTpwNZ1h3JFroaILFG7N100GAxYs2YNNm3ahPPnz0MikaBXr17QaDQQBKFdK2tKSkqg1+vh7d1yQzRvb29kZGS0+hqtVttq+z9O3N66dSseeeQRVFdXw8fHB4mJifD09Gz1PRctWoQ333yzzXWTfTNNnub+QlbjwYieWLIzE0fzynDqog4DfdnjS0S/a1fPkCAImDhxIp5++mnk5+cjNDQUgwYNQk5ODqZNm4b77ruvs+pst7Fjx+Lo0aPYv38/xo8fj4cffvia85Dmzp2L8vJy0yMvjxMt6dpMy+o5X8hqeLoocedA40Tq9YfZO0RELbUrDK1ZswY///wzkpKSkJaWhq+++grr16/HsWPHsGvXLvz000/4/PPP2/x+np6ekMlkKCwsbHG9sLAQGo2m1ddoNJo2tXd2dkbfvn0xcuRIfPLJJ5DL5fjkk09afU+lUgk3N7cWD6JraV5JFuzNlWTWZHLTUNnm1HzU1OtFroaILEm7wtBXX32F119/HWPHjr3qudtvvx1z5szBl19+2eb3UygUiIiIQFJSkumawWBAUlISoqOjW31NdHR0i/YAkJiYeM32V75vXR235Kebd+WGi2Q9RvXxQEB3J1TUNWLr8Ytil0NEFqRdYej48eMYP378NZ+/6667cOzYsXYVMHv2bKxevRpr165Feno6ZsyYgaqqKkyfPh0AMGXKlBZHfMyaNQs7duzAkiVLkJGRgYULF+LIkSOYOXMmAKCqqgqvv/46Dhw4gJycHKSkpODJJ59Efn4+HnrooXbVRvRH5TUNKCivBQAEMwxZFeNEauNKMk6kJqIrtWsCdWlp6VWTl6/k7e2Ny5cvt6uAhIQEFBcXY/78+dBqtQgPD8eOHTtMPyc3NxdS6e+ZbdSoUVi3bh3mzZuH119/HcHBwdiyZQsGDx4MAJDJZMjIyMDatWtRUlICDw8PDB8+HPv27cOgQYPaVRvRHzX3CmncVFA7OohcDbXXgxE98f7O00jLLUN6gQ4DfDgkTkTt3GdIJpNBq9Vec5fpwsJC+Pr6Qq+37vF47jNE1/J58nnM//Y33B7ihU+nDRe7HOqAGf9NwQ8ntZgaHYg3Jw0WuxwiMqOOfn+3q2dIEARMmzYNSmXre6twTg7ZulMXjXtQDfDhEJm1mjwiAD+c1GJTWj7m3DUAjgqZ2CURkcjaFYamTp16wzZTpkzpcDFEli69oDkMscfQWsX09YR/d0fkldbgu2P5SBgeIHZJRCSydoWhzz77rLPqILJ4eoOAzKY5QwxD1ksqleDxqEAs+iEDa/fn4OFI/3ZtFktEtqdDx3EQ2aNzJVWobTDA0UGGII+2HztDlufhSH8o5VKcKtAhNbd9iz6IyPYwDBG10ammIbL+GlfIpOxJsGbdnBWYFO4LAFi7P0fkaohIbAxDRG3E+UK2ZUp0EADgh5MFKKqoFbcYIhIVwxBRGzWHIR7yaRsG+6kxLMAdDXoB6w/xPEIie8YwRNRGpjDEZfU2Y+qoIADAlwdz0KA3iFsMEYmGYYioDS5V1qFQZ9xHq7+GPUO24q7BPvB0UaJQV4edvxXe+AVEZJMYhojaIL3AuKQ+0MMJLsp27UhBFkwhl+LRpvPK1iafF7cYIhINwxBRG5gmT7NXyOY8GhUImVSCQ+dKkaHViV0OEYmAYYioDU7klwMAQnuqRa6EzE2jVmH8IA0ALrMnslcMQ0RtcPKiMQwN4koymzQlOhAAsCn1Akqr6kWuhoi6GsMQ0Q1U1jXiXEkVAONybLI9I3p1R6ifGnWNBnx5gL1DRPaGYYjoBk5d1EEQAB+1Cp4uSrHLoU4gkUjw9C29AABrk3NQ26AXuSIi6koMQ0Q3cLJpvhB7hWzb3aE+8FGrUFJZh++OXRS7HCLqQgxDRDdgCkO+DEO2zEEmxbSmTRg/2XcOgiCIWxARdRmGIaIbaJ48HdqTk6dt3SMjAuCkkCGzsAL7skrELoeIugjDENF1VNc34kxRJQD2DNkDtaMDHo40bsL48S/nRK6GiLoKwxDRdaQXVMAgAF6uSni5qcQuh7rAk6N7QSoBfj5djExthdjlEFEXYBgiug5OnrY/AR5OiGvahPGTX86KXA0RdQWGIaLrYBiyT83L7LekXURRRa3I1RBRZ2MYIrqO4xeaV5Jx8rQ9iQjsjqEB7qjXG/DZr+fFLoeIOhnDENE1VNY14nSRcc5IeIC7uMVQl3t2TB8AwH+Tc6CrbRC5GiLqTAxDRNdw4kI5BAHwc3eElysnT9ubOwZ4I9jLBRV1jfgimUd0ENkyhiGiaziaVwYACPPnfCF7JJVKTL1Dn/16jkd0ENkwhiGiazjWFIbC/d1FrYPEMzHcF37ujiiprMfXR/LELoeIOgnDENE1mHqGerqLWgeJx0EmxZ9v7Q0A+M/es2jQG0SuiIg6A8MQUSu05bXQ6mohlXBZvb1LGO4PTxcF8stq8D0PcCWySQxDRK1o7hXq5+0KZ6Vc3GJIVCoHGaaPNu47tGJPNgwGHuBKZGsYhohacexCGQDOFyKjJ6ID4aqUI6uoErvSC8Uuh4jMjGGIqBWcPE1XclM54PHoQADA8j3ZEAT2DhHZEoYhoj/QGwTTztNhDEPU5MnRvaBykOJYXhn2ni4WuxwiMiOGIaI/yNRWoLKuES5KOfp5u4pdDlmIHq5KPBZl7B1ampTF3iEiG8IwRPQHKTmlAIChAe6QSSUiV0OW5JkxvaGUS5GWW4Z9WSVil0NEZsIwRPQHR3IuAwAiAruJXAlZGi9XFR6NCgDA3iEiW8IwRPQHR84bw1BkYHeRKyFLNGNMHyjlUqTkXMavZy6JXQ4RmQHDENEVCsprkF9WA6mEJ9VT67zcVJg8orl36DR7h4hsAMMQ0RWae4UG+LjBhZst0jXMuK0PFHIpDp+/jORs9g4RWTuGIaIrpOQ0D5FxvhBdm7ebCpOH+wMAPkjKErkaIrpZDENEVzjStJIsIojzhej6nr2tDxQyKQ6dK8X+bK4sI7JmDENETarqGpFeUAGAPUN0Yz5qRzwywtg7tGQn5w4RWTOGIaImabll0BsE+KpV8HV3FLscsgLPj+1rWlm2h7tSE1kthiGiJgfOGifCRvX2ELkSshbebipMHRUEAFiyM5O9Q0RWimGIqElyUxiKZhiidnjm1t5wVshwMl+HH3/Til0OEXWARYSh5cuXIygoCCqVClFRUTh06NB122/cuBEhISFQqVQIDQ3F9u3bTc81NDTgtddeQ2hoKJydneHr64spU6bg4sWLnf0xyIpV1zeaTqqP7sMwRG3n4aLEkzG9AADvJ56G3sDeISJrI3oY2rBhA2bPno0FCxYgNTUVYWFhiIuLQ1FRUavt9+/fj8mTJ+Opp55CWloa4uPjER8fj5MnTwIAqqurkZqaijfeeAOpqanYtGkTMjMzMXHixK78WGRljpy/jEaDAD93R/TsxvlC1D5P39Ibbio5ThdW4vtj/IcXkbWRCCIPckdFRWH48OFYtmwZAMBgMMDf3x8vvPAC5syZc1X7hIQEVFVVYevWraZrI0eORHh4OFauXNnqzzh8+DBGjBiBnJwcBAQEXPV8XV0d6urqTL/X6XTw9/dHeXk53NzcbvYjkhVYvCMDK/Zk44FhPbHk4TCxyyErtHz3Gbz7YyaCPJyQOHsMHGSi/1uTyO7odDqo1ep2f3+L+l9rfX09UlJSEBsba7omlUoRGxuL5OTkVl+TnJzcoj0AxMXFXbM9AJSXl0MikcDd3b3V5xctWgS1Wm16+Pv7t//DkFVrnjzNITLqqGmjguDhrMD5S9XYlHpB7HKIqB1EDUMlJSXQ6/Xw9vZucd3b2xtabesTEbVabbva19bW4rXXXsPkyZOvmRLnzp2L8vJy0yMvL68Dn4asVWVdI45fKAcAjOzNzRapY5yVcsy4rQ8AYOmuLNQ26EWuiIjayqb7cRsaGvDwww9DEASsWLHimu2USiXc3NxaPMh+HDlfCr1BgH93R/Ts5iR2OWTFHh8ZCB+1ChfLa/FFco7Y5RBRG4kahjw9PSGTyVBYWNjiemFhITQaTauv0Wg0bWrfHIRycnKQmJjIgEPX1HzQJpfU081SOcjwcmw/AMDyPWdQXtMgckVE1BaihiGFQoGIiAgkJSWZrhkMBiQlJSE6OrrV10RHR7doDwCJiYkt2jcHoaysLOzatQseHvySo2vb27RzcExwD5ErIVtw/zA/BHu5oKy6Af/Zmy12OUTUBqIPk82ePRurV6/G2rVrkZ6ejhkzZqCqqgrTp08HAEyZMgVz5841tZ81axZ27NiBJUuWICMjAwsXLsSRI0cwc+ZMAMYg9OCDD+LIkSP48ssvodfrodVqodVqUV9fL8pnJMtVpKtFhrYCEgkQ09dT7HLIBshlUrwa1x8A8Omv51CoqxW5IiK6EbnYBSQkJKC4uBjz58+HVqtFeHg4duzYYZoknZubC6n098w2atQorFu3DvPmzcPrr7+O4OBgbNmyBYMHDwYA5Ofn47vvvgMAhIeHt/hZu3fvxm233dYln4usw89ZxtPGQ/3U6O6sELkashV3DPRGRGA3pORcxge7srDo/lCxSyKi6xB9nyFL1NF9Csj6vPhVGr47dhEzx/bFK03/micyh8PnS/HQymTIpBLsfPlW9OnhInZJRDbPKvcZIhKTwSDglzPGnqFb+3G+EJnX8KDuiB3gBb1BwHs/ZopdDhFdB8MQ2a2TF8tRWlUPF6UcQwPcxS6HbNCrcSGQSIAfTmqRmntZ7HKI6BoYhshu/dy0imxUHw8enUCdor/GFQ8M6wkA+Oe2dHBWApFl4jcA2a3mJfUcIqPO9Mqd/aFykCIl5zK2n2h9p3wiEhfDENmlS5V1SMkxDluMDfESuRqyZRq1Cs/cajym4+0d6ahr5DEdRJaGYYjs0u7MYhgEYKCPG/zcHcUuh2zcM2N6w8tVibzSGqzdf17scojoDxiGyC7tOmU80uWOgd43aEl085wUctNGjB8lncGlyjqRKyKiKzEMkd2pbdDj5yzjfCGGIeoqDwzriYE+bqioa8TSpCyxyyGiKzAMkd1Jzr6E6no9fNQqDPLlpprUNaRSCeZNGAAA+PJgLs4UVYpcERE1Yxgiu5OYbhwiix3gDYlEInI1ZE9G9fVE7ABv6A0C/rU9XexyiKgJwxDZFYNBQFJzGOIQGYlg7t0hkEsl+CmjCD9lFIpdDhGBYYjsTGruZRTq6uCqlGNk7+5il0N2qE8PFzwV0wsAsPC7U6ht4FJ7IrExDJFd2Xq8AABwxyBvKOUykashe/XCuGB4uymRW1qNVT+fFbscIrvHMER2Q28QsP2EMQzdM8RH5GrInrko5fjbhIEAgOW7zyCvtFrkiojsG8MQ2Y3D50tRVFEHN5UcMX15BAeJ694hPhjZuzvqGg14a+spscshsmsMQ2Q3tjUNkcUN0kAh5x99EpdEIsGbEwdDJpVg56lC7MksErskIrvFbwSyC416A344aQxDEzhERhaiv8YV00YFAQAWfvcbzy0jEgnDENmFA2dLUVJZD3cnB4zu6yl2OUQmL8UGo4erEucvVWPlHk6mJhIDwxDZhU2pFwAAd4f6wEHGP/ZkOVxVDnjjHuNk6mW7s3C6sELkiojsD78VyOZV1jXih5NaAMbzoYgszb1DfDAuxAsNegF//eY49AZB7JKI7ArDENm87ScKUNOgR29PZwwLcBe7HKKrSCQS/OO+wXBVynE0rwyf/XpO7JKI7ArDENm8/6UYh8geiOjJs8jIYvmoHTH3buNBru/tzETuJe49RNRVGIbIpuWVVuPguVJIJMB9Q/3ELofouh4Z7o+RvbujtsGAOZuOQxA4XEbUFRiGyKZ909QrNLqPJ3zdHUWuhuj6pFIJ3r5/CFQOUuzPvoQNh/PELonILjAMkc1q1BtMXyYPRXLiNFmHIE9n/OWO/gCAf25L51EdRF2AYYhs1q70Imh1tfBwVmD8YI3Y5RC12ZMxvRAR2A0VdY14cX0aGvQGsUsismkMQ2SzvjyYAwB4eLg/T6gnqyKTSvBBQjhcVXKk5Zbhg12nxS6JyKYxDJFNOldShX1ZJZBIgEdHBIhdDlG7+Xd3wtv3DwEA/HtPNvafKRG5IiLbxTBENmldU6/Qbf16wL+7k8jVEHXMhCE+eGS4PwQBeGnDUZRW1YtdEpFNYhgim1NV12iaOP34yECRqyG6OfPvHYg+PZxRVFGHVzce43J7ok7AMEQ255uUC9DVNiLIwwlj+3uJXQ7RTXFSyPHR5GFQyKRIyijCJ79wd2oic2MYIpuiNwimL4unYnpBKuWO02T9Bvq6Yd49xt2pF/2QwflDRGbGMEQ2JfFUIXJLq+Hu5IAHIri3ENmOJ0YG4v5hftAbBDy/LpX7DxGZEcMQ2ZSP950FADwWFQAnhVzkaojMRyKR4F/3hSLUT43L1Q340+dHUFnXKHZZRDaBYYhsxpHzpTiScxkOMgmmRgeJXQ6R2akcZPjPExHwdFEiQ1uB579MRSM3ZCS6aQxDZDM++ukMAODBiJ7wclOJXA1R5/B1d8QnUyOhcpBi7+liLPjuN64wI7pJDENkE47llWHv6WLIpBLMGNNX7HKIOlWYvzs+SBgKiQT48mAuliZliV0SkVVjGCKbsGy3sVdoUrgvAjy4ySLZvvGDNVh47yAAwAe7srB2/3lxCyKyYgxDZPVOXdQh8VQhJBLg+bHsFSL7MXVUEF6KDQYALPjuN6w/lCtyRUTWiWGIrN77icZDLCeE+qBPDxeRqyHqWrPGBWP66CAAwJxNJ/BF8nlR6yGyRgxDZNVScy9jV3ohpBLg5Tv6iV0OUZeTSCSYf89APBXTCwDwxre/cZdqonZiGCKr9t6PmQCMK8jYK0T2SiKRYN6EAZhxWx8AwFtbT2Hl3myRqyKyHgxDZLV+PVOC/dmX4CCT4MVxwWKXQyQqiUSCv8b1N/238PYPGfi/xNNcdk/UBgxDZJUMBgGLfkgHADw6IgA9u3EFGZFEIsHsO/rhlTuNQ8ZLk7Lw6jfHUd/IjRmJrkf0MLR8+XIEBQVBpVIhKioKhw4dum77jRs3IiQkBCqVCqGhodi+fXuL5zdt2oQ777wTHh4ekEgkOHr0aCdWT2L5JvUCTubr4KqU4wX2ChG1MPP2YPwjfjBkUgm+SbmAqZ8eQnl1g9hlEVksUcPQhg0bMHv2bCxYsACpqakICwtDXFwcioqKWm2/f/9+TJ48GU899RTS0tIQHx+P+Ph4nDx50tSmqqoKMTExWLx4cVd9DOpilXWNeLdprtCL44Lh6aIUuSIiy/P4yEB8MjUSLko5ks9ewv0rfkXuJR7uStQaiSDigHJUVBSGDx+OZcuWAQAMBgP8/f3xwgsvYM6cOVe1T0hIQFVVFbZu3Wq6NnLkSISHh2PlypUt2p4/fx69evVCWloawsPDr1tHXV0d6urqTL/X6XTw9/dHeXk53NzcbuITUmd498cMLN+djSAPJ+x8eQwUctE7OIksVnqBDk+uOYyC8lp4OCvw78eGIaq3h9hlEXUKnU4HtVrd7u9v0b5F6uvrkZKSgtjY2N+LkUoRGxuL5OTkVl+TnJzcoj0AxMXFXbN9Wy1atAhqtdr08Pf3v6n3o86TV1qN1fuMy4Zfv3sAgxDRDQzwccOW50djsJ8bLlXV49GPD+LjfWc5sZroCqJ9k5SUlECv18Pb27vFdW9vb2i12lZfo9Vq29W+rebOnYvy8nLTIy8v76bejzrP2zsyUN9owKg+HrhjoPeNX0BE8HZTYeMzo3DfUD/oDQL+sS0dM9elobKuUezSiCwC/1kNQKlUws3NrcWDLM+hc6XYdrwAUgnwxj0DIZFIxC6JyGo4KmR4/+Ew/H3SIDjIJNh2ogCTlv2CM0UVYpdGJDrRwpCnpydkMhkKCwtbXC8sLIRGo2n1NRqNpl3tyXYYDAL+vvU3AEDC8AAM8GFgJWoviUSCKdFBWP/naHi7KZFdXIV7P/oVXx/O47AZ2TXRwpBCoUBERASSkpJM1wwGA5KSkhAdHd3qa6Kjo1u0B4DExMRrtifb8eWhXJzM18FFKcdf7uSxG0Q3IyKwG7a+cAtG9/VATYMef/3fcbzwVRrKa7j8nuyTqMNks2fPxurVq7F27Vqkp6djxowZqKqqwvTp0wEAU6ZMwdy5c03tZ82ahR07dmDJkiXIyMjAwoULceTIEcycOdPUprS0FEePHsWpU6cAAJmZmTh69OhNzysi8RTpavHODxkAgFfu7Mel9ERm0MNViS+ejMJr40Mgl0qw9XgB7l66Dyk5pWKXRtTlRA1DCQkJeO+99zB//nyEh4fj6NGj2LFjh2mSdG5uLgoKCkztR40ahXXr1mHVqlUICwvDN998gy1btmDw4MGmNt999x2GDh2KCRMmAAAeeeQRDB069Kql92Q93tx6ChV1jQjrqcYT0UFil0NkM6RSCWbc1gffzBiFgO5OyC+rwcP/OYD3d2Zy12qyK6LuM2SpOrpPAZnf7owiTF9zGDKpBN/NHI1BvmqxSyKySRW1DZj/7W/YnJYPABjo44b3E8IQouHfgWQ9rG6fIaIbqa5vxLwtxt3FnxwdxCBE1IlcVQ74v4RwLH90GLo5OeBUgQ73fvQLlu8+g0Y9e4nItjEMkcVauisL+WU18HN3xEuxnDRN1BUmDPHBzpfH4I6B3mjQC3j3x0w8sGI/frtYLnZpRJ2GYYgs0qmLOnz8i3Gn6b9PGgRnpVzkiojsRw9XJVY9EYH3Hw6Dq0qOYxfKMXHZr/jntlOorudGjWR7GIbI4jToDXjtf8ehNwi4O1SDcQO40zRRV5NIJLh/WE8kzR6DCaE+0BsErN53Dne8/zN+yii88RsQWRGGIbI4K/dk40R+OdSODlhw7yCxyyGya15uKix/bBg+nRYJP3dH5JfV4Mk1R/DkmsPcvZpsBsMQWZRTF3X48KcsAMCbEwfB200lckVEBAC3h3gjcfat+POtvSGXSvBTRhHiPtiHN7acxKXKOrHLI7opDENkMeobDXhl4zE06AXcOdAbk8J9xS6JiK7gpJDj9bsH4MeXb0XsAG/oDQK+OJCD297dg+W7z/DgV7JaDENkMZbvPoNTBTp0c3LAP+8L5UGsRBaqTw8XfDw1Euv+FIVBvm6oqGvEuz9mYvTbP+GDXadRXs1jPci6cNPFVnDTxa53Mr8c8ct/RaNBwEeTh+LeMPYKEVkDg0HAlqP5WPbTGZwtqQIAuCjleCI6ENNGBXGom7pUR7+/GYZawTDUteoa9Zj40a/ILKzA3aEaLH90GHuFiKyM3iBg+4kCLN99Bhla48RquVSCuEEaPD4yECN7d+d/19TpGIbMiGGoa7219RQ++eUcPJwV2PnyrfDgQaxEVstgEJCUUYT/7M3GkZzLput9vVzweFQA4of6wd1JIWKFZMsYhsyIYajr7MkswrTPDgMAPp4SidiB3FOIyFakF+jw3wM52JyWj+p6PQBAIZPizkHeeDjSH6P7ekImZW8RmQ/DkBkxDHWN4oo63LX0Z5RU1mNqdCDenDRY7JKIqBNU1DZgc1o+1h/Kw6kCnem6r1qFByN64sEIfwR4OIlYIdkKhiEzYhjqfAaDgGlrDuPn08Xo7+2Kb2eOhspBJnZZRNTJTuaX45uUC9iclo/ymt9XnUX39sBDkT1x12AfOCr4dwF1DMOQGTEMdb6P953FP7alQymX4vsXYtDP21XskoioC9U26LErvRBfH7mAfVnFaP4mclXKcU+YLx6O7Ilwf3dOuqZ2YRgyI4ahznUyvxz3/ftXNOgFvBU/GE+MDBS7JCIS0cWyGvwv5QI2plxAbmm16XqwlwsejvTHgxE90c2Zk67pxhiGzIhhqPOU1zRg0rJfcP5SNe4Y6I1VT0TwX35EBMA4fH7ofCm+PpKH7ScKUNtgAAA4K2R4PDoQf7qlNzy52pSug2HIjBiGOofBIODPXxzBrvQi+Lk7YusLMfzXHhG1SlfbgG3HC/BFco5p0rXKQYrHogLxzK294cXNHKkVDENmxDDUOZb9lIX3dp6GQi7FN89GY0hPd7FLIiILJwgCfsoowoc/ncGxvDIAgFIuxbNj+uDZMX042ZpaYBgyI4Yh8/v5dDGmfnYIggAsfiAUCcMDxC6JiKyIIAj4OasES3edRmpuGQDAz90Rf5swAHcN1nC4nQB0/PubB7VSp8srrcaL69MgCMAjw/0ZhIio3SQSCcb064H/zRiFZY8Oha9ahfyyGjz3ZSoeXX0QWYUVYpdIVoxhiDpVbYMez32ZirLqBgzpqcbCiYPELomIrJhEIsE9Q3yR9JfbMGtcMJRyKZLPXsKED3/B8t1n0Kg3iF0iWSGGIeo0BoOAv3x9DCfyy9HNyQH/fmwYN1YkIrNwVMjw8h39sGv2GIwL8UK93oB3f8zEAyv2s5eI2o1hiDrN2zsysO1EARxkEqx4PAI9u3G7fSIyL//uTvh4aiSWPBQGN5Ucxy6UY8KHv2DFnmzoDZwSS23DMESd4ovk81j181kAwLsPhmFkbw+RKyIiWyWRSPBARE/sfHkMbm/qJVq8IwOPrEpG3hWbOBJdC8MQmV1SeiEWfPcbAOCVO/shfqifyBURkT3QqFX4ZGok3nlwCFyUchw+fxl3L92HLWn5YpdGFo5hiMzqxIVyzFyXBoMAJET64/mxfcUuiYjsiEQiwcOR/tj+4i0YFuCOirpGvLThKF78Kq3FwbBEV2IYIrM5U1SJaZ8dQk2DHrcEe+If9w3m3h9EJIoADyd8/Uw0Xo7tB5lUgu+OXcTdS/fhwNlLYpdGFohhiMwi51IVHvv4AC5V1WOwnxv+/dgwOMj4x4uIxCOXSTErNhgbn41GoIcT8stqMHn1ASzekYH6Ri7Bp9/x24puWl5pNR5dfRCFujr083bB509GwVXlIHZZREQAgGEB3bDtxVvwcGRPCAKwYk82HlixH9nFlWKXRhaCYYhuytniSjz8n2Tkl9Wgl6cz/vt0FLrz8FUisjAuSjneeTAMKx4bBrWjA07kl2PCh/vw3wM54KlUxDBEHZah1SFh1QEUlNeir5cLNvx5JLxceZI0EVmuu0J98ONLt2J0Xw/UNhgwb8tJ/OnzIyiuqBO7NBIRwxB1yP7sEjy0IhnFFXUI0bhi/Z9HwsuNQYiILJ9GrcIXT0Zh3oQBUMik2JVehHFL9uC/B3Jg4EaNdolhiNptc9oFTPv0MCrqGjEiqDs2/Dkani5KscsiImozqVSCp2/pjS3Pj8YgXzfoahsxb8tJ3LdiP07ml4tdHnUxicDB0qvodDqo1WqUl5fDzc1N7HIsht4gYPGODNPO0ncN1uD/EsJ53hgRWbVGvQFfHMjBkp2nUVnXCKkEmBIdhFnjgtGNcyCtSke/vxmGWsEwdLXiijrM/voo9mWVAACeu60P/nJnf8ik3EeIiGxDoa4Wf996CtuOFwAAXJVyPDOmN6aOCuIKWSvBMGRGDEMt/ZJVgpe/PoriijqoHKR498Ew3BvmK3ZZRESdYl9WMf61PQPpBToAgJtKjinRQZg6Kgg9XDklwJIxDJkRw5BRZV0jFv+QgS8O5AAA+nu7YtmjQxHs7SpyZUREnctgEPD98Yv4MCkL2cVVAAAHmQTjB/tg8nB/RPX2YM+4BWIYMiN7D0OCIGBXehEWfvcb8stqAACPjwzAvAkDOT+IiOyKwSBg5ykt/vPzWaTllpmua9xUmDDEB+MGeGF4UHfuuG8hGIbMyJ7D0OnCCvxjWzp+Pl0MAOjZzRHvPDAEo/p6ilwZEZG4TuaX48uDudh2/CJ0tY2m6y5KOYYGuGNoQDcMC3DHUP9uUDtxjpEYGIbMyB7DUHZxJT5MysJ3xy5CEACFTIqnb+mF58f2hbNSLnZ5REQWo65Rj72Zxdh5qhC7M4pwqar+qjYB3Z3Qy9MZvXs4o7enM4I8neGjdoS3m5KTsTsRw5AZ2UsYEgQBh86V4uNfzmFXeiGa/yTcNViDOXeFINDDWdwCiYgsnMEg4FSBDmm5l5GWW4bU3Ms4f6n6uq9xVsjg7aaCl5sSGjdV069V0LipoFGr4KNWwctVCTmH3tqNYciMbD0M5ZVWY/uJAmw4koezTRMDASB2gBdeiu2HwX5qEasjIrJulyrrkFVUiXMlVThXUoWzxVU4f6kKhbpaVFwxvHY9Ugng5WoMR77uKmjcHI3/q1bBz90R/t2d4OGsgETCSdxXYhgyI1sLQwaDgBP55diVXojEU4XI0FaYnnN0kCF+qC+eiumNvl4uIlZJRGT7quoaUVRRh0Jd7RUP4++15bXQNl1r0N/4q9nRQYae3RzRs5sxHBl/7QT/bsZfuzs52F1Y6uj3t0VMBlm+fDneffddaLVahIWF4aOPPsKIESOu2X7jxo144403cP78eQQHB2Px4sW4++67Tc8LgoAFCxZg9erVKCsrw+jRo7FixQoEBwd3xccRXXlNA47lleFoXhnSci/jaF4ZLlc3mJ6XSSWIDOyG+4b64Z4wX7hwThARUZdwVsrRSylHL89rT0MwGASUVNWhoKwWBeW1KCivgba8FhfLa1FQVoMLl2tQWFGLmgY9sooqkVVU2er7uCjlprDUsykgNYcm/+5OcOPcJRPRe4Y2bNiAKVOmYOXKlYiKisIHH3yAjRs3IjMzE15eXle1379/P2699VYsWrQI99xzD9atW4fFixcjNTUVgwcPBgAsXrwYixYtwtq1a9GrVy+88cYbOHHiBE6dOgWV6saHiVp6z1BNvR5FFbUorqhDUUUd8kqrcf7SlV2xV5++7KSQYUy/HrhjoDfG9vfiFvNERFasrlGPi2W1uHC5Ghcu1yCvtOl/m35fXHH198Afuankxp6k7saw5OvuCA9nBdydHNDNSYHuTb92UcqtpofJaofJoqKiMHz4cCxbtgwAYDAY4O/vjxdeeAFz5sy5qn1CQgKqqqqwdetW07WRI0ciPDwcK1euhCAI8PX1xV/+8he88sorAIDy8nJ4e3tjzZo1eOSRR25YU2eFoVMXdTh+oQwNBgENjQY0Ggxo0Ato1Ato0BvQYDCYfl1Vp0dVXSMqr3hU1TVCV9OAqnr9DX9WQHcnhPu7Y2iAO8L93THQ1w1KOfcIIiKyB7UNely4XIMLl6uR1/S/F0prTOGptRVw1+Igk0Dt6ABHhQzOCjmcFDI4K43/66SQQ+UghVwqhYNMCgeZpOl/pZDLJFA0XZPJpJBKAJlEAqlEAqlUghCNq9nnqFrlMFl9fT1SUlIwd+5c0zWpVIrY2FgkJye3+prk5GTMnj27xbW4uDhs2bIFAHDu3DlotVrExsaanler1YiKikJycnKrYaiurg51db+n6PJy44nFOp2uw5+tNdtTs/Fh0hmzvJfSQYoeLkp4uiihcVMiyMMZAR5OCPRwQqCHM9ydWvb81FVX4cb/TiAiIlvhpQK8fFQY5qMC0K3Fc1V1jSgoNw65XSyrQX6ZcSiuvKYBl6sbUFZdj8vVDahvNKAOQNH1F8h1yNMxvRBwRz+zvmfz93Z7+3lEDUMlJSXQ6/Xw9vZucd3b2xsZGRmtvkar1bbaXqvVmp5vvnatNn+0aNEivPnmm1dd9/f3b9sHEYl5YhUREVHXW/ABsKCT3ruiogJqddt7nThzFsDcuXNb9DYZDAaUlpbCw8PDbOOkOp0O/v7+yMvLs8h5SJaK961jeN86jveuY3jfOo73rmNau2+CIKCiogK+vu07TFzUMOTp6QmZTIbCwsIW1wsLC6HRaFp9jUajuW775v8tLCyEj49Pizbh4eGtvqdSqYRS2fIkYnd39/Z8lDZzc3PjH/YO4H3rGN63juO96xjet47jveuYP9639vQINRN1e0uFQoGIiAgkJSWZrhkMBiQlJSE6OrrV10RHR7doDwCJiYmm9r169YJGo2nRRqfT4eDBg9d8TyIiIrJfog+TzZ49G1OnTkVkZCRGjBiBDz74AFVVVZg+fToAYMqUKfDz88OiRYsAALNmzcKYMWOwZMkSTJgwAevXr8eRI0ewatUqAIBEIsFLL72Ef/zjHwgODjYtrff19UV8fLxYH5OIiIgslOhhKCEhAcXFxZg/fz60Wi3Cw8OxY8cO0wTo3NxcSKW/d2CNGjUK69atw7x58/D6668jODgYW7ZsMe0xBAB//etfUVVVhT//+c8oKytDTEwMduzY0aY9hjqLUqnEggULrhqOo+vjfesY3reO473rGN63juO96xhz3jfR9xkiIiIiEhOPxCUiIiK7xjBEREREdo1hiIiIiOwawxARERHZNYahLrB8+XIEBQVBpVIhKioKhw4dErski/Pzzz/j3nvvha+vLyQSiemsuWaCIGD+/Pnw8fGBo6MjYmNjkZWVJU6xFmTRokUYPnw4XF1d4eXlhfj4eGRmZrZoU1tbi+effx4eHh5wcXHBAw88cNXGpfZmxYoVGDJkiGmztujoaPzwww+m53nP2ubtt982bWfSjPeudQsXLoREImnxCAkJMT3P+3Zt+fn5ePzxx+Hh4QFHR0eEhobiyJEjpufN8f3AMNTJNmzYgNmzZ2PBggVITU1FWFgY4uLiUFRUJHZpFqWqqgphYWFYvnx5q8+/8847+PDDD7Fy5UocPHgQzs7OiIuLQ21tbRdXaln27t2L559/HgcOHEBiYiIaGhpw5513oqqqytTm5Zdfxvfff4+NGzdi7969uHjxIu6//34RqxZfz5498fbbbyMlJQVHjhzB7bffjkmTJuG3334DwHvWFocPH8Z//vMfDBkypMV13rtrGzRoEAoKCkyPX375xfQc71vrLl++jNGjR8PBwQE//PADTp06hSVLlqBbt98PnjXL94NAnWrEiBHC888/b/q9Xq8XfH19hUWLFolYlWUDIGzevNn0e4PBIGg0GuHdd981XSsrKxOUSqXw1VdfiVCh5SoqKhIACHv37hUEwXifHBwchI0bN5rapKenCwCE5ORkscq0SN26dRM+/vhj3rM2qKioEIKDg4XExERhzJgxwqxZswRB4J+361mwYIEQFhbW6nO8b9f22muvCTExMdd83lzfD+wZ6kT19fVISUlBbGys6ZpUKkVsbCySk5NFrMy6nDt3DlqttsV9VKvViIqK4n38g/LycgBA9+7dAQApKSloaGhoce9CQkIQEBDAe9dEr9dj/fr1qKqqQnR0NO9ZGzz//POYMGFCi3sE8M/bjWRlZcHX1xe9e/fGY489htzcXAC8b9fz3XffITIyEg899BC8vLwwdOhQrF692vS8ub4fGIY6UUlJCfR6vWk37Wbe3t7QarUiVWV9mu8V7+P1GQwGvPTSSxg9erRpR3atVguFQnHVwcO8d8CJEyfg4uICpVKJZ599Fps3b8bAgQN5z25g/fr1SE1NNR2RdCXeu2uLiorCmjVrsGPHDqxYsQLnzp3DLbfcgoqKCt636zh79ixWrFiB4OBg/Pjjj5gxYwZefPFFrF27FoD5vh9EP46DiMzj+eefx8mTJ1vMQ6Br69+/P44ePYry8nJ88803mDp1Kvbu3St2WRYtLy8Ps2bNQmJioqjHG1mju+66y/TrIUOGICoqCoGBgfj666/h6OgoYmWWzWAwIDIyEv/6178AAEOHDsXJkyexcuVKTJ061Ww/hz1DncjT0xMymeyqFQGFhYXQaDQiVWV9mu8V7+O1zZw5E1u3bsXu3bvRs2dP03WNRoP6+nqUlZW1aM97BygUCvTt2xcRERFYtGgRwsLCsHTpUt6z60hJSUFRURGGDRsGuVwOuVyOvXv34sMPP4RcLoe3tzfvXRu5u7ujX79+OHPmDP/MXYePjw8GDhzY4tqAAQNMQ4zm+n5gGOpECoUCERERSEpKMl0zGAxISkpCdHS0iJVZl169ekGj0bS4jzqdDgcPHrT7+ygIAmbOnInNmzfjp59+Qq9evVo8HxERAQcHhxb3LjMzE7m5uXZ/7/7IYDCgrq6O9+w6xo0bhxMnTuDo0aOmR2RkJB577DHTr3nv2qayshLZ2dnw8fHhn7nrGD169FXbhZw+fRqBgYEAzPj9cDOzvOnG1q9fLyiVSmHNmjXCqVOnhD//+c+Cu7u7oNVqxS7NolRUVAhpaWlCWlqaAEB4//33hbS0NCEnJ0cQBEF4++23BXd3d+Hbb78Vjh8/LkyaNEno1auXUFNTI3Ll4poxY4agVquFPXv2CAUFBaZHdXW1qc2zzz4rBAQECD/99JNw5MgRITo6WoiOjhaxavHNmTNH2Lt3r3Du3Dnh+PHjwpw5cwSJRCLs3LlTEATes/a4cjWZIPDeXctf/vIXYc+ePcK5c+eEX3/9VYiNjRU8PT2FoqIiQRB4367l0KFDglwuF/75z38KWVlZwpdffik4OTkJ//3vf01tzPH9wDDUBT766CMhICBAUCgUwogRI4QDBw6IXZLF2b17twDgqsfUqVMFQTAun3zjjTcEb29vQalUCuPGjRMyMzPFLdoCtHbPAAifffaZqU1NTY3w3HPPCd26dROcnJyE++67TygoKBCvaAvw5JNPCoGBgYJCoRB69OghjBs3zhSEBIH3rD3+GIZ471qXkJAg+Pj4CAqFQvDz8xMSEhKEM2fOmJ7nfbu277//Xhg8eLCgVCqFkJAQYdWqVS2eN8f3g0QQBKHD/VdEREREVo5zhoiIiMiuMQwRERGRXWMYIiIiIrvGMERERER2jWGIiIiI7BrDEBEREdk1hiEiIiKyawxDREREZNcYhoioy9x222146aWXOv3nTJs2DfHx8Z3+c4jINjAMEdFNmTZtGiQSCZ599tmrnnv++echkUgwbdo0AMCmTZvw1ltvtfm9R44cedX7rly5EhKJBGvWrLmqjltuuQUAsHTp0queJyK6FoYhIrpp/v7+WL9+PWpqakzXamtrsW7dOgQEBJiude/eHa6urm1+37Fjx2LPnj0tru3evRv+/v5XXd+zZw9uv/12AIBarYa7u3u7PwcR2SeGISK6acOGDYO/vz82bdpkurZp0yYEBARg6NChpmt/HCYLCgrCv/71Lzz55JNwdXVFQEAAVq1aZXp+7NixyMzMhFarNV3bu3cv5syZ0yIMnTt3Djk5ORg7diyAq4fJbrvtNrz44ov461//iu7du0Oj0WDhwoUtPkNGRgZiYmKgUqkwcOBA7Nq1CxKJBFu2bAEA1NfXY+bMmfDx8YFKpUJgYCAWLVpker1EIsGKFStw1113wdHREb1798Y333zT4me89tpr6NevH5ycnNC7d2+88cYbaGhoMD2/cOFChIeH44svvkBQUBDUajUeeeQRVFRU3Pj/BCLqMIYhIjKLJ598Ep999pnp959++immT59+w9ctWbIEkZGRSEtLw3PPPYcZM2YgMzMTADB69Gg4ODhg9+7dAIBTp06hpqYGTz31FC5duoRz584BMPYWqVQqREdHX/PnrF27Fs7Ozjh48CDeeecd/P3vf0diYiIAQK/XIz4+Hk5OTjh48CBWrVqFv/3tby1e/+GHH+K7777D119/jczMTHz55ZcICgpq0eaNN97AAw88gGPHjuGxxx7DI488gvT0dNPzrq6uWLNmDU6dOoWlS5di9erV+L//+78W75GdnY0tW7Zg69at2Lp1K/bu3Yu33377hveRiDqOYYiIzOLxxx/HL7/8gpycHOTk5ODXX3/F448/fsPX3X333XjuuefQt29fvPbaa/D09DSFH2dnZ4wYMcLUC7Rnzx7ExMRAqVRi1KhRLa5HR0dDqVRe8+cMGTIECxYsQHBwMKZMmYLIyEgkJSUBABITE5GdnY3PP/8cYWFhiImJwT//+c8Wr8/NzUVwcDBiYmIQGBiImJgYTJ48uUWbhx56CE8//TT69euHt956C5GRkfjoo49Mz8+bNw+jRo1CUFAQ7r33Xrzyyiv4+uuvW7yHwWDAmjVrMHjwYNxyyy144oknTHUSUedgGCIis+jRowcmTJiANWvW4LPPPsOECRPg6el5w9cNGTLE9GuJRAKNRoOioiLTtdtuu61F6LntttsAAGPGjGlxvXmIrC0/BwB8fHxMPyczMxP+/v7QaDSm50eMGNGi/bRp03D06FH0798fL774Inbu3HnVz/hjz1R0dHSLnqENGzZg9OjR0Gg0cHFxwbx585Cbm9viNUFBQS3mVV1ZJxF1DoYhIjKbJ598EmvWrMHatWvx5JNPtuk1Dg4OLX4vkUhgMBhMvx87dixOnz6N/Px87NmzB2PGjAHwexjKzs5GXl6eafJ0R3/OjQwbNgznzp3DW2+9hZqaGjz88MN48MEH2/z65ORkPPbYY7j77ruxdetWpKWl4W9/+xvq6+vNWicRtR/DEBGZzfjx41FfX4+GhgbExcWZ5T1HjRoFhUKBf//736itrUVERAQAYPjw4SguLsann35qGk7rqP79+yMvLw+FhYWma4cPH76qnZubGxISErB69Wps2LAB//vf/1BaWmp6/sCBAy3aHzhwAAMGDAAA7N+/H4GBgfjb3/6GyMhIBAcHIycnp8M1E5H5yMUugIhsh0wmMw0LyWQys7yno6MjRo4ciY8++gijR482va9CoWhx/Y89Ku1xxx13oE+fPpg6dSreeecdVFRUYN68eQCMPTMA8P7778PHxwdDhw6FVCrFxo0bodFoWizh37hxIyIjIxETE4Mvv/wShw4dwieffAIACA4ORm5uLtavX4/hw4dj27Zt2Lx5c4drJiLzYc8QEZmVm5sb3NzczPqeY8eORUVFhWm+ULMxY8agoqLihvOFbkQmk2HLli2orKzE8OHD8fTTT5tWk6lUKgDGlWDvvPMOIiMjMXz4cJw/fx7bt2+HVPr7X6Nvvvkm1q9fjyFDhuDzzz/HV199hYEDBwIAJk6ciJdffhkzZ85EeHg49u/fjzfeeOOm6iYi85AIgiCIXQQRkaX59ddfERMTgzNnzqBPnz43bC+RSLB582YeA0JkhThMRkQEYPPmzXBxcUFwcDDOnDmDWbNmYfTo0W0KQkRk3RiGiIgAVFRU4LXXXkNubi48PT0RGxuLJUuWiF0WEXUBDpMRERGRXeMEaiIiIrJrDENERERk1xiGiIiIyK4xDBEREZFdYxgiIiIiu8YwRERERHaNYYiIiIjsGsMQERER2bX/B4fTH6Wa3AlKAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", @@ -399,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "605f1aac", "metadata": { "attributes": { @@ -413,7 +657,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.kdeplot(filteredBirds['MaxBodyMass'])\n", "plt.show()" @@ -429,7 +684,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "f4fbbac6", "metadata": { "attributes": { @@ -443,7 +698,18 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2)\n", "plt.show()" @@ -477,7 +743,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "121d1da2", "metadata": { "attributes": { @@ -491,7 +757,38 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\fuqiongying\\AppData\\Local\\Temp\\ipykernel_3908\\1933666654.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n", + " sns.kdeplot(\n", + "C:\\Users\\fuqiongying\\AppData\\Local\\Temp\\ipykernel_3908\\1933666654.py:1: UserWarning: Dataset has 0 variance; skipping density estimate. Pass `warn_singular=False` to disable this warning.\n", + " sns.kdeplot(\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.kdeplot(\n", " data=filteredBirds, x=\"MaxBodyMass\", hue=\"Order\",\n", @@ -510,7 +807,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "14531a07", "metadata": { "attributes": { @@ -524,7 +821,36 @@ "hide-input" ] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\fuqiongying\\AppData\\Local\\Temp\\ipykernel_3908\\49960699.py:1: UserWarning: KDE cannot be estimated (0 variance or perfect covariance). Pass `warn_singular=False` to disable this warning.\n", + " sns.kdeplot(data=filteredBirds, x=\"MinLength\", y=\"MaxLength\", hue=\"ConservationStatus\")\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sns.kdeplot(data=filteredBirds, x=\"MinLength\", y=\"MaxLength\", hue=\"ConservationStatus\")" ] @@ -572,7 +898,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.18" } }, "nbformat": 4, From 8a87b6cd5f42008bd575537cde42d358ebbc7a5b Mon Sep 17 00:00:00 2001 From: Xu Senbo <1170676717@qq.com> Date: Sat, 2 Dec 2023 21:34:20 +0800 Subject: [PATCH 19/28] Rerun three files --- .../deep-learning/cnn-deepdream.ipynb | 87 +- .../deep-learning/cnn-vgg.ipynb | 37 +- .../deep-learning/cnn.ipynb | 40305 +++++++++++++++- 3 files changed, 40385 insertions(+), 44 deletions(-) diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb index 9cfeb8a4bd..49ef8353bf 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -92,9 +92,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Image cc-by: Von.grzanka" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Download an image and read it into a NumPy array.\n", "def download(url, max_dim=None):\n", @@ -123,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -132,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -168,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -208,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -217,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -252,9 +275,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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fFpXKH9x24oswzhkVeZpTione4gFHVFa9mKcag86SRmXBSjMc15ByYkyDV6nCc03ffJlC+lDe0k3LbXqqf3KdcjrUhKZe+R0ny+INFxCqKRtGbu2iiVsUdQKhI90Sz85jNkX4cPt1Crck9f7xolp0odh88H30k58953V58TzrcuaoTruQ+VlcQiILQohY+Gp1qYXDLg1FgRLrMm5wUi5Pc5Bx8rZcP9DSTZ6m2VkslUDqW1taU15dlW0D7JnWeCXYYq8wMlJz3cULfep7VVgUzMmywMJb39HqWcWFzYpCQxUYFGdXjSWy9khNErhYiVumQZAqqmKXryDVnn0ZlRxfLYvdvhOjUBaUXosLLcGvWJpL4QZ0hi0FlNuHZgFZ0FK6dtPEzeGd8pe/+Oru4XZ7ABNYP1/xpo1WX8YaKiVZbm3pnKw//vmmSaSByoZvdU8DX9GwkyewAHOfVirHykQ0QBxXmcq3RD4eFx1WAc50r8sU6Z0DezMCwctZGgjv7sVhURczoFua2a1RUidpk2Rxuz1Mz338l5fV5Sotk9OFcHjcefK86Vs7rT2+hLTbVr/+q3XgdA4VadvZWiVuSXidUi5xuma9uYLhy8DYa7KWBFQolsxSVdlE7hpSKnipoo+UXQJReLXdK5aTjdvJD79D6CxlQtkcGwOirFPw5FmUvuCOj23aM8sYVlb39kMPuJVF4uuChk01UTlaNqgUuoNW+SydfxHudkTYkJqCquKDuBTTGhkCMFUpYq0iaTlGmOiQ2vYNUEMlin3G8964METznSOdjOOUrn/ne9ZTz+enIZNpUqZUAVWag6IqeBYlodBgZU8Lo7oayp229PQn/oKWu9jOsN37sJ2/yKOz6WCALq8z/K6mD6WXP0oszfKQUtcctLf7ovX6Y9wIDc/k8snF97575+t16l9s/+3v97cadPUnvzQv7Pb93uVJ4wXwD7/TVWjwzY8nkp1NSx8OqIhzqtLpZKIKmSUm1gY2tenc6eKZ3CQThfAGzS9ep7e/K5d2R6+XPHCMe9zqLLgXe3/vI/GbR+ezsT36TRv1GJ1jN6b9ntq4dOmhzg2+HPLNeZ5UdMOouC0GdaPr8MorqqRSVFzzWGqpHYHBOZ6nwi1H17xUtCxzqOaUirZaDew6gJxqDRGuilxMMwJx/0A0EsF6PQZxIoKmJlwsCJ6bwTJnOauaPOjlRqsOJ4WYltGeqiJte63ET6fXi0K1Nc20NmUTxGV7C/uTYrxKVa1UxbrXNJscmF20qgH0qa2CzeuEQWHrZqvQyAfagaq1g/PFV9/8Ek7zO31644719VPvKxf98Lv6aVUGBWS2SIl04vqiCYOyRAUtYzbchrOEKjVqeJ4IgONUuahqyEmCzCOeI4DowjRNsMTLFedomlDWiVfguNi7AV48X6IuvvOudj4J1nGmcY0hQakqkZ/goNx/e3uyKKrGaA3MyN8koagetC9fTbmiyFo103lIeK2jERXXoFR1uAkrnvCCwkuMsrrEoAFFLTUwrRpBxWWDOI4hGVcMMITyIod1gwBkDDYFlBURMFzUDRfyXOE3W5btFM1DbxNuYZ8pWLA7y8XFOm8fDHh7Wp16FKkipJXI+TN3brTFUgarTOuA9K4tJyV+VdTjuLWjT6euvIDVDWv7u4c2hVMAfYaOCWpSEJ7OvYCscdZOw+CefC2Zl0+Xf/soje+QJQU8gJ7X7PKQu65frGJbSdb9A3FHf1/i4mVZjKzXL9cWqX77QfA4IP/TowtDVjOhfCEI881UibGlQWHAh37SG6ldpRTLavmF1xmqR1udl6NOsljvPmnU01OWuJs3DRkdqwJNdwg2SBcqRhulcXY04Jy6cteJXFfPfEGwlChambIkcKCxpJZcrdLm7HnBN+A9WpQxrxTNrW8PxvrBcZo++fFLUuBYgPZ7g1xtdNo0m7CIstxdJIHW7Za5afAi6rzVX61rvCzLOK0UuFkVo60toOSdvKCX7oxHgyluNoG+FdWHzMnr16+eBm3593b18wtu59ao3NRRwSZUlTERCvHh3cPsyUbfNiSyaRw2JqFSo93Nq6+GR+ewGGyYvfHbQD4/DUuoOFxOeDB/FGiqoxzq3Kqsv87/yOqB+dl1X16zEilqMF0SSVNUYaW25OXF+Rj1DnvCx/HbF+6Pb5nrRazFdPttiwPoNXHzaXSnY9z73lHwK78sRFDnrpfAF9cPd272ZCUNi3VTRYpY7dLPJvkdjr5XyLWMSZTPXk/LrQ50Swh6r4L4D82KO33l7gnTl/GtbPPAinG3WJ+bjy7RD3/4kP/IQhErg0o5K9753tbps/GZn3WKeaZwyMDgunGWhbovGaah+Ex9fBHdGpzLRHoZ8qb82mv6kqDk0MbqLxnqd/ndTWT0O0JkvP5kXCGct2Ngo2Q++2Df2nBK4Zdoctm7KwjteDH2B001K5EMmgoBS0hyhhGtRMA1s+LxNX8HLl/1OxeDwbsfaTir4ItVnguv77RssII61+PBzslFeXwPbg16yzF3g3sN0MOWTp5OnNdgsEQvvrW1G09+n02q5uhoX/vpLzZdRz7/8hfD745+4kr4b/ID4rdHijLUPvl3byyBTfZbGbvyhYgp/O5JeQCTfL/ru4J1WoJP1+nv3xeDSzfLdsOyM7K8EmabMs4LudDNeJ11B0I+k4rk1dL74d+789/+d2fmW9vvt9FXzzctUpx89vrm23vk7Z1MEk7+w/P23tCDJPJjTcBtHsSXS7eEA6W3tS0t0jJeEgPitU+KzsC9rPbDqFBHUhM/n3gCQcF8tdUa5heLzq4pJG4KjLN5IKeFKnEJZmqVs5IDXvBNRU0b3a85eD1b1pwRJS1V3iP+fNdMr4KzSOnuGORuZ7BO+Tcu0sArKrWq+i6pPaf5hvPFKDi4qr//9/cevaJO5K6IBGXuk4riXr5nNK60EBPxxePZhHG/+Y62/Xc+gm8erS/OItrs/v7Wz//ti36JW7fyvshUm16dugNLwpoo+0lcM6ShuoBKHpRAiqqiY+E6LChCucQ7AkWrogwhNSX13bYNKhnvLa9KvCqZ2mzZsJWtWyPxbJoFFdkOLx9Dqd7V3JOwM3Q8EfN8HmQk9mjNcfM0lzW4Ob3W39kLZboriwRFcx7EYnmrUqM6Y0XMa4p23K59lvi1RBoJQgGhQCNZhQBDWV4yVU7TBPOEANpQACWi84CLC6pJDWCSKBVVRWvKBWmp6/yLaapsGq41hGe4026vz1lVdPNzge8J7obJekeyW94k1DTmBqVS1pLIGoNP2loENVMprUE8D9HdLb37B8fheTqT6qotxa/j8Nyt/QQYpBDY1Zvwg+9srdLiq7MqY9nf/aOhfRO62YIU0tCuvDDvDvR5UEGFH25J00fV7Jeu3hN50sAGqlSqFTGxqr9+4cU+dUvWEQTW5n7373XnjybLZ/kjL5FqraFRso5KQ9F4Ufz20cvZ6vRZXm0CNSvkTYG3u/xADfOaS6PZSZT4bDDqG5bICpRFWbMqbFi0JKEpmISYrlhZmAdlpRTMfxO23jGLs7T1UJmtqk9/ifmiR7syC0Qlcjdn62xaKqNBy5KQxbnTUIIl4jmCyszid4/40scdhW8WhQzz8lXgnSwrldt+ry+3uMwPUEG7Lc4Lq3Jd4N/rrc64ma432/wH2yo/exHIy0fnSNDYiAXjBcQN7pqyXwnX41w3MO8IdFj9xy/npUAyrdYZvmBCtUn9sHHC8ryqRJQxIkglm7u5o5CgIralcJjPfarvtL1lJOgOxuFmU0HOiAVLUVBPLVdJxEmikgrqyCDvaPFBX3XI9veFn52c55f45z8TvvtO77baefPlxeXHwf2j3bAlnU3pCmpGV+FlerJiIiwXsLriCoslXYlJTeH0nZnpPZ/Vmc6o5x0YWhdGq/kam83S5FZz2H3Qgy56/vqkidx7D42uAKWfni4ydPPm0OVgHCuzc3bw4HB+dkYntcJYfVoIQWPK3T1Hn9NknOdMlFerklqy0x/kCLhny8NbmtnH0RS+e2hcPyuvxkCGga7y07B49/d7L5eBbZTbDhW5GNFwflVaSkJ2nP8YrxNajDUcbNI+D8qkSgjOEuoy2OebEGOR4ybXoh2U4ePlwoUPvtN+eZLFpK2aPS9lUmdzOSmleZ3HsczFL8Pwh2bPPUXYIXC4/xdPs9/+aPuJu0g/E37LGbIZ/eLz1/e3D3yz/NFZ9c5B8V/+rYP/zy+WX76hd95RqJqpasBxZZQwjULsUTBgzFQvzostgYmWtM7T8IbMZAxKUxaasOIkvzjCWvnEnRK4gpX1lj6fBu0z7g+Pe//qJ1dPhsHu/+EgXMDNLPn0J4s/+N/uqQ97RFPPnl+LnKFTYqT5xl+3cFmVDcaSwnPMFARbf3QSNiVo2YrGc4YsKYYSTjIYA3kPvLneAKsuERRtrVEkQSw3sWebOAmC1qAp6+pSAoUgj0NabhJFELoEReeJ39Rbd819KPohv1rmBcCrk5Vjizf42jtZqV2iaSJvceskEaqGy/hg02iOQa4qpaeVe+zrdZTynJJpWxVKo0zTebipTYt47bmt55ueBGruX38tJ1r3uGu/3d88f+MvP6/RXe4sbXDJuX4r6NUerAcS5tIYGkIwowBpsFHLNMvTrN0XFIuEcdJW+KXnen7JC3DrgfO6TGrG1XMo9jHSCaIQJLWsglkIxs9y8oFyETfcG6cj63UsoSwMKmmWclsdQ6bY5qFu0Cb17L7y5gKxtTtsYz6hZcaEZWlIJE8q0SI8aq38iBWRZeicKZWgwhRUIhSEEkicbhh5XDIs5IkgiFwR1zIGcZZRSnOeAxTWlLEyYxRACJBqAF0KqYgqvZ772T6txDfPel2206nKaVy62dE7e+3V1eV4nlupaiaAhbiBeYJU3tEmG/zpdf7oMlh4qgr8cjNV2cVkk7xaLJ9evXz8IstypY6nKueOWlsf7DxNGiDbx2873X17dh2mprw4TdSbW7N1o0kk8RruOoKrYh5D3Wnu3BJlLfhLZZrdLkMapecTN6ha9Ymz0/QetDasv360Hv/3j9bX5dCJNZQjLhLbxP5ICSA1prX0y/CYUw7F5h2h2ptvCoLFUc/XtbLr8KquddSdI1ssIv/SJVUj3mq7KplXYo57yQW2Y0Km2GlgK0p5r7h5TxazZEXL03EwoHgXB50/6H/Vc65+vRkVzepkSg670nd39D5Qw7Xi+1KV15t4ExSxjTDXwNWiPnflWfh8mqVt8fhBz7QlTOqRTQMYJCSfL1ZuhnuGSq6N93fuDVNwSxKmkTeOPFeUvhHZLxrJZJ0dQdQxD6ma5dydQwNVISmWHz9/We1luVZJvDdrykZVLq6oVoGSga1b/cBSPV7o2GKv7bA9dfCfHt36oMUuJiXnI6VZJM0au2mZbsnuLr/iape57rW7cvYc1w+simlzUCRw/Y3f+3Vw+e9PW5/W3cn8nfeDMX/+k2efnPmTOw/bLbB2OaLcl8Ue38ZRRMpTR6QAv7m4fudG+xaM/jaIR0oDhc316UVWpfvtytpGRl8Znod3HH2dRwagCo1efbqJPOCpArTt//Fn0YtTYw54NWTLx9delu0Y3IRjX00vr17MJBZSoRKqEg75+qg1fnIteXQZUHFETFnbraLr5wsyTx7IXPe2Tvbl6FdfFLaztGwesc2dPXJL2d4qwZbI1AasN5psPPpq2YpjXqiSu3r4ljZee/se3Yvqh13ZocGoVZUoLvhK1BGgLHerEeBbgrTPhPfKBvXJs5xeeGG1OjcfvWb55MF/tu9LsL215VRkPJvzd/GkpXmkmC3Oe9u8Akm7XamzzH+1ePzR3iliv9SPf0wBPpm/+66zHOpnwYvyl1eD+zvt+52v/8WP2zFnc/huKX2kqvtNc8OjqVpCI5io6YrFAYtWl2f59SpXzwLd9byL5wK3vCG5kMmbjea0FIdpac7Oxr+IZsL/9Xc/Fu3N5vTsTz/58s8++c4/7oW2cUe4Kb6M6ZvSf55xbTvDIkWcPmipvZ7Q6VoPtlqWaQHQkWWrI44T/9FXkxUC/S1bJc1cARdyMBUYr4K2gvd3eFBMgjRJVVgLbDGrhIzNUno9SdtEJAqWbMQFmy5X9aVy4i1//Vcv3UfLhlpU1S8ahlScjme1xzRv1UR+xKf6SCGCsKVADkVPdOj25bckpXedDRMI5/Hhnq7QStKxbIJentdS82zU3oD8SUqnpyfFavPWbbe7BZP47NP1UrTm13AcaKm8jJRstnOUpWeX3T0+0gRbw/56kXEFORY9fx2upnUX8B/tum4auWWQVXFZkW3HeHfvtWo1q8r4xHeuVpRRpFCOo5lBFvEGIgi7Blnm2xkooWvdraaJz0ty4mc9jbipvymqUsxKQ/YxzVG9f1MMxnOypc/ChBaxSWu1rtcIz2ceTuLt/TYyWYHK9p12695IMU1CMZ8wKWmKqwz6DXUzoQAwrdOGUYQVhJGtpjJpFLlCiOaMh4jjOE7XBYmrfvCOevq4eHLqHipppZHUncsC5oLCa2rEsYy3HMCVJfE4xutSlsM4LOuYsQpKMHx6sXbqZnsgbNaSzcpdfrXKBEvpKH/rVgwkfB0WqsQjltdJVXG8KpRlOb+KaMJtW+Zjqt1YQ7hJh3359fPSFESuRTYgW888K/XbXeRj8c3J4ls3qWDILwgtdh8Ob9z4ANz/mU3J2PI/+8VmUWRrf3SobFZp7cFmW1xfxR/s6C+XrIiQuNPduDE0u1izOIdYPFu99q0Kq4ZFDSGRI7nhNjbmHG40tP3YP+TrGGdaVkdhKvFM1ou4YOFmU8q8ZSuhIczjwrcVOos7LTtQlXFet/Zt81B+db5ILq91GSMR+WVTQWX41kBpw9XTaSYWnCgxAlo8RUQZ3O/yZ8v5K69MymEH1pWCq7q3JWqGvLpYLj6/Wh+K/kI61g7JEf/00bKR7cPD7kxU51WTgnokqtSNl6d+Vm62RqBI3MAnZckswFuMI4GgOdLA5MKJH5xkqGQ25iDhDZFPITj/csWEpsWR4UFJr+ddoc5KrpHLNJPNIuVxxjUUSESQ1LRtjxMmOWF97Z5MvGJV6rsQPRp/IWIPVPf+7qDYv3llNfjv/Mbk8hq8zE+eBMCLi5BBJZLLah6EFg9g0fCSMZnC3nvy00muqUq8rEQiy5it/TrQiFDU1TKKxHJ7qyq5crGo6ynbhM7Gc3FWnDnZLpGGW4YgSNPx5uE+5xVFuiPnCTFqTgf87DI09lHWb0WyUK+zKZQpAjWWtR7cO2ohmv74zD26oV8YymHmf/8WXn2R3eBndU2X84v0aXZDN1+ewcNDSW05Ba/fGglvgmD2uWsCnhIcVKBJcmYIada4nKZ2eqoN1JjL3ZpQzXjQWgfl6rW/ZUi5RM+EEpn8+ZvrUIfNUdQxa/NIe/ykGMpdUWlPnrr3h+ajL9JwFn3wbfTN07lNws5Ohwvhm19c/9f/lfUv/82SKtkH/2j3U8r94j+OPxxVNHlz/b9Mw8ls9JY+3XCoql95lFeMLUuenGxULCcebcpcb/Gbkmq5n6iIyZWXE5sT2LoKZhFifFvnVk+j+mS88972lxP4d5ny+f/y1abnvXW/nQ8OBu/fnv/4+tVPHk/q0HnXdFoOVyJOQdyuxEKMklrI88mTDV5VblMDTcpbRCLQHuLryVjmOngrjLwyjcMug7Ir6CqJJhWoawVhni8ur4puV2m8dE+uF17p1n7Vww0r05LWXiDbnC068bWXrQJF1G8MVb+/6zEk7bY2azCX+fFpLKyLfl9wemDuFQqh8jRikizJ5mISO32RuqrUQ26p8XEBNbpOSzhs14EVTDa7ahFWvrZYnWycTQkOd3GYSpNMSFNvh+eNNponGUKcLcqdqtx4+ZKTUtyWJF5PmLvKFFFvHWw14zx84m0faD5PVLPF6W1/lkTrsF3xpkxfXaTd2TQJBVJUWze6cQE9tSj8NC95gyevgLpd6JArNyWSOC2GSmBQhWNJwRRiVHE1Dvh2V7XOXXWZk5YSBCUrxDorbgyk0m3OXy8P9Hgks/hqE+cNBFKSVjyoWcOKspJKhhTEWFM1VOBhTZkXKqBolDyrylrQKxHzgDCKGQAV1xXYykw/fVNAmsn3rfNl3G1gqGlGFVEgm6FXNagjM4+X8irZMsRq6qYyz6ni8kXe6yrNPav//qF6MuPNjioLcVxjnddABJFwNUubuVeuV0mC+rc752epLcSpl6XRRlkU5vf3pjlSCB6f+4Oj3Us/zGXOTzPj2m192zBR6/zzBK2TNVcd77SlS7G8zNHv7GMzgqexfbB8i7WOv/2DP/v2h18++9nw41cpSt3x6vaHnZovDLU+Waep1Iwt6aDI4nEgWP26wwolaK6zm1V6foF5PnQPlNAx38GyEHiLC4hmgcVxQZGGto1WK3xvN+BF/k3V2CTe02RFwtd41ISndf3wfTF+dNG5uv5m0Hu8cYW0bn/9SCO2aGvC0Ky5ZnnhqcPemonVwjc5QR8JX25qk+fvCH0sl2eX1XRWz14XOzbi+b5DWCpiyXDWl0HcOKM27Tq0oAxcNkSy3lXxq6+9P7bF10DwCjBqkWy51GSBU6tNrsyraEvubbHoWtSa0N+VQZaDxkuXbyJtWzaSnNo9G2ebKCvSZrUWWlUjNtQQuech64uc6OqtbO0y3AhgygHPMfnXQVuFF+sZf2DEZx6MNeED28a96AgPLPCXz9p7K9arSnnUuVhRrnS1xVn+I9fRpEDiBd7uOstLXid2Cl/H/eM+XcSoK8oDnAXuy1k1HDg9R6NtspNkcwygSrhwivdNnC2vXlW7B3wp5CJxaX+rZbfyJaNXaTSFZ5fpvmTzlipOK6PysYWbrDSzWlZwLlowpG0EH5/6XcNSsxWu6RqTvXe3r15vcEO4TaGo9e98x/E+Od3/3s5jyLhPThZ7Cn+/Y+UW1zYFvj4jTu93b4R/+ZipskYDc7O+oWuzLFOwXOOmrtmSZtaoxd0YCDBsJrWQs6RKwk1i7+qcpgVXVL6cCxaqHEPw8o6YuX/9jL/hcPvt/ssXMnB4QxG/GG/f3D2p4rHPtgYH+aefWn90c14X8G/O9rfb6AR/ULqbtzT/2p3+uwt9dyd8q1X+zRf7Q+L8Fx+szgv/9QntmZD4qKnWs6rv0y6dnULG726dX3t4cz2/O+wdcMo0JeebkDWWAYYWmjXyaHDU+/O/1DrC5EDImCD+2SffSkWP70z+s5v1Z2/G//0z8cm4d2R0f2Of1WX49Noz1LRkN7zSz0HuEszmdluud9UxRNsoLtpmEDfZ+RvTaD1ejSGvxcoQ1axbR7CrZwVgYexzCoeZvs63OeZXlURqneOJjS8BB6OVgaCgF4kON7TSR9pgf6equWDcnHz+BgtJpUm9twZlHTU8uX/TnEwW87VrDrUF4Ds6ue2huihW+/WlK6GAllamAzbbwvsBaM598/ujVA+vP392w/G0tc+fnO8NNeCWMrGcdJNtk9a2MT3Pm19dat+9vZpFdlQyTXpaAXNfQd/MUkE72O+svn6sc1V6PMwo4x9d7OzZeEcIn5e6UKzHMVvO2h29bLWnI/H4lnvxluRIzadfcXpvzgWhCpoDGzd16bnJjTY2bWG+wlwIyVBfhp7Olys3EhwrCZmkV5sSY8siXTFc52JboKjmFDmYxtyb3LnVEggqwxCGtHmzBiNASEJtXZabFHISw3LFZ3Fe0xoRyCpqcHyZxKUM6rxSWJOsKxlByTYRBhxkXJZWmtK0BZAmXJrVpcSHi1zNYtoT7S1lMeb6PIxw/3IBuh3NUdGbWKjzjHCKactdXZme5Ptitn4ZphCWjtTf0q8LOJ/VCosrizYLL5ZZVDWmQY0Wd31Gd+RSO+oL+w0eGtxJLOpWvuJJu/BjWOFcGZJ1huNpzV0zOeZCRYOa6kXijR31omamKC3OS0rX/+y0tHP4yzMn/77dz3jD6Z++uJBLjlfEly/9gwHfBBWgmb70oueRmVdHO/BRGJ+SBIKkXVWND+oWGZotS0Cbq1QMkq7Z+EEp8/yyFLKuucmSQ7rkgZRKFa9A/yygJnBEzaciLrhXM/+Ajydu0XHkvZ1mdYbHk1rQwHsPOxXhL9/4ElS6pL58NYsLZqvEjauKwJgK5ykIPdHqSIM9UUeV5QgekPOyYpjlz3Mtqg2t5rdhmnAcD6EgOTZGLkvHXvppJn07lhhTRrLr1aZTTTa5ogrzdQMUOc25oBKcXncTRgAAneD2riId2GRWDHlMKjGquK0jhfNrmXL5yl9lsM0xFcBV7ImSAJNIoJKuMCWp47pxOHkyoXs9fWIwxICS84nnpwVTjpT39g+Pi0i364tfp28l2aPPLn/0307ufnAkWuCoN2gQ9+Ybn0kldLnlEm6NdFEu8qC8DhnywYOWXnlgtI1Pl2nC1+tkOZDAMglJpCAKZQGVgJSiKv7g3mvcs/raY1DcmPn1L/P4JZWJbQ3ksClFSXSk/JQ1gHCLhmMGv6yyLb7UrNrbhKUCd0a6VNfpJlquN6xRel3j9bNiYNkJl3x5NW+/b7CWIeq86Wl+LaYNbx5SJsaLb3z3xKOM7N7ScEykHPQwzd3Urxook3bXMjrmNKiyx1GbpgptQNOAMvNf+obiwI4RzcjtLd7bFEXAJNOJgyxwm0aJA4YEF2RYLNrdZcLFm2RF1gff3b3M2Ft+Xa5w7Jadj7a/LLJmoJgHxsnj0lxW9rcpd6vhLjrDPf38NF1sCuW+k9lIzeU9QSpmszoJz/xkFc+7gln5chJa8HEpqwrJeWy0LAUuzgIlpLDNc1z8i1fBjS1Z+D1yfzf96//b8lt7u18e4biCzUtwgBr0Vntj1LCKUIo4Id+T4Ivr8PlFqR+qBPHrpCg30tY+3kIAL0AdRnf75EVB4lXVSkphUxiIYUGiIkyLOmENYlTFJaMwrmtGQRkkhEfTjCJMMczqmkFWl0GCyxjmcHFahRiRljE6VsJSCM8odqurv5ppMpr7VfdI7BjK9FVEiAAzpGBVlPDpzEXtZk8sgJ/vDQ1T5YKLLE94wRbSnE3PQV8mrXYfVqnZ1vJ5mC/Bze3k+iR1Q7SstLu/dXT15whWENVguahu9RyO6O7KJ1nZ3qZJEJ8sy5u7vZJAeBllhAkOqWtJJnzsp6AoFU1K42yvXczOy4zEFUkyud46aF0FhdwUhowl2sQV3xAL2ELhinpOdU3iOZ6yHDWwy5EgEeROK0kjFKHSJ0pHWV5vBpzQdYS6YdtbcnbijyeBvGfkCXCEStF5sWskFVdBmFWNKGKJAlxAJJOEYalmIkZNSYkMY4nFKeVsQSw5FiNO44usQgBwO6IVI1yFgjq+rhpmf0g2XNksI5ok4m1EdCOsq5tCRX0y6tlLb0V4hHksm6KWNDjzyLqMg/nj82KIwxu9OxjG6zZCKs86wtGHN559eRUvxiNUGu1tTvbAQGi1WmssmOuYmyRtSZzKEr1KdhUfO+a5WwGFsU2lJY3QyF8HVZ4mb79rRudJzUlrFuxcYVlEAtB9q16vUB2/vP5/8Pp+GVGUnU/FH+yHI9BlXO0tvYgbiZA7zRafLjc3DQHFsQS3wGr7yFmON9GtIa2S+PHElmBeCE1d1FkSUd7TpLnlxCeargOMBZ43aprQFDiN/qFG54uw7DWoI+3PF0o1tppqPYOrjFukqPeDmyrouOvIywSuQ7qK5H78TKaCutOuSp6vqoGMpF0JQFPxrueiJeQTazmJKrWCFVElCQhWg+Tt1gXHZ3Xgg0CJMHu1UddMm871OKu9vDIGDWqWU05WIO/7rTSlIqQjItXr7bvGT19FbcdAORiAYNU4WcXiVxcq4dEyBZws6OKrZ1e4BsucCo5gCjicYXUIWz0j9WIed5qg2DJQ1iLsSIiDxUDksjXWeYWvq+uXa/XRC15yxPw86+9+8vMnyY7NToOt+0ffudv76v94PHywWyyKzcWUj5veMToNQwoTJFHaQuXaLUVO1BCEnCGApGUYNm82sUcLrgcX1/7+rsqCiBM4rLXMZHahg764eNXSbYAfgEOr/4T8vW9roCguQfH4Inez9hD/arpspNYoR3ieDbvgWtPmizeCLPMowUe7SVbkF8uEzm59+/aXn4W7Gtemwt6+MNGHj1/Fb9/vJhXRErQZpxeWYujl5tfPe9kocVORzLf++Iezrx4Pxpml8L6MCRXLriLCwOQZe+bKXnK7LEMEHFj8Iq9E5pK0WM5W/Hfudm53E5TQtK6UklOIPluA0uGFDLVLXiLNugjv3n4ZpocE4t+7Q+OyL5Qzv3rrLf18PXfS9aNcJBRt/2oxCVr3/v5vnj56hp5O8kn1+GxBdNPatvq98rkUIyjZuaQT7bLaoLkUHSkCvm76Qz7w2oFWwX1PcPj4k4y7lwZzvq+1frD75qdvbhyZb0zn8GyFf/paIvpPf7jrHIHl//OLvWkqv9Vlv7fd/PWbd0PX1cVM5Wdn2Y2ORu81aKhwEz9MUVklT34204nM6yU2lc9eRiXNWoLhLrMRFkxNjYtkoqKqTvWCeabMRVXB3LIhCvBW2LTSRJOJpiCrgJQKl5fzbYldjZNtHZayyDWV7wP8um7WjfWwG+xKk8+WlizuCJtF7e3f7l4+XjaRpyn7QtIItmhH1The7d3bi/7Nk+6t9mtCnJsW+cwVLO7T4BLxrkJbmxOIVlm/cPO2Afb3L96wIFLqDtGpEIyTXhNcupV6ux9eezOx6XaE+rVXjMTAVMtPxq1jM+oK6fNzcg7zg64oCRbfDI7NYIMx5VPJjq7S6Sez8Rju7S7UUIkfvRj85zcWN+zw9x4mT0/fD4U88FZH5lbPgB9fV1onwJaF49TnBgcDLp4HTWMe2uGfvdwb7S06OrqKxTJRiQzj4CQo+u8eyBnd/PraOHJiVZm8HG8N9bNHC1PgcYuorMwiKCo8Z4gwhzJpmhRXEMs8P9ep0CDB0nHuMR7ZxzKPCV5Vid9wmxSqktZTMeoIc4qmG0SYjCVRESgY+2xYdx+Y61/WYliAudGxtEiKwzwWy0gATaJAzSZg5+Z3oUPWPlLFWqbGRY6Kcv2F7566eBmFLy9ErIy2d0JO4r0C05JON77fBFHyne/1wx3VfV49c2lPqhfrVBQBw7Ck7OkkkEZmx0QAM2o0Yc2cLevxq+h4V84n80qDfpwLbfv+h/Lcizp5VJDk7W8ZbyYTzREToLbVOk1gFjbwgdXat8dlyiGxOM+vfvJy7sOd328D01ycZZvXSTGLQVeVbup9R5vHpNzoMTXrqn5ymnz3QRlMl/aBWiTZyUzYlMAzcEus3Z/HFEJn13afowWWnf3h9oEj8NbzH19nfinAupjXuQRb+7qsWvMZP9i3wsKv0kZg4Wqc1vFl7c06ZXk9zcyDIa+A1PVaiOVh1G2pPi5qEEOd09RceLquSnb0Vk916qdhojAesKZM0k3kGiMFadTugesX9fTlVPaFuMiRyz0LgqvXoXBD1wbc042/Rypi5uWY5o0LV8XEZwf6kY85pEo+xnHtoyK3YRlleLWQzW156579+Mdrk8AmxpTi/IFR75XGsSMtKnJzl9tk8t+Smyn3rb3By5etWzfheJPMfvY8KEpBEX/9qrzZbcHcX69i2XZAWPNYgFGRrzPTkYW2xCr/YlxnWdUwaHPcfF00gpA0TBYNaWCsF0jny1ePFqh7+lWF9rd5b1k13DUSsux5Bufp0ZFZ1blSEZXUTZpLHNchVOJcd51CQ9S6uxzlT77cdHvyKgdshd7a6pAcyRZ89k2c2jUH2PgiD+eheJHrlurAmPgBXaVv3zTmAX65an4A2y86pKpLfxo1Dbt5qPphkhZ53XAYyCOV+VcApmkucTuayvcpY0mtNY//6lnRNuwbugwR20KgXi+ez3dGCtLUcYTmq1JrgLrM5683x/eG5QL8+F8++a132m+05uL6+mp93YfGgVUuz8sCry1QdXXyzcVqu0d6H+2O44wcyNd5dVIAP+c6w453Cka7un3crau4FourIO5KrcusLhtbdXVtLT970T+W8joTVlvCD7D6r/509v3vjT76zfcmX7989L9+87v/1a2qzX8bKX8Ts4N7+ouTK/0sees9+fzH41fPZru3R43TjgXOsUkGairkv/k7naZG8xcXJz9dnV8tTQWfxEGx0Ue/3csLJbaaAnolYLoi+xPQsYGESr9K0zBSZYOrUIuWsGxqSFJICIKHx3aesdohwkj0XZfPa87Aox3dUrjLRfLs2WJ3dHy8LYXnSR2Ukzk4vo+277YvP/Fat3wEuJOU6ZYhe64WZGPER4wfmtrsZdziJcYBGCXKjsTcEkTYrIU9cfDqCpxHRSm0tTrzPnWP9gdT2siHOYK+eR4Bn0ZCUQy0iQ93B2jb0J/pdLfHXb8JvXV1dLRDOrKsi+VVwCK3pclVW9clwVbYEvd2P9TLBwEvrkSHrP+/z5uj1Vv/aPTkcZ3uDNUdc882lA0MawJaQiGh0MUkJqKgr1Cegoo0KKGqZZKGZmGZybqWVjzIOKUq4s2yUZKQIjVmwwPt6VcAuDWPeUqa8DKiItUwTn0ANiynIMdNAwUkahEFmcHZJZ+VQORJUjdhnbZbEt40EEKOq2npkgQLdSghLAlSDBxpGlV+LTRXa2ERkwd9dcgKjktAMYBcXEOt6VhhRnpoQynYzPJ56eJBlWfttMxHlutn1ukVmqHNRdX56JbRHm4uUZAECaTo2fNSNGvLNmVi9NU08SSTSWKeMtuSpI7knwh8TGhrkgkcxmoU6qa8Cm/dHP3lL4Pv3bNzihBBKx5eEtbPKAtejzfier6cPXX50cG0YElEzYz2ucZ9M1WwKXWF3nF7nTD5yeb5utC30rqqe4PuJi+UAIxswzoq/8NAxIaUH3cO1YKc1jdC7mtIBQG7qHkuiEZXEYrx1Q39Z1z11gp1LOysqrnvCvvyM6hVPBMVDry9vfbS5mxZRQktcXCx4JJm+O6x2HIS2dGABLyJ0tT1aiOguNMUgIjZW3udwmpqDmZ8i6vcA7LOMFtCzl1BTduR6jWppAd8OEvForn/3cHLKFK5ClfphnYJgaKjMRkVtPz0k7GpWSdP4uH3t6dzuio4+/ag6WbDPxzes97/ty//XDsB7aF0/ug8LwCE9cHWzi7hi5AQREutBgJUpeCiwfpQLk+nc2tLtjWsU1CqkAfpNJdXdL3L8S2tKxCyk/C3yTabvLDqDXLBvHcZ5t6j8O13hM3+0cMttOjsXrru/UVmarpPuCu5EctccIODb4+ehlilgmVxy8LzB6KxAM0iRCFzLY3fhjgrzYZlBabzYkflHxSAuzz5757VYuA/jFsOTMx7940h+UVaB9c1dJycSWSTVKNOrTWbJ9NTThbt4QcCefL1SiiSnbfv54/GcBIPu+L1MoY7LSplW4MdOXGDl97+3U4JSJelmSgmZ+vBR4NflUgIoNx4zx99lYfhStQ5IGh+tXkVZredzr4SxjQ5X8aI02vW1fgkRNmqqpJ0TpItUTi61bn43I0k2bqtkttSWLGi2J/53h7BO30q11fMcXZQSarqJVD7VfjuUSm8ZUkTfjaL7r1zPw6b3Gd1Xkf/4D7nNuM/f/bw/duz6TNtHjeCsFxjxQADhXvkSOHED1/g4mU2QfnAKdK79l2nCjsMvG/ha6yJlOfpb9xvSV5ZDJXTnt08e97nwyfLl3fJb3jjNzfuWUbbGX29+erP/mx70EXva50f3GT/879zdwzXC7sPrRsPe4XjTN+kFo3CEHBZnrjC5evJ4YO+8JakvSSzTy9MIsqOff4q2rsldLray6qQidkeq0Tn5FbtX8zMEhYiZ7NsnlK9ApWgCEjfLYtNR31+Dsqp1OXhwW1nKXJk4kevF/Eyk96+2b8h0V89wz+9zlG4uHR7Bx0lgr/+94/f/s6dTFvO+Gj91l5xUdQLj/YOT9aVNbLPQqinOubiumqq+UpUYf4kDYg12hv+8p++iD/U76k7VhD+TaDMn5y23+3yo77/aGKvIvBO99pza5s07foyPMM7So+xKsFEJv6z5aAAVX+LdR0xTvOrSz6VCpW5lWBOqGbU6RsP379v7tn4zedfTlYf/sbDw7u7k3/1M/pocvM3jl+/Kt5ty34mWVdusdepUCPqQvxmtj3iKx4KoDEJL1TI2trm4vpY5p+UvrI/SgHIXOQ0UuWJklXf28vmwUJio+OPtvyThGmkFvIDFYVxUYZFXjQ5zBVCKYaIZQzmFauJb1aIVw0R5TknC/z5pi70Omkoxdy8wbpNHEsURDuPUB5nClMHKu7ugXmZzCaB9jrvYuDDIq+1aSbmnM0zsPQqkcsUq0nqev7cd6r8ZJMXKdT+WOD2tYa/8c5vtL2+6g935KcvNTYd1yVvN+33D6oa3bwzqnIo0SJYbZJlqfNNFCavxhJISD6mslqCupb4TCLqXk/8+qVkE1s5Et9cgODr2eKlZyjUsoQ0g/71CgSNpRRd0tz9UD4p67apZ6t01kBeIpXK6Vv2uovCCPbA9uCLzdVSZgp5+/0D1TGvLt0KMGBJqoYYB+mSXZ0mdFY+FNvQn6ukPp8EWjsVpOQ6yVCGu0wssZhOmPyiQOJQsOz7B8oKa0lTlFcwOc0aj3a2lMriogwKjagQGzC1XmbJ6XI5m/Mk7Q1tYjW7Vi/VSWHL47kYX7roLPLcKdbjzrd21gM7w3w1zVUP07yJfqcV/hHpPs1+nYClgecIBhsqR3UUeF2nmSwjQZHHc+Xttw/f+Y1DbdvQy3xVc9qtbrOriIidg8JKtEdvvHeVUobYKLBi646ohIWmqDozcAVZQbOwKkFdcSLdVIlA8zxHwwOjapR07HFFlq0oijdjL0l5jfpRths+WS7Hp/7jQn0rcl70Rjv/uW3d1cbf+P/i366vwOzOLQdxengJjaGYXYZSTByJFznQh4w2qPSr8NVK2tGCDefPSqNUW7aQ8GUcc/uWIhDp/BKtJ/T6JLxx/NHfHxr8jizPqODF8ap4M07XoioSsSNr88tMhKJGmpNnfnEdDLrqAOfr1zEro+0H2uZ6kS1q6VtkRrOZQ/uHbHEd75FV6sWRn7Ry3ys356sq2+bufa8lSMDjm7yuC9Veum4m4fig5xeIfjl9kVW8CtRheznO6n4lNEixBayi+LTITsIiQ3XEvchSSSy0GxKrys1X197XuHOo3/72weIzGmfEzYVMRrZGG39B6YxvRq4P3/vAvvDK4jLJvdC+J/31x1fbW8qOU2YvV+Kq+vG/O/t7/6TbbInrjO3sKnXI+IaLAmyV5fSibrd0RoXNNFTtDnvDVrqMlrH2wFbftYSNBTDp7L6b+1lcB7tN/eTZ4t3vtqb3EQMv4jP3wTutMgv23eRnuBneUb6mIclAUpBRbrz/f377GRSmn0V0vDA5PF0E3ZGCBWk9Tbfb2nM33GB154e3mrt7u6835HGTED4R1UlUShoRClk2VW1f/OLJZTGvtvLSLbPLDZB6ggyxzyDzAWfKsqTsaUKzjj770QX/qds9lBSHq3QuXxXh6dKwlJjJJOcSQVI5KW+A2jaCReyf1bEoakXRDcOwqfOgcLrEDZKqhgqnmTlGkpSQeIVUjQkqNa5Pwb1v3br1v3/wP/zTn3/8Yry95fh8yFndzndunFwU/pnP3dqSRTR/eTlgfFHgOM4VFS8tVa5QWzchLwlldWBKaS2uL1dymPf29AVipKZMwcu4amTYktbTvznR3PAW0E7+dHrrPY2074a+rtnWop09FzQe0UQQRR2ioG7TRecunr1ZCCXLDCJlavgCvuW0NlEgVUyApVpUYQCLFQIKlNV6PouPHW55tYgwHh1bvCoJRDy7imsF1ICYjiRDxjdNVcRV2mCB0bhCKoF5NcONs2mqusIlCjZVFbiSpSIkcMAghBfctKkZr8hl1+hUyyjNGtfSY4l3E3L68UwdiKP7Duqhp8sNsXl7zhumMqEVLdJkpIhOW/CJGelKGvOjlrMtbwnJ4w3ILtyWpORe0FdQ7PQf7iqCIMY5IhduWuQJh0yx3iC+vyOfntRXK//gmLezUumIQZCouu2oMNtwOhOu/vxs+/291juO8JHzyceP66XPrS7R/hBZ7Rsirw2776BsVtrN5wXlcqirydNT/p7MHQ+SQ7MKUtXispJ2b4s0y4rQ9b8er0cFH3miJCfzi76trt3UFFEP1UWsWG65L8rr9YxwFUeFDZO32kakSZy/KixF3e6gj8fDmx3nD/bO3c3VN6E+X1DPJ6Kk6nzeFaJFozkmh1EYJkFQ0ma9dCt7BNN7e1cSOVg3giVugizy09VV5YhlyrNlVG/leZblnlS3OgVRO4N1M0e19yz7lkFEPjh7NMNtUEF0Y6j78ppJQf2W2DNbdSn9xt3956fiXp1dZAYbEf7CzcZVf9fafvVnP/ozcKsFrzmyeuP30xpGzNtWQdsxIlLGLGW4in2VFUXVLCu2BliwWnCVKGW94ZqBLELF+Kao2aU/std0CX15Z5usWjyrMxfe5t5v45dr/hcn6d6VlwWOnknDrCFbQ5SWkcyv5EzeVvGT1yjL1yM5qNKbt52nf7XAFs/nMYgF07Diednd0lFHZe8g8aSY+5wx6G7f6ON66ZbL51+O8Ym/t0i/vio7hhp7XBLpZvcWpxpqHlRNJQ7SGjbxcu0Mu8M2qzme7URRbnmwVObl9rG6NEQ4CZhXohrtbJGLHz0fHQ+XuXex5Li+jGfP5Q8fLNr8i//3s+88tC+jQOvISQGwaVgwXz+be9NMvNEhvZF/uaQzLNjdWFJ7h/w8yKLyqjtUYnWnmK8bk/emNIyzwI93B7WARO/HXzma4HPWPC9DTZReu3s72tSfZzr5qN97PS/CNF2cxnZTg11uupj2twTrhtzhuaf/4qljktY/6D8B0eD7h/ykfHJB06wQ4/qgSLiE5lxHlpRNUHIP9WKg7G640XRZ6RhsmBd6bYZ0Jk6Q5nmURW5HhzngPIm7vW198qOzdwiKOmqc8dmry9/5b/44AiX5q3+zHwrt/+RbX8dg8WaDrph40cS4Bl1D06QSqTNQrBTxfRv0L67jpxvlmwActiRVn7G1gPnjvsYCphpcDpFmyUSuBS9qNGGNiXJf2w/95bpo0rmi23tdHE2LtAK1RsS21LIbWuaLccoeHIx+p/P6eTh5sgkR5t4bqaTBIX56uiq/Wn9wGz74+7dPny/Cy/BoeNOUurhe+UpdA0A8H/clazjojeMFGaSGkE8eqbmvWmZ+4v6b//vf/KN/8ps/+KfvnvyHZb2stoR90GVhCPDFRX9HRwdC8cVkp0QXKr+NJZglXCbwv2PHTyKwDuB+r/ajooiqaZIEK3q7d4pyM6KaAleUk3BBbiopngowKgUIfJJdbCZX3vBQPH0+FW8L9O42/+hEkQ4bWZhtcE/N89UaWHJZCNzpJrk7stJm/fiVdf/9VgbnT65aXTNarUSzRff4Mmncz55Vx069Z/cXcW8NJYHVa4rbuNW3S5zTOM+bMoSUw3VtybmAOFwpCl5VVG1YDaAbJ4jjlYSaklhmRRVS7BAuqOrFNLz3rmHp2uWbNQMV58jbHFdgUEmdD37bOXtdffnK30OsLdOW0LBaKCohRA0kqPRx5QtQxtWgdxeSeC24NTp5FeXLTAiboGBAWisVLuN62OHWc0yjaDUPWxyCJOUcUZDR1oCPX5Gbb9uvztB1GuI6r8Li6llx42GVcKq3Sd+733/2qH7ypy+qAv/Dfzz4zh+2x6Bnj3vr86ZPiUbk6cfhzxLkk7rGQlVrA6UWRbMYZ5yY8S1N4cRgVaFVQ6NaExFHlDjLukWaJ0nqhbAo9SCNSlHEXIklWMOXU//hbWn2KgZ5nqexu8HUFPUeJ7kSbjCYY9DffRXn8Cd5cel6j91Mg86WsvdgUFXwMq54KDE/8VeV6RCBzxuu3j1imixmkjif+fMARm6KirjiNJyAsCJYw1vfGYFYpqLWLtHZk0a1gGGaIz79/Ev/SmajG04gbChitJJenmS9XXn4neFrwG8Pjb/6aXIzwK/npbIWS4GCMu3l3tknm2y6vq7s7YE65epdLYFncavJtKzYFqzZ+drFusM3qPAJrVCdzq5o975d8Qi1uTqsvBdeY1nhboMa1uIbsSMGqqV3BAVJva2jgtLD/Sx78mTzciUo3h0Utkvg5JyC+Qte7xxYt+5y7pl/tfIgiOcxk0rp7oetX176+Hol14nrCVJPBUjv2Q7Vm8Gt9sYGS47/wZ5Rv4rGn2yKLsp5OHyr49xEhbR+eRJJSPzirCGGvvPWIWltkYJ9+peLB++JPMlPns6lhA2PxMIU5tOlFF7LQILHO1iqxLpaPp04aqYktfdXbySYLMfL7SNd0pDW0Zx3j76+rmWvzmoeaXIARb2jYBeTNMgvC9eNaQG4tmVsdZYRWp2jIwNnVAtK/ctPfDFatyURyACscby2NEkZtqF0xJ3l1B6iKgaPL8LiTybv/xAvVyVw+HSnD7DEqTKfi5deBWkeXGZaxSqx6ahkE7N39rk3Ty5fFjWUSvwbzu89/FZQTp78+SnvVbVitgyOa9KcKClCsqxiT9jqkkBk/rwxRM2lrWqy4jii1DFrCtmdb5lds6M3lj27ioNMvcoFPLbMhh7tj17N08MHOzMKz766+vgvXvz+D/fdnDWVMCiyy5+leg3EW1ZT+4vMk2oOZYGU4Ntmcfkrl9HcwvD6Cq8/v9rZ737n9nB8lVPKGSafup6gl/Juc/XK1VP/cHsrERSgRDRKdOCWVZYtitoc6Q6aT5clFzcdXvlOt2ObEaNXqVy8LAeHfZOXy6ukYCgCXHfA33pf4ercH6eLT9fpJsCiSrstr8ZLtwbMlOoSEWjZzaFRjx8HoC+GFGMoWHnjw/QP/0/v/eh/fv3f/JOfa394uHW42zNw4zWoqsukrBDvjHQ34+YzZFZdZUQ4VKhJvhKLFo6q2BNNAM3Qi/Nk7UMKtUNRv9vKqpxb0GgSqk3MZzGolLzGSURvHo9i0v6H/+Ch/3RTNJ7J8qvCN3kjZpU4n+alESZ1nbFOW3pTFJFQukV2u5OuP1k76Ub2TzIMg9rtm8b1LAOLmaITvi8y2Ti8YS6eeEZVtnsCY7RtK7Ma5RnlOMg3nChyjJYZEJu4lqiEQSVj1hIAL5GSMaCzpgZljry0IVQWMSFVzREBKBy8+twtVabvqklQuz6VebyYlY6OIiCqTgClVs6a4qtN+X67gsYuTuKKAUU0C9q2hVXDyDLaXMe5zx787dt8G83Gz+McdXjaDBShK2yu6uR0nSSs05VgCsx9Z17qWZRuwtykRR4VkNGGg5cgsWFeemL7Rot8MDA33GLZ/OW/PDv8uwd/+A8ffP3PP//8T35NtzF5a8f0s7CpX5wnB6sroz+MU6TYiUvb3uQMr8F739mpUciH0bNJeIPQqlQ0AnBTVRwFHSRviuxr32mzRUfgbLPcZGZLkWDJQqI7OrT0KYrqlkiW2WqyEvqO5WCuSJneOCpLxl54aA+apv56mZZJ08bHt4be7x5exo36JmNFUiUpK5jGC/6m7rZl2Dbq1Ku8NJxsDBnTEEIWLXckQYPGrtlkjCYpP3TklyvsLa78QL7ZtnWLD9JyMMRSXblBHDYfn7l371oH9+xFxNyjNq3X1bNVmuSt0Nj75ePu8GE7HJ/He5t1GCNk7OHBkHNXdeVvruMCT67aor2MaoXTm0AGOeVHclXGUlKVPNsESOsYai7EkAkyn0B5c8nYeGEMyZWkIRPwir/mN0jS5E04lJ3xFS3C5nuqNV/qn11AKyaRDIbvdqBG1H24e3+4fPPo9b8/6Rw63DQcUk7rSzk0b+Ei+8mb4e7exTgdDmA24CGuukcsPwT5l8/bgTFH7XJMbvTo11nDf/P09ES6tSsCSZLvtMscP5BNtTJFKEwWWMzSWzcGohI/eTmfB77++9svOTS+DBUD3c15s9MZOmSWURYCbpPPpGL0wd74XzznjrvW/+Xuo29m+3e0F5R3P1ve6ZF4Eah+oPYkVKUXAl9dT9oRbh071iEGNShiRbWzPEb2Lk5PV1dfXmxuHdgwVapCkNgbnSsR3w/0JKICBXEIhKBM2wq6az1U7zffZKjR/Jdn/d8cySKMF1HNw5jGZlLUfeX1Lybdgc6rVSGjXMSXXkTdjGB++/e34rr86mfPnLPZvmwXXFN1zSHdYEUsSwZl07us27IiO1lplWWFuYyF8yTllMU6u6UiL0vaIgXV2tP5NSviZ9Fi5r53d2SueJwomc0Qx19kxRXTbl2cHtBU1joLaEy+vrQI11MbVVGv69q2ZNmky1WTzRYjv4BBcjqZ7L8z6ElOVGt7d3fX8+jr1xtxp998exeuAuFNmsvc1TqEPDfY3bZt4ez1+ctkET8fP/xOf+VY+nXyZhMd3b8hfHHe4Sh7h/M43bvO1HStbZCGSnGt5AW/uqBpPMOW4g1VbGE4GB0Ng9NfX8dRaN+6MTK0YJ5omlSLaZXFXF4oWsfF6mUSGCjaqJhU8a4lXJ1mj4TVg//yfu/30Mmnp8avnnucJlKZM7BJ+OKgS13KPx87TbqMyiFUshilc7JTsAhw7aEUHHLrCshT2h9J3mmstlrhmUsFsHzt0aKyVaXLJ4vtfpLWgsKMW4i2zLSky5fXlLnCntJKKggAX2VJaksmJxcZD0NXBrQsEDAanl2kFAiUOM4iiDxFBTvyRZiVCpU3iagr/C0nDg33SdRaVBDLOTJcyAtKLgOiaCQvqzzns4oWuMKIySonJRwvI78sI8SUHGCekzlaAYYNIjpmtEoYEQCDHGE8LJvdW63Qb2az2iDy4TaKpu7IErIwF1guSpqxC3/9ZBM01LyqP3iHS3y6Pkl0U29p4vLNhgN8/77NCXB16bpPJk1VhWnNt+VNxtQEkAaNbtq1xE/mGaZAr5J0kjRZMdyx8J4uxEqF+PHY58pitwsms1rl8PbBtj/lfv5Fdud+i4LNX/zpM+uT8fc+lIbv7H/99Wz5p2e0aXYeClu73PCwc1VmlSKE+SqYZPdukXwRf/X1WNi1+8c9hlAZMe7CRYosbku+0dQ8KtJovvAyUTJ7OuYg5XicwYw1AIFGFUuKnz2PTUEwHZvkyeXzxfBdfnBTHidFfF2135LGhpoLxK6g+5qJOY0CmIyp1uSgiOt5EI99UZeUfRXGsEEcThCNhIrqSakd7JuFHAw7KiV5EeVYyATCRbRKnq7hhdvoDQ8bVW+MVnn6zWpoqw+OySf/r+nY5/cPtjhNwO2B0aJxQrK5dCAqFy53vN+ez4mShD87y7rmOi5rrqE7lqCglK3mP/vXjzo3ZVMVtxTZdLo0l7xUq2gKqnUQV5LUbNw6jXhrKOc8TwGXhMxQEBwK55+X8MJv9wi9adRJmE4pGpRekn/+StI0+eOJhYpcANbQUQVZarq2oxqx2VTX7uVnn9TpWtZZCcDZ19HQ0nZG+uIyVZZlGGFFwe/cNzhHcU0pSeoqTaVghniI3IwNsdhDkyFLm2Rnq4dP/bWLxhtgHam5qm3JHRRy8TUzhFSWAALh6xcLFYvbDtJKH15Oa483tTynVGHh8y9SN8kFqNw55n708UaKLLVjllDQFX0VzJRt0scyC7h1o5RKRToieBPOG9IIOrZU26j9PBzu6vNpvgxoCduVomhyfOjEI1i9Sa5pXcSbGg8l1LMVwkGL13RIWYXCxq7KyVPOaUGLtuheU3FY3+u5K7/cZJui3L4ztDkC0qKV136ddW3Bb4AEqmMifPmLyUBF/I6eWz3/2VrbFNOiffTRgCiyl2HPLXlEIRKanN92ZBFB0DTVuhabJltESha3tZKDGa0QEWo/r2lF2XU9tMViyK/HpXu92mrJi7X7k0nZ/c7Av/ThCTvl0b3fO542nCiTpQChw67cuu/UlZ+4z8ugiVcLfjREb86jow86B3//NijjwufyIDdMYf89/fHj3LLV8jrO5i4vVpDP8qgQQ6RbaH4yaXOuNqpmaxBdrZqOfv+m+dlPfKistm4YimqtxomlkBUsgpopkRsw7K+q4x/sCYRjIWd4VXDmY56ufz13c6DoYOdoR++ZFJYiAUpZhw1oQC71zKrbCZmX1edJorf2dbrUGkhuStLk54tnP720v9f/jZuGaCCv4WmD/bASRJ4FiYTLLMzrNOsPDYjF6Xpu8fw4ZuWXycFAfvZo1ftB121EetEYlp74WKY50htgC6tZvqXJy0UenCV7IyUl6OQn57i4XhDlQZ/5AH/DOABa2aXQzI0bnfLq/BJNA22bg0haLUHu0tbN7uV17uSNzYHlaVCU6cEuKqtmi0dopHhBsf4P4yxPSAx4Xei2LWjzUBY2G1ixHLCaVRAzRkShLgHLa0EggMNZUQNJkhhUOFTSKssZJwoFQITDWkuAABIIub6lJbT2GQkZX6I0r5txACXBzHlugrI2Y92InueCocvJRTQY43NBnsSJX8eDAhfEdBf5xasZBlxzdxsozZ6QL97qYUqKZ5EeREmgny2bGx5xzawsKU0rsM4MWTQJj1A+80i33MwtTAej8NECJr5wo8Pb23bR9pdR0SiPv1x17nC/97294OeL11+cfqXj1oE90lKwqryLMX5Rfc25DGLtB0eq3XGmMi8jrs/F8yYv0mBWtivEXaUWLzpGK+rITpxSXMd8rOxBq43ALEQuWS9DviNHMJb1+HXLGJByRGz6YjY9L2gfjRxaxeDNotRECRbQzLnyNh88fXPxzXhdMIkjVUGVVz5pEm+FG59juoTfvZkjBGhCG5LNgsky1vesLYPYtMprr4pR7ZcdCN5ExKyAppllHALMhnc09q3e1bNJ62JiWPzzwN270QOOldjSjT/arxPfwzybuuQkb/fAa8DXbjGbRSzLcsfqvN2PF1LgLtplEBSK64d6Rb/7d+4ZepWRgqMmZ5PiCrYoCrM8rBFRiwaVJc8jm1ExrxHlJAiXEUVyMZQ7P9wWwyrz8m7L9ICzZ3v6CiiGNMt51rKOf3BDjMXgvKGCbAKz22+fnvulbNy+bV1OG627JQC2zNKdj1RVl6JVjJNaEpvR7c6CAKElC0UGJ7mO+SaOz5bFje/dzdY52JSnRbOFS3Jw6E2fRsuma9cCVnp5fE2kix+92LdvOLv66YUnHfCRCzgLzteOTpfBz07mDOzevnX99TS9tzfctyafPt59eFxV8Pr1Zk8V65gO3mmVX67M02tBkECEzDRjA2l9GWs3LWF7a3l5FS3X3d62l6PnL+ZDg0t+W1Dt9bVsJPc7YsEVX589n7tbBujx0vNvookL7t/okRTYMrpWBBPmDDVpz9A2fLtOhFkICFgmjdCB/d+96c0mihMboqTGyaVIFMTSMN25P2yJ/hI2Czfa8cGgp4K+iFsgnM6sMpYP23TUnr1ZqdHiAhl3THwVyYqpDXh7axe8CgoQpOIaN3E4LoBvE+0qlh3wOM5ElLYp0UC2XHB3VHHNie/9VgvNveuZu/WebUyvval74G+8tPFVa7JeywKcqNKlu7i7323dGqyejnmQYTfiJF5sBFEm4HZ7RSLW5itV6EZCvRKhT6olGe60uo7uPYkVoWFdEIU+0BGDNOQE2IYc4l98452ds4FTOoN8prUcbYGmk/TYolm9+HIqd1HbVshQWezKsj5CRb5Q9agqxMNOtE7T8znxI42CeKTl2yovYdyg/NxjvGEbmlStVpSqQ7tp8Gw1iSuuF3lpJudHytcv6geHVlumu5C+fOq/eV1iJGMibwo3NSwlC6cp2HeIuis3E1jXaYdJGWeKpkFRJJ0C/twNUrL9jO8P9E25UIyyQZqdRFkDix20CdD11fjw9laRUCtbrmTCHws7J7kmaJtGenTteXv63qH9+rmn1/krubMmmdTwhDbUElphna15vk73igJFYavb44voYhnNi8AlfP9In7C6ijbtWdNWa8Mw7a6SSHISJxBzvQ58lAEhY21EPU6qXSZhXHC4SvNKkyqAVYolgpoSlIKoYAhYhjiCKeUMFQDApQ1XQ6IM1c1F0hWQWzSgxFaXLyphMquoI9ISraPMVDSJb16K9GoFh1tNFQAgSDHEZdkYWyZZhtNn8Y62jqJ8ZRDttl5Ecfl0s5lnW7QhIovWqe8mRQNlPjdEpG7XRBcSARRBMpmXyyC99+1u57jNxTLkpcIXf/40mSTZt942+MQbf31y9hU6sjRdK5+fRueXlze2HVr6O07VpLGjabUozTjTCBt14wVzqHzYffdb3PqpO3/iwllcEcG+Kcg9rpFEqVbzIllTAPZYuUNF3+P9ij7PsYXlrsSOGUunRcjjUjeR0BdS30vSJOscK4VhTk6iY8uYblD05Xw5i5HGtR/YpGlbipAvWVPLGt9AWWFQ7PB8wBPZgTylc5eMdrsNZTwMm4Iy7J1vlJJoEtM6HL9tyJyRfXR352c/jq5e+W2j2nxRc2nx7u9vz6825Aq/9fvfUnQYL4p4VVTLiIg8FNVnNd7k3K7V7lAdVNkvP/eOSaOs5xJI3aQcu+wHv30gPVn2BPrxj04mUf7tvz0Mj1tpw/hNkwIM+QxWiSU3vF4sFwmrNGa1Ay/V6hLzWgWxYSniEDguqb38cMivUzEOqajz/V09itOPv1g3MRqOLKEl16Ww3sTnAVQyrkhRHIWWAyLA5VGl88gvfVEimME4Is7I9gVRAqiIEhbSIIh3D5yugfQ6HX9xxddkeKtz+pknrs7zsOyOTD/GFkFJiNp6HTXy9Wx52GfdbS9p80spBI2iFmKOq7o9VLXGuwBbN/Z7W0dCHPu1s62KMSS5wFotOesgjzLQk2wRtVpgEReWjvsWl1yxl596tyRNVcBijcAj13/ppW6xK1Dl82XEsoMfDHyAVkJRj2SVE04nm83j+fwyH/W77rz0gVpzvD5E0TS1eMqLcbktFokqEckYmalZLLMcFZCXbFE0DR7407SzI/AiSdKiZanPXi6IjVBSpX5lDWB3x76erWYn2fGB/mYSMrfRo0buG31Jmm9EznG8Rhcs8XXWBMEmqwits7qmNpeWCYMYJ34tCiqPoUT5nHLdgb70mSwxqYP0kZofOnMge5rDEpikhKjF3Rvc609TrOabtvq9h87iMkITd/7xtbGnMauTAcW+aaW7BG1Rfz0PHqXKQ+OrpbQ7bOm3W5u/8GGKVdtJzDLKqupiIqhYhCDKax9ZTQenTLGc1q5EmqhqcSP/fA2UGg1TwomDQ1nTes++mrqfTnePzN52q1OUT5+Fi8vN+WXx0e/vOTc6YyePJLPNciHJ7ZqVjE3O3MO+pBpsehn7IerfsuSOcnK9mp/MjvpCxYvXL5Kdd9pexzqRGtWuhqa5O+jUTZZMowY0JQE6DKCbq5wAW8MoiiLA6SrJk9IxkSzwby45iWinEtmJgtFpadb5bLnk2MoZqdqW+CbM15n04Kj/+ifnddPIuuy7dccRui25dquzq830FCQdq//ePV9sYn+Nh05R5xVfn7xKgaoaVwC7eMtiS07kZB7fGMwXdYaIvafXFEnXcfr5Sud4JDXqrsWC2jbVMqxDgUoKh5QmjtmWxhQZVZsK1QjoUhzXHMMVxE3eSArhCMcYJjxHKaQlJYLAEOIIp2pymZQUUA5CEEv1Qqd4Ju/f2vmLJ8+lxPqWxoZtfvyspANuzAk9UNMMo/1ucDk3/Yb5EHKo3tRRk7F1tOKkLaNeThf5+eulIsXvNQ+G5ejIaYPxV2LccjT2uukOVZBRtyjJQEFmOPNra7mQgdT+lr4949jl6q+foRv74nYer4rCsVpyG/nhUskWx9+vHoX96Zvy8Pb+gTC5uJqtZm5NWN8iCiesTI1/+1hzHPQqWxa5MeyVjajR8sm4wm5as4bqMDRZLrDQ56TLGHOwjSRvm8st2kNpduVvNK2geVknSm7eCXPyPFxNvZVoGa2BDcuI28CuFieRDYu1SKQ6A0C69cO3rc1mfu1FV/4Kdw4sKeL5JSxKjai4juac0WW1iItxgFUoIRDSOpNRvooXChGY4FCim6rBI5wVYz+nPOvs2fNn8+zFq7t3uylnX1+t8tJCFbgy9L18zOYJiZJ0zB7cpDNT4hQgNY2fM9/nj9dP+2/t5RkHykbooTJPAE4vfnEOXrvzOnO29RzyNY3mid0aoWCZ+TytQ84GZFmSvEZ1yop1aGjCpuKR0Ybr1NiSOQbrtiVYqnCVzScbb4NFL/uyioifjlaxKtHNsimhPDQtCeePX860jhKuvPRqSXmtVvI3deq8JYp5EnseEmzNYIGhBoSIAbCvF8+81Byqhkp3+/nVnCbfpHzY0Efn6UNuuJ/zMG8N5auvQrROaCGCtk77cN5iO6Lt+UViUilZ6h0OLCppI57sDUzkdrTVL68n2cFg/R+vL/3Jdp+PTQnkKcoaDOrkHPY7nEtpGDdKVq90TTKav348v7XTHp4n6WVaUItPTjRcS62WSHPB9WAqjt42TkW5Cx654CZX+m4zlOp5GBXy3b5t6Wkm7mSkwnrESTKKOUpbYZ7/zlayTlfTJgVsqy8ZGcKy2FzViWrO0uTg2KI7IDk5S2UlXcZ5GHUladjXvENEKb345rXcVLt2S2rqpOLBIjwSBYwkLPCtLV7hLEtmCc/yKEoblgcpt65Q5TrKJkyYLstvGJSDJAmi2jGJJMvLUJA5eKTx49rtaXeBkTya3PBqSdXGlnlVs9nJCkRFBeu337HqxH/1V+ftHfu7f3Tn+vVie2hfAUedei82hN+yjsD08tDsGHj5WdRSjWwyPZK56VmUNVF7S1zl5WQedAeSWsZ9UXxCukIjRXVdbant0HMjBetVXUCENnrRAzQOA2EDHXyrV7cK7C6v/tfzXOHdpHaqqnPTkq+fXV5OwIHF395ZPB03TzzcWLZDu0i4uDPIrzOZk+R3R34STp9P0aRo6WAe1IZYDEvYjXn0tr1+PWGgnHvFlqaHggR0jCAmedEQJFS1H9SaVDWE3+cEqcIbE8kSx4X+9r4cNvKsIGbK4xMJH7XrLT8quf50sXGMNAMGZKCtCX15cha+/Xvbaxmfeenq64pqBnnPdn6rre/Uy0pEf/Xn/KZaiLwDWtG4iqv2eoOOQyIyLi7YCHEzb8OYl65DKCrxLBRMUYYwooRlhZyn4wLyjCiMkkbq7XOSSFJJltIU+BiE1dIQ0yLnEQAW56UIQdxDmBYltWXMACsxQJBgTsb1pGgKgJSkwhWlAHFRU52dFpmnO9qWUOOtafH9h8Ljq0UZkLs78iopWi2w7wjJeaTHuWpCWqZxQoGmVDys8kIUq+PdtFxGr15f8cv0/n+9Wxx1xp6/KGvjg30GJFxj7a4ahSzMiu3jzhLQNYp0tcoNK8mpUCNxElxMG8feYgKBOuT9Ip7PR7u0qMNRJxy/nP/wO8af/015/Sev5pPy3R9IHAN1SV/PlBv3OlEquisF1tYuVDV+E065izHzHury7b3RjW7wZJGahNzYuQxgEfgtkc+LxJA5tgyKJFtvVrEXoUrQS4GkPJwjA2fzRG4dOFvD9ua6evoilnjCe354lR4cSF6W4ZbG7/S4DX3xF0G4TjXCy/1y4zVQECVZY2kuEaGukvRxmBFaphlncP4GJ6QMXWzCBpZYVQShJa9rFHKFbBI+qj75ld8WqHWwm0K/EPi0wpksUEXKZE7LNtGa5WGJ2x25RxdJTIMQFeLF59mt99qmVXz1o+CjtxujLfsxOZ/OV1fh/fti8WyztdvmNOPWXa55PF1OIbPYJAxFLo0aygM8KyTFNjBsUBmfX+fHZsPXDSSAAt5oGEyg6OYWL5jbIHpF45BpXWG3xe3fdIoLqQkaHlOd1/x1sakS2agKvpyvU0GE+8f42ksck/eWERUQ3O8EhUCrdEdlfBU9exnmZeZYROIirNBPzrIXkbX60PzWTdZ0oLAjebncELPcVOPZQgwKOk30ih49tJ8H7qYB23eUpm5QW8xo1pUtpIA7g/6qlJ49Tmiq20QsYBpUuWGK08dn6VXu9HmZ44a2OH8Sdm7qMSGMo3IDwpCriJqmNH8yIwW/++HgMyCXfPHhA3X1VNtcluNLZHwX16B4Q3lYZ1PYgbyTOrf4W03/WIRuJCUIY9ITdTepTCo3kVt6NP1yfdASSqlcn202HBUFM8vTVq0RiqwtXWiV8+miLDLd5NI57T2w4ohUYjm/LEQlByVXYSzy/K/PqtZt8863bCksM9FIco4Q2Sv9kldK6tu63E6StGpAlclHyE8CkwA891mpCE6vP5RgBocDPc4LfgesWToUMQiz8eMNnWzaw514nDpEQN2WFMYZKAylmf347OR62RLR7m+1znT95SKECj88xi8+dm/uyMFVdBkInd/fGxLO0Bdgw66uZ0d73X7XXdTiJtNaI/FYtFYX7uVpWI2gn+a8yJVlhgSUtTtHh7xYZEU+BRLHGxUA4nKWL3zqCoJ505lcqo0MD//4QHgxU0LvOgo4ZIA3iE7iaHpucFn7W62QwSCuDFB0pwt3lgUZSZ4m1bXLG1iWJFnglbYQzYLNJCwotjn17fvthefPfxVMJ5sEUExrBMr/P0F4GWzrmhgGeu/HTIt5rc14GC/f26TuFrSFlmEkVTkxVDKpmankh3/kR1L5MZOqxPYkA/aUZUhJlmWpRQ1qvnzOPffw2ftshsX4MeOb59HVVBAhzRBFPMrGBgV9lqIYAqBZos6BuFxtJHlziBQb7AAzEII5HWTpO/UekcMPWMwD60VBW/jWxJdbueE4fvI6TbYK6TpJjSjHwLBcWQf49GyWRcOVdUUpGmeT2J0as0t0+2Z7/YaU5iMKyaChITBtyqwa5dg2niCoH0YxCgFJ41ik9tJCU0BimuLRSFZyFQUXZX2RhZaOphSe4xEa40g/9GIQeUyG0yjlRUmCIShLYixFhygLM91P4iyLEIKlEI6hUiRNQgxDUTzH0nGJ1jqF1drt7ncfsjF20oNcOTea6KCmOJcHK4I8owuqULIGBySeFtfKuXi2EGQGxkjkspw+hKmUgZXOEmUY0Wi2/8S5hrtJYVPKZ4VDe/ZqHBU6KIlRTTrK29MMq+WJ0JHFNllnyvBHnzKqmd/aXG+UzvqL0wO70CC8EpgRwHB4nIHnL6b7l8d7/dzOVerdbcZ3dDtPSM1ay81aOSaTIIJBZxJMDlRWRW0x2Wzn8JeT2g4X4UmYAUoPzB8dVdbKPZJDZdfH6HRumUe+xOph6kMSA0XSZYFiB7gLk0pUuL26trXRfzmz3QVSioJCdmCNrY3KM9bf8WklccaaFRzO/QxNb21KEUypeNwzeBC5s8ymKMuKFwnaJtEYRWKRHCbhRhSGFOZlsJgT7so0g3Me8CYEwmP6WEemdlwR5mpDJPNJjUosgraOAitEBTtfjiZTNcsTeplmDRDqM2CNFv5S3hqHCcENcUAocHvHe56o6sNh6wVwtfFv/c56dYv9lDGcjoCcpUc/uIiHRu3elXklz2HzCcmHgUVDnOVEdkEmLFlfkUgSmTgeV8IJCclMJMRQBIH4JBxg4yFOh4TPKShKwISm6YTN2ooTK8osUZDgtD+imEQt8axvo/UiVKhMdakMhJt15OXeGQ3yflppklmAg5/ud63U92lQESqy5BjTSY5c0BHPeglWOH7q5r4c5lcY49RASJ/gk91blda6ZEx0fwHlKntjefp6MOsvZJ9AmvOkLBE2iXd2+YU9zU7OqVbttswYjXpjw9Q/8a6+Lz946W/cY+q31l98Pr0hki/n7tvvVj46sasiD/TUHU3zb656IYJ4TBrgiYw13lRCRp1TiXk+r6/EIU3Un/bHa85y4j5f4KWlPGwWCFzonyGOc2kHJk+TUKJiHF2NmSDFHYfeLuKaGpSNMG0wBZANrZhxF4xAsCMyBKlWhlE1jFXbEJlatUgdjlGIp76VAARxnJquBferKp1LPLxezPIwCV6P+25G5HyCLgq6idy4GkJfzaQssStT18zVFzZYqzJH6zB5ceLVyZ0pMnPmbpQr0UEc4mmasAYpO14e4xc849mRXF0NbA6u5uRVEXs1HmXU2nfq8eTF65OjK++sFDlwpqdsi711lcV/dXuuecnN0kJS3i5EU5KXhPb5RwdthjQhKjSRC9crIDQlQA8PpiRqSbkn51PvOGkHAVxGchTeDgAB8SnqDzWtEgWpGlVafBTJ1JQKNJISC4J6vJ4PaZo4XCI+1/F0xuRmvYGcsgIu59AwQ+u+q9L0PCGjgEV4pGTHzBPP9bDGDqddep16SYthpnAwzcqo57aatQ4RO5H2crrsQ6HECLcbh4cew1nB00mxSZVXalYczwaOg6bVckaE0I2RBiY9y4lFSd4OYnnllh2HDJklyCtnOkJpUvkiHN6rhHmRPjwhEuIgCEGNp0dWsQziJKi/cpHlqorT512VuTg4R5RmxVxaWm81BAwioWE5edi+ti5AdqtMjbBUHbp2gnLlfCyxgK6U7eHlecDw2sLjWpSZUKC4C2iOlGJOZgi/xEZOHI/dMOPKGAUYihC40cFgguGKwlR5cfJ6BGRGqpHWmUmiPEWTBQwHVppSyJxMnTBhEwyzIowiaRAFKIo7NEt0+OGHeJtwRmxWubOctJVI1zBFSehkHrMtNt9otc/HOiLX5EKM+Kk2dJq7kuYhaJbk5WQ6MT2SS90AS0ShCesUE4MU6/cuP812SzmcR6A2VWQOhgExAWWO4BPUnzlEVdpmlM8jJr+TW82Lky97cRA2V8SIyWIVX2jpvAv8IJ9V77BXq9e+2i5GZ8hlf/st+VUfC81CRItnh4vjaT8o8VQZ3H+rzY7jR309J7uLUXzxaCasUnIzHyeZc2RbA70GeiCKuZISOAiURdUkCQ3SbVmQBYEK4RCja0R9Od+fRU9eDderdDr0psc2nZaKpEsCHIFoJlND1ROzOSEpxbtrKw3l6V8cTg4Gkgwi33ZVj8qRiQ/zXJwGuKtmKcEgvoVyNG66V9cqNAb7VoSgeppYqUjCxTjSiUqp5CVJSfIlxHG7hjoNRyckVc+tbtEnlm8hhplDmcQZ6R6WFmYx38ZkDca1NTFO4YdfzrGU/JUrEru1rtzN7z84ezXxfv7Szm2WHJfsdLDkbIKO7FySqCEWxpLtx3iE2H6aGbHromSAncZZq5qLA9O0EFHGUQFNqRABuG+7ixDnczEVW5jvYHKSOel85GMsi2VMgIAeglklhsnD2dzmEo6lCWsYjNUI59HgtcmM4uVZTJvx5jXidGD199RY4eRmHi8wQ5Uy53iAISSNEJ6VMtPQRs5cKE9DQRSUJXlmeM8+G/QHaokn3HlG1bzJPC7IqKRgqJM6OiMrBEqmhtb3vLixISokgmpxZqthiQ8Q2e/sENr88FIVZuDsuclS6PKbzb1havVTtoau1bGDcxz5ciY3hMob5ZGBDn/U22omg9Ah+QBrw4MBlFtyglPoSwsJhU4UZXq64Ce07ihzGBmTBE3Zom0DWMqJCE6MbeDzrI/hlao/oCQgJTQL6magXwBJQEQpw3XTt0g1cztF6ZWe2l9G18vlrhbSBAsJcON2kcu4h3tWlMWIzeAc9GW+P/UiUSEJrtiiwYJiF1PMg3w5dS89gnApKqxcLQxKJgrEUbNkwPSmY21kCeJkZoDH9jwk6ekcKAI7RvEY8zWLyCMZEuC+FnuPk2qhzhDRMPSUjesFvOwsZ0WOQgYxhmK0IPldVfS8IEm2SYJFCMUD1QziJCygqudOtYlF5BKPoi00qQjIwSxC5cad35CO/vwiiayi4a9X0kIu4dGU92HMQJwkIUb7GcsXypOIXbmd02bJi+faq0fDhOGyWq2iY8mx0+8akI7HcjBGc50bNZbIvICMUz4z2YWLOrN0u+lKhWwy6Gkmwq+Xy/mcX5A3cDh+fEKfBxQBt64rDqQFKj58pWI41USBIBf89+r948mXfz26ci2/oZS4THNdPLBJoph7fU6FdJ1CS2ATlQu3DV9lyAQf7R97aa1mXZ5B/fn0g3+0+SnOAYzYDG1tEZzGpURKtko0PA6tRz38OlnYlrLAfw9HKEScPV989J9neZA2b7e3Pygupm73Zd8ZC+bCRDKG2hXECA6miQLj2YWZJ/EogTdFikQV0jenOkgDFOIJR2JOL8ESiEsRLbF2nOIYrs3nAUFJEh64oZWlKcNFTmwHKc3jKMvDBA0zDCBRkmKp6bMCKnKo56RW4DAEiqAAX8etJ1L9rV9LvIf7wp3G5NFeiuPLXJE0T6kIswg89RNaJoYZigI0h6H8wpVu5OM8h7+0UYQxvczh+XU01HFURDjeGIQidFvyOkIm3cEFiXXeXg0eTIlJb8RWd9qkMNArPbS6tWM+VOcrXcFFHr5Ve78pVM4vKTqDyyG40AoOhxXJUlt0SRSW4j1vWBpNYTka9y70Rn6It1c/f3Jy875muZUbbYFjKF5wOszl0A78udU3mJVtuoHlJWiAoYvmJSFnfnnI6m6aOLgVK0tL3FrHm0M2tZEi3vRDw1N1KQw8/PwwwGy+tC5kRGrh/HalGBOhJ/AVhOCl4qELcylyPQaonPvRJAuG3elLk1upGqMuvbuUCw2xJVhDB+FjoKdCVYh9JyXE0FDJnJBQSWaFxvKSMwNJyCdIzKV+iYv7C3slHWdvVucWdL70mpu17rv1upiEJ5PFmN5ezpkC5Y2neBzSVOYoFJqhUUiITlh+v/3ON/jp//DE+f7wtFDTHg/WZPf+b1ROre34k5Mv/uOjlbfrVKcS69Hk6TwiZbmul1xYwTyPgTO0IlApzcT9RdDjhZ1GO+k5doDXaWyWmClKTy9gPjdzhgtxTTjgrUyLN2tbEkQ+f+2F84nAU3IH5BUssO1iTs5SIGp05Eet9fo8cIiABKdnTALWttZmXSft4X5j6YNvL3UV6sUfvcZOyPKSYBNp2bMt1ucLu/WxD7bZeV3IZcglkZLXSm+naffpydMHfmujZi3Mjhg4SZyZYl4uEf1oul2FvJPi6YTHQirUAh7Rp+vXlPP+wsWT3ieLUPWTkXuO6fe2iydj54Mr4k//qtcpFQMM9OyU2hZZKzUXwVRASoXk5eGoEXpE6FGEa2wXUzIplVNyqCWarvuyE6P2TLUsNUxdMRHjGA0VyS6WpWqOITLfiuIksttiRlFZhOdidDTFOA5NXY5DUxRDRlFCxQRB+YExVWl2vVY1X07jEu6H01wlsp3k5HmQxBCIfDmOy40SyKJkk7PfWckNcDJL+1q6JCpVvpaHxkeLrN1g0tAdHzmaS9K/eB2NhmJT3XyDpw3rUsMLYfKyIYlk4EYZy8s2z3o9M1ZkMicWcDLjwzMHh2EKTLxQRNKRLgjY9a9fHR6fWjbT8i/QsaXQzncP6W9fpaduor04+FAPv15D3Kfw3m7hxakhulSJcBnNC0UqAaRAcs1gYdCJjUkhlgmA2GjSXMp7ApwBKlZVisdGA5tDEU2SgZuWRWZ26P3oYfet37u9vbn7+qNBeoySJH1OeJ1WAXkv79VL2QSf9E3LZ1Q3bGUAQwmkmEO8qEeysd21z/qVm9L4CpeTYQKUcjR3vrEkH8ySP31FXeefZshWPV+o1PSPn1rdePT4EsPr3LfrX9lYdz190PUZ04V8sdogwTzYrIk6Rzl84YRiWihotxt/ffhx3/arpaRnaFlWl6GEJ+xYFnuP9N+rrDzp9yq3JcHODJZA31oyXmuFeBEtghFtzS4jBMU4ejPhae3SNV71nMMBcIO6LOhF2m3kqkVGKRaZ/e5kFpyObYIj1zdzBY5UZ+lc0zUMlcVYapU4kcymqZ7hbIFkWQZP4x5AKnQwWliygGRISrPQ9EOvQOVjNhhPgzDhc5hDAiqOAxzLYCwzKBHEoQ9THGAQIGmYQQzvJaHlXtZnuYsvDn/772AzWbcptNqRZseU7mUrN/PolqRy3lrBabSJo08vOzsk3aIHL0e5MCKJ2HHx6w06C5ASmo2PPZOhW8s1oZqbO055ifBswn/ihV3TDrHdXT6d2vXAC51y/dpyc2Xju3/08s6vLZ9kk+lE1Ux181eWfI7qfW7U67KXsi6ENT6LHa+Tpr5mVncKQru6T8ns/Td0R9reKWodDNSAf5YuHk1ik51FiRsl60VOlN2ZicZB7PkOw9GQIZimFEexltFxREGPS2ewk8+BanV8MIN9VXXdIIwtGHO1IgGZHJ+oaApqWNTP2fNIlsjYReM4pUOsUOBf9M0KDlaXKNlFR9S0oBRcH4MhO4swf0pZGiLDBIEp6ifWIibo2FARZQsLGAnODf3ltI3TTB5q3dQPqSDEOIX1rlRGsaGr7O17mxWoTD5z6MzBgvD9u7sqQIN9F52EuhnKrUyikOHB9N6ygMzcB//q4qlubTMcVcWXBH+pMj3+2evJ/kuuWE0mwUaFHo29QglgCOlirEiTSFSSc7bX9/jEEkTENfAgRqoFFoTCwCGUUnE2caUyoxnTfF6QKslUcwndMo7djSXB7aYvj+Pmeq7zpuKqKNDtBItmg3ka4BHqS7iUphmT0IDH6+XGYuqeEau1e5K7W52m/tI/ZIPjxfOfjdHE7lyyg66T0qxYkBsleO7NhEWgnUFDAwlvFoUEUx1UwbSeRpDZaoMnyojqOHaaJElEkR5BhInlpU/nhTytUeibq+LTTw7rORCsMSRAEyP6+k5TPdW2VxlbFvwM6awUzp67Rxc+nkNmoUf74dlpQMlhSeA9CBlUyv+yvPU1cvq3/eGP9doNSWBKSM4417yGiAh5xgoAGEIajwI9nCaWaThYfpmrb/E1CdLhRPPQxUKUMsvUOICDKDZDClUwCmMBR0gAOL6lNBEbtwLSxMPFwUtYKwFOSp8OVKrhzBGfFeKlPK8iiJkv+7NIZ0U3tsGJrb9MrDm5tirGunMC0OheQUOEIuscHjksZRdEyjvq1UU/lcDFAPzs/35y802hJBW7xrwgEQLtJBYNEVNdSGWZSqkE2K6TxipKzEm6VcxBPfJcYvV2/fGzKTqYpRgNZZyuCMefnGBbzcIH68e2AYRu/p54JSedjJzR4/5je1xjrFKepGJK72X+2JXqjDqPFKXoG5DA4NW3lsIQTip8meYtPUMJ6MoEllrFkuKqJpsxRZmxvajdyv0yU05OJj/8dKJkOKEolRuSsMTLr7VeN9IejwdTdHVVFitkmaNzFGrFwAzdVhWlUCcIQ/au0O1YUEYf2ad5W/vPh2a6VNgh8WmgWlNVFPkXH80ikmy9t8vgpPczAr7UR1+cuIOMLQekzackBgL+8iLoFEkjL4+xeaWozE3rx8PRe1c6MXfc+Gp6/vOM88jObotdqR7NzA3fPdZCq4N4ojC8wN+SuOBgTP9afdRuqw97yVm/ucajlVRNUdeh5AqXDlQ0pUpKXao7xkRHWiC3k8dn/v7D19nUJnl09XYlYDEml/b7GgRuo5qVeRKkJd/1Do/dJARQFgMKZH5GIhgrs1MthSlAMTqNYyRGMT+JjUzNMIZmUByDBEZGHvQAEccoBsIsJWkyiWAaQppGYUqgSIaLGLI6kbgv+nfWN8/ONb/QoGx75KBeKb/OZcHNNjlXxaN5KXQwx7lxKx9lKGPgShxMpYyjMdamZIo+icYexzVWUQdySwytHI0ffWlHkRuqLNqEfJvrh2ReEcKfPc3lAdpa/+Th5a9QMcPjhh0tGT47OntwdZn1QfjT4bLCgvda8z0rXiy0Qn7Yne8s0zOfDJWN0srsk+fHf29FUyWEGkX1xDw+Cxvi2qPFrPY2X21ICDrmFWy61+sBIRPCyDSo1HWqBZcSmSWcEtPMcSK/ry0ghkDGZhHPU91MI1EMIBgXAcwyptpng6hcYxuA7MULs8jqMNnmkwBThAzSbjjWYjH2Wq3loAHoK+JsPmtLqD4LVum8E3mlpgBTFyMxzdWaFSWDdgxKi97U9+cpSFdqPMVgqRHiRcf3PByhmlv5ymIIVuUtCZ1+PvG1YMPC3/klacLnhxCxT2xPD5ViLF6rcrsF/XW/0JskSvuFEdavV1u1ShuQj1WEprgei7beaFtlwYta3HUQ1I08qiZHiy/m862t61foipyNzu8Vx0U2+8kz1nAihgGsVKcRWUqjIMIYwmJoq69DnnIsJ5fHIzewyfDLT47WnXqxUyFkEpRIEY2J1JnwwfHERN20XMKtAGdCZ+xRzeuts4vF8paQGUTj5jLTDGfHkyxBJwePDn2+6V+CF6ftpQYQcTH0g2Ku6/uzJHUGfX8OFW7OWEEJZ49apbUSBdcqChr0dXRBsHgZWVZEw4nVEL143s0lFH39Xo3NxMcQTFORoVyczkjp5ItJtVPghejUtHJie2FfeA6Yzz0UED0noJeK4NFlYXO7IjlPT05IENcrwgmrE0c6uywzG2JhXBLKkWeZzdDaL7F9XS3wjipVud6kF1MojXhoWBIz5I5YIZGqb5sTSFvaiMFrhWzJiukkQng+TQJsqYXtDSSSyvJs4IfBcDEYJ7lW7CrsDoKd9LTmNf5pnFRW4ZVQVxImwbORL1r7F50qTzJQRvEuTkZ9b07QPo7e2ypUa42AoJ2LsYJkEhPsY+gsC1c2JFzM27Oz5d7BI3b456+wX1rH6Kbg8EymGW22oE3jkMKlvMBqUcLk3RRH6aSUwGxwSLBlguVeDfHlOte3kY2SfgCz+kr7nZSwJ/NjUomj3gptx599cvyVdnNJYPOd0+7g8qfD7yyhNl9F2vlVkyEq3DzHI9OQVMgIFTIy5hp5jqeZeVIQuGDYBaLr4cAczqMkq6IZG/mnOK0Nnf7TQS3R2N2ibEdYls/OgldfXMBHh+FSs1GjxGvr5TUiGPoppC5NQVSCRcpcrfjhw5m30GbZ+a3FaLykyJfD+Wx2s8h9l6B8iYjuLc+6+rXfqn8WWKzNug+GjUqu/EZ98m7FOVdLyBxdr++QOB2kzxgBMQHNUeY8LHTKzqsFQzENX/3+gQqWmbTS6Cw1NiL8HJJ6V6ycjYoKbq/WX00CnoI+4fGrFe0jzfpfTm9+bYW8Wz/MIdbwggvdqY0lmF95e73WgApZ5a81Tl/uX75aGE8GytRILHcxxZd3cmSDzwArGSCajn2UqMtF2gGTGe7ZgeZZLEVWO/WiJAQoMCkEz1AiiNEEOCgN3Uzg+cx3QobHcIvSvARDSQrLAdRBEQHEU4iSdJq5WeC7EUEQJEYSGMxglgD8FYN6WDQKhR2EGHjhlZXc0cemGOl/52sbH/7N69qFWxYI7VyVkhmJ8XxVGPaj2ZlRKBGIbRA0kqJIgsVLSu5kEU1nCbJMOyLWf56RPpYtNXAifTFzWrzI1RBrYmcsBZrY9p1s9OHhZ5Ogda0Y8kqdIA4MjpdkIyUoPBWWi4cv5hKEj/bH771RmC0rPiCbHXp6Qja3OvzTOLrwKuvyH//J+eZtaWaT9XWlOLMXHoNwuG3QTOrOIeBrJBAIgPiOQ0LD3dmtcKLYPRu9nAPAR/KkryNAU8ROnZJRFEUpd4Y52czUXZsLmkVrhQMyABebgm/TouQJBhxb5tQUPa66/o13a1E90HgRp640AVOvCLjvqEQaoCbiBZQV2FmCp1UsTTBIWBGCIyWrOO1dehIdS5TP42iYdTVQo9idZWZ06vTxjEwylgZF4L+CLLOy7gmBdjGYDse2DnbvlvIFzuvO7L/Sg/lobPo7W+bITdsUYdvE0dCeOD5+hRvKYNJl15eX1tm8PUoffv98Una3vtYhhMZGa8099runBsGgdR54Vc6YxakZiXQSA3I8DSieXkxDgkt8NAq0aa+fbFX5As3cvrsq8uFFF6pBfOvX2FbeM3pHi6EHYFYOgUQpwdgVo5CmBKJKuaYfpOTRAZGX0EZDfnp5VG1X2dSfVu4CgCdIoYKq1sRcVqi3r1Uez4IXh6ZyU6ltlpgqM1uwIcdcbIlSvZ6YSDgNnVez1+f66nYSae5loE4XvtRcWV+tkzZh2P3hWebMNX5ZckWMwPxKnA1oIJXJ82PLRzAKhiBIKdebHQV1AT88D+qlEI9SwjLJooSn1KtPHP4DfO2GdNE1Kh5oCyK+KunduaCm5Drd8cj+DIOsVGAQkKdFFGaQX6tITKHGlpDzz45ygYCGaBBltEJaNoBBbDEMTEEGUmo+RDSvsoH2Y4QQM8BKHBVV3mmfI+xaDId/+KL3PEZzacJRx5kis2FwoMkVSikVz4bjaKTSdWUilHfuLS0xQkmgh/u63h+MrJPlpjRKVRKGXObremwnKV/OGYVm+M2vbGVU6+NL8qllSRK+sTN/ds6YGF4SWT7nCpJtB8CHturxxYSNF4xIo4ITeoE5TaRSHqJMz8CVWkSEibdwnn9u0YWzKq8ylBEz6Wpqn/98ktlO6+9UA2Gzv6fioyyEzsyL0zgLY6yEQxzxoxDzU6LMSSCFhCLqulMuiaY3jf0YomzGYCGvGDBh04wXfKOUyF+5U1zbiR705meRkHG5Mkv87vvZWjsdLcoMed71shTyQjFAZDeJG29y3Q+P3ttatHR3fE7uJMTOwcR7PfnyfHFx6wqabz94oW3lxCdPVaav7u5IiOmdP9Ke/+3+kKbotVKnIxU2c/WadPFMHY+NM2J2pUNqYzU2zZvb1EHXFPOxa7Y6fHrxF0lu12tjlYTyESvOclpAoWgayTR10Y3f+qD0xevxAKFu/u++9vKzs5/+Px7oq2KhjQsl1qFlMsSLJB2/6L58MNkskad7eLFEXfvaiqovjn90kJXz7EY7KJIsG6F9ldAj2/OkAuqMLM9Is4TyXbZSYsubApGi82mQpElCoRmWRUmMSxKaoDhNkhjwUxSPg0BPWYLGUegnSEgBNwhDH6FY4LsJS0KIQtS3MUilKJKEEIUIXtRGD0v5tXfTCyPJcKKrq/kKXWIon6QBLHBn6rBAaAxNYbKsEJOTeRajYYaxsdkmIk0L+RtFdazDWcgL+BPSErGoMB1HTpD4GETx1Telo9ehYUT1qTU6xarFZpSF00+dVRUkTHxyq0Ie7pcsO2+hd5cZ63OLVBrrMjr/onvrW7eSt9CcD5bubh//ZPCVX71/8PHRxA1+89vF/k+epu/9qrCdbXXwNI+dLxwYp+Dh0Tv/8A29LfRjT2rl5lpcyjE5toiigs5Skald/HSxvpFlnexggsAEcr1pKiKaAM/T1A3i+pnHlQUZtxeeSJyiNpvaOI/NWThwqboSpLHpc9vfurp79Q0vxEsmguL5hgy64zwSDH2ZIUoENKQaMQ+QPBXzrgHRIjQtr7qJziZR6cp7CAgde+QeXF72hjOXwrs9mwu0YtEfo2ulxk7XeNUdfPyFEayuVn5r88P/8qNdGY8VceXWWrFoPfzeI46W8ileL4r+bnlmgKRrv/7s5eSDpU0UebtZdviCI8CCMOv9ZP+iH0FWZgktI+RUA40AO/j/PagyFamCWRea3GFiGcENspgrRXESQBLBs34WOBROXHpCK8yRcbkZXB53qRS+lorCr2zctkT0dTCYRUFBLq6ssdNeN0AtBeAOE0U6w+GakDIZ7F8OGoU1BvV0Vc0vJcQy0KxhCa/8k9LuA7DxGPy/kgptHi6+/la5zKqDE7/RzGFeHB3YHEGYalzi0OCpAx688kdhL6MrSEaExPiTU0rGqht881YDgeTR2ZzdP8fvX69JyqJS18nQLpUozVRGriOze1asTaxSozR2MZWmEQFx+/7mEs4Xil3DzVHUywfT7f99Xni3zgwOZ2O99L5yVY9PB9agRPCuXd/me2cachraHbKE5dCLkc1ShoAgBXGZJ0btEnY5d07OWznOibnArhZbQkRJMHNZFOCuG+Z5GIbwaU8RGyPARoShWS6tTQDKJXnU7y+ICXh/XTl7cDa6UbqDYD9fptKxc5d2Li91U1XLK4X4ngwYijorUAeHj56nXF3OBPnmGqaXSllJQ0aoEHmnZtrcLsy/7DtfzG0Geev9dzRQVN79s8tuQAcSzxc8ahJvVcNCgR8Fto3ZMc+awBCxk8S/4SAuFg1iE9hOvtHQ2VAKC+Pnh5UpUmy4LxwP5cdeML65qo8t59riclRiozdQ/GU/99eXafVmNY8OUGToUKWiTAvCxESUMvX40s2TECUaPMX3xvHSsjg6jfEyFjANfHEeaHZYXvYLNGvN4Jk3I0lmp4441vl3XyAHi8bWRmVDnqUMpU7cL2yEomYBAjnz1f3tzblf6jbVllTBopCYHtO91Ex/8cSdOfPvpMvsfHY+g/deLG6+nRSwkHP9GzvC6WiBXkT+fP6Vf/y++Cta/ovT84PY+vT8dGFBXkBwINTQ5WLzjhyNtusnp+n+OOlB7ioCLqcJGhNsCYQLWo8niMIUCxALfJfxvIQ39flkgqEJt7VSMnvuf5n4b755a/eNq9PhQfpl1/M0hSdZHJy+PEgolAm03sFid/dmOVeYiazoOkUuB29cMdckJJ4lpyato0ZGU3kW0KjYpglZcKPMEXgXN1VVDyeuxxMEIyA4j3AIITC+HWY2RRYoFkUS30JommBwAsuyDGB5GGSJABMyRT2EiuKUjnwJSxMeD50AhGQWx1EC8FmCSqi3GIe+0f/qjaX+l9MyQVYqQH18diWnnx9AejV3e116/WLcH8WRZWxsFrKZfTYx2luIrMBQ1ZKF6URYWyHvUPyMzYhiToFe6LonD4b0PiY2aQRnDntUgvIrikDa43HXb5eSvWIAqEXvxeX9bQbuCsPe4Aor7x/O52zTEYhTzRdrxP4SWyly9hi6pNVYjX/4Z3v/9HdWXoxh6WeDKzw8+8xUKhyVI7B3xVcfhpdPxptyfnHuQd2SsCg+0k5ez7lSOf/OEsR4aR0fG3rw0ljfEYJCQQTh8f45gVGKGDKLlJzScyfoqXHGQQbgo4LQadRKUuvN5VQU60DM/4PVzgKwTBqfvzjmluRIDgO0mC+xPLY5ASaNBA7uEgiMp7bIEVkWiFASAVImJLGIiTqNcQohrMC33ty9r+nDYWJsnV1OD066kcntrPIvX9kPF0pps1VcVtAvJ4vForCalw006BrHj4ZON9r91ZoXK0ePTrZLwlHf27lT09rF9V/bnP3g5eGzI0HA6wm0KIFriRu/Vojy+Xr3cvLjS29h53Gm3zcLLbSxWzlzSCZH0DiXubgbsA4LYknCI0M/HebYQj4MokvD92aL2Km1qAhwelQMRvnytbUSFiCLWc/FtbmNX1B0lXSGnjE2SD6mRdxPAjVECQ4PgzGBuwXFe3rYK15R3BliitkfATUHjBiY4fV2/sr2UW3pZK+L6Ze0QqIpKRJkfKodPTjsJsnNX11iZF78peoqlWP9xPxoyG61UyqnylH3LNpekyAZZPeX2G0qc7A6GwIvTRyLWOAEhy2LeE81Vji/mMtMwyLMqFqlbMI9eqHJpTJPuPUmMzsI8Uz47XuVk4i5ONI0R1E2SaY/ZgI4G8RLJUwoyr7j4x4X+oxUKPiWS2gmEVjZ2PUuVb+HokU7i6ZEYbn2RkHGWVqLEogGdEZDKlmYCZ4EPIa2CQNJVN8Drnpm2+Iu20WiJFAvL9J33q8nWePAoMdjfn1JKOPzUnPUo/yE5SSFcG3JVuPpzOv73NIb+eK1AsHScGHJztnz5165LHOCwI4M41mvqqACSCZn9ut//crsSPRJsjiCb369I9ONi9jLEVL/AllYKZIRSBQV6cyfqrJMIQibZGRk4005I7WxNmfL62LlqjQ9HpM7PLe7WvrmTgg2TuHDJ+d7MO08/wHDf/sGc5/q/cVB+aG35/tKR67T8cHeSMyVcnX5ZJyqCKOUC8YoNUy0zMWDrp0xLpDlSTcxVIKXS/ffLD34/uTCUN/+pWZPzZKQOH82R0dzJi8M9cXwZ0P96DxKYgGR2LutvoXRdYHRXVvNF1vkP166+ln40aKNftz17zSZTrt0tgf/03erGaUeedrtG3nz5SJwSMPhCAqeHhr/8O9vdnvc+KV6cKnXscqVe8CZiPhIq95qJBgNeLB3Eex5GTaKdhXh8cPBekU4GupKjk1TEJbkqZrkacCFJqqiOKrN+w5NkxGFtt7p9CCKcu61Tmn/i94P/p8v2HVh9xbL36id7eG9vVmlQVwtZne+3fb1jb1Xw0svAqoVn1uJkRY2lqobxRfTZDGJs9TLinWymiMExLMss+9Qlj+deiYe06JpholcIvmKErg4y4tJmlgzYzHyam0ZZdi+qmcoILKURFGA42gUSRT0jIBlCIiTkRvSFJplmBmnnhV6NpBEIgFIksR4wXEQK9lBzXm9k+vPqizkSsV55k8PL3Ek5e8UoYRc/vBLaCBbV3K9nHJ60S2RmFAnRwBpSpn6hcawMVpURrZJOhk7NWc4aUKOqCnSnQL14EJI8IaIj99qHBm1uT2b7p2SjxaV/25XwOy5qq+VuA+NsHO7aH86yCdJNQucgEJ/5/qTHzxZZ/CCmTf3p2/Ayc+f8etMkteS75Xv5H+jMX38UeX33/rwXz75h9tfmR+fJwp+/etLqZUMJ5pngybtAzqNKSpeqcKpffonJ2QT37hemwql7pc6O/Pzq8k8QpWmko0WQZQ55ZUvnHExE1abHUpu5For7UqNSoiddt7KXIRoCAgS65N9PcYC940bywsAcCc2aCRPokNVs4yQzSKaZEC9mEPdDC128sk8yISc50EMRQA1zYKziVcris0cYuHmYdT5xt9ZueLeHZ2+fBnMR/ueIRp+VkbjGWo6j9TWldrLoc7oKZpRGYEtr+YHIcQ9lfQDtojDjDl8Noxd/+W/d756tUnfJk+PnVlChs5IwPhT3j74+bRBaZLnzxE6f1tpfHDHfjzyFvZApCMnWJ5rVSl/BsI0RyOIm/IpRlGxMTvvOWjNk1kb02xqu46h/H1GLhPU5MmElOjVGr9saM+6aRTMwBlLSikjpXOFDm0kteZOs0UyqWK6pwANGcXnvPxFgLqJen15FzzAwRPxYngxqn4V0z9+CImzs04TBXMfEYGbwggib/3dm31ghwVaX5coBBpzQy2XGx0S0qlxvaTvHYMM4UL9QrdKK5w9DqHOCVmeow1fLKoQ0yCXLi6iXhIU86OpmyTQzcj+LFvhJaVGJhczkxAfw+D+cnz4/Gm32VFZ5Nt552ngZSqO+IBKHDcAr/fNfJkkAIbSFBOkMuIbEr8mO7041hw0cSkji9x06V4DVL56G1lZC3/2uOBLUMGeZkjAJtmZUV8qLd4tGBEkTFYwZ6k631zxX11dMYD3joP8n/8vf/zf/fN/JMi4kpr8GE8+CThNPe73+V9qtkviyY+786mDKtTten4q0nmG6L0azQYByoD1a0vXm8h4FE99vyZSxz11TKV0brliYGsXXeRzorIdU8vl5W328XG/VMUcw6dBEFJ4EKjTxJ8hsuchJSbLlLzizaMeGul60s50BHl9eFHuMPk2k1j+5Ekiq190lfPVNx2tjCRoBIY6++JToVa8vs0efP3aF59PVg+nRRbhbq9qEQwiyWfCEEU+m140Yd3MxqlPQd/odQqDTOcuQw+n6FvLvcMLfG9c+/Xbfg5x/vYhmYpkjiB+o8jRcteRYZ6+38YoEXttpBMPQRtLV/LDTuZFytpVKM+CvefPjFxuXN1Eo5M9u96sNJAH/0pF4gv2n5cHvyFaX4wV+Q1rDlGZa2yU9JenDolPP3zgZJux0c3+/rvlX61xuPeLS4D9p9N37+DvVQWzbxw8mLTea+YEZmYYjhdDYOtaKi0ivpF3VdR1UF5AJqIxbsrtc9SfqNv1rFNU9vVgf4xI19Z/rdM9fzia/+uHxm5HaxboupSmWmDMX/4I6k5+/SZ9aJrHJ8YmxFfeXD/zHfN0GIcI25BX62IJyZ3PvdnpTPQty/NxNBOKCWEEIENQhRc5BvdQ1MFgEGiamVhuebXGVVinu8g8j8xTGUpgXhTHkUTgqgF5hAgIgkzixIkzJktwlMlgnJJJFCEpQeEYQAFukSxB+KOMbBBm9yitiHjsLCbDQYDB3a+0u7ZG6KPuYLa0WnZNowK9yPLYGiMKlJaC4ThJedaXkLxEQZ8I41C9jCLVbF7lMxtBLj0RwdXL0IA6cb1kE6knoqjI5e7QNmCCkyJ6PCvZYOsaB7qLhHZfD6bxClO9T2+CdCzZkwRp5uYnf3nYGGcf2DXvtdOKE+3TvTIfnemgJLW5pbZSx3CX+fR7J76KrmxTcZNO69nFNIr65vJ7VTSgc+t6+kn/5eFieGLufLWR360bizExp7lSOHdj38GUUMzTDHrjrfebjTJbzjVqfVaSYwqNs9lIS/MYinvjQRhGDitgLEd6mk4LDMKwSRRiUZAHdF6RszBxw9gf44UCE1qZGyblGmLOQkIinQSBqzJDCYGDaVM9UsmdO1eSFI3CVMrtvv0uDvybaD5eP3346OMXx6ezZORUWoAVqkqBm86ILHMDBy3h9uTSKpf8/rl683pugMfNdWqku5fPRsyKsvNbO2pAhNpkvD9bTNLrTEZHRIiRlJgzC/VbW/mf/Owi0b0cZRGcx1fRuW5lMGXxwnCewCImLfH+pe4ML7W+U80zmywJJkLxTi4tkoOHWUEGBpKowbQi0W+txC8iRp+jamR2AAZ8aBh8lZUE1cNmMAfDHMsMNdpLw4QHAh6nFydHeE52CPZeB49Hf/qHoxWa9QjQN4Jqi0FQjMElvqFA1b2+roSLySmYx5DclDh/Huuj2dOPB513Yjpwbr23nIwtdOKGPEVgfgpJbokMMpHAY8VHLx9dMkiS49gkQNwgK5cAHfhJljz8zGSXAXKmX/0Kp2KSIxClPPLw2JWi+AIKjpoOrlHiuqx+ZtWJ5OmRB5NCe4UIREwm4kRLFRbpjkiLwYhEQHGsVefYekFoFVMPe/2njxTNaH1VdP2wYgejoV3LYep0oX2OWRzSKQaIHpWybPZ6VKxQ1cQRPos13LEjU6mQruZTS83RIirca9hV8PLQlX46TWHWqigREZ89nXRtd7WRzxWllbs1w0NfPB2bDF1XUraKTxYqRsX9v5y8/VXFoOVRYym3BhmJr+WzrkcZkZOKuEtHvZlVYxAMmXVsf2JYtdWVzA0jGOsefWuDnvYDaHiZ79VWJc/ULDPI56SSWB0fqdLEnVTB27hUWy+l9xrqdPbF//jqR5Zw55eXt1s5Ma+c/GjIRI4bIqACSyiu+HMXjyE7sw0QAraWQ0rkoHsWsDRVq+E0kj595d25Xi1WwM+/+wpYyTd+c1VjnL2+HV+Mb70hIL6nnWeapg0pXJJLDJpZgyJf8opEUC31Pp9BFVj4xnY/FHpl4cU8kMLcWxtk+/q3l/7rXzr1ny/I+UqZ1z6xcmW0KiOfHWorb5Xjf3Z/TdwpPi49+Mnh4t/MKOhd/UoHUsnJ5445RRpvtNa/Wr+Io9W16PljveI47ixoQ4AhuDqbKEVRRQVr4aGmzaRUodzQSNJgeN1Jl9BsnkPiKj4iCsqbaX6F8EfG4HVfYalSiyGWd6UQLP50+uJCu/stEd3IJ5lwsNf/5LPejRuV+2/nfMIdnPuR4YWaulTkUwYrrBQdglQvTZbCUIQFBJlFMAAphCni2mEKpLWmUkaHhyOQRrUlJcOBOrdDI4ICFeMESeI8RVqOF8ZQUlDdjwMzdRKEURgiLyA4lsUpjgPkX/7lH4DVUvGov0BK+aUammD13mCQZVq1toKSTu9YJBJyra0SSvjlCQDBAviYiClmVOOLk9g+qQgIdKRLzZ/NeZqofbA0ubLhP+raPzRje1YQksK9xoQkZ7KiTtrr1azjOssBHjywM1x7YEx3PePtX86hn/30b+9dSeEq8W4JkpfGBaAubcwTUFHgiTz+6mL0rTcm3fRKqh8wTINz+0evNnbv9kIIvne28vvv43FmPnh1VuBc17rRElOCGx6pFIXn/Yxb4p3Qnvf0XGAFjRKbkc5kMhlllOCZUywv5MpuKdld2vrWN6+IdDiANE3PgEOjApMFXIFKEY4ksDN9Ikeh5nNERaLGBtoq5DEwGMY8ZWFCDrEhyKECgqhWysEgFDgIAQlTFs9MB7UGKpoUoORjRRbz5yOt1MwBDtqLRb+1uuXoEyRCQtQnSZxTeEM7WRyOnr14oXquFkxkiGmlFOqz4koZ89w2kT4Wlkgcxc70zoqoNmjeJGkpF5keA8RoWazHXndmxyDqsyXRSYPI0/dHy53OZBKYz542FIRHo2bLs/P1mU+Wi7A3zBiJhaaL4hmcaP2zfY6GO5vNZrVOZfHBdg3riQgp1oAfL4fnGub/7BSsYaRHLpAQOXOHDBP0yPVrBNQh1G1jtdLX4zuoyPLauAyYWqSy4uLZBVzqbFwvYI+ekq8txhLPQpddrdaSuWXgGKM4B+blca+xyd9YFvxvr1B9/XJG5pVMfTVNDR+ziNm8X7/VTtdXCYaVDZ+ZImHuHrm9HmSakaLsxYPzV3blzWUwDT0P9xyE1A/D2bz8di210hAG6mCGQ3X596+9/un0H1/tjFu5V0N1dRLiMv3RxweVKsnWZQFLz0+jQFXXrih+J5cjcXweO6H52AwQ1K6fxOH6Em0DFC97C3epI8gyizJ8tFG++Oykiie6Y3m6JgsCsSkRszk2Q+IcBvFIsPfOj2YkoWALEd8Szm6s1tal6Z+f328vdxtKrszEzw67g6e1KAwzyq01Cu/czV5eZBfDKaH0Ey6bolcrWb6Ze9rz4r5bwA1QIFxcPPvTJ4fd0T/5/ft6JO/kTNODuUbi2czycmFgZ3ZZHusa9vQcXGkd/fnhxHJLMlVabxOb5dBNnZRnTOMugj4aRmmeLN6pkI5F88Rrm28Vz6nttjn6rP7vjy2PebwCv/P77wVo5eDzh4Wfow7rKdeqYowRpHzUp1i+RQhIsncqFOI+CVySqOeLOYYO3UT30yscMkM0NEC9S5vYFd25WTH7zW9f3evF1OvXI9crkbJlK+blnF2u53FNKZSIijTS8+0CjkHNU9aTmEGQxZHvgs6FhlmhxBN/Y5O+d8WOfuXK6Rfkyvjgwq1IoldWR3l5JafL0vTiol4GsFVaPCd227Aqse4Xx71Z0IugKMird5TLJhMQ9NsJ+ep7e66AdgOM/qI/F5Circ/DTCzxJSiEPmu2OTC6KIq1Ak6Gd+vO22XqF73WEtO7zFpMrtDOTnHHvLQ53ezrQS52cOg706B8c2sNwvOLaexeopHI76ycHszds6C0uUWuO+efvyznc1s8F25VmtVCOFU1zBxcauE4rEkSBGgkMYAgUjL1g1RwA12S81lkqiYWRGKrjOaVbDA0DTuQeEHiac2MeAUHKZFFKInEGRLGPopgWZq5LopDjOBYmoAQojinNBKKMQrQ8ZA4c+YXdsbhi8ArkKh+Yro+c4EGay7mnOmxDwZRxr2ZS1i8qGLaXtSzEaLBFJgIJjpDUzGhHLiJFdjXRLF1HzfGIDGSKEz8GHVGGuFihgHOPDQu5eSaPEQBQrpPAHZs5PLU2jLgQKzCh8OnU7fy9q7foIwnupMx67vsF18UeQN/c82c/2JPaZaqm2XrvPXkh+Nf/s2Nj+P09cf79WVlYBq/8duVvQfa/OGRXMuTk7SiCGnfgyBiy0yBz6YD1/YWfKSUS9Vbby7FEdj6SmPhl2uVa3KbGjvI3v7FRon1aYsiE4GN4NwKoDBzs643aKcAJSmBohxrEqlhvp7DMJSSkSQMC1Q69gMhE7p6VpMiFM0maVBAyTTwM5QESVbL06FuBraHsG5GWtvLhOXggOV5aVNFAUU7VJ7mokjtmbTqKsUm2Sk3V68adnhw+ol5+vLybPTNX70WI7C75x2f29wVqcZjQhNzLTSLJIlMouH05NP5EgE+/6MAeP72G0tISagihjmOYmsiOtoiJZVqSbklg4UR2dHlnn/ld/l5LA+0eYON/KnnDkK+IaHtWpwnvMQ/hzSe8HrgzffsK28KFDFTPwlDBLaquXOQ7X0xcg28tILV8Nx2jX7VA4OLgAwQiMa4H+QwOJ7ppcxTaGQ+NSzZL6yj9YZ7OrKzQfzN31nTnlqrsWhIK+olGjtBkY1l2d75veLry+z5JALf8zglx7REgCY4T4jV5OTCIRsUWlVEJrVfa6fTlGKrCouHR1NBRPTInjrF8q8t0Z1a7/mk4PHgXCvwVYSI93+uukl25y2ufbP16oVfxiCsMd/7i/O1vy+trFam2oWczdo77PHHjj0yZYR6806uRxFRgOAeOtAihsJojCsZkAuQ1VXi0tVOv9Byyy5e71icvLoqWCp19NgSJT4lbGsc8m6GM4kxTxVaifEIFEsLDLMdMYb9ducK02yUo9idIK1Cil83kEGce3CplCQbULko375CFcXo4rH30b/6aGGJ37wmVzTIYvYoQgb7jjYxKxs1tNMe/ATB1Ojrv1y/8/vC//Vf/sn3/pfHm7+kHEzC9t1CquSYMmqFDkuQ5/ujMoZMRkGlZZ1wuTfeyj7716/DqUmd1O693Q7aihcyKUlvbSB7L9XsqRESydoV6XxsnHzp8jSQak3+HWwdQegfHh/9377Xba1/483G7n+z+eHHL6MT63SR5DuEwuYev5zeeavhcHw4VJHQaCxXgYwAE0GcsFVgR6nh+ryC4oUyXIRxoy2ms2L/sa6+mjMA33xnB61J4+P0rX9y7+lTZ3Y5h0ia2jhdSmOOPBmGZcWLfCsY6CtVf3RJV3OYmF87L3oYb00n8/+tnxRXWhdVejUPPB0naimT4zxKvr20bO4fMpqeG2k//cIRa83VdyviN8vv5PmnP9ESLwi/mOtT43ujcHmnaHDCzQ7df2ZMbMcesnLTtYZ2rM8Dr0aRESAlrkrFDuHPVf0JvNGiVjZy7Vry6D/svTjG0TK6UpJQjrylRJNXtnZuECbnk9P9FEr5UCRo3QjzOXRRyPwjv10IZoMIx6TaG7ugjgcUtPUoCaHqA5jhSp30LEwQ0BRDQZw4joeDKI0iys+imIQUQihKRiP+3A2MrFDN+STheAkKcDzzUgwkeOQDBMTYYhaxMUbJNGBIVqHDmIyyjM4ynEHjSddqFpFcq3D55TFDVs0yLqtAuugfTf38/cKNoqA/OMKmqFojxfViBTPnYw/EDATRWp1eJBY6Ogfs3EalzGPrGio98MWROiToCS5BCdJn0/IbJW0Jsuc+jGHqEsPj6c0bq4K9eOmCtNDEcTxqXe3tDSqWsyT477asP9Rml31kZbtehqRHpF/9OnPYO/A3NuKljdre5PydYvZWu9h/pparlW9syaML9FZjMsQ+/MOjxoaMNSuAJ7GeOhzNlCqZxrZ/oDAMUc6VVmuNYsqSnds3b28DUAMAuqOJWGNEAAIJ2leWrNBJKVpJktNDanuFSyhQojDHJGpV8rIP15pynqIv0ovQVb0oJzFolEkQ4jIvhA6ooEmC0laaVcMgSwJS4i/niyWpENOogEEB4gt9WmozEMgEB1AEkCj6zEivE6sXNizRSH4V+CkgcEDjFsYIVVytfvNXYXJXePhZdzQKFw4eIjkOFGw/lOSZCfuXuusmsKvV3tna+UercU/dVgrGp0dygfritdEmhzJR8WkAkHyEZerYjF2bVKd5TkEAGx9oG7v8EIa4NvEH1JIgRzZQyaS5kUs5RzdJzXdgjqvrGfjb8/RqTtmQfnAWl0anaH9KrMtJmeNLKYJUXg/MJRnHrpVGjy4TiXX67rUiEFZkAmKG5NfaWIWMo4WjasQbSwVbgNb/+vLLJ3BtpxauzfBKlUkclPUwM5k6mU/hKyKZeYmoznXbRUliOLW2f+N6mcpKi1FYuKJ/dpC9Hg4ykVQM1/oya6xfmu5SgDPf2aLlLfVvfkRhpJhLEHsaS36SZ/K0l1rx+fnJ9q+/uVHZmI/7699+7ylyCT67mAviMzSpPZiRX9tgqn22SA3Ppr0zS7hVDT28iWIUiXg24BOrBHFfgZ8dz7QkFrcVdr1adFExXzufxnnHbXKsyIZjLyRwd1LDfH+WO5pVlKqTgiSGZSZLVpiwXh32uvb+bBNw7GcgfpqN38iVcO90X2c+PSzfXZarmZZesMPYeppVYDGMky/2jRWpERYUOQfyWxXvRLefd7O1SFlDj35h//Wf/fTO1fLf/8+/+/gPf07i9OYO06ywew4uYoq28Kr5iqS4vSyy+eAH/+nod//ezeYbyz/5BGlzbJkEiAprORyryeciufAC+5FXVcNH4YL6YOPbq0t7/9Pr+yzz9DweTMIYx5R/fo/zoqVfjPwX3b8a4LUdCl2Sg+eG5KcTMiu189ByYz+ani2qb8vpZp5L3WQC5QyoNBaeZKIUIkmGUYFqZM6lPkhSuSC8eYs+fNGTd672fnJ4ReDr1Nufkj+jvMV2R7hw/YsMXJwfwmIFYPPpMM2T8WsEbaPM11Ok9zqYgiCOnVbJDiu4EcQCkgYxw9hupyLwK/XzT3XJ1D/Xs7XNXOsrrTdisxnwT56PDz60r67jiE733L6tqfUqp9xergvIaG924jUd6IRe/MXFLP788v7tvFZu3rlVN9F0TQn89fUWAukONQ6R+NX5X+zb97+1vPS773f29vuOap32zw+nNRYq29zuvbV3csiPfzL3Y912FVJM8rloDU8sANQCjqY0fDVfKZcYhvb7CR8iznHfwNiEtBk+Rmh+JUeaIRHbuOcBERcYPgaUw4hUoPp4irM0hH6SWi4t4haGxq4DgwCkMEWEBEM8B2AMGjkJhQKcI0plIcNQTKIRByECP8kQPAqQEoYs9j3T7Yk4/n6HeLI/hmnsy1Rul1RHU2Hsvzj377y10Q+CPA7QeVq3QhOkmRDs5hRkuHBtZxaBrIZTRcnF+TxMjwZeNp3JzWbhjc3pl0Q2Jd66UgpFYO7NdV3LIubTswuBoZlSnak0ohQbLeJcxMgVduBgoQqkY2c55gorVDxDjw61a1/vfP4fP7d2y4UKbv0YNi+yRLMu1MXFwz1FyZ4NAvaj+bvvbF58aU4TzIcwU0MZAKTKMFc668u7NLm+JK4H4RFN8SBLoIUDH4nMWYZwlI9IAFzYwJ8Fd1doE/DazAsIYncbBYCO3OTgYbe6QWgR2izyyQwJckY+n6VGhCOQwhBI4pfzMCfSsQ9InMQAQPoe3aGNnkejgZJGqetnPBWigJIYDFZBlOlTCFkkYQKcZVbwNEuxKo4MjRTmkYkG14pI4PEshiIEC3Uc4ctfe3vZ0c5phjzqBTAddc+HE0ujM3/3zRa32+GeDR592EefjPLr3P0O/wka8UruytsiRiODgS/m13wLJT13i860AWuibE1hYbF0dhiVKTc/D8pUUOPig3N3a2M3FQvukY3no50qcnCoFqtBebWon1PhhW8qSKdE+FMYCAxHF82k4mnYL/3mrZ1B98u/3lP3HC6jhTKXtqnxUc+4uGwsUylNGHpS4+BrNOavkA5iXyPswQVSzOSTh1n/p4M7b5elBoCeY9lYjNF8aQm0CArhAjqyzw2RIIrH0eW/2A8kJhcZ+M6RNLaJNl/eKh6qwNETDJ0i1ZJ4vWJi4eEPftzRnbXrrOaFYTkk7gmzmGqIAnmu9X5hPfjz+T/5g9X/+F+GF4XR6vu4+h+APA6/WeM+/QSxPxkQKmzWUtiUPCwKem7MC7k2lcwAkyMcB2B+MNETvFxtpX5m4rTuOjbkqdnUjdgqEyTzxItxFmy/0xw54OzVBZEG9kLN1+hustBtgmxEJ4MANyYrEjVOygiLJko51jjrrWL9WrmhTz76X/eoWaDcUGrv5WCYbZVbxC8WZ5afEFkcO86MTX2rQydxBqyBXlySv/oP1o8f+7/4j0/kweA7u5UyFs1jcv8UmWOizFECi+EhDqa+0T27X6CjjWLQuGFeOG/evtspYp7rjxzSGgRKVUAIvIpmIp+imX9nVWhOLLvrzWdN06X5yyI1nDwdz/ggv/2tZv6bytEv9ODx8MUnDrXWWSmwPIxjdZiXMgQkPO73Mq9QqNj9Yeo4jXIVwCCaBKnnZyyQK+Hx1NYRcrnM3CTwXIsbH8yGC/SGlw6OLDSbHlQ+WQqjWESmk0UoUAwiOWTaKsDp2SyHkGUkllOktUibDqiSU35uDWvIfsZkGBv3MaGSn8XoaklIilya2d6rA7Cq3P6l3fYm/9EvZurl7FbeFNBsHQf7nywQSK/+Soku1woMM3voIQlYliNbsc0WXhPq3/jVX++/eKSp0XRuI5ZZkCiCLwcTf8ixHUfAF9bqrQ0m9r7/yWHRwd6oUBKPjhuV619pX4PJH/7PD4RHUx1DxVVx6YYMdeHwVQKtbFgNPS25viozWVpqV3gG944c004hDrGcHMOUFfJyHkZROtAhIFk6x6EMIGM0sC0QIZEVuS4sVEksITwIUJHl6MzzbMvxWJaEBAdZLrQ8OkZdMyUyVMrlqZrUarEXp6ZnYhBJU4gCkOBoaB/zWEG5xLeaPgNOHh1RIUY38xOB7qBkdOn3QSZfq73KEXXMy05PFRfYVQalMc4IAuPMUUMA3P4GjrFEYwSkcqnJMuQaPPJnHEzHlzpPUVCQNINmM6S91Eb7IHGj0FhMx3DCYDWBv7LUVPAKUoh8N9SmcG+mN3LElWL6kecXRLEBMAIjtm9utS78pJyP4In61K6KoHxziZ0aVJ0LNjGyPzwejNdbSpSiOJtpA4cnQ3JNKN39vTJoTAAAANDUFoQjwwEMJ9KEhIMJgjPbZS6FYAl1kCUOQCj5QzHXSBENAbnzkyHA0NINjrD8kMQBlcck6GhphsmhGaQcqMoIRxGosfABRvLE1IPpuba0qmhugIv8QrVyrao7tSWFjnGwAEg+z1gIgpsW5ZqpDiLAyKuFy5FRLIvrDDrI4FoBiQHEeQAY4OocXcswiKRAnErXl7DezsYSAI2NzVKAHNP26YefffjTf/vTD75R2/i9W8bFon84BbSZsJzRtZc3GsyaHCMaw6foRmH44+Fw6BC316srZDqc4qyUCs6zQ30plwDPaAsSm+M9EidRpliSnNkCdZxKAUv67pdMVl1qIOfBbA7vkeirCNkf6KKs3MuFDiqOns2qdXLrt7af7i0qenYwdRG12yyU6A9aVLQ4TgVmjJALZ+ONb1/nwu539x+choeHOF0str9dA1ooOrMmQ8eidFTkHZGrz5A8rUzqTFnQT1Y64cf9UijYnosOJoPEm11t37hBHszyHh6zrpsryvjYxQjth3pyZaHeDhKpJZmIJ11OwVcLcRRgB9YHt/hXd6rvbdB/8f8duD/Tv15+e/zvj5H/dnlBO9RJdk5F5bUGT0ReOHv6XL9zRZbLpQkhSAGMDvQ6S6upGXMkhHGOShzV7A5nVZSfBxmfx3u2hee4mTeBE6vaquN5mkjjZJQUbASjgS+UrHWWd91gYQgCzHFoUCG9cYL7LrubS3bJklyi7YUz0nnKl35zSSkhQnv3HJj2y8+v/Ff15X9a+vFj2v/wwnBkBo3iCOsFabHTUCxT99nT4WL1N640vrl08ldfqnvq0cxOd5arHaEWy1wafILykufhPOc3xKddk6Cs7OzBK7B680Yjct1pUdEnAOs6CE0uX+e+dEOyvRp0h+nTS48Mk5bcLmFf8kvct5o71UXjlfOh7n/+/95nQ+29X18v37wyOBmFJzA2HIPFykuys3cZdGoQQonPRZmCx16BwVCBfaS6GR6UUZRiMXc0cUL6ym6bPexr58bMoY+fTIpleXZs3b65os302Sxmbc2PMCMKmIowPbOrqFMZzykvIHACEBJN8GVs8e9KwuoyPfzQrEpxQJFbWQY80kTw1jrbvwjRYzA2DiWWTrCku2d7z07yaClXgEFbhDM7w8H2P33rcqoeoqbcM1jBe6ljK0sl2bay7nx9E//hn5+mIrb923enqCp+3Fum41Au6ZmfyoKCuBziGjgyfHYRtKtvv9EsT4D1+SsH5/J99exHWsSFLQ5dquTUSgUI1ic/7HGakiLxyrIYCTTcFWBkT86nRVH2IDLLcfwKqkJE5kDdx30SuI5RqRInYoyrSTx1kiAZmBHLRCia5RUMJzGsxE50P8kSHpCuFSVhViiXeBadzhzcixMzJlCykic0NPV5AEJj+Ho2ckmOzziag2QEAoBTBCrEcDQEhG9hZpzpab5U2t5s/s1fHz45te69ldOFcsDhhz0jSSPSS0M00/oRrkSINzuZB9GcAFtMWK4sreYW8yjaN/DUw3GserOBlJmhS2rHs9JoFD+veq1cVaHrdDjQZ2mk8pzwzk5jnLhz16TYdDL0BQhQKs9W0ccnVi+Xxuwsq1OAIn780eU7W/gnL8K1dv1irb68iT1GpsXMsh/Agk5QuLDSAelsbr3onRrp8u6SnE+KteX7rS0JNAAAUgQgCUbTpFIiFAGmUF2cOw6VSfVQAUwUayxLw0zzXRtmHkcnntXXMZMqJGGUeZpXb9QxsgwgBFFGYxlEmGo9iYzJSRdfbhYqTeXgdFJfK4hSoNpOGkuOalGFgmMmDAoBT5wcJ40lbOqBQg4RAciqApzPqdZq9HySpXGngmZGH0j1aoa4YSZw0IUuDRQGA2mUxREeUYAnQJo0YHyuk+e9oL7ElHDx21/71rcrG//lj//dzxPu9dfe7mSVopRjPZpA7GiszvQfaxxq9me+VFUxJEuvVIpt3n7GOh7ZWmEiiHiH1hTJxpMkaxKd99emfUqzdKHMleu1T784TdmkKtDYGEGhh/NEVcKThT185vCsKKOkNfeKJe7ixf7+z8OkRrTaHMIhbywVqCDflOnPtLR/DLT55Y4i3fxKyxy4T//7zwkpyTXyKA8jg2QQdOfrleAYDrOgVKqCiCxF2RoeLg7cwz1tvAnNAlrgKF+KpBUZvHJSppxjWavvcigK9RTzUoqDbBkYo+N3JVAcuq08d0FZpza//veYDLoiQuqX2d6PbS0XK53yze9QPzhN390I5zFc2tdQHHc47HLqKxAngFFOYOtKbhJJ0GDlurB+W3h10AcpaZ1aSBFzkwzQKBUitZUC4pW4PMUo4u46s//R4KQ/LbBJuZ035niEyZkA2UoEp+OxacVxrg1cvjsyLjCO4RM7STJOWZXStTq6LDtTezJMco79mERaX+l4qRkBJ/vbQXgZPPurI7FNrXFccn0NivTwDKRIqum8rSWTh5P1d5XFSXCxmF375fYbX33P1dE6ifYjHIcpzxI9Q8/JaOK4hOpuVQuOvDI7f37+N8+qO9mwK66v8Eg1V0p0ETMnCzecVW8XSIsLj0lrFmHUEqjcEsW9JvPlsUkbx4eaO2PW/v5SdYm/+On540Nv9Gl380q+scZFJzM69PD5PIUuA+w0j4c2NxmEK6UERH5kmaTnsijG8BQIkMsZ2lkqwIl93vVL6+Lyu5Uw8fK5am+cspPRygogSG3cm29sSUM/8wy0gNI4Ls/1LEHAZGItXZF9H35hxH0tzNGxMdfkgrBCMfyhumpPuwEKODG7SEEmHvfdwnaFXClVTJwU2ssrxfHwXB1qRCR7HPDGVonKlsjMPvNUyxUTJtEHrOiOx/rda53afqR9Nly8YLCr0t/5jdbpz84YGAcMx6PudDrqU1nrtnD5EHnyi6fs1TbeZPA3WoGLwKfDrxSDMUZX3t4EXtJ/MR056pVl+eYHneMDEw8JV0cQMo3qSUkM7VdztqQECZrLCQhGWmrqjT1EkAmxenZmZq7GY8hC86UK77JoqVOPeSG1Atr3MTxjORDamWM4GI5ynBi7cDC0DR8hyJjESdWBjABYMtatAGdoG4KCiGhTWyqhKEQSiOFaFIgk2Vnj+2pWNeej+8unGO3+p2c7Rdr4Bo9WeHacZCO1HKXL9FS9n7s8Q7EXGRtYYpnOFOrSB4LHfr29xtFkdzNFHy8mJ7MkBrVbio66+TfWKAC8v7CqJO24VIR5Tg6DJIsblmdntumwHVmnUtrUuRwlgRjcXSkUR+brmMSj4FAN4wyrrU9V8+jIpR3NRtkuGFJfjPidfDZTaRZ1IxzVjH9ppDt/t3nz8eRdTGAxyw/FsrIudf4BAAAAgJDATD0xz2KIe3rk5rzhhV9s3iuLQAWQZ1MMkCyCsbSt6gLDgoTic+7Jgl9aRjiV1GKCAgmMsMxHQJoEPmqmlsTSfIaq0ApmMlNlOez85XTlqpxxpO5PkjDmEH2plfUGR+321sHr49X11irQ+qFRwTYJHDiTmC+k4m4ZOodA2kI5uICARFHcAyifoTgNgGqIORHDxwhoYxkL0ARHnaSKesZVHAIkQAAAAFxZ/u3/468PHn1iDI9mcRDOfbeyXZgMNIKEvk/kOHa3iszJLCrVhDc2kPPu8MNF4522WEUPD2I7zBtD481OsS/n57YtFTEvii9PZ3Sdz9qlNcRvUOQoh+OAG72eqAGnJu4kQN6+Ueoj2Ghko7KU2cmVu/kPPzdTU0em3qoQPeqb5acxg6NXY+fdHaklMx9+afWGybmbZpH+wdX0jX+88bKHHM8fv3XeuCC4qYuRltHcaeG2PX6p0VNivcpfPD1pVP36RqX7z1bOPnskMlikR9eHcYYTT/wcx6DbVckxswzxJNR5rzsFHz358f3tQXuzAYPdJ4u/ngTgCV0slnNExpzp9d45+ctbFwV6dKFzVezowLN8J40ElggJ0whl4iiBnSIvbbeyl3Pn84vuJXblXpUjqe9zbODC+xDMBDEJmZiMnTAGkcImmDtN9EvTSwi9yE0BG6ux5OkIQ2YlgRAs7EA3Px4P5Xg8Q60Lt36TwtbXo1kiZkjqak0ozM5G2ie9/O+ureaJ5xNvR7WRw3neYZx3VhCcOD7VGzLAgjTheHeVtV6Mlm6v4RvV7iZ7YYbXbzGnr4bqDz1HwRajpL1JOdOUzMMuq2gXlzgj5kTENZPTl5e3263KH3xrX37A6pI3nl2wISrJEz+rKmIGq/Rc2H5bePR4n68o1Qo66yUe0t2sE8NRrLTDT0mxXnEHT4fBJcRJ/G6bObR8QvWsdAHKuH5hcIKssySFgcgGuBAUHShV85iCR9M+YMXI1Q1cMkMiX2DkHXn4k3NpuVV5o3Ly0XnyQsW/taKsIwBnekUWm/gJ8I5iRndMUC0086G+N3+6prAs2B2Hy3lwaA4ldPAdp11ymGdJyNrS+u2C+fHicaMIGZabzuprnf5f9Vbe3hFXkcVgwTpUS4oe/vW+FGpEWdh8dyPoMmcHJ2EWuXHPj8LGWr4p8yTkaMPaF8unF7hca1P3KD9hltUAX217nJfNtMIaMe9PsxJ5uTCyw5is5a8DkpqaF4+frF2rLW+W3SsNwlS9CXPysyNana1WK507OxQbHT1yLp77KyVxtSIYiXYiBj6BR1lKUhKYJO7ipXl3S4+NNFfA1CFmFqXYIPpGrHBENY8JCF9ndRbDIdQGRq4oM7HLpQlOQGG3EAcgNGGSuSqBsiXEsX25zOMsH6VxCgQW17ka7VkB7iMQpKgbJ2mWYSiuDpwahx129XqDrzRloPolFnR1lduqhjg8fTwWAR1iYP06e3DgJV9oK6ur8zr3+DWoWOzy2+3KWgHrechjdCJHaBbWakIbo168nqlD86NRUJbEDTHefIcez2xjjog8bUQBwUYYoEo5gkRRd+Q2GiyaYPMUVxEhXyjqU08gy9JmnIEJHULtZHRtO8cITGi53sVgu5GZT7QCyHzWjzyC0IN2EWsPwuy7hwYOGptkvKCSbHP71u8BACAACACWDiSFtU7B8wmrIbQTOLVaXFR7mJSCSx60RDBcwNgCKMgJUpqMEECVanJ/YjMgZlk+gTwEIUCQLAuUvIIE6NzTvDAqtpX+3nBG+y0SURrU0ZeekAeygBxezFgagAY2/lJtt9IaOs4u5vgSHw6MpLVOAJRfg5nuIwJ7co6vXIMYIUTjlBMg4aMAIEwQZUCRIIJhoJ3BNIYLmGFg5rklhF/DJud7x/O33sxFbEoionj9q1fsL3/Su9hYpZ/vz7kTH1lojkS2mhVrGOGi4IAwnETgVe/oyBPe3Fa+0h6Nzxa6BL2Eb5WjohhEhnm5YNo2pcf9/Sk4Eb/2G5shIptxoqeRO7KwkUuwSO16wV04P91ztnfBZgcNkvEozmiDanXIDGY0jaXzeV3ElA6Bhi4nFV8N7QfH9ssvNGGz8Vv/zb3v/vmLH39mvf3bxM3fbr/+oTE4GKAbdaFe0GPALhzmGom1yuTfxshQQyzo93oPXjpbv8nlokL5W/fbJfnpjyaALHMIVgpSZzBkWQKXUnIaa9NZaMbXKmHEjsJM+YW9uHGXfmOz9MWH+sT2UWs6a5D2+II0pMBP4zLHSpJ67iChBQOHQqA+93FS6O/ZgjZ9ayPfeXv1+aPzn/3ZkcwzxTWBIwTfhvo4DQLWde0cwxEZglepFGJykRQoiAoskdlJmmApWc18C8CoxVZwRHsYHx2OOSwDPBPB/PV2dS9DODLGRGu4PytThnSFLzRJkSG8zybcaif/VjS4tIPeqLDBlzplemZEe5MxYta+sjJ6YL3+3kl9Yqyv8ZgEi7xINUUu5UzUB/7IhDEgsTRPQD+l12sAp8kypLaQ7xS2Xv/rQfST/vWr65GRiW12aCSYGqaRhzZkKSVnKVHxczolhJMxG5hrhenZU8T5YCO8y+aUgkSltOEYc2/EZtMXEb9babRIO/TmC5UmAIbigMnGGrfCi6wDZot45UohSpD+ZSBihEI6/sKRS2CrzpAEOtufpSG+JKPq3pF+OarXsMhZOA5ZoiIIkUIlEpB0pnavEchYnTFCjhCQ1YFdLJEYSRqpa3gaxhbEsvLQsmgyulRN3nR7WDrtR998v/nRobrOxLU8vl7Hf/HFMEPi5QY8P1sIkXn/rerICT79D8+reFlWsBgBZK6YyqGbpIatCY4TDsz6iojjVLSY+3YchpnVZI5fexRD9zAwt7RWAypU8nQ/MrMcswuejc5/9Xp5+Z2lo8+05//i4bd/ZysIyRKeMAIB8gqsySmWPPxw3KEK1e1CEksaTutYPhOgpSc4H+2No7V3ds7neKcppasUyMhExdSnsZMQAsFhMp8CRA0ixIFGRsR6EIA0tkwFSYoMziuUFWWzsRN4gFdIkDIkBdEoJsoYx2XxIEhs4JqxFxlhAHmYIilczCyCxCiOwtMs9CcqLSksMHBQmn9/vruGBbdKOg451w0SDG9yk4sRfGmuiZlPRL2LMeWW3tmsWItI0PX0bqFV4579tZFjjShL9zNWxii5w0lXG9/p7Z9+dhEp3vlgrlQ6SUExYRhjZQHTkwgleVGzUXxiowFqoYvJ3kTKl+GiVMucp8vMUDPqBBAE0jswjDMHKVNMrRjpoJqXMoHGu4zAxWaaDTj6cmDefatl9Y+7OCBccqm6unX3n2EA2GnUQ0kBAWMFCCB+imNAOivsh1OMrjYQ2wwl10+0gJi9BjESHo7oMgbsNUxSQKMC4hQPMJGPqVrNilEBpVLEHfQXvJCSkE2rldnrl3psYiKnh7FAsvP92epawzSy0xeD5kbr4KPPk2++ha/Js9ennbt3Dz9+sLn6VvryJ0zbH/3kuPa1QsBgNIK0bzQNbZHPFYoIQXADW48Z8Eznv8MBgJCgC0CbBQRyzO6tiLuJSaNRJsT1rftw9rg3ZTa0bWTHQZdyd6Tg1R8/fTIUb6z3T47LOwUjizVDjWlewBYuK2DxiNrrt6kqvNI0+gPzcR9BmQ++sxKjyeT18wSkFRq5OB8JLmpBUGH02Vg/zQk8xJci/NRGdIS+tANvMMs1S3c28bmRpEkAQLTx3urew8EdXgmYoCAFNs0jbpK4THSePtJ7XQo1AHPvnfUnjwd/1h1T31pdfTF3BsQMV6lcFVlWaIgjvfD6feXZ2VCAsr9SAIIVlcRmC+090RED6f3bx9MIEZ1m5XeIIyFXC81VDSmUgVnGQgDnur2MQkr2yXX8scL4c3dXDiqvLYOTiBt+Yd85IqUwGPtRdSgX8my8OM5SImEu3GKJxx2DYTk01ByEvt1keJ6yJ+nlL3qPj+BXv11X7zejh8OmGs0tw2NAxot+EqIlWoosUiiMKc6KyYpS9wUs8sN06GU4arlB14xzAp6iBII7FBrSRbFeClB+WTBAqmGlPI7ONDMvhFomri2TW9jlgdqcmMspzV1qn+R5nCVrxbz9YOq1c/VOzvaR1FmUWGK7QdealZOXC2yggZbyMDY2cvQLDE0Vavm6HEksMZjolJKwPGFOEpFySUn/dz9L/9vfbP0fWp/9yZdv5OBn3UQry4nR3bV19Mauc3SR392VmPRirwczFAM23iGJBKdkoh9n8wcWLR2ZPzwu3ZTbCpWSsNyE1jwdRoCpFN7vsD/96WWYxsQovi5XM83C8gURICSHozasIuIs8/XXKr5SuXa9YJ6fOohgzz3IkOqoy3PE7gdLbm8RJlYt9HMkcnKghRxJV4vTy4XCCTNrWroGCrvFw5+GxWL+grKOztHlSvXk2ZjbzCXPFojuMvcqqG2TbrrqMezUJcN0fGplt+o/eLLXSmN7tbKQWdKy03c3TiNvI0b5LeoBDCtFxnwYR1w0TnBCVdvNZTjymJJglKUEhnQVsiDen0PB9ATCS52Q68iDQU+K4HG7mL9D+y8n+LV2Z2W1+19eRl8trN9tqZLw2bNpfDqtClT+hsCuFsUYzp+NFYnjy6KvZegiLdHIfJFs53P2bK4jcWM1n1WLweODQ5ZdexN7/Gxv94YU5kvSRYgLeXKpGJ07BTSDOCoR4RHIeNxjHEZiMscNfSbTPITEYcyTOAjyHCn6Bt3gcS/0X0zJOLMthJdDXChUMH8wCsR8LoojnMJBluH1vBDo0Tu/VH99zJ+6eWq3OuNCmkCmqp64LleTp1FYWpEZFBdJL7vwxq/mWMx1rpcLa0tmMRuHGFYiifvx6HOLtQyqU7UwCVeQnhm1W21yX42NGJioQUTlEsm0lROEBVOhrniwSDBpimHj+Wevjz3Tt0D76orAIk8fuTFCUx4PU4xsCrXbuDrwjj8eKBsMli+dX1gwSGkEMIS02Hd3KGDqiXs6R4N0bW3t9sYuKl2jAWq4kE+IbQmEKWAwMEuDfAnVMRbgdG2XOVmYjO01BZ9Cte5on/Cp+rqCZgwZO0gogsEcsgItupd7w3yQKe1OlKIpgTfXOucz03wxWpdtFHcMLVy/enXsTAAtOdRMNw2MDCWJwEQs5Ss+IFY7lePn/SrC9GbUJgDF8u7sb3XTt4zvjbzrTUy5dpWnHTvIKQisATjjhKYM4oGTzTBYkqJsQ0DdKSCZErdMjDXicqAuV6ySSIMGVVnE5pQZIGdssZSSxB/8wZvf/5/+xH55un69OBjMtaklFDmuxA4vrfZylAkBX4xBDLsvtOEivrJFLyJM7JS93uzVp/43/t5SGHovjswljtv4lvjw8xHj+NcofjEaDfWUEJNOBxeRwFDTmTGtsXJbEkWW6B/10RP1fpMOHJ8gEM3GFwsf4PTZE+12HvtEj8j7jQYJ8J7dbpGWwF7NkxkDCIE9O/LaHcnpkLuN6uW5/+TcyGJ3ds5mtlb4uvjFL/rNuS2EWf0u270kalzh4qXJYPgNCSo8QSaambhHlsYysET5jACP/dqizRpZTuwtxCP1Fo0e/9m++b3Rayji1RySxV7iaSO0sS5YcFzCecP2kTSAoc/SMPCxzaaoehRdFLkmwpdTu298/3+ciW8Vdto1aT2hTE8fTXMLg9CCsT5/uYg2t8W1JTkyYqHIYgQxTQhKRBNTy8gIy9JkRrkLJFo4kRbk13OLRn2rXmfm8NSAVAnoZXbi2DxKh12DXxFdNBvKLM2gexFAo5CAIXCchMVQHB6chnmCzxCsd0JbaKFc45bWuE9/OLgukwcfjzMKkzp5wiDsMCizYhcUa5iMx6B7ivheWiPwQqH95R+/XrpVv7IjKBV+zXPcMhnq7OyleutmqlZ4RTI9y4VOul4Knh97wKL6tmtXWxWN2sbt4XOwKZZxXxxZCd7NBCdjqyjnJf65fqZbeMKKd7Y3N0p+2KzXYsNwsZAVUybGEaWBz6Z0niZkRZqe4cdHkSDFUpMiERogorxSChwbyxO47fSfTtnNokyD8cRqd/g0cP2EswLx+xfKL7/JSdi+NZ22a2y3Z+mAw1jZg7YoZpYrFzBePw5VgcKX2B94biYgKysx0C6fzyb571zzirL13N4JU+vT3kHPHojIB3+wrNje5MXMsi3FJO+vl4a2652oLAI0PaxhAc/EQ0NHE2y1ExkWkm+QehYRMYaTZBxgnIHQqRWrk+45/c47Tf0Fcfqjk2lK7exIq/kCvtpM3PlBf56czYsglsul+tW8Pgl8JsFFRJETrq7s9abOhd7JkxwCjTPzq7tbh5ZeuIT3c4gGNRwEfs/m0oZAYKiIJxZiTB2mjOVACGKMp0ASA9MIBZzKYhRJSZ5GJZ7MIphFKOOFziJJZ9Bj+EKDNng/IDI/RQAOaBLLaDTLYgSgOBIjtIyaB3bZs72Q5WuSnqREmEmpKNboNMUyI+NpUi1gbqCsuqoU4z6LaGhCFYEADelAoyx8aVs+v8Od/s3pMoWGVUvxAqzDgfrqB0sbf/OfHwjo5ODD7sY5rPza9tpKvU2NixLW970eA+klkc2W22Nt8HxgJAwfIjSBLtm076Wap9uPkEHoVK+WN8lMVUNSTA0Cawk5q6fv0Yy2tW48U9t31kfDQduucbmKOlwrV7YF15iDQibaHJBobOZNy2ul14i20q4ozG81lwBURw8Fpklis+H3f3zqYCtEfKx5QRkXD0frhXOnWHRGEkuyG1+7CdISwNBwAoN4QjeFaoFZ+3rzcj6r53MuSCGIo9NRKX956+7bB5MXbQ5QxTYGmXfeqOH5PIZCUbYByNq/fivVT3N3VxeH6dZmM0shih19+aOPo/slO6b//W//92//f/6pSttvoBhAr7aBNE+Bh4HU9VgJ87F5EluURlSL+FOPoI+mS0usidbbwqU6jhRysadh1ZXO5sbVv/n5a9rXK23ylANsuUAu3CZDtETq/EI7KSElBL/Kspu3OXXZswPz4PmzZbqItIvw/Xeq4dnG+DxX5OPEQafHi+stMBnpJ2f1m5unPJ0eLBo8UmowLpsjtLCngxrum2Jxuq/ffq9qqFOlJliWGYg5GYnyS/yC92pXNuOZ1njnzkIeKB8fC17kwBoq+3yKd3DYKEtf9PW54IQErmfADWV6/0Shc1pRoYq13uUxRa8e/tSSWaTya0uclIWJWYlzLgyQmFAzVBIYJnFhlBpJwrJ0jcr5e2hySf2nl/AmNm508//m0P34VzZq86HdKb99vXaFaEv7oxVc8GxISPQsCRItlQR4aDoJhJJNuLHNA2ZlTXnzrUo0jaZj5+XTi5hlt2qpVcx7EpFregS3gvdGcZeM9QQXZTRNgR/lEEozY4UhtCBCM2w+nLp2RMl54mrJKBXIOHMdBgeIrds5AZExTymQ7Dz59IlOWWpYzc3G9tWGoKAAVIXIxAbPLpymsLZUPf2jZ8H6MlcVp49PV96oHe13C502RuMEy966V9AP+jRR9HtzkKVzhtgzUy2MbyaoT8Q7yyICwn6b2b67Cs8venM7clP/cC57aeOdbWgePDtOt97cOHveb/C4HweKqItVfMEQTTYjSZxhhMiyCzLHteTweu1egZN19/nPAwa/7C4Wipxqkdh+r7q28+aGEs96q8Vats87jRvtOpJCNDYCx318Kl+v5xttr6eKjSUODSTa0FdkPatSfW+9s+wr9Uvv+Sio/ckPf9JUYm5ph+NrxU44PrHW1wua7lc82shzr8feb98sHTzrLnao8g75vU9evv9mY9rrVXKF6PF4hiICjnIzNeOT3iQZ/6xf+7tXZ4Ar/uQM6yOo4IbDcOtKI9ls9F8c4T1PAEL1v/5m/POPgu++rO8WnipRQDKxy2CI7+tegAFVT8U6ORxqi8skNbhCGLXW6vrDlzjfMBSq3i6hQ3f4xWXlTme3XU0/PAlfRAujOxHY+3eo5rbo2oVaEfgkm1iA8iFGp1yGPorB1d2Nxb+Z3Lq/EzJJdOr2ELS03aB+gA3+h+/p/xWBKXgjhCyiBtc2oYRFR76QeqnCRvaMAgiOoj4AqUTwLsWgqQeQzNa8EMUBmSI4GSEwIaMMYQoFK4nGvsewCZZCHKSFfDH1I5RAMQqFboRTaXbzTnN85hnTlJMyvW9afsYJKIoiszGOuWmBiggjqKTJ5cLuQkFckZlMYookqZAnvTgczx2/pH9k1Vv87Y16d7gwJlGjKdczzHwRCjVpKSfHfn7jFoQhe/DQ2IGLqW/Z+MKVOKgjeobBitKkccoMCyUCo/FFSBVihKAxyWaqCnN5ZqjP+lep2Em8tWbZfWVaU1Zp5s0D2FjPjxwcntnaHsrcuVpufiW/thlFjJvBAvQigNkgIkGVKgIDuafWYBspNAFIIDK9hPk3+e7eyVgv3PnqteYG+uLf/qU6M0kcDmkswyxPWs4tFwGoABQCiLAsoD0WnB66SYhtbGKGTqwW1wjP1EKyrfzkhwff+QOsRQAUMBwovg5TaUJXC4ntW1Us0P10g6U9rcMqoLCJAgDmLloSt1aWGIpmrqyyO9/KAeu4MAd6z0SubWYAcBigLYAIjDsLWXbNgzYvEMjlQqISVOIjxC2Ko2AYaqMMzK0W0PiEzm3UTgeL/qVZLWVVK2tEycSMfBzjrSSNEc6gWBrfm5hvb7LDg9m3rpDuwNIu4rfuK1lX/7Pv6+9f2yVY7PNHJ298827+Sv7Fz5OP+tk/2OEqRGZR5TITT6YQ4SwXglaTtsbunSuFT36cDQcWVaJOTxbtOJAxbexkd+8pf/m35v/pG5U//RdDta7ld1bULwNugaZS7FTEpYb86LsjBEBK4nASpyOtVkN1VJSqzb3/cH74J/53/m7+acAwlSKZQnvsBc+Hy1dLmUG5uBf5ECMQ0kthGMliiiWZKxGuJHftjCzIBaTS1MV//l30jeWdv7RPvD8/u/IPi+WtDzCKdgPJhaAoziI9yFydpxG8UaO47OrmkoNkHEZFib0wUr1rUEeLqxtlpQKyijxx0P5EzUZeCOMkCjwMnVx4rKwsxk4tIRkOIB5OI2gZJ00zSiOfRPCe6QkZ1injEwcvKaSk8LiW4om7kk8kOjk80PwkaC8pdD2b2ImEGEISSVF4PkRTPHl/rTr0LlCXJnhJR2nODG5cLf3JHwcrVzK2zAaWtV4h9IULGHaPkL6ylSdLcPxpX8j8Ip2trvJgEPmTVMWy3lnXfb1Yr8o4QhYKUq3K/P9J9s8ny9LEMO983+P9Ofdc7296U5nlq9p3T89Mj8PMYOAJgkYEwKVIiVpIsZI2tPrCjWUoGFKIK5GUKO6SK4AgAIIABhhgbE9P++7ylWWy0mde7+/x3u4H/hXPl9+zcDI2hoyPZ1HgSLiNIjDVjBwXWb476tpyIdNNs8MLs8r5rIIhZKt5rcFsrdIw1Pxuvrzxzq9AO+5kBm2eESR6WZCZNEnAmV9Yz8VRupU3EUiEnusFQBILu+tstkD7QH481S5v5JAUxPPHjWgMaYnKFTGskoBshV27+gth+gul7/7zH96/H5JJ5pe+nP/DvTvoWFipCYgmXb2K7x3sewy2tEQefjb49jfrL8Y+fryoFuLFXr+REvU0CIxRCXMQzeqkgvfSpeWlXP+9U+fUW9rInvlZ4RuF1pqojoNHp8TqhryyXJpNdW/M9LrxquzdljW1HeqoyEwDS4NNDnqOI/mak2F7jtdYzutz17FCV+R916nmk1Q3snwqJ5FlMSpKEVdXlkQALyzSCMcHCuPDnTo/nkHV9A0hieyQzaQYTSBlrgJo68JfrAttPg4iuLlEn37SpdpDn/SMnw1zA5YXOavjrq1r5jRWEC8CRIUjJxNMonwMIxI7YBiQ4FD1kxCmCE2wfkgqKYbhKYL4IQR+pOsBk0lQJGEhYlsxQbMwwQIvASiEKYAoRDQb3z+BkUsMsqxZR2PXztF4ENMKoF0cJ5rIiJ7bsnmKuCQbTEWIvpZFvr4MK3TfdROeUwsS1QjroRZfaKFjQY/KWN6yzAgYwfe6f/zdU33oUl7auH2p/Ftra9+u0tDjcsAETvfFs+B5lx10lMPO+NyAGcwvS4+74dwGSYi4HjfX/H7ECSxfWiqMYDpSvC6C5rYpzldFISjn8ODO6a1lsRrC/+p3vvryb331rcvXVrL0GgvmmAGNi+54+On+4/ud+0TUywNwCcBiGiQAdLoD2kdTY6K8GL38m5v1jTXw+e8nz58/+cn+B4/PHhw6OZq+cr3BCVdiYHtKlHrBIAOHBYCs3Cxu3hialpMR71+As2dTNjIqhY38S8uP7tlS5dr+YxMAUD8zi2DhJ9gBJYiX2PDOBIDU5ZHuvUMA4PnZ50UBQADk9RZChjE4h7+1g2xckyid9mDScWEKkiRFQbJA4TkfOhIwUnviBcN6ubGaNdP5858e6p+fHBk8+krzTJb8wczsqvRWqVvDl6+VFovElaijlBZYTBDpo4geeGRICkSCMkXeqjDRiZ8OQieTCc005etrs/MYmPOXVlGXzl/aibc39J8OS1H/lS9WX1z0w/4gUQan9synQqmKJ4mHcPBDfX4Xp9cvyd1zB9msz6sbSaYsVHKbNRpRolYUnl1Mia+2vI8exLiHvlkvErnlYiZzpEswlq4uk0eTqiCPIiQWM/27R/inY9NHa1/ejobD9vtPEBo1T06JnWq8IjKpNxhr88R2oK+I/iRCQpZF2Grfk2YeT8NKAQhfIJFi3XTznrjBonXu2DCbuxfZTcO7huZMv6Erg+5InxvnFjpgs3JeSrGEpeMoTGtmIAdYJY2FcvbqhnxrNUNcLplJYhZ4TSSqRNRaZ/1M6vhOpBquHeVrDCfSnuIGIUIQAGOwboRpNAp8FHLsIkASvFTOyH5x59b2Sp3KIAsMaroCvSHmX4y9aOalAVysN8WVBqW7i70RWctfVEpYnh73/DufHgdlNs6F55bKX2Ww1PCMRf7n6jNFGUdhm2OkKhUEEWKGnOId9xUsx1VuFYNGtRDSLMGZbjwLrWngMxKxsZa3gthzUNJPFocOnS0IjbxnBlHkIizgXQ3W8Z90VDIx7h1ZB4D8u1//+V//e7ca/FpxJ/vK29+qbF+hIUunSYIsqQlY4ATCFLbK65XqbU7OA2ClgJmuyAACiBnqxHeOu954fD51Ag93CWZ4PhxMooiJCwTIYADJNxx2zUB2hrnLipTzAQQI7PsEB0rOb/+N//IfbtWI5M7q5df/8e8ox2fN3eYLA1nk6ltfFOcHD8o3MWoyUyO6/urbD993X7u1e3KmXRSXj2Nk0dfJWt4yweA0aK60iPNF1Zxgq8xsKTKXo8Kyl57cX/zsXl5K6RLa6u65f/yjSjXa+YcbSLqY3jX6Px1CNSyho6cfP3+8SOVL8ovz8SU5IIMAnhzwie9lo0WDjc59KuKoSn4+MtttZeEYGSKZWMZiNKOXpNptuXCJE6LAPvFO78zZJEhaOblexsbRBKf4lY2D7+9nkuzcYS7fuOkf6NlTIGm2Tbml27lCiso9N+ot1inbSoBmOaEXw8iOTH9FwuHEywYgimNf1TkRZTOktCU6NI5FEAAyilAcxGmMchIOcBwGEA/xSAcoRvOSoOihodkogiAgAgjASBJJgkTz/DCCjhXGRGROrWIuH1kuvoIj5oTRFqIAMJ6YdeIo30QqYsQQ058a3WdnjbVsqUbBMCR4x9JwTSPf/uqagsPnutL+9HTrtVLztcj7E9V/MPPwoymycaUGeTKYd+eKpmA0DGam4eNVWWxmiZkK8lZyPrFrDO35Hu7bLBZAxzg5S+IgaKEszcTtu3q9Qm1vyErIvPGlSvmX82KmzFOXSMitAhB4jtE5jTkrtJERCBie3XFSCUeGD4/BVZLHC5OJWyo4S1gMr0AwGWbDqfZ7A/Hne5+/uFAq49MRrMxrr3ytJRKrydB0SwGLSGgWAJDiSkpGGJAxAN3oZL5xo3BKUbhP7n8yzbzqv7JGdudDAPJ/+dPRq18BT4xO/MGnb+yuFfbUKB0XLgvD9qeFStIlvDrYJJp14OhW91lkjMV4BNZqIJcPAZXWblqG4RoS6wPGBmk2yqcIjsSo72SMFJNSNSDMdF7NU09Z9MkL883fEA5PA+O8M/Mn1GJWBcvM0OsrWFYIGoxrWufzrhVAHq8XcQvIYTxfmGgt++SexaGkLkgezyQomttizi6y37kFaIu700eWfpuPlcnZLF39xqs/f134/r++F/gJnxf61jwr4qfHZplhPdN9eYXPmt0VMT4yxnRnecewTz9XCCGOIXL7y0u1lfgukdx+c3Xvk/HKIqg32L3DXtYlddd+vz1Yfrn48F+e2H9+z9+pZTZbu5u1/f/v8fln6PYXrxSWswlFFQn0REuWXYcvY3Duk4GH43yAIagaFbkkckBvCAiOTjAs8ijAQnqX5tJ+2izHpVwmKXz+/SHW+GJ9s0Q2RHtI3ZnqBmfIRb7cImUndfTYHXgsg8SaNZpYdIW6GNp2RtQQbLlBEwKZqXA+TEjNjzQrYAFDhK2bWY6pmXFy9sBOXD9PJmSkmzHuWyEFYmeCEDjveWlKxutkvCTS9iQcmYDFsHF73mihA22O06g5SHCMFfO8e6BpjyZpDLJX6vyqcPo8uZFnGzn7w89mjeWC5TqP/m3nb36HOxvN7zy2d7++23tfRTvT5lZiclDrJa++sZR1ShPdGz3rJ0iMNAjLtpm5rahOUaQqODGdhF4QV3JJe5C4F26DTgJgzwiiXgNZDD0YLu5+HGzfkFt5fv6s96uvbnpvr/ugXEGvyd/xEkhCAGKQhgAwEMwms0w5s3+6WFuLCAbHwH/0t7xhj/MsCUwXQZQEcOmaTKfSFSTqPjkv7lDHarhcxORiwQbgvG3lSyRJrFIp0HWACYBGAQBggwQAvPb3SAB2rzepPYgUIJBX/v43hZWGOxsiKHP7ylcm+umT7vj2K7vv7pHX3io9YaWDoyBWA55FNyVhbirG2H3RTy5tcvTMVOaqpns3XkcmnnM9o3vvvbi3COeOvL4i2MOzAyu48gvyp1Ty/oG+c0PabTBjgyBaSy9O23Cq7vJEoyCdn05imn99i3rwb/ZzK0h5A3UUNICsZ6AA8AnFkiTi6xYDQC7nLBap1u4GClqrgyjgmsvFy1eowHYGbZMMMBzBCldRBCwOnuv1y0JXwNwkmaTUs4PzrVc4r40+P9CP7xrbL1d4rpDIJELwjIRQcLQwQjPWYxqeHTopl8UkfDJQczxMXIfxibKIxRbtIXiUUk6M2/NEyBAIZVt2yCIQhChN09YkDpyEYLkQQ+M4wUGMzd0kDsMkxiUyUbpGpUzMLHyk+yQDGihmjOedqTtGAqkgIIoBs4265gwHo0zqUls1/GQ6S2ivnvPgCBBU4ZV8T5D0BXIbLFSYjB/1nN1q9s1sYmkZzsFPjmNPfohkacSNSqUg7BEkHsTO4f3Dpyl1a7t2wnGu5RlxMN2usoePMhB1VXSzQisscWbYmM+u3lyVA5nD6NWVm0sSAKAJUgRoTztmNZs8G0VBo5INZ2FDFH2LOumEmLpQs63NEnrveP72ZTIomxCpjHqDXKg/3js6OB3frMTp+9NXef/ds5Bf4NtXGPHlm4iSTctNBhQSMJul+SxUC7IMQDRZHFnBWfnqNaB8vhJWwNa1pHVx+ODIsMlFW6teLv7Xv7OmHd5/9ZWbbe4MwouSrHzys9nb17ZhivaVxdZ2MownY9QtM8A5O8vnfEieodkbaXrxEyjfpE8tJOg+0a+B109cBMMJnkqEBBloCApF8zzaWNZ7g/RiPBqXb+xum0GhKJmdD+49x1vDbVhMu3uVy1n1o1OFxKxxvMPEk00hwnnCtgUGyZgLQ8hTUsb/6DRT5pcy8o9+/4MoVyWJ6vnF+V+7WV90J0yOkYL8+KlRstLZen744GKpN8e/8vWAdMCz5EZDOFoEDA1cF0heYkHvOUwqIcSeuMzyyvI7FHvRPX2jerG6wgr16N658bK0eTPbPxyMrl2aNmv92ewSSz17rFEZJHypmQlp9aw/Qo3y71yvxoT72YFrHbULMhz6tXqldL01mNrcFbnnz2oh641nCcJjJOrOwSSWMMxJ8JjGzS2eHLdwS8Ne9SDQxv8WDBtXsO8UUi2w0PE5F22JnB/lRbbOp+3x6J6dxnZgBWKLsEOIN0tGGBoZHrESlgWtqW3M5uYM9jvdhdysb3IqTXFkQMzDsedNO7qD4zkEszEudIOwg5J1qW07xRC1UMaNyRzlRQWqG9kVhAhoZj5RZsjYAXjPVz09KaLhHIDscs4LvNnnJ5TjmuvVdCVn9XTnvt2pY/Uing1ChaNffzsv/I8fh+TV6hu14f/078i/83MKNnmVdqe5Vh/zBvO9T05YFSbbqzl+OTN4attj78alIiWFi8gyw0DvKfY8kAO7/yDBl/LZy7UaGi0MMu8j6GS2d2hmmtWr6608j5WlfEBcJq80AVyJUg9AACAFgZIC+TT+cBO9BoDcKInJ/KN1voLpkTqFwlpAwCYAkcRnQLAXRTTMlLJUMUgm40UnI+JmjNJQqLG65iMuCbDZSSUjEWyeSQEGgSiACAIAgAFS/j92AgKA8Mjmm2k0A1iMbrXaM/jSjetxR4GrzR3wDnb6Z2TiZzH44oFaeOVa9+yQrVeD4YKhyKgXjLOwuiqPE2SDiB09gFeXLDzH9ubGzJv2Y3p3fXNnOUPNU28CCpmjNG6fnZEA47fY2Ak5ukt84avKQXDbskar64rn5hh/kkulemn58mmcqzRJ6BMJWCL7C3uphFtjnAhT6XrTPjtp1MRurHAZTpKBDHyHIns9JeqmOS5YzzOddgJe37rKS0d/eN/VPHyNFQT/+KPj25tgrs9izmaWfGY4uPT3aiItp3jVVZx4ClNrbJ2OQ9TnszmY4/Gtsm053HJV01Ndx2zTbveGlXXZoUISRREPsgQFDYtKULmWXYyjaKQjPMUzRDice4GAFRg8CWkk8YIUszWNtlJxmWMyrNJ3nu7blxpEYrn+1HJ5AQrc3R9eLG0XInaDy0+EgaJ+Py7UxaiRw65XuH19dH82f2JLFKjLMN8kVyvEXc1pT6xdanR2L+HbEdHA9OulXC0LDs0FJpGcyBUJBCXtGYJSsaIMgR91fUtGMVyb8prKolCEwuFY5TZxIknbnymtV8v6QtSGfmEuLQel5RUGSGsAHIBn9wFtgZnTbMwBGq61mDThFqiaKlYagFwmIQDWt3pjjxRz4elsTuBoQDgxg7z7zFtfyb5zKVE7x99/dP5L5cA/UYQF/falHeI5BJeaEK4AACDIFyEAQE4dMMftGCkXS3UaUknuxt5fvbj6zciyvDdea6Dg8ph99N5z/CvvbJ98+mO+dd669Nppd7rSYHf/Gh/EiWPHBCoSKc/Zk+ZpjMp+gc2AVQxkb3dAUwDiJYCzwCle9UNjce7HOgNJGqxgIAkR3oMLQIAifzKbVprZWuv1am+cR4Kj/Xvz2cDxuq6PN3aFcS9+YyX7/Y+fk0yAMjLBqBePVL6a2GaYFkkdRVHMHd97URFw+Xpm76HmmujLb2a1i15tKXzk9HUb275ZbX+iXakXFzeTODQ+eWJtri0Vb4kfPJxRMYKGEPETLUEFHuNRep5n2UZzcWHm39go3Kw/7/kBisQ02eub37kizP+c0v58crPYuvvDp9eyi1dI+6NPDlKSyemZwz/1v/ZzQp+Lxotodx1fHOlrX12F90dJX7lNlMVCjSCZIU4yFMIHdhFPCNaZ+hAN01ClCjRD9GMYRxIaRDBgMhaVJls7ZfOMzNTxq4M5iJ00k3IoVeDpBUgNgDJ+uvfJSQbigCSL2bwJldEoxIgQjSwSo4uYi+IkHkWFHF+5JIwN3B5M457z7K4h8MgQjUg7wkSmkM14eKKNwnwxJDyiwSdJ3yl5CcWiJIOqAUaFtK+DTIIlPOkElmKrjTWMTSwtSliRak8ckabSNHGtoLhTpHCiJRHx2Bp17O3LQsqknYmJspQMwZOfKRft6IYFEJh6Y+GK617YyWMTX5Nw40AvZmkK2vQsfDCMbr+SWyvSSRJCyhmc6ShErm/VnJQTdzmOTgMCO5/YWE8FLWStJB4NAyKQblzmW9VWdntDObQB4Mlr10GSgjS1PnimeEm+Ujl4es4uLckbO0+Hxu4VgUSRmVYhAMK2UTkKkaUmwAFI0NCYo8k6EAiYcgABfhQ5Uy8YmDLBzQ7mW60aBPa0b/NpmqnkwgT4CsA44ANgk0AGgIVAAUD+j3chhMEcxDxrnnj5rUv9+BRimIUgIPUlSL35i7+oPHsSt40AF5SF7vrZIIQJ5qcskW0unZy4WxSOstC1FK8/3NysxEeqfpY8G8q7317O5oSTfVPDAteC2y+lL346vvrlAjhzXiyiFOWVUn01CYYdfWCLeRF3k4Sliq9sNO78pJsTZTrHWS6HIj6b8TktjSNipSz3u85NEPkswlsGLsCuagQQmnFEFbGMHKoRGrCUKODbN0VkOX/644vJZ8/zm00j641NkGN5IQLT0/HxBbf2pjTYflkC27htzT9M8IgIB26IYNkik9IcQtNzAct9Led8NEvUgAaoOnQam7VQNQHEEiSiGRQPAEshICIiJxZ46PgEQOgUAb4duT4UiJRBkIhAXQtEUYRFGdlNgvnQjw+1gow2ODfKlvdHPQ64jJtYUbhxubm0EnV/u8GOs/l/3QY90F+ojJAjzlJKA3ylQWrzKY1djJXhDxX3VkS9+YrVniWfSCSqPHx/KhTIq//VupVDxhWy4TuoNkP90IcEClk2G/jsVvlVr0Llc4Cc3FXOX0yim4XlmdEg2VODnQ3cV5vLsWvePTp6o36prIVLl/JgcwuAw9HhPAg1YWBkrsQgMlTARQdo3pxkmCK1Io0vTscIUjT1iGkNAhs7tvN1H64v64vI8iARsv/z//75bgMt8/rsxOxH5JM7YGX9Sr7yOriyBQorAFgp4ObgJA/WADhMwZqElrAMBYDSV2cVypKbXOz6MiVffPTB1tvXG42X0NFDCJHCd1af/MnHN/7a3z71j1ZMLfKrWGZU93BMSIDq8iCACOY+nNDr2QHvV0E7Bl4GfIaAIuMegIPK5pPSf6gt5C/m9dPheKmCAUqqY/4UaCmYpni8Hy3LqqZJ55MLubpyJb4YlFJU8DwtcnpITra2toQT1Tm439eXMzRkMzCcc4ynqOpKbTnjNRw+kNm+Tc2e9tk3G9OcMP2rBxtfYJ8/HxrVpdzCjp+NnqeUuSsTe8HffuvGe4PnfSNgRfH669lpicL2j9VLMjEJJC+WGytCboldPwGWF59cdIfqV0SraUXu0fThI+rtnP7nY3Ne3lqSmuYLY/nVShSeF79xefK+eyWcn5EFsFvcBQSix9Zjjthgdt+81v/p8/ZJssTP5FdWmgXC6Y0QK1ipUF01MBGXmhF+4p8bCZXNYdogsY3qUprHE6tWJHHhQczRz7W8TXgeHPDBvJV3F4imOsM4qSGGJNAEjWuYRG1ls2TePRsV/dgdTpXeXMEw3xMaS+BB7POhb0Hu0gZ/c408mTiQR/e6XqvBHI2wSyEecimfm2kAwdNJ6KIjSCAgEdwC6yscosa1Ejt3sRLsxo5JQolERVlQhzHvDvSJu7AguCFCMa3hrAnRMYZThoueDBK6glQYUoSIa5nlQrlVv/je8fbmmkKUerOzytV09nx07ZJ8zwpYNNwo4tO+TvjM5RW8c2HHR4HXi+NLgsUS/cm0gIk2avdjJEcS+oHKbTVeXUPGidMOUXoykZa4pNKQ1ypBGBgGlHdqKSmA1AII5r//qZrwpa9TkTq4vlFMpg2UHys592L8LB+V6qt852Skl5pCmUumF0oBIKaRyaylMET6AKKPYNo0jvul3WrfQFM0ZPnGnPL6B+fXqMpkba2dgNzIOspzENNupdIEpBQEDIATALKgC0ADgL1QvsxA7YwjcrFe5YjuHBaaq+n0ImRNlCtgu82X1rsLQDBJ6VG3tpIjl+Lk6MUzKb8afPQcqga9BienI6qRWyCMofkwCWtfXbYIFts7SwCgr/PWHAk7fToIqxmuf9G/TuVZ6Hv4nEmwSq2Rhj4dFTJmXwOgYzAzqYyhlI5KhM0rqKFhWLEUIcOBzRbMQB0fjJhi0O0EmSaHSG5sGByKHTk+mytIFukS8OhYaSHuxbuKcjL13yqBX9jADBcdLIyZx7JxCQ2ymq/MXJZBIDKvn+tzl81ALTWTUoseACiarjkKKUEQIqRAJ+OZxhfzw6HVG6ooxEkjhTIz6aitYnXhxrxAT+Y+UGJGEqd+hOKx7Ds+RFyA4YZF5kkPSXECxQQ8HvX8xi56PAQphzGF/PZreU+fGQfT9TAeduHKjRtDJDb9pY1C6F4JWZfgHA3BkOkLc1BITT4smCk5CIQc9CPk4bOZbx3d3ELPUCrdzckxUihx00dgSCwqW1KQUIHihyGFxKEb8SjFbi+LNh6hXTBKkZs3qYLeH0RhV4mgzlZJnDPiSgZ5NlAqPF/f2bj9K1+HYDl17vU/eh5leUkCBBJ07/cIKihc2balfFIqLnqQe7wQIo1MYYIzsjcAAI/jQBtwINUub2eDF4tVUuVi90//f8dffzW9tMQ8ftA9VJkvb30bbL6eojTwgTEwYoTJN9cAACnc7CpBs0ZMxgChhwKBO9DweZjSbtl0usfqs8InIk3DvTMT0eVbywMwSoFSxUYJgYVD9fg4Wc+I9igi8uJsOsZnaaHMgkpcwWEUqw7wY7TKd0IEXgErOTCNj/9v/y7zrDVw7fpf/xu5HdQIQTXrJ71IApaDIT0vuXybSuFVTp0jP5785pX8ZxdqYLvlncbBwbhcrYzG+leu5QaYO6ZQI567FpRprBCGg/vmmsiBmWPNQp62+8NwqcAjMD56EgeYeKtOfPgnT792kzm2LL+ja4+8Z9OJ1Iwnk54YkWZEhiivLxdMKcqK5IWecCl+8HhUoQLN6Lh1dncTefixRfhjrygYpsvFFI0nvYXR2BVfTJ2ZhPtoRshl13cH/Wd2AjyymJOxOOoFmuapHyrvbOXe4xicTQwtYqbjUjkvyaDfVeYW5nkBgaWo62iWy+ZFzzipkCrGQSub/SyHtsfOcJ6mPsKUC4t9lQigeGYslbP3I9KhklKJU/pxfQVQBi1iYWHuTh1NWOBTD9t848r4eZ8RqKSvaxju6C60ErEYDC9ch+NFgU5D70stRpsGbwj4bAhC02TxlMLmgm94ucz6V263H0yMQ01RBpLZjYZakFCMFAV2IFVxpW+HaYASvDOHME5vX86GPDE66Y/SECW5Uj3vOZbiBNuXMQV1Tu/Myxjg87gTpx3VuJSHijX2HWxUuvS+g0HokCVwvgiWSsJ0fcnk6ZSIUR7qOBUhPpmg4ZR64/qaNnK1oZFE3kBRsloc4GH7YbJ6O4sF8N3PO7MP7W9+4+s5UqRxHFIATGzIqWC6mHRH4jxZemkNXMRBOEjHhpcG88eTxnVO16W+alVRmM1DRDYM29LGZ9qpXYhCd3nA8CXA5eYfU8glvvHqDYDZeLhwY4pBut1HQw7NIlt504iIyHbDKdlNj89GxRvb8xS1gsxmNV2NYALrCJoC0AxnQBNDmcInC6tASI7puamgenSWjIeLUzFNUhkVkQoF6m+tpwBAAAER5M4T8ztfl4f7ncdHZ6HF0CBAThSZwroxqMS2MY4wBrlcDrX5NBiFvRDkr6xqBh45JJLJ9MduEu8sxVin77I5Khd6uOvIG0ttNd1uscefG6tFIcfTdJurZyFj9MhwHvOxlUsWtp0XM0t1/bQ7QyW/WsHGHR2J2e7jfkJIg8MoQ5D3ZwaR4Gvv5MyXssR40fnTfSlM8Czvs6BcRwM/zk2BGFOzk4lN8Ze2BVeL9MEsrrJzF8FdQFgu1tUt08CIxBmYosjkC4wfx67imWZYbXFhGFqOHUSRi9AShxqaixN+nkfcJDZtiFE0S2Pa3OWTGMUAjmOYAQMhhwXFyqUq1vvkHP1CWcNIiSGiIsPxFoLC+RKC2Pbq+0d1SI+LtYnm0lzquyHKUHTqWBcetKZ46Eec6Hs8N5otGWcchlRY3Eeh/HZVzNP3z6MEi7CZgweL01Ip9f1VHaJ4OuyZ9tCS2BjEuSvLWYNme3KtwMG0TvRmaQNmmYx11HYCr/qLX79B7X4pcsUUfnzyfnulSDjmVB1aAYgi10Fic/jTO0ihzl26nXVCfIGFiCOIZNxaImGAaXBMYxALVGUWT4Y2g2ie2nib6STeJ6cvvnsXPLTA3//mr62/vpYsDBi03QGflNmMhACYpAABQG3VMikYzo4GUuYwv/s3OrGTTWwCSJq2f/PXM5jwmtt/4O1KyE45TvfpPG7sv1gS+FlXa15aVw4fQVQkFBu18fxSjZzaAA3T+WHCKTH53ARrs5NR4VE1oWR4awG/cuMf/OeZZ3eV/K9dD/yZHBZ7Loj4hGvhwASel5kN2os9aka7V06V5azE1BV2lrT7Ts5PVpdlKmfPMG98dvw4VL/9hvzBo6GnJUxNvq2rZw0szWBh56iDNy69lBWM9HnEr1zaGd99Wn/nZiEelYxZD98obmXDP9775psrP3msNCsS/W6XZclYJqN+dinoewWRHM3agvR67piIClI5mcK83dNKKGpNKBaD8q0VXccqbSVayUTjFNphJlsjAXjpdu3h8wtxbZ04mcfP50yZOR5EJmCjkVtQph0Gn9kIVhHQFS5SQmFueSVSMnMTz/HS9CiNlmOqIuIx7848W5LtEqQCniksFWuq0zwfHtiAY8AjhikVPTIkL2YBmhDTcdv2EjL2cVXgEzXvMIY2vnMWVa43v/PVy5E/ueAEDgR24pF4hOFaRiZM33Zt57lD0IRdj+PKck5VfVPzdFBiBCnqLrh8vswSz6f4pQ9Hu19Zf++mnOkeSBfMXIMUDqaGguq6ArFSkf74ovfFjewFSLJUxDWJ2dO2qkVxQaqUeTwh4CKERV7Nyf3DvgyDaUlAMG/98TlbrkCgyyxuLCaE7A/OtcaamM3BhRUr00WKEAIgcxnyOMsbY3IpTjOpSxwActM6eO+pzdbxr1byt1qXbdf0wb0x4bUnSg+ViNVXri6/ktSQcwjXYQJkYD8ARwvgdw8PvctlhjKYv3g0mhlqDuaJLwXSK5XBD45WqlsUmUwmpCSxEgMPH56MTkfqTGNziHMBqAcfVf7xt3PviPOxDghicPAQVfkIoh3DWd2JK5VtFYB02A4pfa6o5RtZryq2H55uNIGWXNb2n0vbtxJ4goB1F5xIIZ4CTIcJyyCh7eYEyowm+YLlLpBmse5HOII4JJABiMMYxZP4SEM2aqsyAB54HDXXLi+Jxujs+OnJ43vHW19ZlbdL/nzOiRSJ2/cyJH4wbU7spFmoNUjt/jEtIxEpSCxBi9OpG3gFvIJABoJ54q6WmXunk91bIi1QaWphLhMRKeOhBAYd0jq3CJ9iT49NxcavXOazX5EfdRQCC+JWtYpEVYIgQhRLMLFAoBHht/DnXlzomrDbiScGXs+Ol/NbK6QczY0nXnKpGsfOoz8Y3NpNcrXawglhlfN1HS9ldDJlOiSnaXaAxBSayKI9ncZc1uCABFDN923bZ3OCrQRChR+cK82qJBCxDRCeQd25h+cxL8JIK5AoCD0/StOAhJipBuVChhYikiFXbnC8hFhtj8Zzq19l0oOuCRZFCTIbgvVgMVAkpkS4Tcw0GefYrDCpOTBoVZ/REVET8vU6lSwDogOfH548Y4gCz5e4CcxTKFKXLTPibQs9I3FzOo3V0EFpL45xlsJJqlChIwXzgKlbyCwk8IT0T3yGF6dh4JMsVmp99fb2y29vAED6j58vxifNNdyOzVG3zZCxcg6yrKlNLErRVwUCTM9o0gGtOtraSBM2tlLrAsQsGvoLgrWDOD27SDN58jT2/DBcLMx9HUwtQDDgm//gF+0l4eJkRC2Xi9stPGEgAtIUvuunFcreSDMYrOy8WU4THAAbaS+c8yhthbHLTVSxehmihcn4oNeiSFVN6zeuDdtpJeDUqULgn7J1DwYG4mFAiR/tLV4haHArA1MaBYP+Zw+JKoCABpdweNcGH1vg1Urm77/65t8HaQp+/NicnLtEC/EAmARqi0uA6G/kSndPx8sFrChhx5+x8rCVawEd8+d+EPUjPIhe36QOLJQ4Ne9+19Qj++atjcqO+KMfTTLrdGCZnRfe9rfEQFFgL6rw4nJL+vSZUL/GfvpduLG9FCzzKBanDKlkOfoaotJodTcbWWkvjuR06qAoNTRwib9SESMMl1xzdm7Roje/cGA18+bXVu993InmcTIJxl4cdZUci6NkyLlE9wXyhdX68flTfnoOcv6nbbS2XM7k1V7XgGmQSRzlBM8v5QLPyQshm4CZrU+eJRAGUZCSk7AYBiUhr3SmDIJtb+JYLVdhc0dd0h3ByelkAaikQB93g9hLO5HfWuF6SppLIB4QLS7kTbMWu71pOKRy6ZKQF+N8gxmOR4O9MeErQolgeUylIJZjxzaSElyaFXOAiNPAMqz7e9Fas+Z6Nh058iyoZOJMjLGFFflK6ZmCtpICIZD5xhIhwnASUtCCruQYRgqSlYzUR46xNCrTgChyhxdzq22zJQkC3jeS9n5XAlbpGoM47smJ84XLIpYk4SQcX+hXK3nLDTRjblMAumIwDIrNMNZtOcOROBm1AxQnAg5t5dAAQLHKR0doI/L/9HenyQz85v/z0h4iFigwGMZuSBA5wQWN2nLhatJobFSBRaWzI2DqMD4A+Cj1x5GFvLKTH8yse/cu7u+Nv/kLa+ks+uz3P3/lm5tMBk+gQWaxqrCECWBxZgu+t4DMK2/VrMjaXBLBZv7Fz/Z2NjayyRi4NJ9OcSkqZhtA4lMseT47bT8IOWSWk11+g86AbImuvSuoKZat88AyZeTRZwaFSJeMaNBIXBsxhOjQljcRQ/WZCqq1rSUhbyoqVeF9A+A4de4DVkOyfJLyyboMdQCnACRgewUS5xUbOCZVD69xYt/2xYxQWElT086E+tahNzlaBKS4eZWZOTPTmzbkUhLY03mUkMIykdJMcjwzvvlW+vB5IOcqQIlHKs3K7mgart4AqUGkGJJw5MEJkV8TLIhvXJePfzY5v6tvvlqsVuhZwhZbnAGBTKJZAamw2MAF5lCZjH2CS2aDYNw2vvTFzaUN4cMXrjfVn+qao2LlfhwLzNK3V1PT8x+0E2de2c6oR2GTiUejwLETjw1NG091pFgQRn1XosNU80GE57E0snwZo/uGi5KJlEV8Vw9NA89QPkQT4MU+JgpUSgJdA9BLIQ6SJMUIDqYUyBdI3FPjLONfOInilLfzDvBPu73G5eXEBslTddbTclnUxLGYjV0HTQN1PozCfEYr0umyvHmljuqJD3kbES+U3DobHQdpGrmdwfg2T2zw+H7Hw6m8qqOAYzDUxUnBwxFhR+63g+6JAgWhOQ1ZKgKaRWaJEsZ4vgEIodRoNK++eVOmTkHKz/Zle8RUcN0Ih2PIOoyK0wRp7itzf6h7mkYshw3SBgIDSBLoIXQUssCVa0lgumOMxNCEIrF2v82Zou6AJ++rDx8Na0vweg6s/K13pC0JJLhUSjmZln3XJWPV58fRX31F+CYA8tyOc+GP9c4GdwV1zAeNJfIBLjTAcyu0dc0u9v9ET8jmG9teX9c7U+G1HTZwkvLaWqMyHw6yrK88t2dTZn3Jy24UwXyenj0H6h/A8efNq7l88fzF9xBJXZA4A14tgpqTmgezs+TjU1Cr1evV8InpZ+VFEV0iY5OOoOqM3irBsycH9Cq79lrNmqnz+cQFweoy4+m+X6iNtIu+Sb7x27c++J/fNfJVN5vp7BncZp3Ped3f+8T/+noU6+0fPSguX7Ks4JPv3s/WmLZH5EokzqJG/VLw2f61Jn0S+F4mpQ7bbVzCLxXhRWcu47EFeJoEpACHTiqLKMoKlOv6RHmN5fOcjWGRSOjdRXm5Yqepfpb6dAQxIlNMz94fDhwu9fGL/bHYSmE+PHr6JJO4yyzp1bOL4RSTRIuiItvF/dRCQkwsa4RZD+DUmyMlYQUBievIlbywJXloJIxVm0qRbNHxNFEu4ZNJkqG0rtUkcTtG4dBplijPs1EukmaG5wU+HjepcB+LL0iEaC/sgXbMidvN/LNy7VjOiY/GKnCurucP7u631vLHT9vo3A1qYcQlSQp1zGcus0RnIh0OquswjQsBk8ZLNC8K2NOLty+39InyolnzzfGmG6dSzjDD7Ebl3NKF9TX98XNi/VJ5OTk/UOViVl7hIsX0VZMNY+Jmg5Dp3oe9RqHkxlhtPoL17OMSQhcyeFAyxnPuBjc4imjojS5QsVG29Xhx0S+8tEaHNjtZmF2bGCNnN9fo33i11lG+fZ0/7neU8eT0kzYhxUYpK7eyX1iWC2K+iDYpK42OHpjHyqfP2z+31Y5jCxNjYGKYlVfq4r/6TBH7ype3chPC+un92X96c+Xw3w9nTemXfvUm5o8t07LPFg/2dUzvL315M08p+l89/HeflF/aEepMlIa9VCv5R6ZSLQOdQqfxswNfd6c8AVdynM5S+FITc+wffNa/Mh6/faX6YQRaXKqWW4XsqpRegHff41/dfOBQVzCNlSMbR8447HK0kDgaRCa+WVxMlFlJXoF+AnG+nOII8kngvk7QZjKpw3IM3QUgOUOBqAABAABJREFUV6WtZBmjgr2E7LlORFBSX7H5sc+LpPFCR+3oya83Mc7H7/V7JpDXhXJsVmFEFon0SN/MIhee0+koAS+Onk3k+mV/Mqrg6JgITs6HxSwhgFg3PF2hVrue2l54m2uVNzBoy8qR1e/r7SCun7oCE/vLuV53hNTYjybI7atUwcSIXt8LMez10mwxtP9kD6mVyFqWbOXKeWT8XvvmErJcbzF88t6HZ4SrMimx6M3G9jwK3WK9aHGEhPJx4kMQiRRGJ3aKBUnq226EmmFcr+cLqR5aIglc38WKIGBRQ3dIAkDfhzjiBknkBxSC4jSDoCEmplhGZkWGGo89IDJcnRJ0zNJTxGNDaccXsFkXrHHc9jKMsqSaeCMdWAEmU1wwHefoEKIYMcVQuhC5nnM2yUCQbBUOn45YkoojMk8ncBHvt+d8OQsstABFwowKIuOrcdjT5xd6tYD25k6rxBIhqs/tCm0AB8NJorC2kl3fzZRlDLJpv4e0L4RyGMtRDLnzwUJK0MjhjaE9PDJnkRfP8aSYPpyGVdlD2FJKsUiBAA5mdzRF80U5DpzE1hwUSQVTGwchsyLgm4VGp+ZFfraxsdz4Kp/bRFMjUoZMaI/PMZx0GQmpY7cTABHAsFjaa9cRKkAh3W0jW5fJWjY5Hii8LK7UL4/3RtXl9PBkutbi8CRnHMxQD8w/fQwpJmKdw1k8Hof5DPOnL5TlX8wCAQGPj2GZ7TyiTVZ6dsy+cTtPPAuAr4E6noKZwirS1crbOV+uUkCZIZaHeBAjcNVAMxKSE3EK4ldbefdoYWZErFXECMHzLw7bk63t4rmJbq00zj94cf6X/VtLLXlVcuLsxdCpXs7Lcjwns6WUG3zudE1AYInXG8wfzHZ9vv8/3g9N7QUFblH52WPt1EKQEp6hEVtDqV3WZbkEgk0ZnduOa6VRBJ2AyUT0+QubEMXadhFBw9OeYc0XVOrpBh48W0QVuotQtqZkg7Gfg3wwPBgKy8vl1HFHFzqKoIVGPr8G2sduruSnsuTnIlvRqjWMpJKLgZPEiuMZywCjEjcNqdQxpRK5Us3FeTwOBN8lQgSjXFOb2btvyO9+crEraDUWdBdWpUFYM1fAoulAyaIwttBYpM48SETYfOFvlLx4Pu4D4Z2/dZ1T/e5Pz77916rnTCjYsBjZ97rzMRZQPMGKFEnHk4n/5TeET/6XE24Lrr8p6ltIv0K1eOm8J0dVrFqLun/sy4dHYBbxkXJZxE4Gg7VG043csuYSYcjNXT9Dba7RvucrZsrR+OhYS6wAZnBhW4pltN/zznrOrZocGlFCM52O4cVJFKSZkuhPPUYPdjFcJ1MuSgONlJ0IBsxthj7W7ZXXy/2RRtzMTwrFbxLC7//+x7evE0uvLUHIr8r0WgGHRBg77uLJIHJ9OqePvJlx+vl06Hp2vHdokL61vclDEKZ08sFPrVtXiY8etU/x+d/9b/+0Uq+tL0lzQqcjDqHthxf96jY1Vo9rmwI5ig/ee3b/eLHSEnSAPgEBY0RfWMoqNLffSV7dppUF+mx/Rm5KNMfynEt6UzHkek/ovBAsl+h/86/vb/zuB1/63/7m0987fPRXd9lff3v7FgkrBTimsADVZz6LhokV4i7oOWSjsXLSHdRWYU93syUA2tYyj0YLUxXL1zAEUHGVyiZWROIUJAGDQI1cKTWZWOj5KUxKYk09DMwnhyPFMNn6m5CqtnqfHNa7bLaRCVPScILRwGTyxXwuHakRzaJP9DlVJA+P/fqWPlANb2aEZdbgI7kI9WP98T09RxbojJwtKwvdH8+wUkUqbTCekKQzKJChN7TVijWxIyZEMyQ/Hpn7z2flOilwYpngSsWingFEhg4jBDxypcsN+kvU/U9688eHt79Uze9m1IP44IERy4yUQXWXiLkCSgEmm4lD39bDWkNSJ7OMhEAMAQm0prGmTWicpOM0MSKAhw4gQJDabspxOJIgaRwiAKIYisYAS+M4QbBGlgxLpGcpB6pDS2TrZIwiLIthIIBEUxrPF1yUi+IwYSglxiJIy75iVlf1OmMOcG3i5AwbV2H7X32CF7ODAxvdSKhN3n+Vzz2OAkPLaJggWg8zdB60x1apJJth1/OLcEzJlSJ++m4froqEJFpKwISgP4ya6zUZMvlyvdLKUpAoQad3fP+sYxRcO4X4xdSk8QTMgogJY+AE5Lx0Le7euRjNAyAYfHDl1G2u5ypgEi/2+0YvDpKArOUMP2EoCslF+30dIPrRfg+qtuDGu0VybJKinC86rH8ymwmySZFpbTtooAB4tcU8khAMAGQ+1hdkbXsHIDDSf4SrSf+eW7u+LKQzulR2vXx/vI/L1lKeSSadOS2RVYAzXtYXXFVVzcjH6Ew4a0iF6u3SmbPw9x8Fj8/Sj7rPz5hWLs+8XJ/QrYI9IuIReDFuq3voa9+UwK5LdQBIQGajkI1T+3D6INxZEfoDL1M2EoTHGgWmhuIvQmNgZ1OfbUmukNqGA1lhkjCv/fyX//W/+q7lTNu5XCW1vvHbr70wHUPR8KVGWaKqOR9D6VdX6QcHC+eapJW51/PZ7p6uhyxyPm+u5PvPHm0VXyr59/6FPWpRZKO/INOLe8/i9km4GdM7jTQgaxTtwAbC87496UJ3fmFCBHVYA8tlExBQGurZTIasxvLYfbR/0srmzg4H+YZIXSLlnpIyHFx2XCJkXitb3RB4EaubCY+YvkUqcLUQzwxQlgxnkJJk3FV65c0lebmAJ3k/jGQaPwZEOYsEbTuaTx6YrH0t9/ndydrXqvQcSc2IQPNSSRr3F1w5H8wpyKbNSAkxLlOj48GUj7zCN1596qPsnz1dv7SbU5yzu0N5d/lZCMgvrJZqBA3Rsx9ehCU3CKbPx/l9Ww3+xZ145w1hmzdliYy7SZw2IbQSemMFzE4m66vFwbNxkEOGiKSP1RQT8Dhwhv5kArlr6wab9TqnYz2R65hUzicCZjJpNoyO9/raibX6xetMZCF24GRsw4GrO8uCJQp0fs4icv889cnQwVwK7xy6a7KbW8d6nTn+emNotF1AUWN9KWbc4klhu3rvJz98Ob+juTigKn3HbYVpLiDZ2jKPgpmuxt5xVPDrDAecUtz9RBvqYD4Ary8nz0couXybOv/3n89+V/0IAPCVK9nF9H4A60UqGJ57VV44/2w46CUvRZ/uc6V/+d3ZW2n8/gv9N/+b+pXbtT/6Xz/+eDr92v/1VU9/OHhvwm6UFavfcKE7Hg4jWM3EGwUmHHVVJL9VDP7R//E3vOB09pPHP3h08g//0aXqRgrBIp1EsTHalkkCQit2sTG1sQF9H8UZdC0Du2dPqUaLXfRJUgNsFmVMfD+BV5G/GFI32IijddQoh5SBC/m5j9r5IiyKa1hsWliK0l4LePbzIaPgiFu70MuJPUMAiBnr3A6QAJck3wQxAnUDwxgqtplcYONYmDVnCg3qOUJE4scYMeurzMFczpjNWuH8fO+gxIcB8jKgnquOoZA67ohC4mNS/lJJL3IrxfJ+31qVc5YZXr+Z3fOQhTp35ubOWrVRqf643U86iWXgzA+e5X9tff1bDeSOeveZksmE3DaDyYjt67Ye0rLoi4FDSpiNiRxEKHRheBEmAm2iSTBOIzwbqROjFGIJiqconqY4TBiQIhwREx5QUTwbARijfFHwFd+NUJbFsNSB9kgPPKcpJ749k2gYFND5LA5DFHESLOJogZ3ZruoggSgBlJ3a+bDNxSi6tMvjxiS858/GoTXwrlbJqXX06Qcw87xs1XwwJfASiBVmH0VIjlr0yEKBscOQl8iFExE0SVUypbdZXiLLRVrp6Lhur8kMSmVdV+bXboMgiReT8dTX+m08kxmlKRsGrEyjC7zB44ycdFw/9DzHNlfra7vV4tPzF0tEbbWUBYvwcDpoNZ1hGJbqnApGCR7zBEdZ6U4GRZcqALPG54h7tOCbzXVGqm7vrt26nltatgwVTrDo0YvDhC1KDmEDgVg2WaCbPM6Cp5FD2LNVtLL65k6yUM4/n88v9O03Lvgl6eVvL3VPPncZKQKxejSRygYXY/OpGsclkEhIubnzesmNAIfhderhhX2KERPAkVtvvuQENPeQsiJXjavueGj/eOayycblAcJnBQASdwHoWEwIiqmVmmk4MbJ06h1q0YY7O3WWmyJWkeXTgTFyjNgtl3A1tJjE1M+SUrP85Xdu3fujecYkqHqmf38GKZ8Rotq1vLsugjT1dAJ7c5cST5FBsPJGFukCdqdZlKuZFiK3zWMr/vz9u3FnbzOLWWP181O7kPV2iXijFAkk1x+ObR9SEW8FaQ2hUR6aWbZKJaNDX6xBzwpo1rdMSNOAnqqjTy9m0+n2O+uvfC2DWS7QLkTBWGgO9HITFlbrElMHTtvL2gmyCOwIRAk6ObH8UE/xkJlHdLm88411YZe/mPhihNoH04QklaGqzslilnalTPtzfedq5kXbmioek4TBHDZruYunpm5SpRwauCYOUZQkY8zvj3UCE5s/98UshT/8f3z/pXW+dIP6+IkZs/jyUnnG2d+Q5Dt/cSSEXuJq+kVU4jgeRP/176x+8L88w987pg1IvNOEIZe7lO/q5/Oe/YvLq/P58ChG0moyj9NKqHmKU8yQ81HgKkmlIlJ5srvX1u8dMJU8vLSUib2jD4YYYV5MHUJg1q/VCoJ7dme43EL7ipWjcpgfRVOUr5Kv5DNBByBuHLNQIQOUQ41cArGg0kgx0nnxobFaz+zvG2nyXPvWjbVf3Z6D2WKSmMOgWejNjxyjilk+g+QShwoRXGdzdHl7g5ywDx4xI2ut/dwyGfcrt9InAa9S9JPDzb/+O29lH15843brM/3I7wS5arC77O199rO84M/67GoJfXLHyX2R2WgSF7//YCzYJ5+my0RvR/LsSAnMJ60N8vOfTJNJUMH7ztCVILHeyp5e6MMkLNcJnw4/faA0uZ/gunnljea/+M5vJYCLnA/T/fPJoVq6tjsNYalVcCZMNk+nIOkfzk4/ta9vBFVvGpNMp4+scVR6PEEuF6Oq7Rvc5QxK+TFIuSgT40GCpaDEphpAZolQAKCBgZTl7aLrT2mk7D/ST7NyeDgm0cV0s5GTSM5aWFPFrteoNAxbHKYp8QoPGRI1MJinTALG446pR1wksdHMRmbnIXCpbcEqW0gZERcA2rG8LvhGmfCPM6HH8n5fl0WfhgHxxopA6KmOUimCVRg7JkSCsj79w32cY5ffqBR/aemCZrFP+2d74+Cx98q1/JBC/FR/8nTGIfzCcCWYyeCc43AyKUoYmkZxaJu+4rNRuphBPCFC3PFomMkm3rmTpnhESRyNBx6SuAiVhDjAGJJEXMI20grD+TxmWSECMIzi+SwdvTAo3sxitH8C0tJiYWA0AIHkIAlKy0Y80DCSY0mfsAgICJTSY5p1ZFXVYqu9jGQzdZakRk1MIKmlOz0MY1nViFPbxiAaW4lnl1wn5A2663l0ggEWQ1LrZNYek6WNkqIuRndPGEYQbmHW1LtRYaBQauJUGHoUjjwPwQNfeI1nCS+cnFv1CpeyRVN3O4fDseNk1PHcE0FKvrIh8Cs7JMYZOJEu1KMX2nRqSIWC6wQUgks1YawTaJKcD2b5mYlmGVuzZbY7vIDi7Vsvb+zqCS0wSaSj7TBZp713GtQMSYvrlxgA2wCQSyyfAv5iPLH8J0nApRd5Jq23dCIgh0/aGy0mGkR8gB+dnG1vyldWuIVrqyhixBnHYVGJLdYrvjp8eKKuF2KR8QtCA7yFAzMBk/TAKPAgZXzkX/5M96ikzLIvbQLQH4FCgmZuA3TYtlSZllCYYjIxPU6rZTJeqXCJKdQswEjQDEC5wsZUOen7tibKTOwmNSbp9wfZOrn28tJEm5CQHnql2+WsYsx9EeksogISkDxM9EXdC7OI6yrg6Qcd1u674vlzSxo8VfiM/tJff8kPicWP9+Wiz75TVX+4yKpMd659hk83N2+ZjRWJCot9C2HpoxBx+k525mNeQuehi5I0TRAkmVdiczzAeXuNpe/cP/7yt28fnp/p/vB2XsNsYshqtEime918WVAsJHYSGUMRiM04DPUdLyflOIJsIub2ln4Od/fsHMukF2YBiclaOA7IlWRyporZEmp8YHFZrFQpzl3/dhke9JzJes67b26VpKCQTcyuIGGglhndbb9ZyhyarF/K/OT3P92FcfPnlowA9f/ygfQPvvRwZor7o4gJ/QsnzbjJf/Iq3O8Gd85OD1GMt770t7+ADo7Qx3vPX7QLXPXgN9a2R867GnMg2FFNMr0kdFNg2ixbQPKR4xJxoIKluh4a6WNDufClTDZ5fc0zjOn9Z4yOZ3b4FNIzirXz4McnxmtrZUjrRLZMA2QRYbeW9MfqsELEAkOEMUhKmU7bTDGJSEghUsWM4IXoOoakIbrz5a3x9z8f35keMXA7Iwmr8lQxEDSUhGThWjiunY39myWYE8kmI6Lt/uPH3vRi5NkPXhAXHG8Yd+b1OMfF9DiUfKT/O/+k4J5r878IM6wflKWP9/o8XQ9DcoZYrIOyL1WSsf7r18W/MLAwyCGY8+QQgCjZeqv+J3+w94u/8VoqU0fHqhzhE8j1ThcroU+xzLHZ1wlB3e/LJXg4A6SalNr9f/rfff5rRWkCx7c3xOIrr6TVSO+7yMCV3Ujva++fTn7x52T4xrJ+9xwfonQGQVnSNDFeBNb+mN0gjUMlg/BtdT5frYFJ/LroGyz0Q4hRYAMmGgAlCgEg5Tj2tV8oDAeKvKALK8292b4IqyzLP7g3E/JoZY3C0u79MEfYKYPTEzfIKpa9MJEjQu9r/RAlN1vwxZN8NS9slNiz+33gni8vgU+0Ww1HhElbRwTLo+p5feSTBGkuycRVmT9LI3qxNxgsann5QTffhI/b2htfLMf/7ZWobx5+Pg6Vj7JvvcRtF7/81uoPf+/s6N2DMwfwtzAbDTKuLdCsK2E4E2b4VEhixEMJPBRC0wvQ9EhDPGNh0OuVJNikgQnTY8WCKIMRSYBDhApxJEBQ1k+5kIxhGmEcdDwsTwAcg36KqQw1pKlYFFCT8nyVAqluGT7t4wyEhIWFKZPicuAFnTCcxBBNChIV41Q4UFGosJwj1ekSG+C0nNnOq5XsGluenC7EetbHfNaOGCpxEM8BaJoyKBPurhXbJyZBEdlmpFna+P40QLwsjhYYWAizrZWVzVsvgTA4eXYKU8fRlDhFrxdItDse7k92NysIj8SopzsA4UUSOINOKb+2PNGE5zRBR+jOTjON0nFgrbXgbGQv9M55CJYqlXKzUsvESmRt78r3P7H4kD45VHJSQm+xcAk3obW+hGOuq9sTqcWWasskIqmh4xiAF2AhAAAAEKVSozS6KOULCQG8/f45NzGuffHaXEl8JyVFO9NaufXsSffjyQxfTTOFs7MITbLXv3AF5ngjTJ6qyM71OvRTezCjEh50CDCODd12iJxr9OYUfcpiHSV8J7d0nEpXmhvAJ6jIB2YqClYKuu2TiWRQeXkNaMmkj3BfqgOFhigC+hZYLiFZKlUJhuIQ04eE7Vg0USvLTCH3Jf7TB3vDuXflFXEx13TbpagoHNjyErVwYOd0AuaACpirxdVrv778Zz95BJsI/dUa+coM/ofcdMCKNJUtcnu43R3Zq1IO2NxwYLBMjqFtVxuaIuVjKRuk9NiqSpyPA88Jxk9MPTTrDVSqsyRFLQpNbL12m0iRP7oY/8HFm69nfvhu8G/vzX/51xu1gLafoYGKHk8iJIoEllaVwKPJ2o2K08WdnhO4HH2pdDnb3MMuxh+YyslF6XJ9FmjzUTL2Y4RMVd1gSnKuEkZWxC+8Rh2goS54thTpgamjGIq5buaG4A6cS3x0cLzgNnPSRM08e5Gn/N1fbfzJYV/KB7hgL2FtHku1zvnz9Y0b/+Wrp37/Qre3dFxXEh4g5208fmHiFFbY2GVj5BnIJCew/yT4shy22zqyjKAGUZZZF0GO2qpPh77hIdA1Zv1KCbq6T9Ds0he31NA7/+ELRIpzt+pKREQpvVQGiT7HqKhek7tdpVAgQgKjiHTPMnqGEwpsGYMpBVgpXcO4cdtM+wazirqq39bhxkrl3XfPN11jaSUIU0MZBm0dK8ZpsYYu4qSwKuqBhqFUNU2dKDlLENXmy1im/nKLb80+vljnl/i9xQtxozh9CmAwaqZ+72z2k38UGDR7abelXqTu0FLV6PIt8eRsOBsBJ0dtb5ZPDxa3lvCvXS7XGhLGkCSH/OyuL3UUNps8fX6eonZjmTz+zMPI6avXM/vtSQZjAlengmh7m26t5ud9wdW9k0cXv3Azvv4rrwBpB4Dhz/4/9y5ng5Vy6nvh2CUbry7/0mv1ux8cF/VpJkFoCbH7c1iQaQJRpx4ukADFhSUWRQvLGsowjILNNDQFc4DhgEOQEADRSQGuQtxMcxgEmXK5WKncBEmwkJTDk3PQ9ZIQz0pYmlrPn3bzS7Hv+LZCRjbZ63lck5wb4KUrOSlKXUe3OI1aKveV5sXISB4wX/3y+jE6IfLBLHaaKEsfxN2RVyut2B2vhUTceUSjFDSROo/zipYkiBSAm0Qm+r7tiMZXfqnU/a3W4P/8GPz37x6tZh9ckustIsZgqyjxJcE5txZHSn1LNBKYhr6JeaRvFgmMTWM2cE0d9ka6gQSBVBvaeE5F6gwfiMnAhilJu/MAhHbEohGNWjSDIYgdQ4oKB7rPoSmOIgBFsZKYPlnnvMN0EydFGj6NrEKmumWGFhEvglhgYmVgmJTrOKzlaaxIQ44pQhJiWakoKM78Vjb2ocSYlHCmAIDu5pK/pCVfIOsSTIyQDU3j3NzaYLt6cdk1omZzCY5dk0CkILO5Bvcfqb3QvVycdshvffMdEWukIAFzjxf5kAA1wT0emPPjEUoAnMnSBIJkqJNHi3MHbQnZdc7TL69PJ8IyYohOENHM1Ej9FKHwrIOAmaeytLu7ljt9MdY+Dd/85kbY1RFSbrRE1xiVKaPMJwZDMAa7+mYln5EH54dDm7i6nNNg2u1NhCSu1guan6ZWErlR4M+ZVr2xBLwE5AgaFXPDZ5MxWImFlDSfA/VCbZ+dD3WSxSkmQAC7lacF5jqp5+Mi2USB0LLDiCcuNKcPYzXtjQP/2ILFvFRxlJhYAHy5ESI6fWupvHtNoNAFUGLIYYnelrwwPf8pWuIXg0SJorXr9FJSBUCBNgTTUZpqUDPzaxx3TNBKMNTx8o2l4RNvMYm6/XNMQK5957WffX9veD5clYMhSNocgmCkEDpilb2Lmpu768ndhTWHL1RltaC2v7ryCJD0bHyrtJ840cWn57A3y7+Ws01TmvV/+DFy1DtqlJtO5DPNbI7k2j5bomGOoSAHj8nE45FMjuK8OFw48RyUd2roNA6e7Msp/XdAZnQEPr7roVWUfKk2NibFWb8UoZpY7YeACjEz0SKUiR3Nu+NKlXzldlkkhEePRsqTv7y0VsnezAyLwp3RYnyuSXX/ikTEKMIhNFgY1VU2svQol8aR+bOOtg785dhrUN4Czdx9pnJesJkkf/XRiNipfj8xNr6+dvG7e7V65i9VSy4wy4oGV8qftcE7W7n23qL8xWsnLgz+1adfWamzVPA0D2eAnEF4NYeVl0vNxbMPLlcu7MJfy3MzW8sBZDFbBISu2NRZUXZqoqbkBAJ/+1rdTuuBlaiuMSwkc1Q8n7qCPmysshqXB36qxZxcyFRkOAqnCY6djjpvXecWSseZowixrlIhq2DyZF/LM2Kr4ZzRcgYhGWF/ktzVjFsvuoVN3qvX1m+vne23ySKne3brpfJR1/nxw9Evf7kyPHcGz7EABBtCJEZp9rJ8oXiWR88TVoiUYy/Jf2n1JVSMlVjrguwOMn/on7c7M5ytFtHThbt4ONmV0olGsAh6/6OTS6sFNmcPHLXz0G9Ws4Ops4qBFxPzxcExilN0ofzs8XH9G2vHiyE3DNSsgBXjh3eP0MtZbn25HBlYgPeG7kdPZqPvTS/H0X/6t169eKmWp9POJ08fX+y9ukW+9bWKedA53+tWr+5mKnDwwad8vrxdB4o3myt2hEFAk+bETgo4X86mPvbZvz+Zxq2XdnL5K5VSmvpus3vy2drNpYsI5ETgpiDPuDRwAPAhVgPg5BRcXoMgQEHvresCmhdmP533U+DiBEawMsgDP01dReCYAna+uYQX2VaMk5wjd05NIyxm9aB9JP3G320eXaRnaO226AsT1lJZzHlfCzZWa87ELfG4w4YlhFwrkc/a9t04oRWmiMzQIuXHo80l3LIid2/0Z3//k6/9s5/L/OdvxGMVmZnsVu60bQ2Ppy8J1qRnokSUFHCfALiEkmQcYQEVYRMfKXmO+2DOO0QTS30CS0W8Sgi6EfuRhCWakKfsIMVJFCYJSiXzFJKpTUAMRX0mCiLcd0yfZokAoJiyxtQKVPt+wmSZGNZE34caq6hKbkWO8EFiGflmkDJBCgGCkTyNR1GIUAEapKNeAlG2158TGZ+kbM+lDN+b4lF40OU9NETTmCLpTSoaOUeqX62V2pPQnio4CVg/paNg8tE5n3plNFxM09vXbkjYLwJgKC9+lE25YhYPxtjxzHbCNAIAhtlLa1UttY9GzpyqXP+N1U0gdR9+aP8sZIHRO9TBDXvlZtMlkem5LRAezSd4Yo0vJthcE6u8FtneaFjgvLE+8EzTUMxshoshYyzwRhFnXWTWHcx1/6tXd+M4Op8HuI9GHIajmIW4thuUSii/cF7cO2jJ2CDUJyLEuHS5QjNgPtfgw4Oz2bMDpgWK9e16TQjC1uLI5tmsdLkaAIjHANiqROdgqAEumoLge/efu/GEYaGA6NQ0xIVwRcBFLfnmF7ZclxXcPCCKoD4GuBnRNqoZcQzxs3ktoRJEifsR/loBLMbAYIEbA+cC9KBhFgSsAEorlWLcnyNxmZaYOJcv7H1waP84/JXXLn1+546X2CUpmA4XW01+MY7xOqm3LWiMVipIok+ExN/fG0/ChfgrxUH35Hnb/aUVQOeQwT6yzNUWFqkOXWIwhTiXXWlk3qiYaWZ6QWMh47CYEc+hi6BIyCYpw3IoavM8rupg3AlXnWwdlZEPXXTsVelWpkiDFVpuQjE/gCFQHM9Lwyjy0QQ6PY9JRFfzz/saU/DZS/7m62jrJXT0Kb73lwcWz7755SWmlL50eePwrBMazhgAEpLO3GY8dYD55TcbMzteaTHEBfK0FxVF6eRipueoyqZYtdF7j5Tf/C+a//JuRwgjdqNs6OTOVqWIE3/6b+7+N//92+dn6uAwkkoM+uwIOToKPJfI+i9kEoJKNcqUb/KoaTw31MIG3VW006fxfdXIrDHPRnP0ZWpqzUngnf/FKGwUrn5zvRTBOsvsfa7aPJQwDGDGcoPv9lIfw0CIy1IGY8TUpuo8Z6kLz0vqm/zhWNnHEitLxipGnk9DgvrCrdzsc4tfDIqFwKnWQz2MYJxbQg2Pn3gO0dV7M/ylWw1HgQyFhX3jyQ9fXH+pbpfS8fPJlRKm4czjhRehHJoYnVNTTTyOd3DGPUlB8rJ0JuaGJ6TH5grLAp1f3C44s6Fz+nDOCe6qEGsn6t17iiBlt3cKGPSn2sVc0xjUokLZGhnzIUnvlvodsyEzjpCWX8v6inPWVxsFhKC1CMSVFcn2jacftpPH1s5rLQzjOjC+VuV++9tLz+48+cf/5Cf0pncFm2eyxVVWePYEmb1/fus1af3NjJsaWsrHEvp07wXkURpB+ExxkS+XKpmCg744UWs7ucxSASiB0+GeX+i2n79+DZbKYX8YmqopZHlnAaCQhnaYci7EmdSyIbezhgAAAAHAryE3z5fpd/ce169dal5K9150fCnKVvGHB1O6nJsyiOGQpSklMNyEUzDS3P1mkj3WfzhIeICeOPmG6YKpzwYEGWTTQoZX1Xw8C2B40C9Wm9JFjCZqksXishnhGJWRRA1HyST3/MKhv9L6+v9lW7n7/LN/8pNFpvif/b9/edjv+x2wqeAENN7/3uTKV9Y4grDw2PFw3EVoCglMj0qgBALSimorWeOhVSASEKLPBgYsiWRMlTKslhNhAOPUBVjogShMOUgxKR4hMEDTBEZRCKIYwcLUhzGEn9/7e394651r6ZTZi8Zm4Dr4srizw9E/7nr1fC+xDtpukqCQMzGTR2hbQxE+mCUkQs4BJRDGtD1O+WwVoliB6RHItUvw0Y8Gl1dzHpEJ7NFFGDcLNDEhMBTcQUEeN6ueiRPU/hyyvdkmb4ej4OXrL2389W8BUAEvPgZyDCwstfnOsTOD7dnYFXlQIADN1fbmAS+L+WyLKeDHTx99cn+f9AsvNTjYfnwahVe+9ktGnC7nCyWgnOuDYW+Qz8WJ4bDl7NhMd1aK+SVkemKEEP3sg4MkXqgMJrLcK2/9fKu6rRFgnlaWEeXioaPBoFlF3GK9CrC+rtopRkp4JbHthMV9x8ZRGsO6im4pnUarFF/MBVLRHw2IW1S1dRWABQBvh8kERzAAcikAfpimMaAICNyT9P6Lf3vXA1Y/k9e1FJOjLLHJAJQWYtc2022Z6B2D3bcK9BYR9041hISqLklOGrVBez7ukiyRE69kYBakXRxWquDHnRSV0ih5yIAr1QLR3AStso4gM0V/cr/z9TWRcbFPvvt57u3WORx3Pr/72u3sZ4t5RfCUhJRSb16Q+CFXQ0KToYm6cPZnj15sco1vkOpeXH//42ZhOUqdE4hwMnNQlvm8zf9vTyvXidrq5scK6y0KJYMGlQrla4EMYt8rOOZ43LeVIJDESoMTYP2MKZUR+trp4+qLKTo2zizSfO2KuztTonidmFlqBwndc4bB/ZhFAGnOjwuNRaF2HRPNQ00zUpmmIg7uvl2H2cJn7z2CT7WYhLmsoCFxlYhMKWu3Z9Z6vjnU/uqHH7/zT/+zv/zHf/zytuiUto8/G/363yqdPllQqsH85jf99z6NP3eSW0tYmVn8/uPdb27c0aAAY3k6dQLm9pv8+x/HxoPx1f/Xm7Of7r0lpsbO2mGr0Rmegs8u0Kft2TQqrYopNL5EdR+/9nIOFVRZSDU4/ORE3k4kZdzcoLVD8ns/6lFxZvOl7fMV+ZJYIaA8B+Zm7Ibj3lTDuCIW6FhuQzrXQT5NczAUPP0gGHA0SW1nnt8/Wnoz0zkzi6PK6fEHr+SB8hqFdsJsOzZufQXkSDCnIt2O247iSjJq+DGJ5XCEBZy2qKzU/+gPD2ovlev5jHGqHYZJVcJz29XZiVUohwuAtjifxygzdlRRrqP4xVHvkEzJRv1lAOfffe5P1eovXBrOnOcf3PMsY/l1GUnQ9v3ZbOyTEbiykplNZq4dZCtQN3A+lgwV47M6RGNqhU9RVDYK62vNGIuedhSxuBke6dYa8eUb5B//XifQjOqNjd1NMRhp5sz9wi9X8ED4q5+0EdSixMIqGl2+ysBCbvjMmx92h5EIKPHqJYQH8cGZ7cmCKGfZiOzMguZ6VeTQeR8d9Rm+JK+/JkQRePpR+oQAb1yBS9Oecy8uv9NcTEIvQ1TZjs1CFluAdAdABAIUAAAA8EF4MPDMvR/EX8palNP+80e44EXTkWUijVzFQ4z8UoGKzelYW34J2sPDLx4f9Ob0cwQufvvv8PN7+X8vSvmVH5iWdqPWZJOzwycrqitvlD/4GJZfXrpCyNZsQWJUp39KRsj+dHAliznr1PTQKQHj7Hny6//dF4la7s5ffSR/Yihj0lplXt+ByCr90/0z+1lQbObYjZJ+rEYkk88DKXVqRtTjKavDl0PIBgE6O4Gkel5eLlP1aYHO5/jIWZwOFyThLhY2FpCJyEYoRtBoDqRpnNpIsjBDDkEoGkeSBDuKQQYcHX4+q7uJvFYZjxa0K4lS4Td/buOP78wgUi/lTaMzW2N9E0eiHKEu3DhNUYZCU4SgMJFFpmpvPglQOx/W826S4WpMLHKLAyMjorWyeP50zPSttatsLYpj1VJcl12lq0X6Wk4+uBNVsesb3/4VACrg/DPg2iBDAtdJyJC9hnKQWPNzxrFmJOEk9edJVC/p+vTF4tz47t3Pn37y4Nu/cnMSFuywdzFTq9ZF5AmKmyZAwXimtra26C1YyiZwSmC8oWooFuYBZmmzzteGzx+eki2BydRoLvCSSMLoRdx7+FjhPGH9DS6EAZtorhWwml+scaYPxhQqYMli7voabiTRYtYlcNtmTTnPyPltuYICmksBGASHBXwcJg6OSIs4dSxQ4aCHA2CnwAgTqviVnwfdCzjVU/1wTKPOyTHbN5nlDPblTcGZEzu7Dbohep7ixVWUwCSRiyZ9GBfVsNzFEsbGVudR3PZYNELMOWTJGMHOFI/JUhpECjIHAK77cVNeGnOzvc+N5WzmlTd3Pjg+vfUFaQLo/rHTyLL9hTvnEBTDoWaLGdRzgk4ElQEqt7a+9UZTA25uyw7PrZ/9aYcRPPmLq2wkFlVRulrEfmvD7xiPuwQqiwyNCxUqCp2sDLrdqdHTvdkM44ICTjnAnw8oWjIaGTRLkR02DNYpE7Ugw6vAwoZcpIbPUUHeXuovJjBNIhQJA6LIsyVMBEGAJKGW+PR6dnu7cnSn/Rf/6sXAPLhyi5FWREVLzrpmlqZO59pyEzm700/8NLhWPRqDtz84ZXqz+WD2N/7vKx/mLPLweDUTHzwd8g/u7X1/v+Ui6Hr61lrrD41xBJYLlDp5t89VyerXav/hYL6VxUu/IZeK1iGOB1dXA5J9/uPz1wqJItDOqsw7OrfMrV5bM4/IB3/UybBybcm6vFQgi9j+A91mMp+eaNwIu/1m+fPfmydbi+16LnuqU1EsSKSa+iwvlQpIZFJQJgWSLRBeBgSuMYtdpZxxIUzXi1z29VK/Z0X3J3Ap18qxf/i/HzPbxZ+/vDSeerGGRapXrkY1ll/McCbEVC2IWXyyCFYY+dG501oVv/1a8/7Y6YwWchm+erN2/P1D7kkoCpQDEBlBCZJwAhgWOTHGzQOlRIoO55cqFH3vAnXI9PrmoyMILPgrv/FFyEZ/+fDx7asFdWxvFTE7xFiutHt1af/57PNn+wJuB3jUnSCXluhynfcJhGYJSuA8BowWJF7JNDPU4mD0+E+OkHPxtV2ot20/PD/c45dLuEyoP/jnnVqz1aK5wvKqLCD7z6Y//r3DTI1C0RzLsG++0jp1yM8/OK1QzuZ2gark2gPbpqiVy9JUsYqZRqNGL+fQzlB98BQkUXi9kjjtCTykkTy5yHjkyYVFYzmMMdsRWWESkId0hHA0SAGAILWB6bd3xBr82mpfjS96E8GjXT3h4uoyQcU6VqjTQZi6LpZWxIXkap85h1OOqy+pXIiAi8+PuyulXC4gMxiJDy1uSbpMlxbaGLpoKzDiCxN7e+nZk1GZ1lUrWW5wBSgM21qEhpyEkwH6pWLyx7/zA+Tby69/pVy5VvtSffPH5uTp9w7t759rMJHEHE/TsRplMzygsMjQyNS1gpCC4Y1vrXz6nucEim+gEEV024lcAyeRkKIRAmE4EPohjcVkDFgMpBAmUQLIFAcIR1C+nYYhRFFI4xBjVfT658F113vMFecH01qWYjLK/9Smv5V9QKV6J9CykW9TcBLRfOqmFXTqoQU7die9iOXbEK9lSQ/j8WSiJOD6NkvRZOwnnUdPkzkT9tqbtbcKmfgxwT8nyTVVpzPSTGJM1SI08sOHTuNLa/w3vtljlut7f2D1Am4F6H9yRiPYBcfw+RW+KHSn6FnbEsoUhihxGIpkHWD4YgC2374GDfVwP4Y30yOdvv9ghrfMG5dE3Y/oDPXi6XB1M89VcqIbeCHiuO78wtxaq4oy1MdzkEICpOGC4goVJ8hzhHDR2xsPgptLRStbAoCPkxPKSt0gytYzENogMuyBl5IsBkFW9PUFfaWGZdfX3bRMAxqAEFAEAM4sRqpENVUt2Fas2FAYEPr0sMhMaMB0HOxYWTSc/gDMz3qTC8s355ORGWRoPl8rFpYiw13/1puJAoExsHkPEo4xdGcj2xmYxa1SyLl13rVVcHqhyDSDrAogoGzEcDRtpmoEmtukDSBc7Rez9SqIQXrj1qWO9f75yMA3KKZUcD1t91K+fecBsSOpVsCQchHOOpzY7ugByDNlLG9T8bnR+4OYWq0e9Tu3l7eEb3OuemFmpRIrVoJM/DNvzAHowiikmxwVGynielwauO3BvBOXSogj5xIhGvmILLKrASZ6ZNJJKsXYLgoMcLBmCljfsA1ylFJLVZuv5HOzWhYd98YYArpxOOZJ9WhGieHDM0AyEK9lzambbtVu7GKvjTyEdSehDUI01xKnWOoomGJ7W1/aPj15kQXEqz+3c/rJi6/97f/k4R+91/6LwxbJ/rN/9t5v/MYtsYCq2dLOS7KHlsm31n/wu0+oCt2VfbzvNDDN/qVvphmB+T8fO19cX93JP/kfvnfp9tfv6sjG6OLaAGvcexr+9a31r77TAfMHn35vcnhavNG87Rn8kbk/E/XFs1EiLuMoVs/LJNNTp86Ny1WqOf7u810KR+srLpsvkBbpQBIIfYAuMszlFIosZk7RxAoWXBaHXh1BWIz84JmHQFpfLJfFs79cvrPxFXf/b24vzX9ijZDaVumpilKBUTBJIs9rm6XF/lx0E1xOnbOFxaVxGf34ce9b2/luVrb6avuzfRY6175QmPTiwmR8hucASkm+TRbp+XihGDimsDev4Ycx4Z/Z2vMFiaf6L/zKRnq4+Mc/+tC+tSRl8+cz/2aRutEI3ntaqOwac5OTK3/t26JPz2ezcQYD6ytlx9f7/aC6lm/IfqJmXA19cGBxJTRbSGu3vA06xWrk88VCUlXVQAMsdTtgZ724dDUZT1Q+ip78UR+LTLIqrqKqM+ZfLhHn+9S5cvw0peuyLybG7MjsPelIN655EQGGQRjGs/EwqWxJGWSWEovBRUOg54P5QuIkxHSGczEnh5geGiJZcWwAiSSC2cBAbCsel8ASgg4gW82xbAwoM0UL2eVGTh6ube8/nfSV3klvzjLRtWYwn/nDztH5zfLGxMgcqAiHda+y0cc/uKed8bequUdCK6rO9oRSmWFLzDTmSJ1ESOHqVna/HYI4oHMxn8GUFL3TXZRutPJeH4vdWg15Qa5Uc+c/fwO980LNvxfdiXiE27MKoPlWFh6n0bGG60bCiiJPZ+yAQImDEVAFgC2Cnp22LiNcA3nyo2EzTYmKRGW5nEcTspymuHGusDKvhTYENM6gTkIwBIbjJPA9N4JBiICEpHGERBECQ7DxmDD3Jukm6uiTOPFlGv7s+Lhxc+vRMyfSUYKnBkOTKIHzcbiWITEtKYUez/kclWSgtbBTGpBX+cV0YrlzCzezDAFkBuA7S5Va7eEnxKfPplIc1FkICNrm4v40DjGaLZGS7VuJ84XbK8vitRTcNydDbkma/XSe5xifUTeavq4fhD0QTNPbK0GEh9N5UjOcwZNOiS0+606XROLl12/87o+efu93n5Wr+PXbS0tlvEw4JG1mymRGp2cpwvrBRo21Qk+dBTkeYqhhORzLYgyWyAgJfJqLisv11sX52BNyq7cLVEIFk7NO5PAwDSGV5WhlNKQQw4/FelYEtCCjJkgBzyOIuApAnobAcxKSgOrMzkgWadCgKCQEsELeiWwn00NR7bO75MRlb5RdZXzn+YllAgE1nf44Cjp+i54hZqH8Gr+2IWfENDL0/sl06TLOugtF19IgIsU4g3Ej05vHFIlDEFFyARtN7ThGURIMYp+GuJVQlgWDqPratQYbgthzU5yiCTF7a6v9gzt7vRlTzZ2N3Z0auU86shJUpwldKp9PIbZCKUiSi6gnD9BcgdbxnBDi16XVFZPXbKOPYoXvtMIM0/GhMEeKbpI1LIeRcAfOHqjlOLEwozOeeCm2er0RJSBOIpKHMCFEGkecBMEJHAEmkw6tAPKQk9ixncSxIdRXbv76myug9f0XfzF5YvGpp1thAr2F5WOZYiqFDR5zbFvpLUYUYAqZUkDr/mJwPPVwn4dAj1ChxF57tXFuTuexeTxLJv+H+s1fXfneZyfxY22rUP7s/vEX3041K/3en5++9YtbJ++q1aHAvFrd9vhDQGSu186cyN33V0keZa17dxfLKNjJJ+r+jMD5nWXuJ4fWAM1/7Te3M3DrR3/48P1HP505o4I+EUsF50xv7NbjHaZBlOeNWjABg/sLocQVBWz9xnjQ9cN55GVLZrWl5RiMdCOPACkhYHwBumycRlQw10LP00kKxpYX2JGiOl2W1XON+YgoBOXueFbKtP81aIvgtVrOeJG9WR1xosfzeDh7NsPX/GxN4ouxFQSaOy/xgeJMmhw9u4h/fNeNVvP1nUyl3PzZn59UjZBbaaBFOUNRNOKWcLU3nsIkYEFubTPbH88xDkFdW4rHLhHm+p85T843c66/HJ0eXzhj2noBbmSJIwMw6zF3dXnvmWf3sK36CuGlmRzpEJJAyto0CAI8L1MOy0bVTJGTAn3xrPeiP7w/HDJ1STSH0Uo1m+cEB6dwF3s2jpezISPZpgLrNapW5V1eKkSkYSXPnlqo7ZCInC/FS1kaY6RcpXj+oQ1cCBKfEriEiKYzzLN7ZuKPRmOyJPafLDyoojwS5GiTIIjU50OfIzSnH2Pl9S4qNDqBsJwRQA54ADBVkKYAlkEMJXTdA0ySwlxA3K5zXrtDxkaxKXSPFOCkdJ4RYExeOLhGaleF904j2NwRpcJvgHf+WXz0yoIM1Qg4RPfY31hr3N+3KB2hhDiIwng0WhPR8UzbKEu2amwsYF6iogvQ+TPljV8Dw0LVbnClG447hm9mA+WOMf/BwakobZbkLEcZc+hqoVTBOm1rpYhmm9xxxyZZIssiH//VvqHEIYRxtWILbsIxKIchoecYiZCVrVDnETz2k5QABIPaKA5sgLtoTKaASASWjIMUSZE0BVhUAFnCf3hyQO/WMhItWnC3ypNKMLEOe5UVEZzECTuXmZIhwWwwPnBTCRMaIg+94b5OA8ye6r0wnGkk21ZPPzsEv3BzzPDZPbU/iZkbpS9uUscfjbSPJsiYmuZjw0WqjoHnxZT0CHNwb6W1DKzhp+8rhrtJZscwoPKpN1N4S47Ox3d0rv7GltV5Fvmi5+hcWcBQRI0jabXUxJ7dfe782YNeFMO+wL0cJvpM3Uvil67Ii5FSW1mHDqXPu89PLsorjRASvESX8+zExyY9xXTMJ1OLArO11zMR4sUrFAsYK4LK7BzO4FqDt2RcV0LbDWYRTCBEsMQNA0y/8DmtwDVpsQWAmUaTBHRw6noCjwQ0k9JApIX44sTR0wsPtifh/LyLmL1n4zJFgsUFvVzYaDHpfP+ju5qXx5wxD5yENMPA7Q8mz7S3bpbSqwWmVEo4OzjjvK4tNXOGYU8sP6LUSl50g5QVmcTwLu/IA8vWnQBnEGjpYrn+1Wsrn4LSXxz42/H8DETlXSkDMGM+hzgxGy8aPFVv5S72jriaNHgxCFKWaXo2kjIf9eTqkjHAd0iXIUL3VSTuD9RZgcNBqcGI2ZUwPFU+721W2ZEFC7h9lrEMyHkOr/OMZyUXgwuvjGWL0uki5QP3WlOw5n0Px2c6HrjousBCEmtJiEQgYBYiviNjuXlEtFa4o8PzlXVNS0mQBHKeRVElkKKxlYqq1m8bNY7mMzQvhPFI9S+i0JidumEhCyKGKMsROsb1sfa0M+z5ynKe+9XfXPr8fz33CjcrX6513rV/5Rc2kmyZb81vXKvn2dp2JIH3O054HFcD5fl7+zostXZ1TRSjfnltqbvnkCMvv5U/vhiN8Ury6sr7h52rhbJ/Pv6nf2XclhhJEW4SnR6eYwn9ICOJkaYeT4QggyoL/fVaIZD5cKgKwnvP543759m3W7UlZGSmTOQ8PTLyGFmW8xYDXJFVD51ai0bDOCAiigA04UcRE9pxtcR55ALoFJXfevbjREbmxSzxO+B+G5R+ERD2gt400EDkEy99TkTmaHy6SFa3uHxa7vfB3NU1Vbtd9xalwsen4XreU/+yu/n1pVu/RD+9PwlOTYWNDQdeZryndHrqEuuNMopRfjexqbBFUS3EeLEYMculX6qWPvmzY+vmWj6Xmf67F1/+zq5t+VjHKF+tWDNbKlpMo/zpx2dSYnA0U16uvDhdrBYj5StX9z8KbiySjlF6ei9d+ZvXvc4h/eJoFrMyRgRD0xpPDxZo8wv4UJ9wM3dJLk6eejiXrObzApWqk7nXnQxNC8qZchZZbmAOrq0tl0xL0W0i8tQiQ6nnox4ijMqBwMsrpG8FTD0fLcTsoz/78Afv9sef7P3Zn/0XnFS+/3HvlS+2Fr0LKlezrUm+Pc0uuVCPwN1DcHsZ0FUQvtuDXzpLPl3r36osT4dBaQkPSLIR2vu0uEt2HtOHTqsG/3lfJesUr0PDBGkus8IXOA+5iQp7E0Yrpqt6zXowufL6m7rXvUBZQyYqW3nitB0tFVevcOpCw9bzdFdh5LAsYsx4MF3BqQJDOezPPn2WrfLYe469IeRyaw8G3eVfvnljQ4IHowMzCLMSJcbhxCevevRmubc/w5uSFwxnIZmPiZqPTyNYWsoiCSSiIMzjHspnJmikhw7ALYAjIZJSRITHThIiDqRQRifjNHURC/ICFWJpHKcIwDA+0BdjpXgJPxkv/CH12TTn8CKapAsB9ZMDqM5FTqL1jHJGGXaYzuxqhpr2vcRIEixjO2lNgDgSsziBCSReCNR1YOHR9T7ldYcvPprPpaC5k2F3KDRIaBwiRcpxozgB4RAVW7ktkALg+wunul6+/0jPbKYGESaa89mPtWAQ5d9a8Xr9w/uz7ZcphUiBjTBsggf2ozunny+GwE++tV2u1tgoVgqRmMOCiMQpDGdXljMYlwC3rRuzwHWhQUWQSkF3hrIVKpgrZgTiCMMyAlGUFTeAiOphgt9OEUDwDWriuL2JmcN8K44IPGAyaBx5jsrIvFsScceaX9hWHMb5lI8DLuWGYu4SyEdRZKmGrk00KY4mw1kQUYLeVl58UqXkyCtCZv3OCfbi4fHixV+ej02JZKh8AQREfcVj9wK6llVc0sNtTmTVLg4QsXWlNDIARppATH3txDmzVjfFFGFwFMQCxyRU70SHKbFalNmo9OMP3Tlzyl0ajedBthCeP8ryNEMSEJcJMRYn3dkrt8VRlNIZxvFkZeyc/oeT1lrRikU5yIUc0+KJzmnbTHsbP59pQdUYIek4tj7TljbZEEySZeLR3RnAY2fYg3GmBKJgEtI4UxVTBE+yqulrKBm71gyO9ADIGAFjjk6RQEdx2rTEmMeVBYk4aeKH+Yxw3hmm1vyOzzFzBeEsxU8oMdanXp4BEsqEFEjHqaXamoAkGeraDk2vNeshgmnBg58dTud2OrbEZby2JFdY9qINJgfhlc3s6Yf3fuvLxQ/O3GOvbvlCd84y7fjSS+ub2ZrwhYNPR54lGncGnYqYix4ra0zm/8+if/VstiWGgd5aO+fw5hy+nCrXqXRy6tzNZrMpykpUsAWNJWMAW8BodGNgDFjGQGNLhgAPBM+IpkSRMyTFJrtJdjyhT6g6leur+nJ+c353znsvX9h/4rl62uMA24QsmSwq2N6uy5bYb/4N+vyV/ur5RSD5UCXWOAIX1dJ3RbF2VThphcfl6hWR7551f3WeUJizOy460DJbB/ut0sj/xoIg317QI+DZyUUrLFWJopxk/ZDn3Md7Y30lpnKgi8ksEwcRlcT21MEDzAYENprbFhUPx8Nsrf7GtZJmLwDX3R7fKda8T8H6Db9ygrIkwqQSZWqWESNjZgyHANjUnWup0xCL2sbeIyN9TSix+BKHbIw4/dn50pawUpKtOWIYPJ1GmOfM+vHlFbV/jqa6T6SFOKLnAxeowdM+9rV/0fwvxuDY9968tAw9jCQotlibH3UnCSbf3er+9OLo09nb/01xb7eN72LWap73JdfRjvtkpIFqOjrYnmXyhc08ONt9IabsrcsSubh19CoWV1euJ5O9n7z47Ne7HGOOW/GdLWJtRT1pGdPT4519f+Mypw/mK2+mxwBnSHf/YOYlUfhqfq77bLa27dprikCl1DwVW6YZsfGZybPs4Ke7vrio/N1/8cE/+z+w3/vh/72rUstcgDkIm1oEpMLRoJ5D2KBr2DjIpEE2Cn+yu0+dXMpWw+H87pt3qQUCucUGQ9kgJwAgpDY/fBtYb1/96V/9h6OT3u0czOL8oBVZQ5OvNg81hvTB2FNzlDhzsIV5su2DJKN1d4cKy7gjyaEMN4N1DL0ck3jgWUcYJLGZYVUU25yNY40Yar6y2XBjMr+RrxSD47ZVTznz49nJgxOYxhlGEYwp0gwijEcz4+gB/tqd7A5Jl1lhtbmAoXgWkHlCkS0XQyRCXgISXswREduazyhORDzFedbMiAguSTgcx0EQJjHmYxSGYXRg+JpmShxN0TTACaIE9J0tS0wFt60pRSu7Ij7zBoKRyYjD0IvvNFvbuDvEZyKFbJmSAaN7nUhPpHzs8MWxk2TGwIUozWZjOR3tdfmPyZDngsngZSVbulvxwfzcCxWXYTiOvpgevuoXm/hMUvZfnV0uVS6FCiDD5rfe2/3Fk3FkyMn8P//J/lpDFfKia57N+7mnv9xL1flkNLcveFmlLO38WjH91vv1z1tsKkou1V1guocPh0hQfCr/+lbzcJIs5fFiKn1+fN63baHM9CybsGMhIfKQNXumJFI6CaBAClcqIIpIgqBcLkvDuUKMTScc6BTmEqnImfpUyGQoT3ch7dgZruyF0f2X1pqtIyxdWuVJoUTiDACpcH4x0Wwm0cJJREgM7sQIgoefffb4aHcTP30q5yHWGvXNcJL+Zu6WcEX4T+M/mESvqNmIw0uKVJCBa+nBswO9supR5tSQ2cZSPjg/tQeE4btpGXMxxusH47mPWLRZz0JjghNcOouSmTObQe7eaj7nP/7TT/EzT3eEFV5cLqfGYkpYHHUGTpGSJhOt1QuFd5b8kwfMnXzd5DbHoSxVcTdyZtBdAzGTyl+7VMU0CLTHk4PDfVpEjKJjrJSkyvSrs72+lSP4CQQyXxIGXYIsA1HHaNujvLBvak7LXHy99GPdlIHdNLHYhEo9RZCeTTGq3h9mMkEW53R+eDIfxVCqpaqeK53NLwABJERP7DkiGA54DOq3jElKSm/KJYlQUtLxZ/rFCjY+GPXvjyXoLy8JxbXK2RcXLtA5kIwDTK1RBw5295qMnbZ/8nTmhErOdnCfSmH4Ox/m2TXF4L1nZ3a+aLBsMJQIKgz0YAIggUr+nw/GzXzO/eiMqxbwrdrOR8Pez9r8Wm1XYspZisunoqHf+bjNXtUmL6ZYp+dlMocIrVzJb1RSXyymtRsNpm0U0wa3WO2edsypEVxJOwIrXGf6rVnhZrE3NKmhmduoFRdT6HAaMEiIcEQEPuUljM25riMG42ODS5HN63Bk7nhUbL947Dxj38jj8fODzeXSxWGvUGL7WqCaXgWH3RC3NZRV464TnTluZiUVp5PBo52W389S6Ett4f317IuXQ17H0kUqJyWTvi4HLgvNKSHYtslMhZUV6u46+NXPn628uWxenFDvsnM4Pnqwf/0bTUfcOPujP82UqC9jnDIl/9UL5W++DcajhY+m1gkMhJpjbJe2VnY8iGcl3NPH3aEkpFLNnBfDtfWMkriyQHS/GuSW8+INOphSxXy+8vZ6fBJkK8ph2tl7ZZwPxrn8YkzAsAnmqYSI/dHY2xsdvxxN8mkyYRWexxpZWc3zNCEkXWfKxPO5IUEsQbZhumQ+s6rCF7/efbqbLOTl//mnv7v9q+enX4Z3Xk8PTtqVRRGMIyyKAS5JOQQ6Q7BSiL975xIAAKImAgmwEOBiMCNggQ1DhJMQA3tTsJ5m17/1jx/e/wP1syfBfIwhmiF8NyZFHatuqlVc9HncnqPxodW4kvWViGKg2gvkWpQDfpRMLrhmOPVipZwK3GoZG8ZAFtAwIJsCpfk4wJPsasE6NUVBjValvalQWCi5BwOiwsM0E0ulk9MRup6nF5zCmQYSLlLJeKJJbERilCLJjMsGrGgFMCmEYibDi6T1pOcDSi7xpmXZpilnCI8BIYclUcIR0DGD0ESIwhHEEgJRDIkRMEYJERDwToF1OkY2IbxsZB70sgWKJUioCxyjnO8ELoVLKUic90VACkkaz5CIIydD42LYr99UsRTwTwzrHM3HhsSTxKJJIiup4otVQfI8FGLHe2bnYpApCc0Cmafjk71pbo3+r/7OG6eveBuqAmC0EVx5Z+vwR88hjL52s0rh/sNnBvCMyePnAWkUlYXRaOpCRmbdMh+9eDCm6/nrqirg+tnOsD02vDlbXC7euXc1yleWG7PnLz7jIV5aAkQ7Ho1tmg6yDJdNp1fXl15ddB58so8j1MzXaUrVUdg6t8k4xFxaZSK6SGotB4jhcja03ASanOs4tO96TuBZo+7IwVNpXBXLMkHH+PP7L+tVUa0tESpAhuYm2LzrWKF1pvU/fX7yi18/EH1fXm82w+l4cCaEdf6H799rXkOjheTTv8qSEcDNugIJy5l6cqWqphrpgR7LOYZp5O8fGkkPvL7MWIyM4mRyEpcXMgYKEU+3J2x9uRofB+PBpJ7POhSz82rKEzZFU6enp/qJ1/WSXmW2/D6tZuVmRs5j03QFeZ7ly6of1cs6Mdk1fIAbp7YdxEt3K/4lBo6Jw/2uMTnrR5NUlihABGCl8Xbjcf+k+7KT0HbJQl+NwnwxYx340nCmg2hsUjYvujzAUlQNhtr5TPWQyvs2YFfXFAegHnACitIFxtFiVS3Il/NBPBo8tKi5ftzT5CZXZuKTC4cTWCdwrClv+k6ZKa+s0J5Gzi/Gist846Zy5thEGLz9gXrcnh3u9XfO5lSU0CzEQzaZOI0axyr4qS2nvimOZ14jz5dFUqioTIHevUgrH5YwAFeWBwe7y0e6m1/PJ88mWAQffWlceo2gnQB81Zme+d95m3560HmxY640MoVm2mI5pNv7j55v1bMZzoOTIDO4eHyovXVXpBDWcWk0DCcxulOFto+0oZLeynOYSbAoQAyM+aV649nLQc6006psnGuu0bJnzlohGk5iHyNJEZM543jsUFQQQayYTbU6rRKBm2FHWXTyqXDyDGy+V9MSEH8VbImuMWhHfkxUshDhnEsvk65zbDQy2KDtjEU+R1ONSwvb2o7enwoAOPzGravkq6cz4PsbBZ6oAU9gj/twZmMg4ai8QJaszz5+MLYnPt589FzLVotPzqxiVkgukcfj57QxqKVFz+hVtlJ//ue29W9+3Nt7vhhMgpmoLKetQzEHvMD1ohSb8iTGi1IKCATPnB6PrH4YyKoixjml17MSQFR4KyKUpa9tqYeZdmuYY/StTfyoddx7Mk+qse2RgzBJO2QmIvKcWn4znVlI6fNQ1ySRTM6PppE2kPHIQOkQ46OALPCcmBa608lys3j9Tqq363316bRy4bNDQ0jhF0emnGFNDimzCMgIzHxwjQeeBGiOgf+/BglMFO9/+YwN841VGA8ipZwCCfRDL88E50lQxUh3oelpo71pn+VJ25ERqaZ5Nm55fc+2JJQTSqIqZfPCvDfjwliRsX7subhrEzTynDAJhEgjgO36FkkQYxPS5fyLIZGgEDv1CtUUgqDl+zjw8/dIhCjnlawPovbxtF4SsCTpt8OqAtom1PZ69dXs4FWcrtG0pFhFuZbPtfetJUWZGrNoNjx6egF8IFWVmRsQngMA8iAAGEbgNIgSnoh5BkQQ+gALOBiHCSRigCGYAKJ6Pnq4Kl+5U3zQ6dMm9qEMPYu2e36kUqO5zTApIbazWm+boU1hUR4iksXOZlGBZBFH41NERUk2hb9cqCi6U3NmI5uuQz8jaM8/8QadQ0elv7vCDO/d2DWD3uPz2q2GkEkeH2q/uNi+Vlr80dO/+P6ttz/7aPj1HzDpbPXk5bHpYPVqfDLpFKVpNxY1H5bGwiV++tjJ+ZMom2Fyq+zp0VilRi8dYzGbTBxeyi9FiDeV6MnRaUUkiDg/2I0wBVuqFkbzxJz6RpzkyzDyveXXci1t1j3Wuy86C2vrNp4Ej/fTVxsYn2BRiEI/n5fORp69o+WEAORlZ96JcSSkfGMwV3EY6LiXgHbfKsXG1XcvJ6dmt3dWLmedxOGnnkR7ATf77MsvHrx6bvOxii1P+8GTnVf9BkjTvv0H//2/Ba9v5a3A704hIBFcEBXHj9S79UBUo8PxPiu5sqW0X5bT+cKtpUnXMS3acfvTEUZzRkhwtuU3y/nYCUO+WLlZGA96XhwovVc/brmWoKysVSRo9wcX//oX//Lu/jf/u3/1Nwa+m5yfU5eXNbbSGGr2qHcyMM3nXapYunx9jbm+NUWTzot446x7bvOrKxFRLecYx4Yc4+IXZx1H6sZqdNmfzQVlUOAqM9edHiVCuXVOLmYAur1xM+MPfvTKm3s9PwCuXivlXAF0J/qFx3EFtmlxhUjQI5a18WRuMkpRvh3C+aDcYOaA4DyLp0A/8bp2gGPAKGReq4oItw4/2c1eXvPXrtTSceZw3DHjLw06l6GWt/KTCyuTxhIBPg54fCXV/uyicL3KeRZ34rk8chXp33w2uSnFxKGdFMpBQlW0wQ7Derc2bgTO3HFTwflcpODp9KRt0kFscSH5d6+94PF4/zRbqoK/s/T853u5j50CjsOVqJXCQr0AbhbI28XUX2+fV9PmWT9HQiFE5YsQfzY3e1YJ89GjA46CUoqGtuOczth3cncvE73xDN0qeHvtpcsrHiX+MjDpLP06zvUd5yhS6AJS/XkPwmF7pKrRSyLONYQBMy5vscRfGeOfJ+nq/GIn2P/fvYn2pz16YUSJfEidWN6qyHTtIEFMBHFtqEGJ7fssf/syEC6cGTy+/6z2/Wq1FkdY/GBP54qM346teLiWQ0Qq0Y9mZTZ51tte+kFp4h6ouTa1VuRmZzAp5V49GBxcqinZ5dLCF5/uPOfb6ctsXYU/+M3Fv/y1EMzlxTzppdi5BYOUGCcMTqDUwFxL9AclZgSpOSIa+ZRvJNrpaE+LC3k+TUHoE92nnbMHp+qSWP5Ojd7vfvMfL5gMPPz/PLsAlQxCYDJnF51OJ4kQ8/FftzM8n6eZdt93y41iRmIReXycyNloDnHSHGUrIhHgz//6oHCZqF2vCEu59stZo9YkFDpxI17gRidavVQB/BxsCeAXAfjh+4AGAMx/+tlx5eWXVPP1m98shUAdhoHgMeYciDmcJgSaBinQn0ZAJDfOckF7vf61Kn/6B39EbSwfv5ivDV0tzbNVmbG8gMR12zG0YSmX2h8HlIYwGOYDH3NmUp509W4t0nazVDfKiibFmlSuUpi0LGHWW0r4A9JkJNadB3s/2jc9sFRPMVcywQv3ZK+TXuNL07lKC+ICM5iZfpSNgTs489NlEdtSEZ0JsszhL1/YhiMwmswCqpFySTbw4siBEBN8msXFmCAIIrYBRoZuwqT4wHdjGETQC5IAAoogSOIlSdZk5XxnXMgLnMNF6aAWl3kLkEvikykugDgATmzLayWMKNRhm51qmuqGNOJzaRmnE2s2xxFG9DQwH8diuEJPn7zs4LG8ei09/Dtfs/Plj3/v8cH/0v+Nv7fYWxBOPp7DCNuoxRPNEEtjl6YEwH3vt2ujPfDWrZsjNflff/8+xmKFWlkkGG87EXhsfEq+pH26pNMUfdjVIBNJDZJW8spcPmzrAyvcWqnm8sWLoS+kEwwPF3N4/1XU2jZTWYGLFFVO1Bwx1ZMn9jgXZkSMAi6uKLiMTezRKcPCABo8x2NxuPtUL+cywBOgFx66cxqEgGaSwJ3oNqszKVUxdDD23JmJFxbUvSeDWrooUsn4dLJYEqGbnJ2ef/TVx3/xYJdWgzeb61SUev7LnW48WVaWt3cfAgtg1Msiq7y3GTy/4KHYIIpXr2zgWhxNplpKEFfWJ+Zh2zRp9YaKeZyUldyRrrKULmJaBGiOoik+4kSoUDCkPH2Ep2nkux3fZJ3pG7cK/+lfHTx6cX8OfA6otmQZMaqVZGtQcAP+XqM23R5pQdSZtJZvyYu3t+QMvdefdjs64VO+k339O7kpyE9Gw+M2keOjm1k2cse709BxQUErpBTaNhCYRToido+0Qr365vvNLxXu2SD57jtrw0f7ng4RvTQP4skxKBUFiaDTMCeRiuVImQZJs+pw3gvw2CWn2SrRG8RqHnUQ6Xku4YJKFHsRW0uosI+OW9H6u3drC9kzPfr5I225xpMLokCavQGu63jlUnVu+aPz/qTduXUtb/I4dGKWoIJTbLCvLW55X/uwlILEaJiABjh4/HCFhiul8OiCHTlUBDmxITZrbBVMf7537uHqYHv73bOZp4dESP+Nt8Xf+9Gz3svW9Q9qVJVhKVtgycDAZo8nGEWRFg/mfomng3mEeJlfwmgxlaNde0ImnWmiMntOWF0qX7nOnh0P1xBoTbDFdLqN49Pts6X1AgYh1gWWwKg0mliWAkIzCIsq7jAg0VxtN7jzL9M9w/NdXChJphQyIC+s2Srqz2eWGeaZiVcoMZJCTtvQD9DFyFMqHMez3hxenISo7ypRdjkbjSA4/5nGJgzbUG1aAGFsXHjzQKzhsWOZIiCPPg4y+SacqZ0B8sJ13CzrAiemS09H4Oobi9P94MmBTV5evDhz3/jtK0+ez4vVfPHa4pGOWcPgg7duPD0ZoJArX1oNLlpJ7LF5VvRjLaJiRSzwuPn4PMaSpXfysqIgyymvkN09n6xnim81dyfmdscNCf43CwvGLTOxk6tbl9DA+egvdoHj6L25NdS4QhREJJum6Qw26tt9A1u92rCMyOvbqho5o0FjIW3TYaczCCcxIWffeb/qJ6F22rWGXYUsLJQYrT8nNZ9/YxFsCcAGqL8HaOHrb66jN2+ebrePXtIYtBwoEiWOYWLTsiIeYwBiPFZlZCI29zSy4cD208npf7HXpvsqEdk8lN+m2jjqfDQSQURJKU+nJo6bQCAp+OgsTmE07TiuYXIC9RRaA43BbbvCC6bJAJfcbGTOtOGop3uW4SyyeJGy+Hw+gcYAzb/sb6woPitoIIyxoD21iSBJV0hgTzxjjkekjoz4YadVi2tpbLChYgHnYzyBhV7MB0aY4qn+qSdnqAQndCtkJRrARMJxwADHj7zAC1AMUQAZAkEswXECDGXqP6LU/eHDG9y9RfgQwX0T3gkgrblzyYk1QQqiQC1WIm3waOzRxciNsnhCkFwhpfrQ7YX9C2OEE5HKM3JJxEvL3xQrey9aD56d61+cvfW1t+Vlh/OTnb/4ypawzKU0P8VCCugu9WIXLeheuz6sKlc979f6rpWtoTfvZKba/smp9sVpD+GkoznNDJ3UMiWRpFC8drn+6OGFQXOnk9npcV/h5M21fA0PM8AH0DRMIlOX7d40r2T6kW8gb+wwM9spQG7pWsF14NlwHAQ4lAki4J+9al9Zy1Svrhqn7qdPH9x8u/nm7VwPCMzAgX1z1u4RwMXqc5o3qumRibGaO20NWUmCAZIMy8UTzrRYbcjlYzyIwYtHB3rrGGLY9c167+QwHo4upZk/nM/cxHCfvQThHUkx37l7bfbs1whYPORXC9lyCuRW5NmDQxFI4xoHd6LVhEzfvlFs5Bk8YDierVfNjgejoFzAEpCvFMNIJ+GFyUs8G4cABJPONLS8a3lwg7/4hy/+agbGAIDbzaW3bpdVTmkW5Y+1VkEUaBp/sTc2I9pHfChnXN+39i8oW74sRRbIc2ueM9n3jI7tCX+rrP7lUfj57CJBwS3Bx9ykUMc+03GEJZo76yj5ZDkMcunnR/6GRF/GCX0yeiFweJKMd86KK8U4n8RYKOOlNBtjiQEpnjIBEYU8TSDatsajgPA1FGOOj6YISpI31KhCBvgR7eOONeQEaUDM9dOp5MqXqtJeUbopQXlsjwvpo8kMnJ97A7e0oCiQZ+e2mKV6QSQ0ZWE1vZCuhk+PQz2U8uTPraB5Zf2NpevY9q+IL7QbctCn4yjCtOPReEjzpsES0epKBHuE3jULQny4xvzRyXmw7/7WO5tWNT3dGW8kgMbjGYaR8/6pHorjUfV2UV0pusTotJeQRfliFhJ+wtfTzoJI5pm17VEwtg/82SCdSkWaagZsf8hdq+o/OfBoq/rGatIbVWvSV8+MlYI61gwvS/Z9XcA0IrFoj5g+jeFCOdPrWE5qhkPX4+LZRf06Ymvy5XHB1S76ZzYeGjsv8CQM3nsz3R9AAuC8or67xk9wcmraQBEuv+mgfF1x56Cx6Iksz4bWoUGlWlY0nYFICSk27h4dxZfJUXcCb6zEZ4/2zCviympa/+ivXmEzv7GxktbTy/lWb35JNl8Qw/smpC9fL3/2kvFhLZ3t9JKTg+H6tzNPW+0Sk/7qwhsrHCu7Y5M6CwZpwm+8lzIVCZ3sPy5mItBvHb7svrZZZGbRj+9/byUrcqXtL1+lt89ay5ufnEblIKi/Lasppfvps8b1Yn/InBwT2S28c9QTEq6ykSFq9MWj7lkvWMlmzal77I/YtMSJxKYYT7od54W5N3NSWbawqs5HCI2ophKDyAJHLwGqaFNH3mwc/OjRyv9mCwqsvFLR+1PaissCNmwPuKWixnBkEjEwihkGwkRm4vfX6t4o/B/+x5+v/PBS+Vp2vv3seb5clcDJnz6tkiSdk8ajcYyBie7kalXybETpocVGAz/kqdgJp8AIaouSCOy23hIJtbc/Ur5+LbfGYj65XMqfm+HUARSJowmgUcgHrmUizElmrltURDP0BRS7UaQyBAGxBOE4hhWsID4YnUosyvAjl4oIJo1F0QzIDB3MDMAxhJB4jM8SHg0TnotCJ0wQDr0IT4DEYVFAYxADOBH5MdG16HHHW9wovr6Enj07U5fSCJ/ttHxGtzHBJwFn2jDiYgsG5ig23TmrEoVSaTI3OjbUdSeyAmW1qW4VzEfDVscpfToKMUYmuDffz+3tO/7B2S8H01s3s3jITA8M3bJnTsCY9NXNxmF/Ep4NioOjqtIIL/Fnx2NimFZeX2AGi69zj/J5USaioxd9bxQpFd+fdvhUhsJrwNTC2YTB3SIJqkU2LMrNtQJkcQ5hzsvTwQlYWMamaOBTLqYWOQmLPFzTqKOuo1JJQjCWH1TzqfNBJ+xZPjs12AGJsDsf1hyeu+9qthassB5hz+Ucmy8o6ZyUAGzs3veSCcoKhVjBMOSN2LFO4BKth67H4dmysrPTBhn28uLNeNtYi8a/Oo0IyooE6Vtv3/3jj7STaPz9ukVJUWv/2YOhtbnZ1A1snkjvrlA0w4ROVCwiNR3niKgorF9ebGhsODAS1psoBQFjkyRL+BFHM4E+8fKSCxtk/GywkPHPjwxyMnMnyM8RP37Zeutq6cHZ5HvfvAsTG/Eem4MoSuYorIizp9tnAfBZrta4JNauFg7azsgjCTQs4pI3HkCJHI/G/RX74rjb6tc2Re6c0LqDqUTIADEHg8gfBzAe0+4sx8Ykm2f1sRkhHGuKoftEkbB1OvrpPJ9hZD7sXiTSJclMYpqLLNNTOHZgOrRljAZTKgVUPnRpPM7xJInTmjMexjQnIiuOHZwsEm0dqy3ykYjCIPJJYsAGpj3ujBh66gvV1Foz7OwZpQp1kjCxJE8inJ6EqUWCsPzeELALi2uX1S/+5Cg0yC0BTh6ekN/0ySoHlPhwYJL6KKRINRV2CXs8d1IigeLk1tvV7S/d85P2rXrj2UsnW8paHvniP3UaJVaX6VxZyUokv7qVpwLernyx3z3aHRkHWqnJmrNhcaPMbanRwPI7Rv+rlrU/+uBrGb9vJa4+8HyClDpH/uo14aRJ2nQsAe/swsov680sizQtBYm5QVTzRKZIvjzzFooLeVaedmR/6tMGSCD9yaea1MyAk0x7gAlWwpQyAucTZOYHlxuOG0oCucJkWEqlOWXc0y+tLI4jN8uIFx1ttZzFPC1MJIsAaR6D1wAAYJS00hjY9sYFCvJNSuScG3lDEf2FnAOLtGcHj5xr3/zhPYFL7v/xY29EKHnlL5666Ub9LPLfWcodPstCAoVM9vJV/Cc709iyszgVBoQjiyk1byS9Au5H2ZDCotOT0+2d89fuBMue232oC5z3zc3J53/wUkwQtXzzaqp88HC/vrJk4QT/XrYGGr/+5dPjIzMnbaRWl5c89cX4RGsNQmAtvcHq1uDxTztmxxYCzhvglItJjMyE5jQ2W/1ICjQcVSsCYiN/fOE38zKiiPZAz0s+JY5iIbGDgpLP0N+q/Ozf/WyA6CtXbtRvCSYOevqguZZLemNaYhHPxwgnGCxGRsQnPJ+jcuz6nVerS+xXj6fUDF7/MNfZG3B9M7UpxIkWuUFWyHl8yNLOeOCICmMYLuPxILAnNr1WYi5OdRVSl7I8Ew/ayJnsOPUmd9GKuJhyKUjYOE9jLAVRihaq2c5IYzQPUbE2jiiBoEjWTwBkCTbNTs5ddziNXReS/Ey3ASAZlSo0eStCMiMJatw6NxPMtXDgYmEMkZx4oRHJDIhgRDMojhPXiASJSygCRQCDOAHZ0rQ3G2zbWxdRrni1/WgsCLOhzxTEiKZC53TqFomzBGi68/ZvLSURnRzHp4k3Fjl+NJKvLrN9cCoG7S+3c10gv7HQzyKhbyJzfnKxu/BWo1JTn/6M6/YdqiKvyo4TIR9LLgzXxMjzSLxCaC9P49traApS/miHKucljr442RuOprIknOz1XR7jcex4bhUUAXHg5WlnBG0ahVKG8Q2EYNhMSZWZ/hSkEom78/2Fn/xRZ+cUmioxZNRL9ar/cp70DYHFEEt4fsgTQiFXOD85+3h7h0vxNzYVS5QKAKtly89R6J2cF4IsQUSxIFRK+V2CQdEReTHKlx1QY3pzgETXnUfTKQoQz6H4poJak/xYL16trw/HGnTnBBEIkCosrczbB+eddraU/b/9zVv/5s8/+bXuhu2emXg1eaOkYFmGXrl7JWSNnS/PdVuuXl2goyB9o0GrK3oEzC5YqGdHJuoGFEsH6QVKn/mYQwxMz+zOlmlG40QWj2wSxZA9MHbssb5RE65+fevkSTwbOev5KoHVIC49O+kzZV6R2SDCimlpmA7nJxf63qjH8wJvXzCCSxMiz80L4aMJIfQmS/JaO7ClHkVICE/VX2gD2wC0xNV1/YyXUJOTJDYIyO6ht1ZVeVx+FiVpvrD4uP3pK90hGFKGZZXjWVY0MXPq4IhlYn/Oi4QAp4NEosk0RQ08Nh8xo06fCUDMxTAxzZDNKEzkYoTKmgINW3OJTk5wdrU3y8C0aAfeSjGbZp9NnGFOklwvGWup0HCrYncwtrX0rYYQmrH2Yq7+Zv7yP//Gw89e8ROvQhPi9hC4k89JIpx7eYqOUDQa+7MMFtrRu1tUbzAhJXHjO5WvPjWNw0kJS6U4fTDyqzkuVZb7GN8sy0mCDu4fTyAhTXr4WiabYbggzmTRiKJMP2nvhcnArEOwWqSzby14LIZUXsLjvJiALLn91Omu0qVl7NUvHpvXMuB96Ufbk28vxA/NcC2VjA1OMl3B8Uoq9mzX2/tV/9vfWSSasUY6RpX/8F7lrQVdqX3XAcLJC8fS5sU6tdh4HQEUoBCDPp5QXd+JcCmTYYOhFSGQ4IDNMue+I2O+o/l+TCAMZnkIAeCSAsLsFUaIwfAqsx4nWl3AMfTKLd5iQRZwT6p/n2HBdwA4LzTa0rU3skS21/mVkirfEA72QfVso/sNo/5IqOXReaN88mqvu/CGnKScQqzv3T+pvMGW01hXM0yMzlHzrfe3ajf94YMOyam5G7fh06+s+/Paf/3t+dKVLz/5q5AkAmEx9WoYPIi2GbvxRSu7lctV5B3Nlxq5yftETuXSlhVr7YMvOvNQyTYr0DQBH00twm0FasRjrELwlAFD03LnPqwvkBhO2X6UllmH9xgJ68wIkZde/fqQmBuN999G/5JeeLLTe/Jy/2fR7Q+u7kTCyatWJp2RRQ4g0oyAgiwMSRDDAEJjY3zl3TeMVz+VLo4o1bSf9R//p4/ublbGrEJA3BuN0hSKOcaIjIlrJLxgI0TY8cgOewXuKLSuiTxmJObxYFmEX9SYxEReyOIlsT9ObEfLOYFLYBAlpo2OO2OqxChJPJPY4MSGrJytqebB+cWLM0sRpSt8p0fmk2TquUEEKCxyp2aEmQ4nJHQwPbAtgOLEYRPe4xIURSROp7JsENlMOrStSA4xwwKhDxhWZCgUQEg088TePrq8UBue+HwExyajXsIOT2EIcEZHqRgxU//NJeKz87j1o6OjmXQpL7iUyDGJwZiyw3ozV01sKUHDOFE4rliiBT6kTmNDp+//r11J0e7UM6IKH35+KItQqdK7B/NL62LvbPR6CkxeGJr+5AuYWVul9iZtOsUeblNTP8pUU7FOlcqiZXTcgemPDM8Ljuba6VksZaPTU+16apHP1y3E61ZqVGICQ3u2bRTLaul29uXTWZqW9P3Y0O2NOn08JPsXnUZpqVRVyYgy5liUSuXSIgNoJsQELN7d9zB75grR3bt3fYo+P74QOVzFU+selE3FMxxPqhx0XZB44Yltj6wyxWBBMIy550OC4VFWxmGZLUD34thS2ZhDbEXJsI7dnbXd3hkC8HdfXzrRCgRxOSUFr9ojz3KbBTIID//yJxdMKrew0Bz3WcqPRPHU4o0LikQik0uTmO/lUznNCjUnKRVLfIMcn5jDRN/f0+uv5aYDLKOobBOHe8H4sHVoNwhp7Xz7ZGlF4PFyXSpKnjkP+leuUJPYa5TZLz/3kG7Wc8zZrqlhk2sLQgMlyHcvbKd1mJSrhfWImxxHc1FiSAUaRMnwRMgFbnB+oHlicnmRTQBmtaazE+utG83yWvZ87jz03P/TxpWLR63IVMtbeZII/YgOCK4zinkF0CDokQaJR+1zBzOMAKMtKijmcmkhg/zYM6Y4mEZ6mM8m9tBFPFXeSFkCGB4nUpopeMlAJ5IiWn29ADNM6FH4lP761eWHPz3hvBE/86faRE1Ed+zOlSSbknjH/C//5jS5k339imyOCQmBi4PulEVEjsxgpGeGIocBSBOGfT5Bz+242mRffuGt3pLu3Fo6/8WJS5q8pWJ5FMSghzSM4c47w2oWzzSYokS/vM801wu0TJ5PPFaO0o4l+DEfJtRyxRUQ8KPItzo9r2dgUkTMzCiMzTIdhz1PrMrZy8V4ajM2ATKShvmIiDpdUtDjVCN30NaZoPT2+pJXIKNh1hTzyhvMN9a/DwD0AfDCgCOpFNFZWVt3AIIAwQDRoT0lcQWHfOQItGGaw3ROEPUwCLtqQBp0EuEUm/GIaejNwDwUBYWiEtYGXhDZGAym2qGSYvZBsgE4HpAAAARX8YAIKYADl9jcqlF3hjFoWdP10pYHKuugUqayIpfAWIcAvPn66+duOPdcb6lC9ETXbPfPeaohwIEbjMxZwm0W1NOz6c9eYr/zgxRxYPzoD60r7731xmr1R7/66vSzzo3fvDIC9AQn3729+OSzfWaQSV0W3CiD8uzVy4WpO3E0BVTTJ7343vdruER88snw1YvZIiPwTkTapFpMWlDxxHiseSQAOGS7rWlK5C+mEVchTRzsHA0K5SUhp3z9f8989a+/nIE4aDa3bvxg9Qb4oz/+2Pt5t/7GGobn4kwW4QQEkMdAMqEghYCUgMivpEuVdCPaEsvm+afbT80wZpcreoKno8Tq4noLW6iJMWXqE5NBiI6gYcTtPlQbhUxWGU/OB6/sUTidPRsyX+cDLsWTkUKKg9NYD1mVydGi47iG4ThJBAgnlizfNsK8QGMMzSXxrG+Oe/GVt/IJ7faOprcv5yfnhKMHmVpGkf3W88nMSYo1yZl5h68uUjXR9/25awtTXGQwgowigXQVNKUDRFO4y0kkzyAFYzjPNlFgEbHulOogROjG7fSJ3n1vOS9bYwBTwDjY3w/yl4A3if54b6/rZO2Xo1Bt4ne5zAKlpGjg+q2zIewgA9GMSBSwmJub9qnoXEwSGPA3lq+NZoMpO285YYbE36hh8xmAzo2rmcHcScz5RMmCsjSywvbLQw1UClsFTHPysR9y+LhPcCgo5bHjmdtYpI0gNzwapKtFqzVZyTFkxDDufKVRHcvyyNWej8lsVpWd/ic/N++8dy22RtANb9XU3e2jaiXnLipfvdLwPWOjpCTp5qMHe4PZcDrqYohsvTrD+0M7t7KZt1cRZLRwQEsxbc2hu/3VJ74u37pr0bQ/GfYQDlWJjsoQH5j1ItvPZ/MR7Y5ZQeHm2un2fxzevFP3yHjq4kDkL11VpJt1b6oNZmd/9FfH63fqxDE8n+n9JhNHAs5kL869k4H99o0KwhhSIo53umsrmaea2Qw5Oa+kBf7iycsQCOsShcOkvlqmcAkQQWalSRPnBwcTdwKpJBJr6WKJ/x+k6z/iyN1EPdTO765h125WkRkJOeIs9KLVFVd/OXESgiy9/vc//PP/7l9hRVdd9S5y6l8O4dpTWFqNvyS50qK22uoIBWarLn5ph52WIWNxlnM0OzA73uKVBX6d6g2h/fTETTD+9TUNYNHjtu8KC3fkl4+3L85bSdETM0HXDVMcOen4Gc73c1lnbslOjGdJA49Xy+I48RMUxgzjdqFazUdzvT2YeUuKaQaNGksuL3q+u/dZL4vL1tCYX9jNb2yC6zlsX3v5LC4xYlMUsOOeUOTJbDm8FHhalD0Z9aTUmeUzA3u92rwkoPvPpsPztokv5UrOsFSyp6fqaUwF81whOfNtKiFAhKjEniQrCk5ezfgUzxxaNE96ocQR8hTBdIoNA1NKpoAgwoOE5GU6OzDYLAc7BjYGcOLqJtIhlRGIiKd8QCSTeSZDX2B+D+E2JWRF/AY7Ou2qDOtgvqmfjlVeC59XhPS0kKsRrraOs3roKuny9So9vfN+hkh4cA8AEoJ5YE9iVkVJH2Il2tilegRg8bLKBXlKSlAU6gEhJOSMTSoAEGmx6MWjGE9PElHkBRBFimhwPvRpbADUpRp0Ip2cg/HUCzUvVY90gPIghewoSRmiRWBCCPojYJyAYjqUPAW4VqJxR/bgGsBxII3PZ+u32JN9ZnH21tr37/e/upF77WXnaLmCMuOYhBeJNHf1Kf5u0YiT6dGEy8wETIOr+QBE8ouX2XuX3eHe6R8Z5Hr+l795mex+Be5PF/7eHaxYuPjnf97427cxgNxf76RXSWqRsnFSxFPtjx4Nf3KIlaRLv3Ubq2FXXyscbR9efTPjhNbuxYvvFODLY4IheqBY9gOyXMDO+0YpW5saXi4V0SopERN8LXfhov2WpTAHlXfuNv/rr7f+42PcyHz8h//vD373w7/xw7dHEHlRXCewMAQojub9KFNjDYWSAEqSECNZAADoAMTYz/pGFKPoaPbd719xQnO0Nwm6du6NW7g8089GvhcUc0pkxziBYZsZuUbDg9PxSL+/3Z0MJtfqi36MpfEsSy3M9UY8mXxQkh5f9LpK3A9R3iftKJAD3NBxkmZdn0wF4YQgw4seoarnyBHmGj21gCEbZwbMZwKzPzozWDLiMAxvjVEq1bxdtnQbx0ht7gb9CKVpWiKi0/FkiQZZ286ogTHGx8nCSgFLMIgnAQCEm5Fv/c21Jx+7PSYkxMJpEAy3s5XXSh9cfg9/sKs7Zwd7z1+2qJwkpK/j9eLK1tW0ZgXDnYEVRRwnNZbUUEoiKBtgRsFgY5EMY+q0Y108dIsV9crtIhjavaPZWp7pO+jZYauBcLqY2Xy/ub89KAly3/EXN+MxOjLnpCzKWBICS9jI0KftSX4l3R0oHdsvqbxBOWlerKqehCvCUlUhKCGfh1ykP3zaTuL8jS25EHz1LGyUxzXaffHCXtjwx/hs+zBZf6/5xluNi1fGp4/NVMGNY1+S6Rur0sHIE5RwGGK168XqnXIZnyOFM7dbGOGsXklPGe3opf2krTMhIgTc6HswzXpBLdPIOZjoOO5wai1VFDizjwcjJS/0Z2eEbr7/9dqgRwQjsbRSpVLyGjGxSr/66KvPcBrfXJAHgQ4TP+pTb28tzzK+muFPe0M6tN/+xmqt2Byg6Xy7bw1b81Flo5wqXs63Ri4XkASk0bSLhBgBGArUZjN3fKRXZERCIpQV6p2/9f3RJ2vBeFRIc+ry/fvey/t60Y540iUY7ulT7ztfl45n4ObKxvL/47/6sz/5s//lTz+9ficllXPMZm5kDldnegkwuRiaY+aVjtQcxkILRZPzjkc3FOrNNU5Ew1+PYhsU+bQoRqHn+UOLyXDVFaztDsmMHcsdPCZCXJQkk3chEwSsH5tdI8YQT4XT0xnnh5rGzMJEKmZwHcRslFbgo23XcCEGoMinmI01FPqHnx2XuVgMZjaGr33vcv66+KuPTv0+puZJuaZQWNLTs82bBXf/qDOagHONQEWCgIHMyzw5tX2sUl0ppCIjFHSwP0Q9z1QjKsd7Ho/35r49szNK7rQ3Vz3hh7cXvjg0r72eP0iIJc+f07HTanMlhhI9nFDiOSLUSrEipSohcBJRjZUwAGYw8RHGkKbE0pIUs4SsOQEVyVgcOy5h46+p/N6Rd6vO0W5M2sOjvaRWy8KU4h7x37r2Vps4HehzrrZxYzMDwIKGKiIEAgAaCCHC40QDPZMsyRSQAtB19qfKWgWJBgQ8DlIkGtpzk5axthWIGM5wPb1NU2OM38zlBMxDwXx/LjalqUVzHgembi0VQz4kAe6ggKERz9qU7mUDJGZIWGNBJ6lhMbIqsylIbVYiXwtnaU/q2A7JL66nEThIQF9r3gEA1V/XvzyR78H7X5mFr7s1thI4Zq3AtCxwBVUGlcVUlXT10bDz7PP7/mJRUavlPU8msfJrFtx5SEvZpfd+O/OxNj372ClmJVQM/ZNDAVjX77HTwR7m9oZXmmElA605fjz64qGpykHz3mK6mT591uK8weEXevlW42//xqU/+de9ECKfBHbg12XXsCZUlqRcw26BXJ4eDfBKSdrT9QVZvnS58fGX7bNn/ZPRF9/93hb//h1+ZcH9jcrPf+9R/aqtSIQyimYYSdQkksMzVSFxXAqLwoSjsBAEFGABygGSbP4GoZ9r0y9HU/MrK2yKIIHlZZ4X9Yv9C9dz02l1YEBMVDjakQ0rOB7gmv39d7PPXesLJ966d5PNxolNo6SGLFUQmbarY6wg85g+ioMkoXiGyEAZRMgOJA/xDKsNHVMn7l7mv9jucRkxu7JwcW5TJNUskRd7w8B1lLVCMg11w1fkxOWpmekwooAXuOHAmSQxZ7hcBNDEIQFUKbwkKfYoDgdTLp+OEoBARIhE9Hwagmoyavn8vOOYwb0fvo0X6Cf3v+j0wnwx7unmymrmrSU82Cod7/h794/bF25+TSxuZfsmPDrqzwJj41KmfrkKaqrnk0MHl0OzGvpFfdr+bEgH5sTkLHNGUvTlH146ejiOQbSKueISPd0dYHLxiDQSNyTPOOra9ZF7bqqq7hsO5nsxpuYyeH/MChRifBEDH9yrPHrafffD+sMXY3sOmkJCirSHZ7ZPgZti0hLZ7Xc4LMZoYjScv3Y933k+e7gTSYiRy8IAae50WLlK9fvu9ImelcL52aD+9nWhRnTcuclHtd4sGpJkjrPb03QRRZcFbNQlfFBLue4QubPELDLs1gI/tATcTwQEElEV2HxasKxWy1aaasq1GHsOxCYbN7mke+Qfgu/8jfc52nn8OKYjxCdnb7+3cu6JACdKjejFpyEB/SvXL1dvNM6eeMN9I8WFjuOjwbkGsex6WFgugqGDjBArlCDwwWwK5jEqcCzgSDACXIBlijGWxb/zHfvf/lsKZXLQtrvm1XwxT3PSOvvgq8nK7c2DnZ28kD/AXiwsX/3tHy6v1c2LFj44uHBDB5CzqwQxCU0PiLNGBvfGAHlmNKEoqVRnvZCRYlFyDYlnZ1kysg0NgtGBdjnPW4XM6HBChv6slGgTGpObJVY4ddDIttVK5XRk2qIIxlMJg6ZCz1hkaVhQym4xuElFw6GGDQ3LCyVMKfIid2WdjYlnv9wpVyQOhZOJq5fzDgl3/uNLJeBXGumjxbRuxsE6F9Ao/HS3nXgUx6Xc08AWxLJU0ocupYxtOX8xCVlKzWG8DZMmwOYuFUXGOAxYeyeJHJmU7LE5xHMfLk34ZiM7zaRVnWrst3pDB6jF7NGCkaYSP3GhKK8pkTcf+0Ui4dhjynd5vjFICIbIppBLwlDXJgadjSjLxadUCgRExjCt3kiaBdFw9Ad/uBtdb1z7p7/zDXHYR990v5FSAFBAbRlQNODBwQu9qslcBYTPkldpDB+DyzLURrDcAEA0o4AnAL+oIhTBrgfKCELOAqenY9N/NROVS8Urzal1xGE2u3nHwQ/CpEBAebaWSwOzhcA6A0KQTjA40UxeUVI5BNAIcaI1oUScvTia84sczREME4BJQm2VJtqchr5NRomv8JQoJASAYO6jd74DMxAAomJbD0Cy/63vf/Cltv3D9JXtWFATkGExC/UkTCYsrSjJ/N9+vfMMsgfne7//ayWzwDV9/Xmf0/3aMuW+bK9Np/gUdUWmFGWf/Oq5gs9/FiL7+KCYQqjIUoQCzludz3V8qciqZDcc/XoHQ6r/5dPZtdfiM+806zka5f7oi9ONO+/u6+3zDqdUMDKYEQtVvQN9PcFonlaLEWD6p4PGSuHq67nWOdF5Mvj49520oto77Su/cev9f/jh4OV2MrNGNXKlsoogZvgWA3xAyjQYGjhPJBhEcRK5OCUCBDWpLvCHN//Rra4Zo3afDULrevawN0vZoVQr5QBpBiCvKInFYqKGMGxXoUdpZRwdL79Zzq4m2pQrxYyYkx51fJ41PZqj4tmk6xdVcmLpEcBx38QyKphNT2M+C/0IBEyJcH2nBi0xU8IpyTTn65fz0dT0bU+5XpPWJOPpnM3KQKb1g75n2ekcNEleJiMcixGKQx2JLqF4oTG0cIzNshLqaSxLh44eBh4xCb3JcLy8kDo66jSWs5nY4/jO7o9fanMvXeAfn0RiqvrBvUsYzp954+RanM8g2z20wvjoIs7ITKNYLIuqIQoyGYwfHmb/LoM3ONcqykUWy1rx8bgrofJ6FvjYyU/nNHa4lPi2ERxZVLlOw8gs1dKIJ4mpS0HC98mLPnnpFv3yqZ7PMjT0EjuyA+SMbYzm9Dmez6VMYDoi7WQyp8+neSnnWKUrN+rtF3PPtN+4uXjYbXenyfpW+vPnMWpJ5Rw2nMHlTTbuzS72Oo2vxYko5ihlPsVxLBBsxBFuNhWS+WAwnLuuWLu3IqpV39h9fmyGhG9EaW9G8Fk1wmajGZFT+VRIq3Upnuix4XOsNOhQ2TI7358LHBvG/MlTollTuZx0cnS+zCd7vXPyR2pZrZOvse32WKSk9Ebjhoj/h39zvzRhV+orMHTT1WXS5xi7u7yerS/w2y86PMHVl9J6exRYXj7FYaoCwAwkQoARBEXNL6bNpojJORiaKB5CIosE+cZ/+3cf/9XD3WPq9rXUjXvXxmJtAsfO8ZO19eSrV9qtb4J5Mp4CNQVSXvEb+atUZjCjDvVzL/Ricjw89ruGgTqLeNjtmDLFc7xMZQt5X0WJGMcKLFuRbtidgSkyjbtrhGk8+LNTWU4qFcp0iaSwvrnUGH41YLORDSIh0APTwHwjl+cVBw5P9ci0hWI+jwfmMJrOHBfHiRkIp5BJ19RSTne5+XicCBCyfq8VMlL2zhJ/+LxDT1HhjtDisJ5jvKfI9otzs4ekBSy/1OgFIcZB7YWF7CToe1w50IipF4WzgWmP+BLJhhAHoWvHmEcIgZHI0E2hiHNglkhEqRD0TOtg2NJ5bNPxQOzozrWq5HbYAm/FEpOTfZiYOEnQJJtLw7BntPfNkClDww0s1woxuShPnYRI0eY44iEd4kkYs2pq+d4t8cVp/C/+/T8l4AoAAAA07/qbFQAQAAcks5IGGI7ouuxTgLOtT0bCjUhU02ZkCzILgQMAhZANEZmYekgzRIk0YTgFdhXUrhf7X+7OeNbpDCb5/ALFSwCAA09KJX6BjqoQ4SDBMcqEpIjpjo7sqSYrjBNYuO/pDspmchDnKlnw4HhSqNt1jDtO+qqftvxRzpthKG7P47JlCKIArqRvs3BiLP/seeuDJZ//Wh0AYg1AQy7NI7QC4UkLXMniWDsEjTRrTUEQVDPZv3ftDVi/e7B4nspKn7RH2XXgyK0+gZ8dEpfUTMx56NwIPppTcw7jco1RZ2wOR37URPHZ7x36jkulxPo1lsTg7EjP45GSCl89Ox+8lR8ASmoqt94rPIsIbOsa77IX28bFdA5tpMdmTV7FADmei90eVW7yUwOj9STwAUGya19bAZHAS36ye/JXfzR+52vvlC9fRQAUAXBDRJMgjJKTIaAZQDN5iCOAM5CAMCFAAgCmZoEKrk/D065ozgbz4F6DGXTGwfFAzbOsyDoeh8MAJDgt8JPADi0sUyuE0zi3elUoyklKyZTY0e5Y13q8jDtBEBK2KGJkiGN4lE3HgeumKNvy4xlOMGxsESR03GTqeKWYLzFzw5xZmCohPLBmw/nCmkoyhNF2CdNn4tnFiYdDotmUQ5Ag5JMSC7Ew9JFuR65HOp5MQaRTQMU9M/DNge1EKMIhIZACM+exh4OqwBMx08oRxsks3cbKH2z2MlAdd5cv8b4m02xYA3kHufG8VVwqM1neduBshHKrrL4o0tdT4FcnCwFmH1knvaCy80L5/l2YEhmBzprm+Mcvy//g9a1/VBz+7JAlPFUJ6DtNYuYxfMRm82RMxjZMGH5wPsrXVUMzZcUzZ07CCSyf3qCkJ8cXQOIAxZVEXGHhvD/5xz9c+z/vvuyQS/IyPdXnoER9/pP23//bbzzdPeZofK0gnVf0EY8XRboQRL3YuEiINA27h843L6eT7OLKUvXscGhR2dHUmSOXw8NiXTBj+sXQMT9pXy6Y5WURBLNRQLXNeAxrl94XuIczXsJga7Bj4zEWJfmMUs1s0eZgLlQb0uwkGA1HuAjTAjj51WMTyvWat3Z5Yf/MPe+EUItrlcLCkvfLnaHnzZeaxKV7tLJevDibW9NIKpKRFFKM/yd/1VaYzLd/UKMJoSHjw2lKZXEAjd7JmLI9yBExH+oeLrmYALNQqcAohRIAMeD44mzpktH5vM6pcm0zQPTg/qv1euazL47Shcwwn17I34Xg4dOPP1GupgRylauguOLhZpAO8dfSX2fR8KuPnmSmF24HVxQpWdtIyZSagrXswsmf/9RTc6cxshIs0SbnD724JFSEOMznlte5nWMvmxKCfiwz5Hy5AFqtqcs0c+hllMwCZJ6NyYIaiVJus9LfG+SRPwslmaU8MclUCJimWiM/3fVytAPw4PB0hCqrt0sp72gO90b89ZUJK+dMp5p45qk7HNkWnzUKteCzM6CbTgVzSUsRGVzKABBMrNAuCxUNiRKH7EgLQF1l99sYHkPHdzO+43OsRRmUkAqHkYODvokotCdtVKlaIjbFp63TEa5WhYSHdKCDi7mx7ECjnte6zt6uE5fqcRASFiPU5QjlmJq45KrZFKHfqKzEcRzXE8kjPCFFeO9eVf7/rMdg1psuZDIAgPuHYIHq5rG8+eNX/PtbgAP2x3/Kf7BqABEA+7wN1quEPrJIb0REIaqtkbQIWcLsmLDqcQjvRmclEzLfeLvA5BlkQyiBGIAkyT17FoLrz09/X1jJVm59b5kGE+uEdOT5dO6J+KTzkEa8WqWDiAzsAbJlXNXvLjCuM8EwYqVGu+MXuWzFevxYLl0rX1kZehhgsZE1C7FUKDvyFcOUxwp6E8FdBwBhci6nUh7EkzDExN2Ex8Dhjo1Jcdul5oMgxJT1zOprNyKA/3ZjjIDSA0cmor8Hh92OEXQO5oHPWZ2tu5lwFI/+YsfmgsoCObH7Nq6M16jlbB7vd5y+iXxNlOT2yy6+KY+8VjxXXITvPjy5tWxnl7vTTEpaSWnT0RVkfPzReTehVm8s0Ps2pKQY42Y9cznD9XDc8gmBUzovxsW/+f619XeuASPZmQHJ/eOPyfev056H8SQMAbNQBY4bxkZIciRiAwewOGBoDAAAAAQo4T95ljDxi0Esf+Rio192GSWXfl3qa2SW43KU52uGQ8KJ5px8fnFzbS24knEFgrWTXD3pd3wWCAHLYtMw8JJirhz70zigAsIk40HIFvpd12rkYwjIUbu2zM+jlEgiQPEcjQLd4XKshKGd43NUq1c2xMmjTsCIEYVmIxsGwCuy69cqk8MpPpiZIUUXRTYgcNunKvkZhnN06PhupOmB4xBxAngqCCHBchEFZv1JIDbYPAj0hy3tKKgtC0dY8OrQ/f5KDvgGLkpOGuPmTHzW0j1BquLWFCM9Yq3CYBTlaHZ0Mh+fGyrCxDygCWJwIIYPRoYZ0yUuRSR5yLZ/dVG/UmhcrweEMJgMQw0LdmIwIZYLrEGD0RwKGd+OuiRD+5BNZYiDLmE6tKyw/TO9VEsjYtaoEi9OtHRdnkfisC9IUjqkGJYBR3uT37hb++s/5RwgnQ6Yi6fd965fqQk0TSG+yLc+Hcan1koUX+aj+UgFU0CYkAp4YKYYnr+ckzoHw9GBwdTTOOeDaf/GWznZE7YPhhDC2zfXFmgQYvOvfjkcGV6Vw2ReyDEMRcC2Zu/snzClXFX0h71IkNxOHJ8a/aLj7vnjtUX1qz3n1j2XVR3JtJQF/uXRmRFkbt6o9QeF1DoxN5zP/kv7xnpxfSmbKeHTHd3SojdvbTWWKRoDRwd9t8wvr2dAkpx3ZvkibJ17yHDsrr95rRlZyBhDBXNBdA7TDYAAx8qBY156Y1PBvEej42GPa7+Y3Fijem3t3uVCgwmN2UtedNfLIivkHpwd8p5XypEVQxmcB1oYbFwr/OCdD89PjlZrcoQpYi3dNqZR185kuMz3Xtt9OPonH2zs53Ozzrkxjrvt+dfe3+hpqHPsSgYh8VSoRyzPj8+8kiRQNDo4cOcMqm+Wz088bsyvXCGHz8buxPCbqRJNVlNsazQLUBJYM1aUaZH2unagM/V6Jd/kgoG227WYhXT1CqsfTs72h2w63k14plRcuUc/fblXNN13V+UHjgM5xp35ScxGCa1kud5eqGJ+a+TieCSw1HxspwGpu1CkKGfGYiA2/XRO4kOEjfyzBLS326is3b+RMz8PnaMpHpbKT6dTdTeoXBETAIcBWLLxmJTqy6s9Kt/I5vI860asS/G6JC2CFHS9AikYrpmjHXMa85MuYnXElWEq74Th9ClZWrADBzBshkwhkLnydPacfS3xLbMee705uYEIdvtJEkbLuYbf0tMZHsjAnwN9rBMWKyqIElOap1jEzDsmqMXyNUqGEIdA8uI5A9XJcLx3hGUzxldT4m/ezAsAIBSl6RgKfCljFCGNQNbaHY9nfsRKfTNokr6hGxTLBi552kGXruSf//rnb/yAklIZJBWiECd6LljiCLz36vnTt++9XlPyITB//PKgvJq+ToGNzI3PP3t0/e6trYpz8dV2ZVN2AYZDXGfxaEw17zXcJGZhMBwZpZyCkFSGN32oa4jNlQlQ3szcgQbQ2sN577hED4eD/tnEczheoRB9i4jicWeuDxY46jBjO6mgdTZmLMLLE6IfmdPoUi5tKQWXT030JO7id7dU/PiCSQnpraxUIlrDyBFsYxbDLNsOA0VNexOnIBCmIDoPzsI7dXyEwQUZQPFb120uLWEgAHPLakcaRpNMzGdQbJjW2DACyMkyNDJxJmFpHMCN3/ytzZ/97OUd6Px6X2OWMm/ezB487jhuuB1z5awgpJN0mp2HRLmRGh2MSKSv3ZZlJwJ7LaNNxZXSDKEBBgBIXZxHr62V7O6A9HzHw/OSDVSx1Y+wfEqWcmrkQx+fYVyOoUlIeVODBajdDQvFQn5LOT2eAidgAnPqEkI6vbQhng9849RIBjaL4yEEkeEwJAEAnqWoyMfjICFoYBiQVWiA3DCJ/TAiWoOBreRzNdaOyfbhzDmyU+sr4w012O1shHjbCJIitXxvZTSIscMDZ+xiaYErsjCxTJtXb5Uf/ex5sx7hoVMl+VSKDjq60B/mlsiBQHITV+nrU04Svr6AdvujM82PQGCFcRITgaV4PnO7aRawWd+Vc/6xYbJLSjhzrhcEFNIZmZpP9OViRe8lUAIT02ri9kcWXt6ohDH88S965a3F2rPH6Y3mZ1budDD4/nfqv77Y/sH/5c6rf/f5ly+0q9ebv/p//Xnun22mrme1L+MqPp0j7upmDsH4pD/3vYTEkUfA0+MxS7H5kl3C48J5H1Kbwm7vZKBjKxVV4S8OetM5yUBz1ArSG/V8NY/7RDyl5A2mgPsnn56eTkZRFDnE3MaJzQ82cwdgZOpc/dbwZAqmqP1yZsvYNHYDgrny9vpgpGChKKad3aFRtS6+3+TXv3+5r4Pdx2cSlebXI4ADdzo/fjXjr16lyHA88nCGiIfz3pmeqldYlY/qkA5tKs2DfMFIziWYRclB5K3G3LkpCo+3+8s48OLoT/94sExY9Xurqa2qWQ2GYnB6sldQqkEpH/58Z3WlKmacF6faxkK89e6K9fn09OnAuI1duPFrl9KhTVH7T4UIOxi4p7PT5ntvhv05VUyoMPfOt3nAvvt7+/eZjlZf5fGz0eo9sjMl1EKwExKdybwcZcJJixRF0nKjl73NKzjRcSEGur2eeHkVw7y2B712YNuzomJ7vDIIJ4CghlHC58SlNO6dJWCgJSFiL+cvHve9PbtSlvwC3qRA0mCx7txrzaL64mcKtQ1gTUxCXgIkNw2IBRPeWGAsz5UTMA1ww4uqNE1mLHLqyEzkDeMwBtOhJqQp0ht6z6c0xVl7fTtknDt2a4QtrDbe/t2vPf/jvWvZhcLlzEmWqmESK1dB7GpGwoVEWiEijBcjIGBJlUuBAEtclBB8x5mVqZDQdKwhIUn0x7oHcwP8QF0rsKU0B+jHx8crzUUyMZzJRWPle3vWcPBsZ+M3vwvHn5NxCjF2OLSYZIpa5jh2NIksqBQhF2PLpMpCzhrR0PIyOYCppq5f7AwLK2Q2UwHoOZDU3NdLOabyT7/1909gIgJDP+958EyVtZmd5JkUwVn8coajKArAU0c711G9TCNcJljVfNkOgunVb7+5+1l7481bZkK9SuB8Yn4rZYjq5uW7mzh49SVabkDvu5c2TeM+iDnAym/cuQK6XdSoNG4vBZAkV4uMrfF8cxTQAKRm8KwYnTLKWhu1y9AHoEeBZs6PphSRQKoCSDP0ruW2sFxxfDZvVM8/E2ec0SU+6Xkr9bRk7fesWTUVxf7uq2m1KPBzAR+m17m8tv2KJZk4TZ1k7XwSsReKxJa6enipgVqac/JwulHI0RJ8sD9fW1ua9edqqURxbkiSlWuN7tPT04eTWKmtUzXAIDolDPbGNG+na/VKCpiD2bSvcXKKoFKptCpiBAB9XwAsPEBw3QYxC4i6mrd35mvN7Onhyc9/to3CYGWjqLKSTAo+h/uEA3yqeLvkLYDEmE93x3E+8+WpS241VqKU6c5jRqGtmYpxZJ543JPjwGyk1s4NuF7hiNNBicblN1anz78SMzWbEjM0re+1woSjSxRfyFQqvPl8YFxMKgvpWcvIKoIghcZxDzpwPDGaaxWbpHEzwWwjx4tWMR2IgiQkRDAd6mTksTROoSQIQtOPI8KOg0Twds/8TJoDHD3NYMwCEqTEH3op2Y2refxaYTYcDH68y8QelYPsYraNw3KKYDBnejoEDOOTxPxsmi+IZo7PcsTgWNtjMeEOpkClcxrltmiWw/qn8eIy68dgYITVS2krCG2VDptyD0Od+TyTxRxajwGBMGZuTXATJ6O061iBCXg2ZEVGKC4dGx6S0OER/MGH4unhMb7b4qFXzmI3mv5uR//+3dIf/NlLdWD9o28yf/iHe+u/W5Bl5/jPPrvxxmpmrdQ/0mtrqhn5k73+lQ+au+OFL9u7yWC2kVReX10g1dbk+Z5EcKkV9/DCGRFxqcyjiDnVHTbUs1np8tWt5tXlA1PigD/rD457fSnFLW+WkykRgYmMm1/8tW3VyI3lhU//w8Pf/A21K1LzDux5INeI6HF4ejBxk9x738ppNlPhMvPZE90vrN4uJzEcn/ZZiY3oYpT47tR2zNCL42sVkE3Rz7YHkkAlIFIK5LjvmRaxssyaJHRnIVXTJCyTBASkCiSNolBa5fWjnGoivOcyN98TPsxjoeWWOG7gCjmsOqDiACGSxqfFwtbytQF6crY/sLtW+SKobdQ3I/jrV4O57xnpmTvRMMzomXbjcmncsur+wOfMQSSRAvrZ73eUa59dorDm7UWKrwJlRnF2hrOLNJZj0Lela95Q7OiH/Sr9z8T1i7/+yx89Od741man368UTMQQkzGeqYhO36MU5nB/XFsn4NDyWFIWMgWFCGaIYSmD4qCjB8NxpIfpWpovqMOpR0xtvN9h5+RSml+OwZPnwzdVArNizjE6gZ9jsQwBnRGIAoqmxBTHJQQNsMS1IimXMCYQOUzHSaKwAujYsbHN1TJjy9dz1yqq9GQ6/9bvvLaydpmhr9U+3EEzBFZkPg6IhIE+iOaoN42yy7wdR2HoBBHuRhb0DNuGCpC7F3ZejodaHw8BreRQokdOHsvAqr4y04awFKATF+9ejLNlJvLrS28AAA52D79xowohBLk7CUcCFgXtEWVlET5TPXtwMhimILcoVgrS7LxDhj7knMChgSwkCVkrzZ/tH9Pliwpw1VpjnasnQ7c9+dIvKQEZ8LjlINJlcIqmsNns4HCKU6nlm9fjhG6mS0N49tUnZ4DSN2+krtRB53i8ePmKsjGLAZoMR3Uhf+92ftoZcqoz6M7c48G9m+Sz7YskxCtvvgMgGr76k/zWKshgEFWS+LI7usDCkGEFwAsp6G0PvzT2RqlyUSr6SUjNuYD1BWqqY6UcsGcxmMcDK6oyMRBdJGYba+8vbK6Bs5/8/udqlXbn7MXQrZCKNXLIcXIn0xSRfBaWLlMy03L6A4LgZQoxZZgxz+2lWgq62e1J8Lt/b2nwcrf/zFovMLRNZzGCMwzfClAQ86Jkz8JyOcl/ZzOAFEwwszcLbOTiZKop2qP40fOuInBsgUqpdeCE48MxU8IomrIpqM28ejMN3IRjEwjBteu/8Un71+PhKSZlb7xR8CnEh+FkABLX9+a+K1BCOoXwOL0YJ5NkfEgkBi6qGRKjIpqctBOOGQezRLiGumdaCQZLGan3tPXa1bwOsYiRLYFeyHEnEGvUeLuH/BAARt28krPJBI5nzx71IIzEkkSXJYFlBBaLx6EZJ9WF3DwroYUKjHyiGw0HLgthwtIJyeNxJPGiZ8FsTqBiNB2bFIMJiCUiNpKnXjYPDs8g0Nz8exuQSHo/OS2UihE5pdcXCgLY+08Paork64hLUeV61jgYQlqIQdTabq1fVxzX9WIXJfw4RQeycun91VcfHeoOxYZJimUkPdJHLoqd2XiOy+kUiebb2mimixvFHC0wnQnOkmMrUAAXt0w7IY/73Q+uNfiU0hm6+y+OY4JMJZDHEIURPMvaMXp17K2Vk0lE/exYvWnwYgU+e3Y6Zi+uMAuPv4wFmW2LuT/8k/0P/8m9r/7zj8xnx1deI+VSHIChyqtqXoDGMG63NWO8fm9h4wYmC+3JeOhbxEtXbCp0Uk/5DBtZZj3GSQJrh1xrEssCM+qNUSmNZZllKp7pnJf4vu2JgUvkeNamf2ORGpyepe8wv/O17HznWJTpMdALEtc6nVRzKiPilBxdHA26PS9fzMokX76p5pabjx91Qz6ztKqEZ+1nv/hCJIUwJZXureqzKBqO+WyVC8IXY/+9BfFCjxqv5RLPVbNIxuoGcnHQPXVECpu6ISPz164Us8Wc+D/1o9N/95e/vQTbhZvP905/296r/7O3je1OikqFFhvEtsSPX01eJr0vKnV5uXrp1SASJlMW41YaEgSB4HedYhpDFo5nQ4qWs8LRJx2IY6ck9/rVNWLcsgdWqiz++hfn9fysnMu7kup7dKISwNA4PwhT6kbpQ+fiMRCZ+jf+t9/92pMF/MbH/pfVajrO1FhsNDEJKHpTqnqWWWZH2xVSKvDkNPYShBkuXyBokyBgnayUEzkVD03yy1nCjb1rmyrN6nNEfVWvkuFcQ7iLUxiZwVWviEHasYNU3vVQpVlI6x0nxnHOnM7BJIVIIpch7Et3VtML+bOYPtXjdTkqKWlkYnj+BgDhW0nHBmEAI9N8nM0QKMPAcFKh8eSkH0LFBsFKU+y77kmHLlB0DGEisrhjRUpas+gcZnquL4Y2c23FB8T44052lf3sDOMb4Xqq+hw8x1mb2yAfuyNzFvwdNaOfPogGJnNrCwAAEgcLqWAQUaWsQ+EEWoCWXgbK9pFHmGfhpdUQkwIiXKvX5QS1d+b63COJAHmmrOlSNUNNtZidDVtYCj6jp2y8sSZU7tYhFibolf94OunDEoF5PpZ0QywXgYfB8fTyh+XAYhLLIjK8ZFLd4Shm5LmnSX4AAj0QYzcwnXHERkSYpBJy+do95cX93QoyADakK5xm7JO7Jt07wjYTkSsFg1aslHBjF1tYz+bx9dzGfPQkrXFyKjZGCpH2XV7gklGKzWBYyhfa2ngMSyPLYKYyUwNs1g8+WLuWWs70R/uff7JtR9rhL4ytAn65mH/610fehjyTJO/zc0W0c5eEo5mpBDAJWRVmzB6QdPK5Qw2YvD7S5L/95sPHj+V0JUjwmCNnBLN6vRBrEUFxZ6eRjYK0CEvp0CUzZBy4U0hgXq2Zc/2x2Udcmgxm1EITTzgpTnxGztRVKkn2sURC8BCAKy7R8lhcjCEaWd0WprXGHEWHTsil8kyEiwvZosyfPHnk1yXNDTPvVDuD8LLJ8c3Fg4/bhBcrGcZboaSMfHjc/eDq+nSgrVa8fIbOKJTZ0lGh4trcleXL+pm38VZl54+fFhqZTL10/8efUAmq0iS7UR+MdPPZFC4wpKom0wnBsxYdU77TmQ0TAiulKD4das6EWZAoKuQNY2b6KYGJ/DCd4byQdFyT53hi2NPOPbOSk1e4SKaxwfmkuip9rltLl+HYpfX2qA4phyVcwlt4LTOmQarECJ3I7ps8FZlJKHJMX/fF1bqYIZ2OaXUS4lJWfhMyKoM9N5eqRLCsvjjyyVo4P5uySbxQySdsEV+W8w3V1QPzYipz7sSPsTSrd/EaF9pa/OyLvdffW5SLMo4MP3ACP3HtUBRZfTTyKO50l1L4KMyxBOmcHIwLlaiKWRddlM9C15vvPotW14p/8e87fysp/+BW4/jI70yjy3cy27MJkx4DwQLzSADTWjX8xvfqk9g6/WJYplIfXLu82/c+3R5f3Qw+vFMm9bBzPJ7OA2ZBRnQs5dX5he+3ToQGb6CoWGf8mBmeTPrPZnfuQiIg8HQ0nYyiV5T0Jv/qeHDnXnEa+5bnT2bJaErRFKyySJ90c1xYyQnHU9htBVTGpgH92jLT2mvt/GKbZNRrb6+dtCf5NKG1B/tH7cZdgobo9dsUKfLVBTsre7gkHD49U+phe+jVpAi4sxCAwX7nYIFcyjbDeXQlQ3RT2PaXs7VM7//4966d//4Es21OIFyaAgyL+XFsM82l2GUr//m+PVZ0aClhDpfDiAz07suxJzH5WmL2Inc+iUP7yqWC0TPxqcn589iTLFhYuqsmNL0keNAIn398KF5ehnLuYuJ3Ws5GWspSHsFgV0Xms89fvPnGXXgaOaVt4WG49O7vACAA8EKKHGUdtENJvkHpR2vcsBcmCfLNAAClQuWoiPGtAYbUAEwuhhKVyJGXyTMGbo7a83peEifjc0dfq9FPTw0iUAMSUAqxmisbFsVR6aFcTwmNS+tMDtzx0B4OisOEz1sD0nZABBbQtCxRdIEFIBWN53i7Yw1n3DVciExAQiBwwHMgQBAgZHgBgxMKFuHMPHSMMXOtlGPDeGoYIKFZEfMjgpWwSIPtYbB6VfGQ65izFIPRYnyPHT3sXHyiSrQUXi/W97r+m+V6izn98kdf9rveu791B4AU0ADwApAzIM/r9lxgRDgD/gMRaxLVawRGcKCYborpJECRG2CkzwvjyAWHL51rr11JXy/gAEMAxnFAFseW8JYXupJ4E7ggTsYBOy8HHSaN4RSe8DwCQ3va5sKwcG0VYGUKb51PPZpjiYQjMG4+DNiMRblIVifthxe0SnqmUs+IM1WJSJwCBXIZuoCZfhaW716C2BDcSiPIQH0QMlh3FCxW9PavjRAZjeXrGGQzuYXzB181ipLWAzgRk+5IS7RQxYqQRyyHAmEyTXjMz4LBzEDJOFm8tTCL2XWlvLC4/v/8z8qt36RjV5sEcH/grF1j04R3gXQd4tqc5SrZ0OLSLEp8r2WB/CZ72OpXCrYus52ZN/BpPxsiEmGeDwT7tDXzpgE7jOZ+Ul+rxn4CKdLUx4kFWYygMNxLdIpAhRz27OOzW+9dBRKHAQDOx7ob9DSpXq2CiMHARmzNRSG1We48fGpz0Lv4dCy7YWozHWYoIo9pNpBUtHs6I4sZhqM4HpsOQt7H5Qzqv+i1Hg7Wb+fHdoT5FFpA7RPi9fUYBZGOAzJiCCzwE/YS5R1uO8uXxAQnBp2eoFJKRWRsN4NTUklFqmCAhCUZJssPe6Y7jxWe4tOsjfGm5eGOn0kTemuYTNxsWSTVoA9iqSKSE1Ii2f6Z2eoDVhQRSYTQJTCJWS6xJycuz9G1N9bYdmf06CBfU4YzM85Kacd88dGYr6iz+QhSQrFKz395keWx/nAMFrn127njva6fyss38ukQag+OhXj+CyadokHqvGeMJLouVRaqjZVCbrD//D8/He7ZhYwJ765EUyyyR4MewiKw0sjmfd0yDCUDhroG65Rhx08OOmq2KMuV3vE2SROmSw2moVCRCt7gYMgbGLFCwkU53HlwukUzTop+MeLe+dqGmTnf+2SnukDd+271Vz9rvfnP7561jnCGt3Smzkr9DlesEvNjt6bmXic7e8+fTXx+RabzFKt3ptKlJl5RdTQ6v39enF/0OzzzwWW+lvNGZ3FAlS+p3WfzdIp7sNM7vegWFbp+7YaJqf3+Xv1SenLiGEX2/s7Zm1fypCp/8WySzsloZryznH+055nTxBcqBIe5puklgJaoxVWOyEqcFA0DI4ud8+yIXlVasWm7g+BwhivZG99atZOCo5kkz4QGzaDo+MkXhptrTajKWN+sl+zOpCzwhMxhZe+znz3bRy+lD1KZTPPv/ze/8eR/fEl64V9j9PI3CvDTE/KD918dBnl8CmNYVhB2fp6g2cKlxWDmyIKYKLzdnTMezYgC4eqMYVKeT8jU5qYCBt35OcArGVH0sfNDJTb6cabbRuh0ePPbd8Ub6fZTlIfhmUl8/b13+t5p2G5/8kn49ncrt+4WUTjLAVO/H9TulDwkePDpDChUJWAnEW4ZeTMUGhnFm7thzG1JtEuEtodnJJJDaRGn7UFx9Qbj+yt+CCnBmY/p0MgFMWmVs6AzfaZhWEpZqrJZoYIMMOCWSvmF67Un/eBugQVgEQDIwFWtu23ra8QaDwQh3t+DEhj2Q2jgOOoJAkfxOldg0bhlGyRNICgBHHnQxQATz0JZYLEwEnzDtHmBUjycHh94iCWQHsWREweGAUxdHI9uvJsh6IwxP6H2PbuLcYvxvkmHk1YpaWxKk/H+zDgiP1OEGpY036wFZ5XQ9RPbtwGk/TB65sPrnCqpbuSTWnJeL2y+IaZAfx5EBnL3h9pqmjTs89AKC2VGrizgVS9b5aPYmnTOFEkMZZCwRqwlRFF+/tmnRWUR+l/UMjafD0FYsCed4+FQzcPCegkHNwFIQ7B/cQYBIwWqSPRsE8lZRk9QYtoojOigTna1uTSfX+hTQVeffgJupQdrV1QI2Mqbl03rGTbGOGsKNwsWgSHfJz0r4PnMe0XrxEfd/wKKfwsmHp3PBbOOkEYjwsl5vsjx5+1RmmUxMa8ypYPhtFoExCREmPgCsYsXZ6VcLmDw8dy6881bDVHS++PeJ/13/2klWTHCBx+LxKgpE165wFaV03NN2mg4O1TiWvZmY2PRC159sfjh+s7++eWb+G7LuFmp9FvDJhY+H54jFq++s3WDq4AERCjxDA24pBdFNAEGdtx6RuRVzy4Ub33rMsTZyXRgbTvpZU7lLEkwMC6ObIoAAGMlhMhQsl3v0dgOM9+6SylBO/QLcaJ7EZFlmAAV7AQR1OGZa4wtahxhdfbQDadD0snSQQblc/zBqZ/1wit35H7/jObLaMwsac6ua/VYrDIcZaD09DRcWKBOfrl3dbMRqkrv/gmVknWeTGTKedZCwxCU5MIVuTUneYIEjkYwJhtNBqVUJAaBYagYcgg76O6NiktsnIzOIzGO5xaZSYmcxJAJMrWIkOJk1vE/2CrYrWB2PLQgfOWixgIOgtAdDRKWQ7SLy0SpAsB0IhflA3Ncz2N2Hg7m7pV1NfCTlSvKfB6NxpG4JMoFKuwZ+RQ1edRltxhwaXF/Ks01Duuy+AhduypSebEz0U+G+ChBZQWzReYoDOeEH5N6c4MbP7IDlsfY9N4sLMzmXFaiCvXxrCtCOHY8CGJrEtE0SUDyi/uzjUaGVhLDwfw4wpxZ78lZfHT8Torf/emzf/B3Nv6n+8RYXszeJFFsnk+t+qo0CWjXCGMy8sLYd5PsdLj2zoYfZrhU4zTM+YGIgeBsDggeD7QCv8irbCZ0iOksWyoVu27Y03oLknLt9RIW9M4/Phu9mi5lxONpxu3K1QVV1edBqJxPQOWK8uo4ms1NpJluTEkx5lkurRv2NGREZuRGRlqoFfP61Mym7fbh4HQ4D1JYbYEVhTCHMRIU+qHXmhKFtMt48OAXR4FF5q7AR788q15R3vn21dbLvgdiA6apFKOW0tVa7reuG6NfteeloA/C6yDSSg5TKJ7P0w20ceLMFkh5TbEjGJIF1dXY+OBgf2rKVxhVkdOA338+DdvOWiO1UinefzSbPJ9dWc9sUMH85awgRBkeWGS8/dgmOtPLl5oYl64X4KeHp4cPH19ZZ9Q8A1CYTBAOis5Ov3qj+M5SvPPFwcadzVFrnsOKVC0hhWIEAAkaLmgloR4BO09S2sGsVuQBj4wwGtkwQaTAiQnFJEys1LO9YaZUlSdjS4ghBUmKa7x1U52PMdSLWGJemJy+dSeDm+DcGYu4RCwtQDr7shtKHAQwAsgD8JH2bKRc21LkBLnhxakhp2lARAQHigIP8wkkGYSkCJi6oQbA45nQOrPSeT7GaTeAdF4JrDAyfXbkFzOunUx9CGKXiLEiCELgggoH7CiuLAsET7gokhVyRqD8WsPHpIUK2lz+BzggEOg+/ctf/uAf3gEc+/mPXjw0+eoCXcnKM9frWv4mSOGJG43cTiYxxvF8gL/1zjoCwE1oaPfiZGYMes/sWCbd5SUeA6Q323v1Rce9wvo+kV5hfc4QAHE6uUCuMd6jimW+fgmYaMuHOhlHjm44Igu6oLycxoCCA8M1EkaSM3khwn0HeBRv0KyANN2aeYnEnutMHid4AmTq3p/+3tnb9xZrN7G//p9flH7au/LPvw3xOm/7WFYIu8fJJ6djDuNFslDmw5BmcDK1JDhn6fHug7TCFYqYseMTzKycw4Ydx2QBQ0pWGFsv+qVL+dsEf7Tb0x2seEleKsNwMO+/mmJV3grot6oLoQZrjWVTfdzXzoyWjcaaMBmn1prbI1TN08BF+BgZnhP5pj3sbV3i/+TJ7J/+t9l//3+9ENnU8mqqe6F5LS/wBaVevlxYggDGECYYcCyMxFRB8G2Ed/S5HtFvf6eM2qmdg/aEEtIV0NPC0nVFVFKeaeGGn0z8ie6xOUOS65ATGsW15LWrP/qTB03H2NmZE1wyIznH8a7cYLC27Y19JhKuLEqmyPns/DwxYwYANSrytO9alO6VSPLsoqtWmP2Do63liFd8LwbEvF2uisfspJltijaYTSQylfKZFDDCUUykr5ZFFg4PB0kvrpaztogbMVFVwOzXIxvqTI3PrKTLZTmeWflqiorcwUBP6kKxzDm2WSgnlAfRfG47EVtOQj2GRExIItgxgt2O7vPiyLTTpA8KEnAQzxP4zNf16aFUYs5m6RU+JYBXT4esxLV1o1jPkLu9yJRTr5Wdtll0gK7ZZoUPtLGcz/i+ddIDxmT4/Xvr4WePik4+bNLKWn65OttVRQ0FUI4kMJ7k8jwNsJa/HF2cpRzTvLjYqsfpHPiMEiVnMqSyunZ3kee5EtkfoZRM+FE75ikausZQIoHVa7FS8Wwe+1hQX0WxfiBahp8VlWb24VcHb33ryu5HHxVzwtoKZcw4vBeWRKw7MMSR0zsN2uPMhp1bz21dkCKhpwXdvjhp83mVx+e+A2NB2ChzyZLiz3yaN3H5hm+eF1hmbCSho2Mcya/mja5VrVBbTeLJyzlueaps0wK5+7j/5j21SLCEb48k8fDcq2yQ6WbdPJ01LxV0mcVC50pj0UOxQEA4Ce1uGMXE5mJaKnCxb9uJD3OpxNQwgepTDoVm0SK20VjPMtTsxO7iBdP3GlerCiamNR9IPEAzC0o9KAurUe/Lx69/nwWWWJrKJXqUAYUo2MdeSzsf/VXl3d95/uCLjLKom6MybVdfrwehM/1KY1KllVQeew1o3kAq5W+8U24dDSM7FgL84y96xaXc2jK9sBgvsNnhG3lKXASm4Tl0qbLCBJrX9zojDeXJCAvz40EO0GBogfwNq6hBv5tvVgdaUFAbF0mAQaBClU9gHXMHiOLoBM0VTXPlokBCDUjc8AzDMCG0owbP2J0wBYF35qbaF+KlpcS2ROieHXvVvJjkRS5N0Bn88VdnU5/JLxSbpH+uMXGgU5BcLqWR+Rj9fB98K1KuXQJgBoRVODrjZdSTAXkyg0RmMg9FbxJzEa7ZY00hGmg48LMYmaoRMKBYgMVR6LYmvmWN08pGlUYOzpDTWIUFIgyo3iHI0iNAddw8hKHtG+MOD6uZfIFbOEfp+sX+Y/tV78mr7uKNKzeuyl/7el1niy/v98uSN1rMjIH8q1fDy7fy9XSOdOe+GRmMR7qx5EM1XQUAeABog3HkYjTA43yBo/GGSpy3zkh/6o4HeclrLlQJ+i0HvaTjBIOvHj4+qL5z92tX64gIWiBbA1QMrCTZPxHzclYSWvPHPz8bhtM3LumqvIpQ1DrQQ8FiqAAlLtBc3vdFJjU4GUwGEZKLKTHe/ehLNc92Ds/rS863/8l3XERAcAqe/wTSfJIlyddvg4uHTcHp/eWEeO0Spim47oKlprCSZk0snDtw2Kdqq8c7z5Cez1DS3s+HSkmTXmvarn3cniznSquXgos5nOz8pMTfq22tP21ELyPqjg703RE2wci3M1vff2vvy/24NZFKgsys5EsFckAxWSE7dflYw/I1b3QGF5enxqyJEU6ULG/KT/7s52//wX//hz/92de08lPptWoKOtA8RPESIumERygyIUmPHSWwpc1iHnkPvjzwLKRAMs2aIKIvL6WB7upne6dP0dbtGuCjQi3jJglIbACUhGGnJBdz6qvd/mBiX68xfCnvyiUFTjxZ1d7MyIFjTo0gJWEgEotZFXOoEYZbuKBrQUlRqtLRn52svH5T1IqHjw4Wvrf+6mVnRKkV3360P+wslXiHK3Ynyrqo+7H3Yl4upuYGvzDqJ8OQKmXI12oZZz749SlRyPQdO9dQSt9spHH7dJbYHUTNMakoI9mTJeSwycWTs7epLMsTxbv84NRlY9wPAy8KCRqyW3ncOgoK9ThDwLPT2UqT5xLPjRJbJI0uWE/HA02n8ZzrYPrOeP2b+a9ORisUj2XIILLIYYK7eKoidqETeJ4XYIyfnF84X//66olpzVszIwZN3vDypTiz9oLVz/20jmt5bm7CYTwd8xRHGoxnzrPO7IjUYKM+IykilQNotCIgkh2bIZ5WM1TI+tZAyhK6S6IIQCISkmhiBYQ4ilHgja39eUwm3Pt3K0+/8L7xjnjR0lfLsZmN2NjutsZcIpMyjVnzUoYOzRDn6UyeZylivGtZiWBN+naor7xZmGJoGgZnr2yS9iHLrGPm8Uc7ie+L+eNZty9xhMCHoxcDMhVB4LCN9C4ppdnG6pp2MuoZU1jmha0lQuu6tJ1EGF5UsCGltbeDhXtSV5AmAhOwUl7IKmQegCBR5ns7IKTYxrIMjNAhkuH5EDghO/FMfS7FZMJ7Igw4CpK4P+uOM2p+5bV8iIeBPk7AxB1iSShNp6NQcSoFUqjSJ7s4CGJFwLgMd9HyNr9O/em29cN3Ln1yMryRdCbn3nJuNO9cPD9rUYF3Y6OeSf9/SfrPX03TxDDzu5+c45vzeU8+p07lqg7VOU3kUEwiRVFQ8mJlax0WsLGCjQUWWBsGvNgVDEnetRW5kihKFEVyOKlnema6e7o6VU4nxzfnJ+d0+4P+ievj76JgiquC7NOINpye9CZEZAgEQ1eLWYje/rXo4FfnhxlyVahhEbL7iVErmpZlNNtLq6u1owswJRDpKp4Xhf19e2CSzatXf/yXj9//5uzlleVs2gNcrOJzf4G18o0JBCgENOIiqJzNh1ns59BoMZo83MWVuu9OoqogIAFJAlc7TWiaXKmqmIBOU4IzQ20S5tm4lSb+7pym5ae7UxyJy4y60iqIAA2GGLDHrIJzHoa5Z9oXkvr33kSwEIAzABL49c+RlyrqkTbc9Rt1vD82EgSxIOmaMYko1Y38RPN2qmTKhMORx+OcZ4YknmG4X5VTiXAtnfMhY5JiBhMEsZHd3oVn5KRt4IVSltbeUgCoQcAOHt3/1ccnr32LgfzZZhtvbchnD8//8T8Km/n8722LbMW/ezfKSdZ3bkrdzgJM53NaD/t+uURkhHhwOF8uwsjZg/BG6EWlam50eIwP4jZmQ4cP6hUFVkdhXz+dvPYyC5EIBVGQDieHViHO/ua33yWLrwCIZ+c/mdXMiDg1h9PLKi/04hKBnc09LMCWKtVcaXM8DeQgq+IWmSOnQQREKQ3x6UTmcMXFnDwzs7q2rCqrG8tvX/sAgNm//wf/5jcvH0WvrjI2AVAZ2dj2fnGPezcfDR28XREk2UdXcU6ExsJ8dpFMMz6fEmyGYjql1ivXN9NBlifIym/XHn/49PCT3ivfXhodxgdH7tp2q6UKrdvbRz/x59EXIul+71J9kRXpPEcUY0c7oVTx5p1L+ri89xivSO6ZBgSa3z+PCJpwfKzOE2cz8WYOcRaByIufHp68+Rbz4EfzN8HF9xdG/u3v8gFwd8Fz3G1tgDyQAzQKBDKEACC5gi+NDd3sXSyv47i6IoYBiiGzMz3MmMg3q+WktY6FIfQ8NKNTQHBBkDKEBTORw8T1OidV61ktV0ujh8/DZgXFI2iNR8cHyUaVrpTgyEy1CDZVad5BShwAYZwgMbqYyyRcLwfe89PtJverr6ZFHxlmLkfL3ti5nmEFYKCC5WcOHTIxwimXcwTKRSP3xYKutJsrijqwvekzz+2FS1dy8u8tsbYxvOc5jgMZGEzxiGI8haUofKz7+TnMRdys75wRnpzDIyceajaISYiheBglhpFJhP9iwVxqZBXfIOqNLEZPn/ULzXyRyeTBKCqqhSwn7HYCQrR1qinWmLn5WBUIAy71/SuvNF4Y/vmFoybIcivJZmexjT1QhcKVEtbCNm5vjjxnOknX7DSSRNJltpRFogX0wqCMTBwY3THbZg+OGtmly+gCm0+mmETVY5VLpy7m4VROxoNcqbgx7j6dDB9ibhAB0ggy17UxEcNwHw/TSqOAR8ZBNzh8Ni7mGv6gm7HKoxdIQaWFWk70FjQrdUbhVi1ElHBquU8PXWy1pJTZJ0eGVsqtVbhiqbixhn7xZL4o5aovLzEdTaLC7jjoudT1DUGGVEtELSXvEQLagngeHnx5vqb321cEwwfu1F9tFe8+WuRCIicS1HYLBMnw0cM6X7y5JX91d6R9aVz7zUvySi5GiPmebruWV0K8RUZIbIGzadtnAisYmjExLRWVoZHxKzUFRueDxfnTEbtduZHvP7nAsFodI2t0Okgmej9JiIIcz+aFtDN7bpm9aIKuiGtb9t5EhBkhs4Uns3QarJG43v/o7XeXL8L52nsiVwSX6zV9ndJ980//4l6Zr5Ql0n76YJJmK68oKlfwRgl2Dr0jzRLI+s5KcwXfvTf64x8fqQu8fn2JuDjk86IoJQgJw/Oxc7lFCsXjrx7uXFtdeLSDh6//1ltPz3pbOWx+MWgV1xNR/E/6/PfB4ocdYr32sKgNAYxqrBp+eKD3VohldeN6iymO9SzNUixMSLnOepHvMVg8mIURQuJsjON6pcSRM4XgpdjBociFiYHSnFhw3MlMT3DKR1RaZfwUZTEWqu/vAAv89C+D1iuLzY06stYFmplp6DJPTOYLIxJIxkF9Nw49srbkRUzkzF+cDE4tnaaUGpXiZE4u0DQyNnw8GOt0gSsqskhFpz6ax88zSC/zhD4dLolMe5uKBtMFY1bkDa5Z+t2/YZlcJLHKHx+bv1P7vc1vOv0PTvMf6wgwSi28Fja6QwNOD8d9o71KrCqvRJ0nxokn3RRqKu4RMV0pGN7eYi5amgvnwBl6KhlygXY20HFlKrVQ8moTinEw0sL8FzLJOokrXmuCySg9+LOzfhjlkc2KcGKNYjMky/xyu5Tq7v4Fhtf5NxvpzzpHJSqfp7jOhnL6IBVsVK3hLSkjSZw0h0QuP/C6w6+czTv57pD64b/8o1sl5qXNq9RVDxg+2LSM+6R0dGRwgLYuiFcE789c9LuvTV6E1DO7hE7wdZTf4BZHRo1wz++loykotsupMYF+QIrM1e+9P3+yu3jQK7ev8FguxZ4E4FUaRde/y30GNtTd8/DpfqnATYmibEMmD1Mw5UEjn5cK7TTHJ7uHdlGS2HQOWBxEqZqQhSpfN7OjEZUrV3OlFW1wenuD+erhL775dsXsDecvhL92CYXfKZUQPAQg8EiUBQkCfAz4Bfygg61xTK6w8tWT/TpRVQsJhuYqa8L4KZfZz6B8hZYZ3DFPTkfLLceNFQAFHAPUUkm/z2MXHedUzwoglvj793oKZVxtFF++1sDsRd+ElhHIco1EqU1GDAlETBYLHTJhmsMiF6b+ceeMriHruWR3kiKVppucz02K9s2OPm7mUujzcwKwtuyOSZsrF1VkrbIgYn1qwqkpkpB9f30SLWbPp+XRPLDR4npRzMmSqA1TlJkHdZKUF6bxYiBul9GKT0/x6dkIzaKIV0UMTVEWx1BYkDJjEVVULwzxRKY1LaRTEsNYAqcdP+xOtGZT1ed4xwXba8jUsjgWP0/TaGGXyEImp1/PnBQmb92snx+6A90ZH9utl+pBZWly0Jt9cgaL2SR1q0srESaFoUyjqD+n5keoUiwUUf3wNEvPx8kmKLmU3SPtpKTCpVLMX4xsJEIwgTKiUlFcai816TJ7uos7wzPbd1kFQJfuHNkelcEYdrVojeGvVGMXcghLzgCTq4myjNsmctqLUZRrLGNCGTvUDOAEtRU5t46c6/1Bv7T+Wo1dlyedYH/qMc9ou4cIkSzz1PB8N9XQb9+5jr5T2ao3unNs3MFR08xiykDBDb6kvJfrPtrfH2i+CbJT7Y315jfefen+Lw9/9Cvz9/4WKwvkPFM9Azk7jjcubfCqiBWX55rhHF8siSpXwieTBYXGGLREyk3nM+C5ceznJNQbeYlhJR7y5VOzcZ1qlUpApO/9bBGT0uqVXLpw3UlSg4yYX0TWOCB9w/eGpq1QkpsiN5bizr0h+pYD9mZSVTCC8NIr9Y/+6S+//Z32UpF9Nlg43MifQlSsL1eaD3/2tFJGNl9by7Su4tnzgxQVA1FHQBLvHoaNO0Lv4Rjf7z+fTtdK2au/dh1v3UQQ/czTji4S7Ud3jUH68q2lCgp/OfV4aNPe8PMv5+/82u/eWq4iALMKq2eZv4LWHvyTDy/9rdaKqGQJKKlqiiqWV3KkywxR5ItJP10gJobXszRIORFF2QSHgCMDdTWHFbEHL1Kqnvpj6IqkuYAipYAAze9sTfadWENQJ21xoT0dcbEvMX5MVMdQVMoqpoCX/pb00+56e6ZSKgpi1BSGKBYXUrTWjI6/nmhBXL5U4UjeMmMcwxqqmKsW6cIGz3vDqTPxnajvEAKVS2KPTHtuSKA1Fk9khJFfSRF9sq/rWnL2p/9+8Vf//kYFFHszKzQkRrjhjhJ1TWgN3CPhPz5+uPh2q1D8xuZx5+CHn43LCrmcZwcYdfNGc+9kAZghI+GMmu7uzSmei2DaKNIH5yZ1fGK5cWh5rbU8wdJUSb6EZ8ZIs1CqIqMICeiCYkFVQct+dPfi4Wk07JKqZNisIouYExPzGpdl4+fkWLddnpKWmgTr0ZbfKnmumR3NgqkLa8VLfGkJc6ZO74WH+QzpM7IraPjOFfHZrwxjMQUXAxiXl35vGaxe7x2N1ZMhWwthKcsvg6xvYYqvPe9VbvB5SliYCla3CGTeO6eKhZp7HC9QSRvNr7YS+g6ZdALfs54dYTev1Q72YtqkUzLlXcsgf0nh7yIAfR00042mc/oXdBAMLk6GFtJOILKw+CUZD5idzSuu66xcBWcLolVyeFujRWHedTY3LjcuFZWa6ZlFn1XO7Oydv7bTdUMB5zff3PGZsipnCIIGELgxyBHAg6CMAJYFe/fhpRWZUpQZAAcPjCu/Iw1P0+FAe6O42biOHX0EXak/RdYVtprfIWeLUBIQEiEAAJvc9vDKxb2fnFSV7NmBt/EOXb+t0mIh2U/OH/RlOk7DWMbqq1cL0TTLTJuXs6kz0WXCiNLVSuLmfX+GY/Nwhye1Y3flNeTrL83uzDCd8IM3k1JgHQ1GphHmaq5LJPnSEkR9ve9nHI6QFBToAPoocBaTRWYBU+KKO3KcL511oxIlFpUM2o47cYOAAfNUf26ZLLm8roR0PLPMNEckForGGT4IXS5jfDbhsuysa201i4GA2F2d5sMpFnD5hIFZsowGz/oY6QmiTMyNU4hGcVzzkXopmwiE7psyJDuf7fdUrlpmwoXnHyxyVaqLWsFi1v/5J95rO1Q5aF7dNElX3YsB8Kd4NDoJtD5MItbF6AIRxTmsUhd0SpjYqMBkTIhBIqOKjRJO5iE9GqJZKkNCCfCWbfatSc/u9zZb5VOzz2SSqRtHUzZeF9p1RKb9qa06Bkk6zlKbzGJo+/jkTK+tCnUsHZ15e1MuBSRPhU91e/bVM/ee446R1y9XtZVVt5hL908gR1e+eU2p88+fd/cP0nCKpm5SbbHttfxjG1zsaeDBg3aFWH9VGYAa0tmbf2X+r//Dj/7mf/fe6++tzUYT/eMLtlnefmfl+f2LnWsckhGlN4pnUOYXh8IrFRtU8gAl61Y6MFP9EDLG+EIXigUmnJtjbznhPDLiMG75hhhtlUe2axNU8lKOFos0DHBrAdbF4aNhPBrbmo5sFczhqa2Hq+J0Wtaee8o2OgY/fAQr1+IVpf/R/qW/Xbz1v/2/fvS//OKD330VaCYShVSD7x8DVTwBJYrCAdS79u6EuVHyDVfVLJQGOI/KOBHMtVZZ3T8Xdl6mF6PB9MXF6ZeGSsJdwP32b7XxbTWeAUP3Ji+O2tvNs6+PljYar79X6Q8+JQ4WBl8uvVxIZ0gqfGp+vOe8p7x9586PE22Et6efdF+VbeEbV8xHfUI9qgeUjke6DiUMCw0zc3COzGLHg5Ic6mCrKXEZVyhgKPoG2gAgO9JR8gKwCA8LQWb3LHCq81QkE0EQBbRkcDp8ZICcClpVUBeIycFh4wYB/KOJuHV6nr1UwgcDPAH0S3/ltZQMOYxRXP8YQ+cJKBVzHI1Yg2A0DHwnTjKhsrPCkADyFBUJLQFdnMxpKYFHPsi7XNU0vxi/9NdfA+BKcv6g5iDJZPrZuIMLJGvkrudWYOfux/cnnznJO/zlUrNy5wN68qXpnJwarbyJwCw2UktaDLUCxJdrdVTFP37kCRwuSpTpTFxfvnpbordrAODAmACOVdgcDGM95FhLS7RulM0M7iTqj0zOFNfIfiEHaQxi/LJQrGZQt2ejMSWQkGXoyiZr5de//8X+bZZCu3rH5N/cofl89dHFYoedEq+Wg1480yzIFuLj/cY7jXH3OJ3H3/jfLbcZyfjsYfCvPpPeX1UJCGMim8REIUEdHHzSKWwXzg2bx7AEAX6CoB0Ddy+RhPKZeOUbbwT7R0NGciHCd6aztZe2GrsBTMW8WqUxjJB4iL2jYCQAIQQMAACiQFzZ/tfnyZuviKtJWevOIg/C+2aJzsDlleGTQ9DOvbPeOIdjnoZmnCtuF/hJ2Cer0xJ8pWQ4Wkl445oK5BokuS0AEZAC4CNoAsAMAJYEAAAWgMUMhCqsiggqIc8Q0Dwa3qo5OJlytLPdgK5vUEl4bs5baG4lH6GOi8ZSyhMoBCaEYYh48yzz4qXLBWSZlPrh4qtj8Sw+TZ0lvlTFc6oH5JrEF5RZbBWRlOD9kYeaWAaDuWBlzrzA59ELY4EPzSTHeUo6sqduURqPw7qwcJKWF2KFFRGxWJ4s+dV0TqmkDqN5lF/l0TA6ibH5MOB9B1HJ2raaRdCZ+1ngxhZhTId4AxPbOVvhsk6aZ4GEpJPUXhwsOhAwOYHjJDYI/AjFEcPFXM+MLIYgWDJAYjYZm8E4KCto6tF2ZxZjIaGH484YBYGRz0VW4mtZHGJcjrD4wAmT0HaCUTA04tqd6rd/o/00LzsPTOPz0dYaPqpmT4bla2/s0PXACzSQ4nkFhilZiYkC2pho0ZS1UzEZZRyP0K4thD2FzcsYAcI9c6NAsjw5emESqlZakljOYwUwwdJyvtQoI67kTmc6LlMSjlUqkRNHXz3tnXXUW2vo5ooIY8S2jGdH4cbGsqQo00G6fxTdJDGS40EQDs5GaSPz3OjM5q5uSDe+Vd1uFAIBC0kkQ3LaYYA901QvW/SnAppmCIal0flQvHui77zVenUTwxjy9Hk/MU35LZTCsDd+f/kf/aHzr/79+HpF/vaOYnVFLUSQc9gLKFCocdXtEhBpxKjm1nSY2IhjAEoCgcadu+mpOwlb7/LmHJFXyiJEZZznz0OcrwFeCGYZXmjkSIzHizhik+YkmZ1ik6BBW4iKzhj0cA7RCbhUQ6PJeId0854GqQA8OkauSngR4Ypw+vVZ6c6N11+rWkFCNfPdi5nAWVsN4tmDvZXMoG0aQdUw1aCLSWXe6yUshw5nUbGUzea6H6GIKOSpuNSqax3nzst4ghZGz4PP7k23VzmE5HZPJyTO3vlgZX/qTvpJtXony3a5NZFhicyVyxxizJT//r/eICkIAFh7cTTPGd//zOhviH/9KjCEHgnmAEMYjMJIgsMoBxDqcjmIMmdGd0OSNsksSRmIWY5ueHuo47PiIuSScR/mInl8NN5Zn4L+aOYCyKcUlqWLKc5nN26cAnLl4snk3/7h5/+X/0MlxEkqwgt4gDRoWa4kIG1erSGB+8lnp0uyzLixS2M0kp4/m6i5NE/hVQxigsy22imBTWaxZEHPfBJAlPNNOq4MzuatFlaO4hOLrXGbCEhGj4x6UcTdszd5lLi+BEll4NT7IPydPxihTP3MJvY+PBY07d23qv0TvqWApyfj2EKu3MqZibX3+WkZwPpy9aUmc3jicuFcitLtnQSsqWDUAxUOxHN4ZMFFOjAwoii8mFkONFQOoctqpZjoeW6a0TkB5io4E2XAQ81BsjBQCsZYlvGqeuGShRyqUspHf67TEbncZhgQQXlYTJ5Pe1MVyvTVeilMZmf2q0trvaOjO/VQugSejY1CIZK+qQg7okMH8RzHyABDEgvBhSBCchJZk6JhSLQhoHBcFR58nBVRhGKRm03988Pzr37W/TzgvvM7LVMD87s+R6GL0yRmyQvXy9sO22w5JhAlBgEAQjDugUZj/TdW0qOTu+OBt7OmlFc3wMgAR+cgg6uvbpono2f7HaSYKioP0XTmUME4oIsJNvaezoxLy8LkKDTKo6agLhAngHwwfYoWVz1PTB2syNBhCigOKAuIFQCoQoAi5DE4fKpv/8ZLCYaSNsjVWLxSzEB49YPNorji2Z575vBcfEZzjCiZGXKTAyM0whbns6f7xDxfF1m0JDnhAp07I8irJVVZyccY2sesyeRcBByfWSIDRpnjG4aUxIEHIJXFmd7mCS/UaV6xMo/zEv3RSTA36qUa+fqV04OF6vgMb/sjHMXnSYQIJcVSEiHFK27C1POCUhFLtOnYvWcTKcu4UspzRCaKZ50BS6NrL7X52Bl1TYJIORj4EcozBAqYIOPSjCKSGPdVFrqLhE8McyE1KzSd2QsfKyZ2Md8fRGxR4pCce6yRAplh6CymPVfjIoSRWehH7sjFCyTLwOHCkzmVYOjjWdHclkrgbPC0szvI7BArvXu9UCm3se58bEqQsiDFoCiakrI5HcY0UwS1gtjcIsvrfHFqdggRsrJ/amcYyZaws8MuDgrVlaZFAl7glFBhl3BPn97/NIH2qhdMiVF3zwd+Ar79MvX736KxvvNiGpxybq2E7yyL+wO/exSttr3LDThwkN39vIj5i/7ZwbP5SsB9+1sF6t239Cm10J3nM8Pw2UCSl9+uSlzX3Dfsh7NkswirUooVpRqd6t61y1KepY9+drx5c+mVq+u7v3oQPZxmwfxHL85eem9DXcWd3XiOOPLr7QsUAuWQvnadAFurAMnATLaLL/Qjorpewk9EYLvgxNvfw3KjZmZXctvVfALCeYqzYKoFdsxbY0DnCKS5Qrd8EEx8pyXNAT70hDA03e7emFMIRsVv5ZJcibX6A2I+ZMwYxGfIpJg9FhAOhAc98N6l0ye7FHIsv/JrvXuzgsrdtbNvKBYi5BHO0HkmC/kqEpea4rPOjFJBjoK2RwQxEgUUQ4naibvZzPVmE57Bdi+M56l/9aa0jCW93cnecahsXWYqSqFMxSG6vC0ff9HJvvzSKjG0x+RDEnneQd6pGY+FMNiq7o6g/W/X3lRW0Hz1v13/6SPY0TSlVaJ8O0mJ2EEQPJ1FHMjs8dxPPIRerTeYFd8+t4d2dxyhRYZDDClXBbjLsFizQsHIfm4ZnYsjkojzuWUntuUqMZ/6shJ4e5/+v/1nv/zjRx+42IfJtf8KowFr9e9jmYpPWDt0jN4YiSKvLiTNChmLTW3YJ5QG08VlDGU9ytNSW4ppJHt2shiGAj472VAWvYBeU2mwrVYvNdOLr372ofat33qFIIqgc4wqFbDZhMAhuuc/ctPtolFhcDeL8SjzltUr1ZJTmky+nr44GG2uUnuOsrRUfDw8OLo4WKvmrv/O2tEXdu/hmGtLK0Vm/NXYjn1Qa0I3QqIFmDsAmUERAoEkpkQU25QcpKXieiWFVCuGphfFr9NJtxNOz/QMkImKnQdmmKF5HmFkni8yxhSxdq38ZoNc2SI0oqZNzp/sra51a6KB5FouXQPoJWIt1h78UWW90rCY8dm5xpbRwPFCvvd5KJUIQoQcFkDDgCLmp3zkEXnAo9P+eShT/rD32BHKyRWm6F+58pTgf/D12f/0b39l/dGf/aP/6u//r8/e+u5y8MxHbuUyNjQyNVcisSzqeWiTlYAJXojZToZOn/e4euOh6F+6vSrB9cv7x7uKScg8D5dup/tHeI2U16gqIAnonjx+WtuQ3JRfUQz0QFst0ncJ37JO+ifR21L69TP9UvJllluvMT3of8/BKjRbJCQAAIAAIBvIAkKbA63noTchlHdXMDzQd+cFiX1B51oAyBMs/mz+tLYiNpz6bdqysMsyBAAFEEAI1DL17ve206d/cTyOZyex12wSl2r1D8RxLwUTG+/ZuyLDKcJqkS/29Z9O/WqJbaaY36zsH3S0sXU7w3lBGI0mQMjRtr0lyl881hvbVdJkL6zkNmAxSZYxPzZBtkL5KVoeppAigeayOIXGsRd5u1Nc0Q3atrMUx0sczhKThZnRfuFWDrOd6eFFOPGSOcB5DK2wOSL13ZR0EyLKECuEKYGXMjAdg/YyNpt5QsXUUGU8jUmRQECYjxPEinCGmYWJXBP1EOM6djyBpgRFFrEdwKHYZN8hKVhvNrwLiPaABc3Wy4pTqssNB6sKaLhT31S0OJtOE0Hr5jkRw3Avx8dhpimNYoEa2VmcMXjF61xoD57ExZtYbIbOYFZtsNMJsN1kfbtgAFJs4ChSJCYznAqfjvytmzuqtJYMzpjJfi/xfvDw/E9+MttaIS41c5VlMaLoUwvSBFVfVycmsvt8oZVp5VZeQNHxsXU2SNdbNSrMGX5bOafnA2vo2GU0eeWteu/Q/vSffzrrLv7u//6deJjVKtSpAXhUoyCjH0wUvo5nzuCgPz5flJcby5tFd2Yu7Gw+TGR3vq20LzaAO6MQHHoZV6fqEBS3AQIAgJBHZ3CttU6iIAXtPfhFqOsbRozSEtVeSruBTdgh5ScDh8mRaJkOdTdOUbJY80e6BmOxKgx7rpAlGZtjuHyrotIcPvYWGcmf9UdLKhE00O4jY93zkhrxQJlfrx4NBwt9RO681Pzq83vvrRirN0soll+FQ9/v0Xv3wnOHrF4tMwyA0Bo6S0S2WBi+SNEyFutJklEspEaaVV2NhxNzcqwzpSSZu/6LIypVrl1pyzeuAAT51/+fH03zzLzo5/kYREGxwbBIYu+eIN+sIlYCxrvOoehVmtvfuAm8xwBVUjDkkNEbLVQjUkGtPn7mrxaJhecrUkJHWOz6lWXRGrn3v3zWUAYcBhQ+X2qtA5QEEEAAoJk5vk37wUd/cfLN72DDuymBerEaTz3scCy1eBSri+K0G/3zH6pH3l/9O+9vbJQ1kIye0FfeaczMeWjpug2RqvTmjdcikJEAzPeO8VmSkhhDUZ3h3D7NktBdvS0bg5EX0zRGzqa+bhs3bpZBIQW94X+6n31rvfTdOwW8KAB4nO4+JkkmCxT0jARSe2NVHc6Onn1yvySxztxzxuG0lMureqkKX3x+wHl1ans094lL68TB6emzn5/cqvJrtyvHF04WxDNtZOjuG+sleOS6IsvvtMGzQ2ikGh/ovgE9BodIlOJl1YeAcU6OewaYx/HUjR2DUjE2JDBpFS86wFjALPBORyg3H9IMn8PzD39s7GxS6O1cmxVPvwbRcAZ7M3JzxdFCvz5RQVFZVVAslBhIcuj8aLiar/aedqcerxSF8URAyRT30aOj6fptTKyw+gFcjGHpkrKUFVd//2VgD3b/3Qsb6Lri/I2b8j9487+m/+jnl7bMlW9PxCBn3+tzFdoYYXgdWDNTzAFkoOENVXCVuBeQm/wbG8HDn4Q4MR6y8euvQ1aQzbGPiFjMUPVWqQ80BeCzibZVLGzf2OAQDZl2HDpVlcyytJ0CLaDpO2/wgBWWpolw+f8hABaCDkhsDi8ASMcwxhIU4CgASA5BkDTLKui1Ckqq9NCEMcORAk85mVIAR6edk/vaG78WmrFMoUxORs8DHQuShoRDD5AsArDXXvvv/m9T4HBfPcruzvqfaEYD1FcqREtFhWhNiPpIMAwjyOG1W+LkQYj3MrGZiYJIk1n31CYFL3BoXiJmniAOaRhm2+2KcFP2EDSb0VKKjwd+oUGhPu2cjD0j8Ed6jKTpZtUQoJgjVzF83AkISJRkyhhZRBj5AyOig3QlhyUA69qQpGvrMs5h44Wvu1Gk+RnJIURMoiSGAZwWMAcsRj2kghK2XDzr2oGzeGnnErQNBkZWGbXjyDYmNZblClywO6/kkUmejD1PFIXUMCjN0XCqupmVXlpBIUo9WZRzIqa0W5fzHdd5RjIy6ln9SZVyrTIRke6CyWnzHhkJXYnlNWZtLaGZvH78CHTn9WvLplLIHjxKHDQFlDGP47yC41ihJkRI5lwcEYPpbKJvvFW7vtEGXTf14wzg8nEo3cn/xUN6pM1kld3iQJw6UUm0EZ60cYVm8QpISO7suZGGYomnQIGYnM2mvemU5Yun/stvb/qrPBqiHZ3GSKzdCFeXCUIpfPXcapgDvWeCxvLxoBdp40krx5XnS7cLHJt9Nk0SN6lp4eQobm81CkvC48OuPxFlVD2m8rdXJR6U1wCAYNEBOSJDKk2IYMgwAGPkRwuCqyUeHi9R8Onz3kiOtL1m4wMlwxz+fManHo6vbngixWJ51CMoLhn3J+0sppckOEISK6LEsvX0gqXI0WSaseQEYEsWhNySFZbDhHdWkBmajuPUHS9CQG8syZ/8j8+932FfWwurck6O/M6Pv6Au0/RKb3HOMRc8OzVomhQr9N5intlFEk3lcp5PmM6544Vg57K0s2TOWOnnH55+jyuB91/+6v4L/GC/tbX23W8sD4bG5iv5bBySBI/UdzJ3V84CQIqRNyKOoyub7QnHW15yhNRvWIdd3H4+Xlxdur6FtjRnIVJZjKIITGESBT7CCOgwskIrbd3MFSReAtth4HrmF5+HpWLyYj17eRAPYsflQwNRUOAEiJcGCF4oE+uG/+MnjuKPtd14+3tX/vt/qDz79/7RX9kg7d1Cd5Ko7VGS9D59Kog+JxckkR71vjzpgjslIWdTyVbl66EN9/sET2/tqNzKSqGhniGWvghmd3WJLKtEdjbJbyjI0I5W31vjpQxcTODiANgDFFqKXNV/cHJkx3luVm1ITcW/V6dRaSW3TLDQmg90isfCNG2/eX19SfAt5n7HhCVOUuRXrpem3VDzEFbiVTHhqsK/+fmXuDZfapNyBsB9P7u5ifYnec+isuxMDxos25f5gCXNcRojRAa8qD/mCaK105zXGkcvjoJjh2tmci32kZx/mLA5OF+kYv0sIsPug+4Hq98DLFh5ea3/p1+zyA5+xJWwNJ1oE+cMdzCEIcCpT1+tR1P9R/fjVz4o3skFgMDgOIoGAygVrr2zhqLWL//DcP3y7Sw2r+cI3+zQL2Rig9ahSgaGYYCPPBLys38Y/bPLxOSP/10nX6SuExB4MccCAlM7FsfnlAute7nBZ8zsMWu1Tu6n7bWVb70dQy/eO/zLL1+8+2qzWkoSx7f9LEHCKkAxUFLLhbPuEyDlV3kUl4TEdgCJiRXSngNMTaFuIvpYzBdg9PHTZ/RGDiFLLIY/nIJAyX6wR/yXEHxRAH+zDMgMQxFmKs1LyMUwK/uMWIhxqlglIDCWX2rh+JSLif2zvhmba1d32rQMaOQ8y1oMCgE4GWRy/lSya4USX9wJVhFhDoeCl3lsNHWJdLDAB24gc5lxjlTZ1aLwGIGD+fyKQGoCZhTg0dyJZlq7DpCSEzVW32+uORKZwJS1AoFCAwSd406luubC0LaJ3uV2rlBaA7ZXVBHNsfbOCVrOFaSQyzTLQUCGJqHS4EmF0lgksgMlVwQYPfbQEo3IIh1ZCcAQnqFDiOFoAlCIE5AScHZ5FY1cjvUsxkiRGDXtmCZQx8e8NAoyFKCc4YBGhZikCE0wzMgU8rygYCHGCUFKQuLMoDbWgVgWEJHav5gqnlRq8Eu8hOh2egj5M9OuJDYPyxgRHugkF7MSppeZmCGGKURO56OF0Fwp2AR48MsjZWa+8XY5pon+Am60iHwu5dxT63Noei6Lwys3RGqjPH0673x9enmJomgDGYer28W/tdq+/8WL6bF9PkQLq7zLmNWq5CagLAHT9HGEWG0JRt87f9o73T/QA+/d31ld2iwZM+fRn9+Fpdza1Y3m5YSvKb2ZS0DCPnek0O+ZMZMn+Mv54uYaiFLpyubo5PHw4FCRc0u5rEaE/mzBJVZdLqk0fh5Xd15aUpltAIAPwAKAfgwuQalsAVqhIMyQeMRNsVK9iiAZXnhpWGYzEqWTA0pNdgg4fmGbA6lxoxHZUoSmGKpoC+gP/FRBBSyilnB/vsBg0eYlcDwrpD70CCVGAVl5bq2mtfYoSfAC9FMzpPQsF1dp1rbc45+e3PntTRkVPr5wpmN045sVSOZa7w2eT57AFBfriBywYDII++cIplSaVSCSp714cTKf61ps2ZyiJD7aHUBLt66vVWzoTx99tP/V9Bs369TCRZxEoABJRCPb/eSHFzcTXF5OUoZGQIxj88yew41lBiBOnhfJggGoJWCtLNf1LPPTZNwfCzQAAHJUyFAIJ2JDK1umanATBdCeJc5CP34+REtb6ltKfghz8955xIZbbTnx0T0jnS0khMm0/rBm8eilzW+rxL2fPsqvlodfjCvruSu/hV9vX8qgfhRjW9eYp3svSC5ZWlGIPHf+Ij4ZWDxifqmp5XyjmMvfzNNZjdLGiUyQjmXZPd9Kp7ROrVdr9XwVnRTE9VLQQnyM3QAkCByA9xFaBbke8h6JGq7SLL9ElT1HM7zo4stxnWWWNqsZ2QbAgtFjLkEePlmwghvX5EW2oFhTZJmQ4QejBCTxyZFeqKCz/ax+Df/9X1t/9vPOFxf9FRkI1lA83hPWfYFnTmdJoXJVETJvqo0PbEdQbSpRc8RrS8vEHCIsU1UVcVkZ7mvDi4WKgXkARaIBMSTP+V9NxntnBzBFr59lfXX9CrP1+THYSCZXviXCSMuipdxIs05t8L4EJyyw0fY3c9F/OK8M570P9aZUnDPMlSulU8sjn+voaOANef9VZpo0KUEsNeqWy+Xw/OvfTO9+2WU6nf1O/KMR+Q9/b7lw6c5/Ufr86cV4faMw9mKoyGMnxBjBSeydK/mTx18vlxrbDE8j649/1D+kopvFyvpGZfz5cHEeCjVOS5CISeceJjEsjfBFyIIm9ex80MVQxMcplLQMgLP8+SDmqWg8SWpLGJMgJ525TBVM1mDspKMdw4IoUlULZAG4tAZoAICCgD5ZknHT2pQBVyFDlPIyKkt3f3JU22SJopJQ5M2ttTixnz2eEjn1SoFtC8h/Xq+s1oSTe3Xrsy9/eXFYapBvvdkQdQHT4/6jg3RZLrZZ9qYYEITdyeuT2TwzyjxtEg7hEBSKldrFs4XFYwXEzVge7z6dyBti57ELOWq5SNh7Hf9iwLIUKccwwddWFVRVJRMCLEtHmjXVSQ9zECM2EjIHcCrL1cgsy1I7M2YIVSJJUuFJ1pg4iQMmGl5ZFhlZBdAw7QBHsRSDAELkf/r/fsfqjww8ZUjOnCwEKKOeoVwuamo+ur8IStA+7TUkQf3GZdoMT5/rbLtABDEfa6eSLCm0+8i5dKMSZTJmBmFBEtRCaiYiQhsZaJagZeOC0UtsEy+NdAGTu8/vD1Dm263Qo1BQqTpLoTZk5tCQWQRLzz96XOdLsEqUC/jJLDggirdvwK2FC0OumW/U315DE1l9/OTBgwjzSIQyytMjM5w4bPtFELy8pAJ8+vSpNp+pYouO5HCbzrNsg69IAh0++nTBV8iaArzDvX/2s19+vDu+8d5WoOfe/N5So1ywCNGbMWIZFUD25f7MT+U1xL3+6oZ3o0kbXWOsJA5wBtPm2sbqajg7ns9O51mxfGtN+NN/c3/l9rU33yrHeGxw28DCnDnDtaGMIHMAvS5Qmmf5SRso53ef4VQ4T6Li9R2tvxiu5kVwaB1rv4gL0yhMqe1iCWfpWYUtinO2IONMarqpi+FEjgSJR2BxPHNmuoIw97CEssuv0BHCTkFzDdxduALF2PbJ8wXNezaWpt4hh7ocztLMikDBZ0CU1JqRVSq9+GIRvbqJgtchyB78Pz+dVbLgVamyWbSAEsMX2s/mCqZUBcDcWsawavzVn5yRpYZE+/39Scals3lYUsojPlekw7qUrb967fv/7HMA06SV/9ZLlcRpZoGHJ2NkEvAtYXT33FtGmnfeI6ImcOxJSYoAJOKoxOeeeAv85ISXUYGOooiTGRz16AET2jbcatee7s8XF/vVJQarqUXuVhF9riMrCpAAAMD407MnWXMtMoQC0Xc5GPrjM77MIjk2DQTreAIUDtXn0lUSjBGosyO/+pwX6xvy3sGiQIOteqtYUndPxlQJrmLWPKviRGGxvz82DKWcQNvOUVXjLJBB2GOSbmmjkqwtVdEIBQsOaAyoE95ROv31fRvF5kCFQPwc9CcpX0DY1tmf9ld/fQXk2f7dYQoBj+XuhZSQDyow1hmpN8Dy+gjiMCksI5TN56RkFnjPxi/dWXZldTHW87qFWJb07dLu1wMYDVky3mrT2de77uGht8RLrR26LQBTS1HyZFy620klwlOWKneuyOTCB57vZ+qzoT7t6nqOwX1nZf3mEic6IdkQ8CGr//m//Nl/+rMjNku4Su0bl24TJfTbcqX5XrmXhvgsoxLP9/nKBoY8MAHaR36dePKHj91uhli8LGOlnXLuLRLg+aOfnGmTZPPyCivh6DXefahLL10HoA2y0y9/OJmfd3xcm9yfg0KZSJHxUN8psK02uXFJQkg12Nr4Akrvxq7dGVdfFzx/iJ7mcJTCmyHkYOe08NmzGXt1aaOKH3442LxWZZEYbck4sIr+bDhBqHa9iBAQhmlwTDLM+PSg1G76J/t4qTSDnYooo6gZJ5FlYnRMeAKRo2MAEIBJKPjoY/ASAPw74K+A/xxpmE1nkFLMeYzQkPZGHgbkaLCrEXL7ap7OSGscNtYYAGDq69nc7bCzDfkWRFMEwQD4xcHji73uwd5hDx7MZ6vSxlpzAw1wEvnl8XBZpKDEr7b4jHXOL8yk62YCxyzSsCSFTDqc6+zZ+N7A/mBLSVc2FElhFh4o5GU1/Og/zPJ8QpYpotmWOZl8MHfpUjBdEDnIF1VcZJsidpElC32UjKY0B0gkskeWn5KVcsa18pkfOD6bJAiLcKjvZASSgSzOkpQEEEAEQBxHcZQQd17K6YmbI9nnx6R/is10HDjK+i3h4H4H6XoKwbgQlBBUwcEGB+MkShjGn1HrGcyCkN/KxQH0B16Jp3jfnHcjocVhVEJqYWcaSmzi05ZvagePp3xePJ5gvEAOXH88QN6r4uHxNEUorspBgJ3vz1CxQFYkHaf2Z5m3yG6uZPrXpz/+cvDeH7wlXb920c3s80n5wqLimOdDHo7ibI7KaIprk2NjX6Auv3cpT5vwgYYKWESxASNaHsgGyY0bOWXZPz4+4VIW+CNdDyutQrOem2C0M086jptfr9ECN52aahP/1htXhfp697MHDx4O8VGPMWdivURCCsu43tGZNUPzfNZHUZbFvjg2BV567WoJCHntgnjYQagFYDCz8wV77QPCd8D1cgbSlsk7x7sIyEmUrlDAm30kThed1d9T4fQxSyS+kZBLUl+rjHyQS4oFi5LW2DjBeruZjwG1ZtJeJpB0uDBYX6eQwVUy5kD8r/7vxt/dlLO651fbID9w52Va5X27Pws0e5KtVKpWFE9Mp9BYH+fLA4tNsIQsFktV2B/a+A+M8lsv1biORIwxlrn76CDvzgtl4Xq7GSH+fOyfDcNSgBab4tRLlndWl6vts/Pz8bOLpcuZqEIMZy7OjLLjv/ZmK3/9fYDUO09+rGmTikgrQgxqMhh5dDHIqQg+GcHlQjwNoWFiKc2LyfNhh3OzVi0Xu4NRNw0knoBcPsfmM2pyMdfq3vISsbR1RwVVHIDd/bHagkezh3zMyi5Su/KN5fXJYLbXMZ2lpsBThaPRpOwmOTQzZj1uu3DQgc1NicEgypNonkkW2UpkQZTEcoKlpaMFJEWsUZQpKuscu3GiIXRiOXo06dwfLGQ0NuuRKjKEJKyKuCjSwEefXgAnnAUyrqyI3dlzzDhEEQZAC6zlwHkEPAxJJ0g/KZBRdDa/uE/VahRX4qBKkR+eP9qfvLGztLFZ2lpRaC37xZ/v3bxScD23e2S8/o0rZ0Fw3J8UCbJcpkWB1A8X8YFXkAtlgXjxcPBof151sRy1nFoYvVYCDgqlEEWRQsK8RVISU9Xo3ORsDqLUTRDPngQChQp0o5o3hpU6ihhfd8R26/Nh1Hi9+o3f+3appjq/ODoZ56+kDBu1i6+092YXTjtZVZne3flmU7IOLR2FSKFQPIZoc/nWazUKrwDS23t2ePjnRyfDs2sb1eZKXXr72vSFyz9KMzyGWQEBACArcNN96YM1/Wz21MFPfmSTlzBV9V7/g7VSVYmDoHOcActaX8G4TNKen0dHcUbMUCqbj9kvv5hLDeWNb60vHR8Ph0/ZVqPY0jeW8yePpmIdKQxCrCXX2ujx01OoMiQOrSnevtYwrXnepwlhCfMzVik/H/lI5C7HRJZZqcQb48iIy6d2p7RK4MqdYramQhWgZopILgACQFgBGTiKKgMUJHgFIpmNZQpLUBWWHfa8CgczN0Y5gOAqVc+twxZAAQIwAwBtAjAblKQS+jIvveZ+3dVSJDsZ2Veul2691nKOdf9icT5wiSperBZmgRDF0MZIYFPGRbBU4/i1Ja6lTR7oeDxAahgrsfOu++K+BTACQkShc0RG0abXz8jm6zlZLmaJPx2i1tA5uLDHblCsYpTMW36UoERxPUfkBCTR5yM7QzBFlTCWMOYJhERoWShJsxyboSGSAhyDKIYi//hf/4EnAAwP06ln4CxjYem+Jt0Uyq/nzeP5xYd900eXWwL+EidybO/utMSR0mrt/OEiPPapAib/WpslheE8T/asIpW5VHjuR3Iyyxq5ShUZ9i2BDsK+G+B+LZ+bnh5aRFq4knvsUSsBVRqs5nfymhaQEjoamyHtk4e+7iFQIqiMbOHheX+xXpU3/tZfvaN+8+Lww8VuB5w68iQxrA4oGNP9C7bJ7mrCbiB/44p47U77+f68f+HxOTAbGmvFYrFcoThiMU8LSwSOT/s9UDl/9A/+578k2/VcmRWVFpsX7YCm5MKNpipdq0uo/vThJMvfWl3TJRc/O5hgGAZzMlGVWiTTRLC+nWra2DCiG3e2Fl/ONTJd/9bVcDgzdsdm3ObaN69U0vvHJFICbQDr0rCbnSzHZ4fTFrV8+cVfHB1pR4/+56/+m//xzo2//mvT+z/aP9grr8IsRFr1+txMpNWmrQcUzotBMqhfouEktaIA4PEwk1Ed9RcF86wzDz78fvCffuD/1o5YfPO9O69RaCOgBpSlGWCxbxJZqLJLXFwUOYA3RhcKXZEFNtHr5ZNTjKaJAkUu+tP132RQFNy/+3NaMy6vItTkMHBZDSnEJIFKnNWZhq6xdbtxodOMi2IIFSLDYo61hsfPLT6pLV0rYosRFyaAB7ZaXkELVqlyS/t6LxkPi997G/zkM3AjATyenbXiXNOJzLBcw0KbTr0Bz+NxiZl17O4Ay1Ol7XoWe0jYImg90aKgtBTZg4Pj01TeWY2OV1rrgC0iQIPxSfcY1ME+Bt9ALjmn+jA5XKAIEFXanHfWtgvT40lxqz0+SkoyRBRkfoDQBcUYWAlOwdXy4Kmly4XNnXYFOJlpdA4v7BTNN9QKqdhMgCLG02nKBAParqsQEhxjTfuhuLTUyHVO2X/sutLh3eICJmXj3dvzjbhVZFkEx8GgB9IetAIrk6St4oTIFmiZTFJk5Fc/2BrPreGju7krW64H3AukVeAFGeXWr9y9++X08fzmNQWjhIWQw/yY8jVCXVZRCOwzkSPO+qf6FE0z7/TFAiLP2jJ189KSiZO1dQHQ2HSPHE0x24liVm1ckigaTayotpy/p4dnnw+zMGsUSUGW1ylUadd/fEJznF9x9GSlzqrjo2fF+miSE1VVCLQsG/qhiMS9jvfqNdExtcOv4jv/x1cv7nXr6hgN/fu7CQYnXY9tFuZSfWntRmv2S49AhDmKU+vbjaUm1L/6/Ii4w+r3z9RJrMP85M7alb1/84xNXPmvlk0Vy40SzOTqpaWATuYjv36DgrNwPOufOJo2ob7z6lWyzNz77Gj1eknvwF+enoPQZ/Ibbyzj5jyrNYvS0MJ3VjI8nGo0I3okBnxfz7IcJMeOqbdzrd79R8Ka/+eJ8UEBNsIUpG60MPW0lM644o4wYBXcO7QptYbWGU07PVrGyM32ZYAQAAFgHIbD44GpsJtyof/Tz/O//sY5iooBzA1isRnlKGqI4KyZUsIkATUH/Txb5JzF3aMfGyN+9mjUudZihDRZjBLNC5hIq19fExRK4rDBY9euom6clJgswCR2HjlImFg2IiPQseCKavSicmL1JxGDixLKoiWc5nILNyrLZZKg0rFh+qxZoyIewUdBU0D3NfqqAPmYWlCRkxqQBcOOJzAuw4oba3jIUuMLCxokhgOUwhmeiSLfBgiWRCANkQRgBIKiAHcdkCRAkVAIoaKgHi/Cic8F/ifDwa1vVdT7Pnea5ELatFD6GhXPqGEPFdRc/hL6yfeftDZl/iwhCpRaEVCMGw+HoQXrYsYgyOjibOTgeJ4PKryHSVURL+jpeEbOTFArMcQ4SXlKqFKhn+IEFs/DOgwef9WpksLqsjxwMYkG3jglsCJRvv6Kug1iz/aLU8pmbByfj6QCfewnoEK7EJ8cmlTC+HV0kLBqs4K7PSQNAY4VmCTKxrjHLpfZ8WL85Py4WSsP3Pjcgu/kuXoRN/GsWebwDKsugeO9noOZoSTYk7AlXjz82aisqivlnCCIGMXsTqPHJ4OehOy8Vqdp0uj5890FjqZpnCaGYZqLVI2XlDFXuTh5bqJYc/cA5EAcCicVxcRKaVXwNHtwBh//9KdfMy/p0m+SJvLlk919JU9SWRExk7FGknKsPe0kDawXplKGjvdHNJznaUoLEYrPRTTQRrSb8zx8Gt5O105SY8ltXdeIHYRh0oGVhVnYaBXTOYGTyWe/OL15pUK2l/KvL+PrKJLxqKuXJNE3zBDwuXz20weuWoeXb30jGYxJu6sf9K0MnyFhRKZETbp1Z2fWGT3fXQRE1KjS1VLyg5+M0ycZDYPLt2tH5gwoAiknRV656LnWwSNnmN6MomlnxgYUcEhgU+DzJK0RAwLNi9F8mFLmIpeM04tzQqpMkBCNY7q91GjmaQTt6RGRzU72dFkldsrxWLeXBWqpTaOw0R8adpQpwCgLS81NqA21g58+ZMYelqRXNktHvai0WrIWi/2D+UZTTU4BpuGwkEcIrHCZyUhG75BLOfJ0iKJU7sbtJQxgoWdk032IZKXNelXIsSjlHS/OT0xKxAWlVKsqSUiUtkRjQZx2PWN4sS4If4+ivk5TgcdWv9WoKyRLM77GMxMcaW6BdBUZXGSIB2TEsy1H56anZpuXGIbF3Cj1VSLIXNPJ3OxRT1eWS9X8VKKo1W82janlnk523mQ06J6MJptLMipIC5QkGTLfarFUMp9qUpN+enbcfRhQVa3QLL142p97RK660qpIozh89swcJDZP00uyIPSiEpIJW/VZv++ezT0u3nilDiTu7Tfkj/60A8MkORllCrh61ey4NksnZhbwiLhBWSdHwSvX1FHvbDFbvPKdVegMeTbyUO7rJzPH1BpN8tqq2qgXA1aZnOBepdmuE1I+QNLB0z9/jOHhK29UP/swxNjoLMtfXi8hvvDWdRXwOZivHS8uhhFa8Am0ABgdNmvp+f3jjEH2J9N2g40zdESBBmy1Xs0rBKPuIAYCNgqEgxDmQCNVjC1k2jlTQFEEChd6tq5KUaRJLNHte1yVjgGVoWJ1pe2roAT8EE5T5xzQISFT8igg8tVknBoYXILLtVbBc200CdZ3YPTi/umPGbUtwQKp9TRtapQL28fnnYSS8r7mneCvXxaOvMV85M+AU2o1FEkBmQDQTIDF/b3T3Zle24HzedJgsWRi4DjRupTfvlKY7J33f96ZJEj9UrlZ5p2Fs1xhaC804yDGCIpLED5lWXY4kARXyopsrhQX04AjmMFFaGYQ49C5DgsS3x9oXneytF30/FlAMeySoq7mKyPUnzvERJfyguezEZ7iMoViSDyzHw8jqSLyMiPU+CxKPc+NDSRD0wxADwKGJIkkTtMMQoBnri1HWWwhBAzxJIMJJqhyZAfVL3xTCLEqIekAxVnr5wavtFtFefrF1967tMvTpEobho/3ZwEvrcwTHPXMEu49mkc9j8lDAUv6p1g1RkoU30EI1LX2n4xmA7fEyOYp3KxX11fyUiYNjhdxnonwJJikuc1qVVa10QCZgD5OlzMs6pPbH6wRoAFQTZCqlTRd+J3QNQzJ/MqYrfKT7gsv8csbLWFzO1ctsMfHQwchcxVS6zv3R8HbO+VoDBCgoqm3Wi7fRo//yefPaQCOj/ojTMCpfATtYOoJYxdbqZXpXGinK3eWVwqIlGNpPLyYLy4ezPN5DGKl164I85SxO3bnYgIkQa8yVC+WUt4f43yumfK+dnyy92zyn/YOzfs/EVDHuvNfvHMtaUGbjO20VHp+eHxd4PZk/OZ7N8J5xCVBISs21sW9zl6DQLKcLEnpIwvkI7Fl+yxT4KJMoGKcRcv9cOjwEYjqBH6q5zFz/NaKfFEZbdxikWz+5DzPyqgOubmNGRjEaRnzputvrDV4Hnunbfw86D1jov4L+/UUv37rauh09g8jEn1Twn7x48HO9TxU6N4pjJxKdSt2FslUSD0QUC++iImc7Vq1Zs6eRvtn9o01nk2sPY39F//x3rUbayealyBS9O7Sq++9hYDD4wePvjwcVRS5honwh/cQqQbyxUyNyCzGObFUD453R7YwrOYNLOWTxM+tN5b5XDw6pQVSoRFVYFw2yozQhtqRF+/w3sM0LSECnZMyr9t7Ns5zX5Mvv5mvX7/xv8FeGL8iP12kAn1wuE9hydrO2od/dH/revPrL4Z3djYA6r04Otu5fnv3eFQRQoSiZQpzrjU4mB5PJi1/IbogbqidKcjOXwi3188FdB6jqhuReDpCYKuOZzjjyNWKNjt/cDE9W+ys5/K/9xuSqpDx/SmvOZQQ07IwcgZPnC1ygV3LKWkIYFhsM+jYV0gGmCiYX4RBUl0mQtzduHNDf3LWonl74CyejGQqMjUxjNnRwmv0HIAgKlvAFuj8dJCqWEBaw5NRZmO0HL1/rXyzW/uLj6kHg942myytrmzRLOKRMhoRKxUDO8JpgiqzQkpdnI2zNJRLhe1bDbusHs3Frx8O2rOo8sr6zjpzfkSUeRfEWDzwSEVNQxFQ6e7RvMhlsZDefXKwvUwsrYgZHn/yFyO+Sh3O569f4Sbd/pXfepsAEhydo0bG60hRdE8f+TPzjKIosiWtXU8+ORmaUv4qSb58fStfDcSDIagQ4DIFh5PS9Jx9eTvTZL3v8RSCCV7MEDPNIkLWHwTvvv/u+GyWSedlU4jzbAYQ30h6IB5lZd50OZo4PJnWgQVsLOY4FUL32JhEfp32l6UNgBadjLfP8H5uk7Tu16hUJCMs1wJmkmUoQvCwpGKj2VaKYpzqaQSeRLY1QaIG/WqTOBoiFTxEsCju0m+9tC3K+1aQfmflAMleuYqQKLJ8pcKh2a6ZkheHoAUy1EeypD87Xltl7s2Ye493PdJZWcq8GW65uNYxh8f2N25KW3+/9skJJJ+NtAzP4kIa0X7kzgkkhi4SprLI8XLRzeymjy3X1ZjC0hI05mlUDJLEBaGr3C7RiTN63BNZwTSwlRxm46nujHc/7AiXtkCDhnNk3F3katScx1sUjTo2IfAeigKPxPzUIiBOE5GbYqkXexHDU4DFE5hiaQITBCERPCYED0kv3cwHdmTO/M7TqJJLUQKuVyvPP7PW29W5GWo+KdKI3SVXr5e7khdOWDAyl5aLHpoKJTFYWAvjVBcjdBmrb+L2k2Byohuat/XyaqYJaMaKVmwFkEzx6lKb5URX4uw4NzVZq++HUTJ6NpTqfBqRrmHc+3IEoba6XqgXBMwCuSgnFXIAgNOTOUcwKI1gGySpkEc+7yb0kCjydfTKygpXWmYE6eGJb/lSbj3vzM16M9Ri7/6DMecTrpgIOcBJ5C8eYt8/ikpNda0lkyXcRABGh4rCFFRWtxElR8cYMxsanWMHY2IKcUlOvLwj4opgpcmZ5pujCdMScwI1ccH0bCYgyvLVJiawh/d7VcpP5haFP7lEWz9Ivthara69ue9B5qQbF7fUw3MLkQnfp95+/32KJpeVlnNuru9gJ3qIFBox0F48HL11vVpCsPgwS0lMfmtFdsjT4zgmsmKOHx1aSIokWCBhmaERi1G2WV1Jo2RhEdPAtk5sgknZVBmeYTevQZxsSOVi3wT58+gvf6VtLKNT/dj9VaedOsJ2bXszvL87aqjSB9vhV5+fFG8Xr//2G+nzSy9e7Nqp8fZvrgwRZu+LmbM7u7xGmz2tJqmJxOw9nas5WFilN1rEN6/nE7I9PvGDM/2Tzz83vjz5zf/zzupV4eCTU/ZKEaoUcAUkX50OzwLPYfALyzfRJNb8qH8eYjlPqlBMHIfRQjdsTw8TkmWjKBsuihLqzUNv6OOreev51KX8tkRcLl8Cb1/PBj//5J9+RIsoslwp8EDdyo/1RbuYiVIAiMzLgij2UT7yMXc2mc2mvgftEHhknYC2mSuH80VypqVlRi7xRdAga5VLoY0i+ovxgyOMwFc2mRKNOVroJJruBhLkcBeU86Xq1eCLNNzPMnHml5bVDLQG1vmuh7TUsvhaHCmDT/f0DS1BcT5zY5wIvBjdaPHJmDz4ZNgz0a3lwrQ7K6cXku2Mnk+TKMm1ldjNRpq+vlZlX60ezwIuzqgw1BwDxzE1F5jn87keiZRcf+3avq7GiPLeG/CPf3HXfIigFEcRWL3EIyXUuBjWeZBwZIQxKAdmTshjBB7j7CikArd9jXz8lf9opHOfzXZy3CUuneuJY7MxkREgU6oi5PgmSs7Pz1gJ14YwiryHn82jcFxub1751qVyuB5nwyf9KezZ5IvZswfnL2+wJFUF46SwJuoYu/7e8mEG7nlmgvhby16bExq14S+fD4JuuBHNkpCYgrB5azXEyWKJmlizT49H5vEit14FWaXCR8D3EJBVCzTUo9HoaIKy28o2Spg/+ldHv/F3bhYUDGGwUYSyVIaOn8FqTm4vlbCVJgChAbvHtuYhO00Fg4BGAJndigJ7Gp9PHJNBlhInFeqlwQXKZgWOYlKURJwwikd8FKIrxwgANjimAT20o/4oWWuomAi2pXL3JLyyRqZBgnAEBwAA6AqH0OJm6ncxpg0QAAPps4+fS2AqSNDGjKN7kVwmAxxDErKMkU/+Yx8oxLXXNsit2vO9GMdRbRarODswTD+nVBnJ7Hk5SV59s3H6eDD7uNe+wkXhgldlT+VIisBRKu8Yxu4cNxdikQ9cIyyAzMpeaxK9Sbz38XOpoLz9XvXTr73ZhRWXxAjLBFLwwgQSOEIQluOHScRKIEFSmgUgi30PpElGEgDJMhTFUBTHWd/R2sLZST/z3dad7bd4y5yje14URFYKMnuqKc18T+MqLNM5d0pcD5d56mRMs8yUxeIUI+0Q5YpBnAq65jpsRgGBoxwgxHHqQI5I/XkPsCmZSRguCyyHUyjJ5incCqanIzXhvcwDVdGjUYCkYLPxxhscCZhUji8uXLIc6Iw7QKa1i91E4JA86z9eiCoiJmgZJV+9VuNmdi7A1b7lpFPKSB2EpJtlObBNNxCrXO98ZlrJla0thMdu8g//Xce+py22rgqXiqExW7hwOfUzJwiImMJmpg3sw663tl2aGWlVDAlRhAtoz2JXO3coQChSk8dilT4XEdUJEz9xZ07+ulxbVfz9M2eeelK2VROHdgQ3sp346nIOZwi5PzjPSWQqoNx0nlZKCa9u3FCkaOpAa4zbUM94+Ox00K2Ip1ulVezk6MJpvXbz5oCH43E3Ogo8GEhKIc7lL307l1fjn/yoq6oTi6CZafzu1fqgrrI1NhuO9j4/zbT+lfffVJCg/+XFd7+xA2earfN/+hdnr6LU4RG4c7lwbzFYfH/xL/6np/nNGNuqfXRfW8MYmnC++OE5ak6kxnWqBA1UOT/9uLJS+96r709KH+Uy7GIRcqJAJOyqNK2/3HyC2lMKOVkkK3V/2jmgd25dbjMneQk0c8hi0LuYibRSZJc7Z51laPfHek3sIks7AsIrc8JIcQvCeOOSgrBsuBic2jgPJIoJveDxE3/EQqDgIIKv3qjmpNIHDZDB8cmLuf3wz4W/8gZovPrmf7kBT74/6fUjPdRYkc2TBhrXcMIYnRVv8d3jZ7duNvWLjh+lpbY6cwx2lUT87kVApjgvWLC4vKViVf34LpNFNDJsiks4Ej07CTPdQ/N2eHm9utUKI3z+3Es/OVJvVx87wa2t+mtN+t7ZeJ3dA0C1gB+4hav6qa+yAVDLBTl/ueJrg3AxXypDQ4dekAYVld3gN5hELdf6u4Z+jGEvNXxBM7DR6+9vnU0utLlTXFEMT0/sxNdhY4UbvJhCBQcYhoazeWA18vJ6pYnwZUozUpnUBgvSHl979U6pXCrAFNPT3oszlIrsMAxoYC9x9RVwUJebCL2NY0pOSrq244P6cl3F3NlEO9vT2htqlk8FxzWCjNKg7vRO5oxCzEIQ69bRzPSM53ruzeq3b/0eTJPPfv5EqWxdjEeYMRmYt9aVSL6iWN+pEeh6y8FDbzA5NrEY/Vln/MbBk8vffe0UYZhnGdN7AYIIlRlitUIy3bwRoqRcdAFgz4rt+irsPxgEvt57/22qJ7VARxud6tW1rc7+UXNr9eTJZ/O28+qt9y76SE6Ej7/WZcQrXl7htlVwckYj/gwldaD5aRJcdNub29SJpqMp7PEqQaDXcZoRm0jVphkijugKTqBRIyGRZTlDMKBFSBAgjgA2awCGALobRfpI08sImbsjJ6IBoYwWQUskEQTgLPGfPX0cABpHMjB9jravIR0AlhqtG4/Sxz9+sSuhnfKyOPExniIZHtMcKcfSwnvF/kn/6f2RX69nebTg464K7UWUhUGLZ53AQ0H0+adPvv3SpvSdpa+vhFF/LhHVYpFOXZTgQeZbx4fjcOFSl1slmaMhwsuW4eNn949WGu2VbemrT6KPPz6Wb28unk+lLPDQKKVRLg0XCzcLSRpJaJ7DSDTEgY8CLCZwgBEQoUDmgwzBSAancJolVnNgCSCjc/P4q0cSgQpMMUcQSWguiZlE0NbclSIPcVHciboPmdbVUoYkokzxOZHGCI+mFYWkMh8BAs4QKkT4erB4brRW8kwOBgjaWhP8RRqzlM/woeaycaBbHgkQnMcgigoySyMklhHFZdkDYdz34ol3HMZrVxgIoRtOYHQyscJSuz0dOUnsQSrKhEAlqZnJEgHimBChcXGDAjRSShyFSZE4yxcInCMpkrhxpUIR2E8+65yy3Z+ejXSfeG1DClEMUkkx1+QNL1+RfQOzJaxWoGmeS+yEZCMyz5haEs8SRsUxJBFKvBahkYxyiSl7kENRscawi4QNZ/bFIwIlrt8U7EN7/0HU3sZew8grRPDqay+F4uKrff/yJtObzHjEKUQkBNFcQ4orMObjghQU1wl73/6tl3IXXz91Q8NSiWqJyZCB/ThEC3YUSFaGiig669sNRolDcvuOCjnKZYid9VU6ZMdz+LzvxvNFCRXRTNj7o+e/+d2bVJUzj5McpmNTsMPrsyH+4Yn35HiKlTtvX5LrmfFo6jBL0Hke6x5g5fGNnO496StrHLeGUnr8xx/GjW3k773bMHcBzUJ7HiXmiEbJ3u6QyU8d3GYN0zVPPz2exH7y62sIYtp9avj8MzfuRngGOCLB2bPmagheLV0yy+ncZRNz9/FkfkrKTYlilykfhamFo6EU2DRBpikwp1GKoa9cLtAkTlPUbOIcxkPEXWxWhfWrK+BK+uJf/Ilxqcw1iku1Fplf2EMi8UVdH4MwTkHS74ccTXgzJ5ThzKM8OhZZONGHHMT2J4aA52WmSNTrKqhmMB66pioF4ZwtqvZwgZICIq4Ljh5fnAwROiuSrdZNNfzTY+0vf6HUBOSt72Ayd3XTPb07VtB7udXiZRWzTgF5/5hoLscBIGVkoZFstTkybH1mkbwQDvwXd09FHl//20tkg+GT5sy2DCsMISRbon7o5oR0s0ydnPqAB3JF1nwUSHRtO5fKYkbyEW/jRg29cslPwvHhi1JZXnh4Dt1o5RtbBW4xWKj1qkzQKGYJ2uii54z2u+dqYPClrevVCx/kRAKXiElvNJ34rWVqKS8TDfRkEvRH8UaeLJAZNCISsa5tUUaYjkZhrl5ul8SVq5heFu+PHv78//VcWFN/9731F+NxI/BFMGisVH1sWIeL54/+8vQU6Bidyy9nqUg5Zmu5XY7Hs18NWuvb+/0ZiyZcgzucznA6I1kanLt8E1mMO6fdwwKD/dZvXD08ne4dGjvvVLMiYXnuuXawf69fEqhb5e00cFAAtxlgXjjXdpYI1j8cOMjQr1YkYGNx9mARJVVeJVvexeMvFI7NFlFMliYpo43V9ZJA4Xno4NM0qUisfaGnFuTHaQqNxCXwFCGV1SwWdNu10HS5+EZeTivABTAcz4LMBoHo6UEq4KFkk6nM+gNcKUOIIghWvUaDXw6L71QBAMzycu5lrjAd4bvPjWJ7Q8rBi9PT9Q3R7hkHfowtEzWK5azQtLBBiMtuRrrhhorqPQvjsLf+xtX9z/d/+dNHVL1f3SgIEhIyVG9ikWY2edavYIYboDmCqUh4kCCXiqk3tdZUwuORzmlnMlRvvb3x9ROTdJJmSzTOtQKfEBmGKMRSq+BYwOrNw7mVpWRMpSSRQAjRFCEoDEszicYBSiIoifsoRvdTtKFGFbIIiQRBEw+WU5rEo9lU90S/S9LlCp1U5CTjsX0dRD4tYbO5w3EugAyWxp6Gc2JKEkkEiGkSy0iQWy9iMgEIqYJmoY0RFFsQ0IuFLrMihWCoRDn9CRRo14zLaZQeWUDgH3THZcScpuQtUFXWy4hsfP598/1NYOq6v7bm+DgVu3pGcwjhE2KuLYmTBUPHeSYmRdqBqeVgfmu1CGBkMwIWaJ7BlrPOfKB17icBsesY7Vb5eypD+9a+TjC5wEmylIdhGCJkXChLlrYwE4wPzSJPZYlPuZW3l4WUJvECMsF5SJNYmMluRhjuqJA7niLZgqtkdnE1o/PKbJ9rXaZMPpqao2CB5ldXbCY/+GyoclLPs51xr5QTQw0u+TGJM1KFgxLmauP+oqcHZvhgwEgJgVvM6PmUQ2Z7KKhu1vPgcRpkq5fqzao7fHH2s+ycQtfeWDpMOScAEmog+91OAi80r1lNfC6hZ5YX0SHwyJS46JhLS+TjP/nRwvJ/5hQ+Pf8Rh5cP0N3/k0R970312jb7H+4fnz7wmlut+08f2KVsePH1EuKU3ryyffud383P94aYn5zOT0KxxQFZwoOEL5M5K60yiFcsPn+mqSrx6rtbR3cXT/7pl+Mnc+xG6YM7JCi2hjGIUmR/b9LKse6TwyEpMHE4+3JxfuYWVkr0nQqiJzSB5Wi893Qip5hc5zpH00BSX3ppKQR4b7ggSHymIRUY10Sk34kX0dG1S/Xtv3MZWB8dPOwcpA6jsED3r75U/ewzp7FRsN00M0Bjh38+nXAhjhUr5HHXr5IcR2wGUwcIUqENwboJE4g82484pKkYFxPc7XVOujOIJTq4simWWmuU3THv99M3EzSrMr9erz/VIuzw8Uyr5paZKdfmxf5pJgqmW5aYSs4/TBLHfbpVxmIsFUpXWznv3nFSxBI/eLGA+et1ELkf/rNfVqXc+vXV7nRULAgHH190HqlCK4eeT+e+aMfx5e08UcEGj33i5TUZx/AK7Wscocetl1oAUJ1f3PdRMZGlidmxKiG7miFL1BAyuGl3xlGFTvIMunWzWJyi/cAhCCE7HYW5Ujw18Rit1O9rMYUAAQAASURBVCkN9S4mTrFCbbTzBd4bdvDzzsSMUBSyMYITm3JZqG5kxiB24iA7m+49u9/DR7Pr13Pbf+vq1FsYHr28fCMJly6eT351f3Lz605aL733Wu0nHz405FZqz9yvnzCvC2COrZzbQ3eQL8vmIq5wsId7bWD88m6yWsnjpoaxCa4DTlU7+fV8XvDuXgy/6IkqQckYNfdu3inP53HjEmtezLLJZ/VvlO59/9O3hTWNYZl6wTq2qp6QBINECNfapThtA/m0qsBprgrK1QZKpgD42QwiVDbv8Yk0zbxJHEmpIV3dCLEgWPgkheMZDXjSW0R5FKAtFcKsjFAAUFFol0T3eHKickWLDRmUgqrowSlVLQ6weQ0UEgARiFwV5qc/NdrfWK69867+P/x8MXJfrnG9wmxc5i9TdIxhCAjCObYjkvOp4YSJspzjp5RSSSb9eYyGw81i2pt/9ocvtn99OVcSkb5xuN8NMpQWlJqCDkpSrn4pn+p0nsTCGbBdFCBEGlfNNPzlCNYK2xvlORoHIltdIbpP5qWXl3DCWDgQIVHCjzLbE0W+UeR8BffjACABlQIswTMcI1EIARrGMQApCTN84WaLWXh2atdWmXouM2cepYg6gqv5YsCLfXOkD2b0IiksrJUiydRl7WCqcCKfRxe4kw50xoRK1R1e+C4WWAYOATr1MzSJCZbNPMNL6RxHeVmYWhxLEiqEbsfim/R4EYtYbHei2eMZPPHK325LVwrR2831olw+FE7nAc6pt18PGCzpnunXlhaluhS73mQyJEpMoDABTmEImgZOFjphHKU8Usph3TAYAZ7IKVmApZbHYfmCSuU5en27Oolqj54545gaHy+kDTkLxwIVcVmQzQOMQLI+qCBoEC0aTTE4Gq0s0TmW4QKurlAewyFfGZADocXzFdKmVLK9+sFNRT8hO/riwwG1FVGR4Z5niZ8i6upGxgSsgM6GePtW+WRodNxMKtYMkQNs1WEV6JO+jjJcApfLF96k8HLNf8C4KJ+cYoO4yCeUi4WJlZ49nAp8vhjbh88vwpljQY8R8o++OlNu12Yp/sXEakSGWZMWjnH+/NTZ3f3WpthwMGwy8LFFE8f27g9V9bhv6j84nyzAHCQvAAD/YvFHrv8H1/ccFg6Ayt6nk2RL+vzhPihNXwbh3331+icmf/oZfWtHpj38tdcq3aFDFHgndBZhZFM5NkH1GcZP5aMH5nnvrLlRuLLRvP7XL18seM/CO4/jru+COLq1nHKE6+Pu4QvTikEZldfurKh5caGFsY8LVDg6WSwG3vobtbmXuTa6fBmJwXyiZQ2KpyjQWmfhJAidqCQgRBj9/AfPxIp08/b6+rW+NicWU/35k2lDLjZV5vyR/sYr6FHsjw7tkiQBPJn0tLaIuR23JKMJxqJIBXCbCODj8dE48U3HubrORSXisGtlQ2dlte3jgnwaJk0NqHlFOp9/ss/e0ln5g2z7gkgreTPT56BzumixQkmOZ+exa7rrcg5Ug/4woM51VsI0hGE4it+uXeyerlPEsR0ETAEQfqXCi3xy7+5jrszIOfH1y6vuGGVF4stnyG/ukAUiePTh80uXFJYSMpTR5vPBs2OKUy9dySGI5HonDhFulkPTxK9tlSwYDCByNpv7MILrhYBhUpq7+Ko/2o1LklBaX7KZqhLYUoHM6HD/YKq5Js6nVZ7isdB8eg6juHqjkTmLIOV1XRaJ+P5/fHpBElWWGcvExIlNiBYksbFWWWdKwUk8fNC50h9LkpTZJ2PA/9UPbjduCjAmfvazceYWX17iN5nAQ43RY9OfOXR7Y3zWz8N8bblpxEkAg1nKthpIlmXPH+vNbVUmRLzLKpKOjOdZ7OoZlgJaTShrON+41tQFdDjo9Q/O1OXKavkGhYMf/MlXjWul9s6S58dpmYtLpUIOA6CqQ3f2hccYbH1dezI5nOWM513xreUcDa5kuToIwCpTGxsD2zGBNuZrwqEV5wVAxhHuSFCVoMPxfeOgZyGELCk0ZDGlSA/GR1YxmelEkV8BAnDmxTwJozEN1gCOAtvLRKqalfb+5PHk/esfXP3e29wf/sA9njdI0tZ6ZmR7jhbb5O98cMk2aSOAuVXeNiEeBYuBK6O+rWlckWk1BH8vffEzjS2zlzZb778LnQU8PHXtPIgsZ4vIs4aYzNx+lrbKAWp5WgIUlZLX6j/fi5TEa9+gtZ516fXW6iu1z3Y9NMNqbDrRfLbEZhBNRp4TxQRDwCSicJCEAAdIksAwhjSTAhxBkBSABOcUUS2Fpy9m8cXo6XNPEBTR1hFWObJhQWQ2K3QLp5HOfPZgYFGJcGdJvJYfTEI2yPgcEmMCNk/jhFIvqeU6NY0Cf+Y7h+POyawaV/MFyqXQAYKEMaDJVAKpNw0WfT2gOM2PqO5CWq0WX35zqe8PO/baDeGLJ8aYgPTHD26+fr1o9++NsZM4IjPvaD6fMqLKIoTCXVxMo62qKuArchKkmEQhU4QlKAAQXExhqYSakWX7Nq0S2iCqFSkKl872DzDBoRF04cBam3UCV4pC02fiLGCARGcBi6JIAUKzPxqiSzklv5RGE6tzODetg3M1FWprmQ43a6ymUtGmVNhqQd0619Ht65U6bmeW6ROywGUUws9NZI5nXOTmsMA2wjSOBMizKRRTNpekmhXUCtDFWGJquiBaJoF+GhQyL65xp+NjupyHbkLM3aLKtWiw4PnOgZvItFHlmxLpokIOEcRyQQW0htFXa8zDpx1tFrHjRVVZTDpBEyo1MmHwwHtmHKr2ffzW+1tnv9OX/3/WHGQAAGBkG8uThR+Pa+vIGF3sdfzVndE06KMmyN+8CWB7ITE3f++ddWA5n3xljhw9cDwKjYc9tSAyTAomWYVY9FSAVGuFreJwgTqjcAf4R8+8XhQCTiwxqXS7mq+vgc5za7qQcaTwwRWuM8XF7GI6pLKEzNfsycDPGPr1qiED82yCydTJIk5PujmCSulYFwgwDDiBoxVlsYgKKvduk352d3T42TCJcUYi6k1pb2z98sG9V75Tm3cmTwdJpuRno/GlYnl+PoBsyQMhsDP51k0EiUiXuQAYPu7WORoufMsM3b5jwEhUyO6cnBmTtbdWMWDjZ4NM4OC13yb/7L99cTC89QqFIoXuXyykS43qMhUtR/e+OBEKvFQX0lH/ArLqtavJ5OuGz6Yo0R+OJfIKWGGUFyiXw2SOAWPty48P117eYS5Lfne3UV7aP/ULpLNeUi7OFyvLwq8eTjdy9AyQM0J++nWntS7mVETJi0uSiqJoNjl+8MJmijJG4bTm6a4JZW5hhmWFEVDaHKcioWqxqa4si7ib0vQFJg3mIgcWnIzTqxlTKqh9J+4sEAtwWIkhCcO2wehJbV388CddNpdAASuiIUrhumlSdihFaLMtXNqufvpnjzCJkOpJ2tJmt2uwHzAGeuf928Zp/4f/5COpLeYwvp/5l0X2XEdMtvDWH1xFss/g4X6HXpnhSCYgLdVbny2IXFBeXfZ/tXdQVchtpRoi8nEXOY3Pp24oUFKzPD06zb92iWrQs07H9YmFmJPb7UVnsVJ5/tJSsXupLLVX6fM9hE3RWiOY9l/s6xXr0eo310uv3erN5trdZ/7BonjLuLP5qjg9GHFKicGRhAGgIESnPgJZHADPYegITWdsAQeGxyUepIrxnrMuZ8eLoRNhkMojkLt969rDe+evN6M5D/IIcOWHFfxGkfmXkfddkltxGJRPP32EcuOF37l4Rij1Gd2slGOxIDhYA+dnilRbVHJjngSfOoQocCjQUnZrvYrSROiCuY9yA4fZzne3SkCL/UN4Nh8t8hArySxNCbZTAsjkwXlCsCg/E1VQrbkGGTOmPHjo8PLKlevU85OuJdACMD76Ovn2e9fLr9b2pv36NK7lyGESFCuIE5LeyPK9FMF8miGpDEKFQVMUwaEduIKfMRQaIxmOkSBEyL/2D14dHplTb7EwEWNm8k5Iqfx4CpmIkAlq9eVmE4OT3vjzz4c0M2y2uTBKYpCqxTxZU8JRZvRcfRJnCioyvNIsVAkeovxMS2UhiYGJI2nq2f2J1lhW8CoTV6XyFfkaEaY8OzicYzPMOdHPWKTMZrA7vG/7l8zZ445pPBrP6bS4UmjhoJYnZmcJwiUJ58qCF85iwfVBlIQIitNkECMJy6EMNkNcAkaADiMnrbfVKEL9ANJS3epOVvhYkgMIbClMfcsVFcSICRKLJ8NMrWDnQ63J4lubykYONR+fzBcXEV0+FeExQdxew/Cn0ePU1X3E8+FlzBMQ9lYZsiAl0yQnM72urrDUAsA4JnhRXuj4covVBlOSSqoU6jhUQZWSBK81I8u4qC9JAIRSbz4dRwoj+lqA8eFadUtztJFWYyqXLGnT8hZEAkRZJHlOAplSZosk/mJfO/koQeIw9XVh20VnC85MVZlcKy+Pno3tKItTNJ55VzdoLTqXRsg5jRY2KdATwcACALzd9IjCqdfXoZ46CGXF/ucHjuXW3nnjhpi/2QXCmwDzvxx/+fGPJHK2cbvi21GhKFo+hQaeO/HUgmyE2OTCzyELkEYYxC0T/HTCF2pbqzcZ1ndFVXVo9fTRqPuVY5D00pV6HsHnCyTDXWMOVtuqSGc4Q8k1ZAYmBw89ZGGV6jUlJ8aSjGsQplHs6hiM+/1A4mhSiqYLPTPxrU3sbJzRiNM9c0JdXlmWjo7ObAu+tI28OO/JDE3JbgZ8UsyqVDqegVe+uQNAcfz8NCLT4aRTMiBA7EHXpdIwDHFY5bc3L28Us8f3H5kXBxGkZTIDp0G6MWJ/6xL455892nNu/dY3mrfYT/78Se2y0H6lvnJD6Hbnjm80ivTZPBVApl4S47GvOXo7M4DXB+NcmcVdPVgrlvkry9HUH57YBhWMXgTvbMZIZP3gZ8+QD+KFG1MJtrlZxHDaROaNyw0jRbk4xpA0zYQMuvEgnC+CzQ12YbNuYGNsKBgRgyJGJiomSlzMxTwTQTMmYs0kPYKlK+rq1WXq3MLHWCS7Hz8bIQq53lYRgBCWL9QUmOqJ5jlTh7tVsWmt3cCqmyxjYDEXeROEaOUIppRO3aN/3+t+5m79rrJ+g3zxSf/qEv3lVxZnafTD54PJxez4YACLDbI060WTmRXZi82GAgC9v0u7e2Bozt9/o0kUcEe3tdg1h7r28ZCM45WVgtkZp6VqsNa4OHUGFFmSxFIFPfvCgr4uc/G9RzMshTdeVtWlVsckDp/pm6sV9nQqcpPdpxeiQoiG0DX1l642SK7s7R5i/JOGwIUr69UyVd0qW2iKk5sqkw+6Y6MzLK6jHKQDhEuZZDE2GphrjftMBNFUjT0HX+LZFQI0iPXj0d394PIr9OnZcFu5zGcxFlFBbwZahYKx9eBz9/IHf/8vv3/+O3+AkEOA0DtF/ujoIvAn0ZuvtLvtmjf2nQQpK4IPcriT+l/PD8YOHCRX3q3HFAIE2iFR4wjNZtXChvjl3hHBhAyzUHL5LCCCyE87c+PYpHOoUFMKNerq36/vDwm2WHr8ovdF181zlMwwUgV/8siolHKWwJwO4K2dnO+Gn5wNC82kLlDjiStQiTt1nlpIca0IFQVNsAgBWRbRECKaG6eQrTAIDaIYoEgKAYI3OOysyT4ZGPceDJdvcNmBla+Lo4FRit08jbmSPJsh00OTAGG1Sn/3vdr+V11jpvkwhUa2mER0CRGNKDZjyKpRCCieHMNULfBSzE9c3PMdFCc0zZULCl0ph00pxwsiHWuxr80mds8aZfGBnyvhdmmURNdK3dS4ek0drFP5d+7Iv6lBIxF+1dUOghmPSB5keXxuy+iL+eVqcWbHeLliGU4kCAsDNgWUSowQogkJa0Xh2SBs7bSNhU2EFiAIa2QyikxCDSEpcxAwAjt1fUpEgwXSkuggTsoIqJagG5+9+NcTbEr3vLz7Rvk7H6CtS+ynf3FeXaIdsZxHU46NfLoUed3Uraslcr5ANB+jGnIXDxBFlab+uDsna81u6HoOxipqlCQ4hQlk6CYxkxOezwXh3JaWJLZYVGY4go+k2mIcWhGOsirza6/X0IC7mFFjKAh0FkWzhye4n8w2BqXq++WLlVBQlvN6MLn30z/85BSDFM/kClUMN19sN4t1jCCY/MjFD0ddDih3xsx/uJh3dnffB0EPsN8Vr1YcfNTFgtIr06NfiJvkfHT+5dP5Zq64+c31QuMSeXb86WfzrH+4Q6Pbb9UhyyS6fnbhEgERuRhPEsYI0JWsRwbTkS3k1M1X1qsEpLdq7hCLOGSsLYZ7bpS4oZUVCxhxrUU35LPPHvEIgtFKuanwRSIx7DiK4ZnhWhNDx+hSK8cL3sgaOGEe50Q6TpI08EOKjKHhzcK0ISExnj/YP5PXVdIzWjn88F5nI1cpr1S/+NS83qBchuNjDSW1kEBDbX5uOaXrmwiMwv7jro6V6pCZODmZmPR1G4kSTlaYgDdMtwu45vWbr5LW3QOmfmWghdXGCDM7AR/A9s3wuP/Dxy9uvd7c/I5wcj5nJ9BOURCmjM6fGq6Au/DguFBmRl5G8eLFYQTZQm31+hnyUA292JokTw1MWrx3vbGIkkkdfP+z4xtXqtu/vTMywvWXW1999EIkJWdmbzSpn/7li8urFWdkUnwxRWFKSa7h6ikZ9dB8OaVc78xxQgckam5GYKwNNwEak9bMCWiUmJuup7aaqxt2t3vyZa+Rp2p5vhRgCz9+8q+fF5pLVUUIxk7EYFRR6tjE+U/28iIoXPYIlc2Ozm0kxnPFIEjvj60yQuFg/urfvuSQWe/JMzBYzD6aXzya18stSpyLSyw34l77/ZdiaDDPY/USOB3Ryb2Tvf/l3ovh5L13Ct/cEqjJfWNv4dQKCUvEgRuLyI7c4HNSMvR/8UiX66woUmqeE4D5ZJdTN9bv/fB8Y1Ms7GyoCpyN3elXs7ZMUzwHAg/p+J6L4EzZE3m6tXVZndgaoASKurzVWfQb0zngeQkvDZ8OYtJ0YqZe1cOmWGteQ6EJpohBkZ0xKPkhyqM0VZoeDUrXi9o4Zu/2yZttIvSQ1SpcONOLC3Lp8rOpkVtpdMajvDIfxBGrCki4O2JeRjcKD8/HVxrlT76aka53B+U6x/v3yNba27UX/+bZMif3x87pxG76OpsEa1fE8ZV1ocqb9zvYG+tKrUHuv5g0ge07y0wIZ7FTcADD5stCgAt9HJdVgTvz+VLRddNRB3v6YqKKjhugCiVk2iRxCYKnl29S3YFbLjq+TwA/j0GyeG6YMU+REEUBTFBiXc2mQz+JA88RTIzKZ2gSkywTzTEKerO+qzIAECSSRhBH8OGBgXgRqdAtGUN7VOay6QLNs9h0Fl9MdCkHRZRyEkgjcb9HFspk9ebSLHTchY2CxEricGHkGKRSTkEBgDVZn4JwDs6ee/R4xDe4CPEZll5Z4RmWnXuBNdQow9Z1lybI8yzSbfDWf/PKqab4v3q+92W3/oYgXWXjL+3sF8NC2Z+6LqGl81lULqGQQD0Gc2KcLpDuzBoNoe8IHoFENE8TVOylkRfk82xop/3D5Mq71a6YhjM2V2589dVTkGloQYIi6swziYIRxxOABontpSYvpahnGx6gCsTU/f8ThJ/BlqYHYpj3fjnHk/O5Ofbt3D0zPQEzwAADYBfYxCW5zBIpWbLkKtGmyiq75D+Wg2SXLbskuUivpTVpcrkEsAssMMgTekJP53hzODmf8+Uc/Twj7dX5N15bDryl2YkAF8pf2t0/BGLz94RXLSfwZ+YxtsYQk4/Po5BX9hqxTCQaivEQxARSZLmmHamajKNSBkQeHnOwj2ApBiEuNo9hqUBOFUTKiScXflUAuZhg8zJItNkEiGVhfyCU6rziFnM8ax6jfoDotm/bliBJUZJx1hqTmZ2btC4+vfh8P/yD70mrO7e0p879z93KwqNBxZhOfzGcXr3hrX1tzXyamotkFslgbhW3dp+dnn9wZ92ddEYRYyPYbDbA8vhigS83KnZMfOPr37i59iYG4FeTOCYnH/zhLWE5k2Jm+7B1vPAzdOLYPkFCWAxIAm8PbQ5Kv/8Hd0pbO3KWmLcW42fQZBb4JIT4BLEqhohgKJYckVk2ffyrQbOM5TJMItKKgu2f+I0skvpIQSAVNbOUpQp5iWdISFcLcECmkO8nSxXOViHXiFXNT53w+SBcXzOhBJ4eTfShun1VrhZy032bQ/EVpDB9Fi3tWQcfX2S2HBcRdSN76fabXAV6+NWEwVOpRE8djxbSKcATGhZrYkixoR/U6+TTz5RrpTnMbwH+HN91Fj/tJDM4H9FQUBQysL6INhK8/emL12p8l4wXDra2ztjdlE5pTmJP+wtvBm9TxQjTcdvYyzDdwXzWtYp5DiN9P4kpGkojOsaw2E++/zvbD58Yj76w6ldSrWMHxIjVAkq1Hz1Jb+5IihqRM0zxOC+Vggw0gPmu7a/tiLZuTp25N1gkPA7QhDL1KxkO7qlkamn9CGEgmxQyl+oUzWGQdverC2o64AqVwdNZd+LnqTAMJNgldDgaORFaR+cjmLmU1a1k5zrRWngQ1jMd/cjwiYDJWJPw2bz49topTRYJwtLDRQhtVqiPD8e7t/N3rtRzMt06OEGK5fbj496zsYRA0yUe8Wf7T15wm9R7//D9vRWq+2nH7JowM3egsHd/WtzkijmRgQog4A0bLxWS/ePZ9gYdAEOsYkSqSxB7r2/nt0tx7OkTD409jsNmZ3r1NgoilNtgPZvZLcjd/mz8+f5iMlycqdSSIH2wtJHZDNDOl796uE4Ls5jsPg+X3m+oFh7RpsimJIhjH5Ek3vQjByKfjNzNRklpR5yS4mhy4rTd+/OCAFav1nYr6b2/XlSEaEFXSBskBpWpFu6+ONq7Wkd5bf+jn//BDf7D35xjS/+Qp2caAh2Op5XK9EyEV5vZYG9ZIRK6nNxaknYv7xwMdbQAB1O0FaRqTtrYFpy0faQM5XdWV9nK+LMpzKZ0CY6GJo+ieZmcK2EWTzES1W3QOXSkMqgQmDWlBAjNSKRmOhZH7bt+giGjMMgTFECi0cySc5IR8L6J0GmGILTRTAtSHLIA40E8L7qe45shCVMewyc8GbIkSCzVdUUCAgECwRCKw17SiznXLiNcTuasd3IzwxaKguF4xMIdDfymQEJuZKigiAZqywlD4MUJzggA8vMFLDCDeO7NlCBVnMUEECKDydXsZTjkbcUzIihxIQxy4MFcd3QfrUJUnCYiU2hm+ZowO8ef/g+/DoRm+VvXgeMMVHEdt3hWU+qZo18/RpISM13QK8U0nPvtdpBbYgnKsCIx9V2IETZ5e6Td2Cv95mFnbxWb+76V4LLIzPLIYYBsvFP/6sODK2u31ndp5awjL9Xap30+wxOwWeHp4RBCMCT10ozMBIqJuZg7sEu5NCug8y7yyUhHZTLXOVr/4f2T3/ZfY8Fd8uZJsE6rUU1pglcj6p/cuNkgYg8czcIbu/K8e2oyksxnnXFyeUeyU8yKXJYWAt8rV5kkwIyJtkzRA8urZ0R/1TDTeKAnheYa7nk8GE9se2OnwMhFKBYBwlKcKwOSgfCQi66/sfvklyetTztZVq/d4aYGeG17A58c2Ifd/XSp/p3Vzs+/2CxVur3QxJ2P/vwL/NvhHRkfwqXrRY5IGJl5+eFb+KXlZQI1n0ebiNr1hwb3Zu7kInj/1rvNPb+E5S08YjJTCsVvLzVIthJCCto5OTycsqGDmyYOhUhkWiG5tS4PTUaq8mpshAenvShyItyP2VxBEukERATGI2dtB3E9thEOAmJZCqlsxvDsWCMxN7ws45JgjzQQBV6EgNFQYSVqcXLiagClOTYD5nO9N1JpFs7lJCZDxAjs2qjp03uvZ857OkVHLy9mpYIUpnGFo9rJHOLjweCEyID1W0sFDObeeR249OxseJERrmRw+dwq5sQgNYdtdXtJRJq5KKHHr+bTU+3SsnD2q08b63WLKZPni3yZvGjPBlPryhtopYk8+O3864Ior1XGuoHAXK9j7NxubL0NP/xN5+1vX5585Ma23aM5QbAHfSWhvISKQ69TqsKvzidLxUJrZMl1bnA+jsP0ywO8eYXyF1NTiWZOaDycQwnd7c3zXI2SQ16EXSFqlJiEcH2bBK7aZBNjpnY7Vn6T1x0PEVMKWpjEiq7E8VDdXA0MJqGJSEncBQ7TuaRouByNb996bXdHNM9f7eDoRwv/xrc3qk7HN1koDycSSjmQa+J71Rw192UTIKFbbwrBZBq+U7ai6YY9SwOTWkaGZ08wSGJ2Y5JK37l+1SZqjy/6lc+PYEztmQpxBGoimUMSv++ZqlXcrV39dt0GqtLR10qcu7y0GAaHJ3rK5AiRwCEn6Y/7Tdq+US0jnJ7HYcpzB8mL48HC7d/O569+u+rY04xQGS9mHA+VygJIkFSZGDPUpwLFi7wJPB93phl6/c7y+u/ROEDV7iINT0dWiF8vYWyxDHP4ZehK41qapiPo/ng4XC7QPogx34YDOI0NxA+1oY8v8/0o5oPQDnzPQXJiVjsNsI1lK+pd9JWNW7snP3m+dW318yMboqpTl1YNrZWtnwZ8JJLtk18fC5T7vMWahJGige1/8eJQlhTYjCkTgKb05Hhsn6nFHMmGFEIhs0tLbjqZ/uKgVIXajN4vslK1OfriwXZmByHcgGUjbdqg5ORwQQt5Ya9IUdnB/jHPwnmGMs24fWoXRYagsZYAh7qzVuc030HK/CxiiikTeTE/i4k08kgyB4UOA5MkG6oOXhERJsQhkHgBCLwAULOZS9CgmqEQlorMEDJT1JJwPM+OE9u46Jx2exTPEgQ7OYkSMWysyespRLgJIicox1wcakwMCTkGAwlISdiDXC0R2aSUtV17BqOxZgSGAs8vJiGEcUhCep6H04USq/g+1aDRgMmVGXdgxSDqRsT0M2W7WdaDrPugTeA4PZjt7YnDM+/5DNz6G5XnWaTO5Rqmf3imJQQRwHqecaMY5Iq2NTBTlokwxkZRw4fffC3TfzUSaIQTS45rMZgLx2qOAARpg0BPTUefgzevs6MFzLAJJHOLvl+X6JmC8aWM1h1ak16tVg9wZtFZsBC2gCAjZtgojA2LyqfoU3BkAHXPSisurGJGKbPz7ubqZuavNGC3x/KaMKWokBQKkowutMhGuYKkataknd7ZzPT7euoznMwoiq8liJQR1KGaK2Esm6g82NfMaglh0MyTrwIpFpdBprncHD/tD446mEDPkGzt+sqnT8ZmTy9E9Lv1Ol5fv1IVNAv86KdzIjavvbuyXaT/mx+6U2UxV5TUsZdqhO8O7n35ysIicZKlmKo17O9y8zrw7h6Np2OtvqEEBKTbFQ7LZ8oF7dRslIurQrECMMUbZUgarwpJb/Kg5RjG4OYKRs4QAoddjhpfmN2x+uhiglQlVzRYknSglKTCWpWwplPYwjJ5eTxMEI1YrZGCZ6mKtZIvtGcBXcyyCRQBH7a1VAggAgIEbvYCgZeAm8QGvJYntCDGQLp5uabG1ov7bTvyr2/UpmNVgvTh6awU5TAkITnOd7zJyTzG0iMdp3NMkQq74+LN167IWWZ/5gUdST1yyjmhLMbQwLcMSIpDR9UlMZjZs5zHJShV2au/+kk3t8etv5WL3J49Pv3sc+1vvnudXvcf/rqtSrPcW1feeS2+GI13OIQSqnyk9/b73Yftxs3G2Hhx1u7KRaP7YCwu4YxAqFMfwosU7vfGw2qRzhIeFBvjmd8oEzIDmMiaKNrs0XyZCtGM7JdzSOQaKRvYXmSjZ0MDywMlsL2EDwwok83EKPBdYzHGOJoIB3bW9mE47Tho/io+7YQakw5IwmQ8CEq5nB/b0wIWpQZxrYxTCYi7qtfzM3kZnOhmjzkWKlmOsJWe2bUZKGadQMTh7jzwCXlpg7PP+9O+t5MQx2dK4GDITsSM3WNMudTIWZBD99PDBw+/unj59h62eynrzrD3apcu/V6+/SKp5f2Yov76ly+yKE80wlq5CJUloxeoZoRG0a2rRUWnXU1V1YQA/tG9Z540nrN1LJzbhrUsIxaMns2Nz6bad/+G8MkPxzcbtJRJzybBrIQC2CttU3wYcm2l1LR/+xN9SaJWv7WTQsyXPxxU6tJES2++XapzIXSgS5mlcOHyBBQrB686U1pIS9WcGYZCAfMVrsEmF0dmLg7UjocKFEEBw4hhLxBlzvVJOpdZtEBueWniYiUuOpechdeyQ5tDsUnPnPldZoygpSw75zUZy9OOiSpsCgd6ym6C/kSDtSBfRt2BvkJwr85mVEQS5XzNh2aa3fTnk5/P4X6/cGkpiRaqBd34/Zuf6O7xSSgwIGFTM8OtLuezEfLi5xfDL5GVN5YQi9eNWYSEGcbnMKozDrXYKZSJw5kF6pA6NLgCxoZId4DW8lmMDqAgwMPItiD9zOdqclvxqhLwfSSloAgGaZokviUxkOWDhQbxUJImEAZBqOC6jGFCQq68ypat8Fk3JiwoSwLgop7rj1XdTiNAwYzE1DarEAYZBEybRtzVZZKwozCEnFcJun15tcSn1caWrsCHnVBvzXtWKGWxHC9mcrQDbI9BgmcDx5sCM+rqFr/XAHof+Wrw1t+5dDofmg+00I8nL7pEpYLebev/KsYiajA+ZW/vFGjYttKu51YaaAoASGMkdePW2WyDzTYbwYtB8d3d/WS4lAOeB+MuVJYEQhJ72jRTyfpd4MeieDnbOtOz1eLM6uVy3OJkghEEoKNFiMeIVFpeNg0usjEvTEsSYIZD9DhW+bCFEb462LHBIQbUc57ojInVnc0KsJfRE+eIf5UW39j0dFBwwbElbTVFt68XrpYNqkB2Z2WBguUKG/GatmAoYneJnnXV3GvbamuSxgAaa8WdzZTjc5gzvDhcqhXyzTqpIO3j0E7R/FoFh+JjMk/lzHu/ebG0u/GafAnfKAIDHZ90Pz8Ir/2dN3bBGyaU/tf/xZ+97IUJULO7pQdPJ4tJ/Kuv2pfeWpPBUXcBrue9xIH2u1bcXiiJbKSHHaV8bzphUfvS5uKLX3/45u07r71/hV7LfTLsQ7kVLgiQ9um9r15xmfj9dzfoixkW6MOLhZPLNWSWhuNv38kfTtx5x5sh5qVvraVDu38eRDgvZ6kIDQAMS3QccuKpnuI59HMFpgv1EqScqnHMyVlJAASKsAZsjnTDcE39tAUlEOZBkUewlGksHtn5q9XtaunkyHphtqtr2TLq+AH5cqTQHEwmnpjhcoKkzMatlra8wn/28cESX2g2pXE0axcE9vSsYrA+5+IDfxZT49xyPXWL2bLNOqPpGNOPaUlBi20uik7vX2y8uY1U8usFjtYufvCzg1t/f0u+5PYWLvGyi8IkFoXTl7PKbtnmyevvLH/+YFTea/L5wnnPX37rGtM5sboxuQ2hviugeoDLjavLGSnMLmV9zROvoqamlYtgeIQtfaPSOrFYSukqGJ5qAZTKJWCi+J2ldBBSpQbVnqEZDospzI0ZZRFrBsuVSAb1n78cNil7kDIS7o4CLvQC10TVsZPGKQJPwpkXLpdDj3bP1WdPeo5V2lrCYd8pdbzbxYKIwu2EfrU/xViiLCKeHkAR6FhQvlmc9+eLF/FspFxqipNW25uMuUJRMBG7r62L2XihIBbiDAIhgq8QecJynxxHsIjhwO8/OTueig5Dkb3h917jI4QzFdSa2dSD2d1KdmU5uEl0Uy//4BWiGWoKx0gzvr1DR9D41TBMXMV0ERJCie3S17+1fvDxBWCYla3lUTvM1auU1cpSmJsa0MsLsLv2/Mnw8ncvf+MfZvr76mf//Is9hsFBCVy8WNleBUEy7ZvQYhpz1HhfOTm2imusVBWr1VTD8vZozqQwEBHEiAGb6c8PpAwSulp1pXx/7Ms5gSBpBUQlAm6NJqFpNW9mR04/s8KfDzqj8lruyAs1RAaQmsYQ7HQsM9DwNFItq60/z65+e/N0hJVH8xUMzkQTv8H+/N7zRpaH3qgOgrBxEeVEctFRBF2Xv77iH7jXYXZoxhe/ff7um/nzZyOXMAdjSMb1gY0QV6vL31xuf3Vy+KklXGb4GPanjp8gZGRkN/hZ1+GjsCbgKoN5K0X7fEivy4sUon1LRqBAcZgUlURy6CR2ZLsc1TfMNQ7vWHaGohPYZ9jEDCKMgMI4Njs6jgKIZlCS5x3YyzG07cemhNYrLI4J8+OphrLnbUXaqRYyjOQ4wzM7t5qOx1qa2paT5Egf+FZ5lYgTCs0y47T04MWs88vx8o1NrkYv3brEp/H0sHNyOG3PRjzt0UUITXTHIVw1EnIyz3Gbd5b6v33220evdDYAl+j1f/zaFz87W88m1XeWrCDdWkF6L7uHv44zHLd1rTy1Y3M4r6/ldY32ZHThe+nJYGlH6tpkxUi4QnGuQBurmcN26os0zfDtLi7JZRyl0slQir2LRXL57bXz+y5hp5mVDNBAAMEhwFIER4Dg+6GpI1mJHWnsldu125u5e+enbHfy5By0AqDDQJykX//uZen2N+duxIfq/CzZqNHd/bnhwrU6j6VBBMVeBkdRwo2Szhi6eS0PoxCBJI0i0+vbuUslc98AqldbK8SRMRgoMpdydXZwhtl2kSU435AiDxhemuL0zHADDVu7zQ73ezWG3yJoJkOBIBl3Ok6OWn/P03rn7ULWOZluXaK/+0eXHnz42YuzIV8vnfZ0gvVOZuZobAhVZuz0yxyaRLlfavMZgCssr+fisQ5jIOBwuJZv7r39Hlss2mdmDWZoTx2cDIyye/u7vDXxBAygzRVAcnVZeT5yRzPfVsydq/wyTSM8enhkzx6MQ5eqbZUVn5YqSDBT+31td3MdZNmeivtjq7gilzIBeuKWRGZCwjTl62EqYYnVVefDzuCltXK1lquvyHlGQTIVGVC6Zp5ZIkFUm6A1soJTvSYRIkkmAKBRDACmDjzLDDe2xRTyySC8VMoLpfIFTIKcuMUgW/ka0XMmTPL83LWBrbeHdpIqCIoTMzJQAxbYJ2rhlVZfkT/6pCM/jv2vbBzmq0tScxI49wZ8hJwv7H4SbzfLcK55rmrxKAGeWVnLisdpq+fUMkL/SM9c6IpNjibzVhC6qUBIG6Ua48ZUaDB46sBp6vthmoaJkZSWmNahX62j2mlczqDaCEV41x3OyYRwQi6Dk/ZZXMhQMxWd9QOMTkIbwqJwdq4PF0axFiImTBkGgwmMaeICFY0cr+OTSJD4VLOOZlVMsv1Ba7qYJnvfLBYqKemBZayC1zKuEoSqKVaIQoPSJkoCZ7Sx2z9VtUVSy/BclrM4aYpiGgKHZIQiAo3ARdRaL4qDmatrPo9lugu6sVxHJXVuuk7H6L5aCDCo1MLRMUU7iYQjmgcHkD49GTWWUNL15iNtJnq6S9Ji7epejk4Yhzh92e6jecoFkIlRAkdFURzPWToOcyYfHKeVkvjFF61vbuRjBJ51lXIJVtuQeLaQUiT9fA728l4aulgAXd68efV1EHd6j8cR4tOid9Gx3cEwKnBZubx3ZzeFgrP743kyvrEj+oHvKuG0l16+tfrbTx/PxZCs548OrZePFu+9JQYxXynB/WE74Ki4AI/n+lxD/aFxcdgpXhZiE86kymKSLm+Go3mrsWmcAKyRT8ePyEJN5Mo1Zjw623evf7vWfTnO7UJyicF13X3Z7Z3NL57qjZuVhCEJMfQXmtlxVAr+nb9/+U8Pj2dDBm9yHsBEDA5VCDHcux+evPH1SmGHe/pozM7KPE7G+RSD0uN7ShbyBRzuj3yUxDptLNvkL3yzSGFIEFqKt7TRGC4MKNLDOJbgGLhRIU6jJEFkMrWTKI013d4sc56W4DjmBpHjoCkFwUGMEmTWr6StmV8l2Jej4DATgYW+J7NQpVi7sowlo/b9CVaOck1x3h7Ox3aljqhwCPEIlBCWGoQp0P1AGyh+LRuxAtP0w62VZ/f23bGXaTDNS4TdjYHqRjzWqIvFtcL53J4bhvLgCMtlyO1iAQFXm4Xnvzgq5nLX3FhadIXN2pefD3akyuRvbUM9NjWsmLV1OTlCRyLRsJPY1R0+SyHOgoZTbql08ayTeXvp8VcnOxgl4HQYBgxFTBkUHbxypNXtIt4+6kvrOcXy2TAmfCwh5dNJnyvkV5bIzkvb7Zjl3exJx0UgoPWSjw5SroTdWkpm5QxI9CueuYKCj5kVs4rLw7a/mM2KN48t69l5YMxmr+0020/PGYkRQInLl2YvX9RrRfl6ObYWglDSfEOqyanVjRKOv75+caGu3KwkKiJcFqyxg6YGUWkY6tC0klKZz5Vw5TiJtfFSXgwrdBFx7ZFVT1CmKtlkioRDdkke0yA2q/Hzzw4+f+6WKmD50i//9Cdxdz6bxAk1+Z3l4IhC+7EfYqJjAPOip1OZm0L8A224TGTp76z9+V88IYAnkvkSz37r+2+tv3uLhKjTlw8RkewcXtRkihFK5y8Gy2Lm7PHxZkMAfpxe2toTF1BsxiN/iohquMj6ajVfez40qyuZlIMWiyg5WeD7A+y7t8Ji3tw/Zy5Q6UZDpDVtBlSVWSeyeRDpSIkj5wCGDQxe3pNhmiux7O0aCVWInBMgM52swf7ACghPrlARhC0OTVxg1MQPAVj0tGIOk4uknYCBGhhhZJ9NFTXyIqwgymvFzOyoR5ZogJB5yn2/YBpmqEUzVwe+B8WqHRFW30oEJujQgBj3czlPT2b5JerHvz55k+A314T+wkVosiRIthWezsbZQp1FPdIbOAT12cP9199vPr83rO6UjGcjO7biJaKC4IBGlrPVZrmGIiURoHAyioCJZ4kMGUMB13/UZxPahhPNDCOaRmDMTYJKLbMYK6xEmypiYYQThKSNoDQVc9BkqMUQIoUmJ3jDCEJSpFHlFUlU3dhJAilRpD2COJr7kZ9SsLi8Ge2P0bm6XC9X63iSowHlApFwjMni4Fw5NcAtkcR499lAn7loTrx+lbvzO+TLn/ZHKIv0ZxQLo9Hc1dDVMqbP5w9f4Naxhwdptuo5WCxRyVYJMtPDgGW3i9aDIwtlke1Vcr2EnU+UtgEZlkOviXAKNfncG28IcTfqvuq97OgoHxG3V/zYwfohsS1m/Wh60nXkbGNZLOgO4kLZFaq3H+YjaHI2Xns9l1mlnzy6aG4Is74nh1AMZ096sMXUWBWIaVjJ4YOGESHT2cOnJKbwErvozgFqMDJVv1wYGxI/mHbP2panVav4eq4ItOnpHGITqJDDFx1DI3JffPxhfWm5VhG+8Q4TIgicel3NJ10vzNM5KfdXH7VvvcWZCEJwrGMZMutM5ucINcVKWGi2BwA7dECEJYkzRXOirx3eyOjU6hR01DAaQ7/+spNdM1Yya8ez95qS9f2dIo6POkpgp85CqVQlYr16iIX8EjLyO2mCuZDNiDnVhnmSX807J4/bhVUhgRnITxPNn6kWtJIN6oJ1YWxdLZzNppzEO1MaNWDa8m0tJor84qSlm+V+jKE2Kufo1NEYDrO1BKOSZ7MFWU/UvilkkPO2RvO0o8cgjtOUxkgKSSPUhYFU4kd+dJLC7C60xKT4KoSnlNLTjMnYnU9lGthzw3XcFAPFy42Z4VD5yIxCx0VIkvF0JOHx4mUcaeYFle4iwUozzqgSHXaniuoCs7TBZPN5jApfPRo8P+oiDIpwfr2E4rJl6mF7bGdYgFtw59fdmx/k736hxGtR6bp4/6szUJKvf0D3H/j3ThfcjjjDjDPNrRZYxhbMxHed6Hw02NyWT+PI8x1GyD6apbfXmx999vLWanKVj+8/gHfeqbdO2gca/u47+YP7s4JU50RcUY0r15f8BG0fj6Y9m4gTHElcyrt8NbeWX/vsdPHkwUV+twBV62m8+ulXTw+6icZq9euCWRqX1rKL+YQO4WpZwhqG03oVUHjtVqMTmvNDdUuqmwTFEsiJrmZBipDAA4QSJZ4elrLlh30z6XsEQpTkEPExWCJ5WkYrSy9e2dOQ41Eymzd6PXLm+Ug5efHQ8EwgkzwWBsP9HrPCRITFTe2zx7NJ11iqOFSigDS8p9uF8najOTu8P9YT72zat2OkSIuLuWNH6DIXPDAVCAIHkZa8HIikJDERm62vrd56bXurBDjfVkLI6HYmCePOYUims05b4N5c4TXjYKygiZ7Vif7ZeFXiqKWSP0tQL3lx4Gy/xm3mIoxyD57H2eXtGOHx1UKlyH32sLsEQfWVHJsJxwfD1Caa5TqB4n4Yplg8SyA0wCKCBxOwWshc3+TTXAICZBr5zZ1lQG1nvx3O792lHH9ZTtBsjNIJiaQoHFMR6oycnECiOInhYSnLTbWEFaDRAnMHLpHg0cT0z5k00MmikPIop6Z8PprHaJqFU4ONASJA+nxqFreI9jGKU6mtugvJXamjxyOl5yPbe9XBBUqIQE3oQsFL6IXvmVOFef875R/8y+74QKfcjDsPSwV66kFmwGgBntku52kujzQhwPQ0G8fQrBimGBgeTGMjJlkixoh8iWZg2zFTyAUMDtxjJ3CA4ZmziKNz6N5Ksddzpi3FYaIMDjOF1BubIZJkZTk0vUkLNS6mG7vCOqVqbXWvzo5ltK/Am+9c8R3CsH2KQoWMhKkGAy/mobWg0i40AV5XaCCMiprD0G57K5cqPdf88hfzjTIB8ay4UTg70uBuuH1ZCKde6yh05zFfg90y4ePmq2MTW8HPXwTXcvQmhyMkvvBYX3XX6iyfJfmVRr7ccl9O+925dYZeudGQ1laCGPa5hL2MF4bDBQhHLR1FkmWW65+HFC6lLBbomWWqnDq6PV5EulkTUSwPHt03Di+IlSX8ox8rBOzKUu5MdfkiczyZU26aFyAptVHWYhNv2h9LGPysNSqt8KXb6xS9QrQu4pk5e9ml7DB73TB0/6KF7kb01IyoApcTJdj1Y734h//ob61cEz7/0at533yphVyZp3J0hCDFHM7g0fzFwJ2MJy4XeuZGLgjIfqMGneAmnS887jqvNXL0dPFH3FTtWToa1i5NdaA/6+zXism5vQAYHjVWKC+flquFKtX0wJNz7ehcGw/VN96oJOXi6EwvxYOXn/T1BGGqqH7mFURSQgETA011MzRuhkbGSDOuG3ZghMcomOwOY4ngLduYm17CYOetQETpYGHzaESEGh5AEM7EcVLkolRPXDfGA9hx0wTCIZiA09QxHAnHGY5OQy+hGESEcQ8LnQSkEIK4aJ6AWphUKS6eW5zvzxYdkyFTfpDg/oKBYKlKFUV2rOhuGgMUnk7NqRnlPQQTBR5GjRCaAqiBRKDmHrb79RezwtVtW3WyVcgbk42Zkk3chZY+//JU2BYInqgRpqknEhSaQcSlJMpim5DXK2Fv/md741eWmsPTRt580q3e2TtTjhja0T57DqfiWjGdzU0mJhnRixkhDQCWpOUSq/rWk1mYu5Rf+PytrcyHp2YZprEKezY8WqvK5FtUz54EqVJZo6sc+ln7ovz+ZcQjJhdetkJ3L+aULF4v7Jhncz8rAOXox/cWVcDeuME7dmi4zuC4/eqTpxnPvUMzRnfwxiXrt9TlV22F4DIsSdcx08vVhxcX9srrUC+6xqWOTRT2pMEsikHKY+jZuS4KpcMDbaTRxovz9/aUy9sZP4ClxIkiYNUY2CfsWR9RkdoaOw0jpzMYLea4GBNeMJq5FEoxBUEmobGmBX78tMNYZ95K2MMDYr67ebvi/OVv43d24f/q3/znrw5+efR/e34vRP/HE+aNjctC7Cfq7Hxox2Q4NuyBnda54sSYDA47m7dupDUsUYmVJcYFuZk92N93eEOjVVNYKY2VOe75lOnMf/WkvFtELRdBCWQR0hh1ctxnQ6xB2mSu8KWAj54or30zu1CGYXVN963wDL7yZnn/eFGwA/bykhgmn3chm1laX8UQze5BTMpEzBQVc3k6Md20X90SURoHluZ37LbJunzIf+mQ2iP6diG7wyVHVn8QQzQ/WUQkByzTIVhaSW2JhsA80KZOkZdmEc5Q6DRMvNAGqKQFWUS2xza65cTSDCNKgqL0iASPvUTVIFGgjQDNZtNYDfd2pNbYKxUJNMOUCr7mAdglccq7+t1sGoN0xOhBh2GxYmH9/M/brw69epk/HYfsRmPiL0qZnJ6kpdv5DK3nkU0aqMMUk9OJnzhV+lB/1iJ83DZBg4VRUYDmQJsRJhJw1cLw6LQkUEZAZ8vZmIIqMR6zxOFB30bxFMVDENIR6sytNAoNj4L06frrDY8r4J0FsFyBJpZvUVCShkaoyfikiEOdcwyeMqzIpi6d+mmacofnnc1KSkUJgq7lcb8TLOvjB3c2W2sb8MVFezL/6Kf2zuXl/Ov0tXfWn/6Fm2Wg0pv5wcctv+xTtANj9pRHg4HiP0B5PNc6Taemm78CoyJ4I4vP3MjsuXrSi2Wbld2aKQoeWTbjfredlOrtmZ6T443lLEpYT3QKSfjEdptEep6Ywq4UnKPORC1JIVIjEzdwL2ZxnhZzuBGbFp7PNVFFxep5SfMCUcZdI87sCOcKVD6fzmU4iCkyL4gSv1rFGDQiTHd89vTssVXdRnlCQMtCvl4GoGUY8rQkagJc8kPfdgM7KVTSl6+0vdfeVffYh391v8JgKAsfGeR1CZ+Eg3tPD5PPzufbNaeI1EIUG6V8hrTFuT5IJACTXtI5tYQK1W8teBLCeWw+mgUsTKFu4kV2yG0XRUBULmG4cuGcXswujkcOQTTLGPN2JaWTZ4dGuvALpCbSYdDMd8carfBIiQERAls6jbtTekkS09n5LLeWU+EY8RzDtFGyhOSFkJqMeiq6w8NRnDWMruI3a6iCJRicJBm/PddoCscQyFMjrsItEpRXXTOOyQoJkrAocCiJJ80s5epagFa3BMKioMnUsmD0wrCN0bT30gSIQrDRZqBbScqSabqSiTFaEMmXnyg4mZfKObEQzi7UnCTAKG1rqZygSALXl+jNwlTvn+A9a/R42KzQWjszMG3LcXY4LB55cpkkbooLiNR8QpYxKnXzGSSwPYcg41phDDh6LdNqJ8OT0LHDt76z9uLPnfjA5Dg6v8oTBu4euDglRKMkgahw4dIIIhaEziA8V6iljYYJYBYUDqZJrZ4pkYYaO9fX+LvzwLSJtebuk1djKIGKG4VTLcjjiXeuS8WyoyTiOgj4BErSyQg7HUlf2200X+d/8vOHvb4DyWViRU5T4zU5vL0jDr4yNjjOctETZ0T509x6sbTbpO3IeTR5tD/rfeW/ezsOAtCdIjBKq9OUC93pyMo14rNHw+VsZTBus67R649PsrTsqbkSF4dFtFKuQh6gIkAVUmwyGc/6o0mSsVRtXKYFEonjEMusyfOh+vjuAN8ROYGj08AdqydmiNWQ3RKBwenl5rRQ4n/5bz/+8a+/+AeXl9+xcGk+skL01WEX1hQ1i1s+MkcyDM1ms1J26s3nQD2yVuha9colSV5tnfqlTKkiWq+eAwSVs1gmhUNOkq/8Yfbu/+ujRuLEnmkr40vNitBgexfSb+/13ApfblY3Nqm7f76vNVOQoEt5v6WOblytp3E4G4eVWhMF0HgY1HL8/pSLdB8HHgWPFd+p8Gmq4ThNeHwOyMgC2J5jBovF3LaoABqPk00+7X/cqt3mXYpGF0mMR61h8Nob2dCxk9j2NTWcYVmUPXkabm5jydT0JQpGjMNfzqv/2I4T4n5AULtLUoGdPWzXiSQm/ciJuSTK8yGahL7nMoCIUMpWJKXrAYKVIlKLcQROTMWxUrziArmCruXTB194X95zCcGvBezgmVnMZt0A8ifx9q3qRMFKK9w2tHIKjtRE85MFj1iPTVVIvcmZU9zOJWZuLVuHgAdOj1KD5Ouy7tEtOEylHJwlswzx5Lkt52jTJ/k4AilsWynbpNYyhD5MPDN1bezOnXVlBNttR9qgt0m9d3/s8ji8lzk+1GUarq7SY1OXOQfPuwiCjqKokUej/mji+kLk2rpt+MQCYKaDiFv5r37wDMt29yp8LYv9ix/0Y2bF/ncRtlci6uDPfnl+tXBKZDgQJ+oAzeGCP7dzmYbRg2YOI8iBkLUdYmpOcdHHUYj34mRkximOF/mmVCQIB4mMlIgRNzA9R+nbKAJXyqWSFka2ZvqjyXqWyoUgVD0QulPXbXXDCMMyJFXJAIiC1r9eObs7aj+eYTY+68EXEDxRAgHTLuX9FcYiyhTw8WQ8I3wssJ1z3UIxA6VTvkKUSaa8sm7EJcpSXp6Yv/3JqYCAW++LJyPV79rTGBRvF3UaG5qIRfIHVly1M6W3d/vAIecSfKF3X/Q9/QBOzfJWPfutrFOKddWq531kKdVlkNstC4IEKWE7ydQZ20Kt9kgrUpS+oF0rzsvZ8XghNwuBWOl1aN+CCZjb3Mr8wkNe+1sbbOyfno6dceT62PXfyycg7j4Zo8Dz2kGZJZIYIqAkcnDDiF9/O9Em3slgmr8kKgcL2Ad0ikBQLLhGSAaeMk8P/GRqTtywuCxoAQSFVqrhHBwhgUelMSvG1iyyF6iUpfUwgmCIsJECK6U2yMts4CYba1l/4lokQmTh1MSB66C4ZRcmfDm2U7EaeTMGE0madGlg+yCYuheP7U2aZffqLkwqB08whoGIDOchlohmmBhx02nga0PMf8GO5rGerxxTcqOcZVvjWUqans0I6NkszsmSEznTkeZqQZVhzGFULtKiVDqtyfmX5uyHLbSQbfvMaqszewBfury2OAoKuD2b2GsC4tPEQsF2RP449EzMHB0ai2TiZ7BseRmjmaZXVIEUC1SojsuF+qzfuYemcPX6+eNI2As2ReyVfcLJq87o+NJbt4/VuLZG5iexDWMYSQ1MF7xWItjwybNnb92Qrvxu7YtPootj99JuVNhcXsef9s/TXsS5snfj8qUKxSQiOh9Gz358dOaFFT4OxUb1684r17/upFTkLlXJbleV95jPf3VO75vAyXDegLlMCzGGWTN32GFW9lotI7uFAcMEPLnvUQihQJMUphSRsxk4ToRiZT2b2HQmVKdW6i50ggKdqSVOzdpG843L+RmSU00VV7Vj7eLpqffgJ2ctPC1Vk5N2zxz25we9o/kkg/C7l6WGPttfqWin3geXKoG0M38xC2UXK5YjInEQ6P6H9//+728HEhtbaMzYe7f38NA/aE97fbP2J9tv/fs3+1/uIwg90+JnX3STUuXaVeQdU+z06FxbWV9hjG+v9DqDq9d2DTNuNGibTNTeAJazuUJoqAGbY60gvUpxZHV11D5z4lDkg+6YzfIUnuqRJMQIygJrcjE767pclbzREEwXQRoYaiTjiVJYKRiTGSckl4sZbWilIWGnLsRhCzesViCiDEMcyO2xZ51B7b2ax6jPH833XFb6L/72G4B70X5EoNJYn2J4SGGIafl2hEFhisrycU+VSzjFQeVi6BpaMEYgnC7ViCJO2xRod7XembqxWdrbzeq037dooZZJMZhf9d99s5wYssBAW3LQtnu/8sOsM16Ta5rpeIi0yxokwuFVDBDNkCAMI8o5Z6AMQ4KAjsaU73Gp1FgpxqE+PD/X7Hhpa4myMQwNdRjyopTvTnud6Pr2SrhZGF702roJpNjrn1UXRTyMLBjC3EVLwacMrk6trNlf3yulhH2k+Tkpi1FsFDoqFJMCTka6c9ipLi2LC//jX53it/fezsGRCUubzV+dPV/ITTbSw5cvoTv5t3a/zWTvTTuDNAAwjEEe7Ad2IBB24Fh4ljGY8dxSAkPwgwiCpnFKIP5MDSte6I3CJ4lRxeWGATQ2AyOBp85VCVml+baatiyDz8INGQA/hcDCT0ARHoIyS1Sa6n3dRWAnSVXHEagIuMpKhZ9NYD11INLAaLZQjlumtruSOXjYvXJz1Tj2oBi//rc2Ht1TsQgN6LIYBt4gnXpWODgwOaPEk8lEef171zREf/nhi9RL+avba+tFPzLRF31PLr+1JuigtX8vOjnoxeZkXM1GMyeJ7OUs5Cvx2btC7UpmunhWAkb6etF3bTiOtzkKQskdOM6sMhcoj1LtQKZhOKlnyHM2QksCS1URjDj5qCfJzfTC1hDqoZikV1aOLQx9erLkhokLmKaIIsSLr85dzxNzuAlCn0ylWubxDw6KsEty6XGAegHq+LEHcLPA0C0NKlA5MkbH49Qz6QydbDDVXSIisTSXI4+1WDcIOGywyTSbzLXImDkCipc4CMmwjpuA2VhACRbBKRRJ7Tjrw9bdAcpSpOGAYgFDQwSBURsjZlOd5DnXMmUWxVHI1GOCI7AAqohkuZAjRRlhsWefjcg5cf2KECoEZsBsLrY8MyEZUiSH5zFuiqMT9cYqv77Jn6b+fI6wNG5ZiYpBPIVYSqIGKYLivMhEATqHUDyTQRxo/oOLb++VnxoI9HqjugyxbvHpo3EzU0CZiOeFWNEnVrpcgvyY0DrW2f5gGwXLlyrdVqob6AJhK8WVzXp1qIYFzGwrkRN4YrV0/KTPkDRmpa3jdLfO98f6Oih3UKV75lx/lxmd99dK9NSlsoWyR7txSmQ28D4k//L+nF7L7b1biXcWL++egiDYel28c/Pazd3qX9795Ac/O9jZQDZXSYKo3FiX+5OxpThlqjc4iEXp3KQrIR2pQyOTkQKbooIFK2W21uVZV4UUbOqir3+wdfJ0IdG8FtiBygDfoHh+OQBmhBF8cjinfYZqrkhq2zl+pAEUYjmKsxaITrz57tIktPWhr8/0MUJLAlxjiYf7hxftfREOisv4ejU7UDKHPdeTEXFP+EOBVdVwNPZmQ1w3wdVKc/PttbmaLgYZfX/KJba/XjcVqH719sY3dyAA9RF3PLL6U4NDwz/87jZ0sfjNf/UTeSUncPZyuSIz2+ed+ScPOpwBik1SS+lUTlEnuLZZeBXC7YDzkGCNxqbtWSYkr61CSQrUBARZFsQ8A9FxkJ71QKnAUrBDZyLPm2ekNHYdWiQXz6dJd/TNItFXhu7Mk/KMFzMsHV28GHMxjGGYY4dobCkDM5NJDdXMSARJIprhVTczr6bsxp7w6tk8PTK+VWdetFBxs7qamgDigqOJY0e5GgAAHlsuD/lIkqARbLYcIiFhC9LtlCOl5TtlOJMBhgs8KzIjjhV4mUbD/qQzmcy5TKW5TUktl7y+wl3Ei8WILchsq2PTvqKmVK0S0dGS2jfttIlU6N5hcOmKD5JaHI8IJJfjacBvAf8QMGykHmAlUabY3tBGomRkSJdea/IIePJoSOUgz4muX68UWW4yn3z+02OV8iUOy93yeY48QNJSlqlyeHkaD8f66Pli9VY+RsjOOSKpSQwH044hZgRlgXQ8QySRhKJoCPLoQqzzAppmECVw6MvfWX3xi4lkUOMP6f/o+zezqwQO4oP7btl9waWpn/dftiIkjtdKXNBX6Cj2FIifW74LIQDBMuV53/ACv7YSKrNxPkOWqpI6s+iQUFvKCOHyObd1oqKFBDCi3jchJ4LZCMNhnkfDLM691li0wwhe+O1BHQmBROhdB4dIg/CFOgIQGMIjMQeKSdqOw9PpAk7TqcbsLBWZEgw5NCGVX33Wznwxa8ZIkLrpLDE0XM4kJEpiFQ8EqgYQukaMZu0Hd08vV8uXfve2n4JnJxdOGOU4aKXOQxCEgeWt95flvdrJT37cMsfLhQQPk0W2fvnWxlEAjdIXhfnZ+i46wE2YFdJUnD1zi0tEo8BJ6Hy7Ufr0WZCtizrB84DKZmFSMYJhbxAFQwUmGlz5EpyvY+NAwZ/0yVO0ezFFOTwRCcr03F5kTHyOga2WU8EYHyOKKPJ5yyrtwvkc5hqzggxGPjnVsTJPKbZWxDGMhEeWZcw8bq/EXtoQ6rGm6EiKWlOWxhEWYQfDhe1OZZ6lKpKxsAe2UfDpDJuEOiTiiDWLEwKx53GBhhKGh03XNBMeV+IQgiGAZhkUobFJYM6RTFmE1alhMCIOEyaEYLCUj9yYNM8uzIId5q6s8GxkTaJZjYNYn2FkyHBj37OSUJAzty4b9B+vqbcuI8dIgOiZAslMZmHfzuzUjgwLUADAVJgXJBgPpovsddpXUOzZbHYULTUvvfgvf5i+W8rslt1racb0PZs/twCgSabX8rqBtNEcV4vvfJ0YfrR/NkYGdipsF95u3JIF7wKAItvRoHCSw6NDVwgW2zfz5+Ph9ZWsSqCDx63NFVSOkkLMCPUl2u0c76vvfiCN9PjF8XC7tt7/9BNwafXKN1baPxQQTlJfzOW36oWdpvfCbL86nPOmINt/8CerE7vWmYSGsbBVmnKj9Rpzbx0fKvGsP0ix2cYGlEoyjhO6G589HacjJdJif3lJ3EbC2bgBibKwRb6d7ivz197e+hKAtwAAKaB4YKbc3HAZji0K8enRpH2auE/3hRzvX6JQHpe+vqkp4RxC61fEIhl39rXUM1XLWd4tXEwtvizLVPrsed/tzHqqNZv4RUf7zWeTeUTV8txSlVohpTdfK5y2J7/6s4Np6DQEUfaQMuIXw2729esQBAEAzobq8mo+L2L2ufbb5/a33l/6xn/Enn/WOR2o5mxYkpn1AqzsZu49798R+OFYYwt5Q4vMYVssCYRphXVqsHBFgmoW+KFH9jR/ay3Hdt2gTggo0mpHsphda2DtsbpEhEjAQL7vunxqu8+/WEgnCSNiG4k2tzCoxo+mU7kg2jDcupjuvlmx5oHhuViJACRonWgExzYyNBQT9UJhNPHKtPPWW0sffdwpCfhCvnzf0JPz8x8/+Hi5USQYdj7vUQxpRJiES93RNMPgvpgqWogwYRQDPbKOvlSvlkVayNNLDJJDAcLiF9YA4ko7ALehj4ZTEqClBuuFfjOEzmP6pKWVRfZJcQ2ghngKVSlT50UCcpwJurO1AiALzOcIeQsMPwQQAzaW0kEVghbITjOGNX88ArFvBShRxRxvEfjQzg15zsbjQ/f8uHVuUZX3y6XvFjehuN1Z+Ihp9ghcB0cfnQavLTWWFiIFDTvQOonMEC+hIKWzkItRgjqxai8WyOjuQUFkVm4XvvrF890cn98UJ15ilLmCiKQX3e6988u3v//3/+f14xdOj2OzcLDVkB++NKu1QEVIVk61p9OhGksiq3hJhsMRLmEdt22mUhgB1KMpcnhmgkCms7lw6sVzl6Rpoklki9Ss48g52kWTbJj6gziXk4SZmoMCuhHDZRfQe8s7JTD/71cDNwlbmVpRkqknLxa2No8UJDvcTMdhIQ7ngY9n8zmbZ2hz0h4fD5u7VfajXzzDuBK9y7SmvfLK1tOX2hoTFLIl2J+bDI4AgtSJmZdABJHayVJZqFzZnA6Ni6MBhIr161kE0xf6xNbHWClfJWW5sJr8k//lx73/T/LhCyNk619fmaDKyiBlzi7YqlFLx7hWveoe/gTdwzaWnnXM94TwkZ39XuK7MtVEoBdFxpyb4Vw77Zj1geMuLe+9vllw/dE8zUgYq/qdxx11q1r9+lpypcDgoHf3DE0oKl9CYJW1carEapF2/qS7vZXquAmWGzN1QadUfSmizlrsehGvkPDoFFpuCiTIVFClWaBJgvzVE9xECC6250km9WzEEiVcoAh7ofUhTpAJw7EiI8zFkCyyPIogNGYiQEMAZOksSfA5DliBP/GiMAjSBE1gLDbsxIPKZShceFgMyUjiRkCEUhLYJBSmmkHM04pAQX7Ss1xAREkOQbwwcMN8AfZbesDJCzSHNRvl2xt4xNit4+++t/HpcVsgSb4u+16CY5DlQKxMsCg+NUCNguGjAVrMGau8vcsWOsbxlx355Mz5Lzf0EixMgyaHKB5yYpGFUoNPu1++atkQ99qlKDVTG2Skb9+5JuzkADsLkKnVKlsOzA6EZIlrQJ17g/qgfUmg0SnFyzKzzE/HyHGrZSto6ffo+70l8Q1knIAaM6FrxM21rcQZjK108CARfE4QM1PMC5909rLY0hoyIrmEkY6mod+mXATkckw2Q6HN1Bi52swRZPqDInyxujifmpPHs9JNPrlcwpxAed7W9KAigVb7Is9gtWIeZQpa115naFSbm9nSmwAkAMAQmD1K+Y1YNWOORs2pOhuGkBuXrvOhp6qGPj9Pdi5jZy2ksZ2PYsKbqTgae1h8fjBjemipWGZDZz6Yd09O+yfjlZskKsr+Mz0NhXfvrIRucHSofuPvFgde/MVf/xbCow8uVWddD5uFNFHAr1SlUAIAzFLAgmh9s3Z8Cl5bz56451/86MnGNsKUyO0c3zpQ3QW0zGeuXSZPn09NV4cZ7KCrfOf7xcdPxjgIfdiLLQyhKZQiIhhylSCTY1JFIECYhgkGHKGEoSmYqnicWWq1hlerIPTjbAZ1enA5U1pdq4F1HEA65xophyqjKFnoOQwABoyd0NJcnkoZMp1asBkwLCQAM0WQyPMgXCT3z5HaVrY0px590uP5k9BJ20+g5YKIp0SCmIAkiRQIkKd17AwFxZYdYZ6rQ24GYrKMncTFpngw9BJLby5wkgvyyxkctdSpPmmrJABCyrB5c/TSfzqNiwRGlNJc5NDnc+qhxa3m5Eoehx2AdlOf5WUWC0P70YH1ajP/P4Ogjzf+9V//4k/+2yvQMg4WUZxhTh+7ecSFsIQTaJRFZmMPxSCjrZq8Uc6xtp5EGvLi7hgFya07RW8RioACRC6u2acfPZmi7mY1sy7V8ITFE1TG4TiTBlLCyWlyZNF4rsITpfXV1Q0BgeCnY6JazgOLe/7ilCfDCy/KERniGv8//ZsDZnv99Nhi1+n1DwqqjWeX9MQKF8ftm3eqiw374cukY0CFMm4oNo3Nc4HeqHGBmpydu4XMEo9UchwwHgUt38xJ1BQ4juRLZUps0Nqxi4aQbtirS2U1SbERYG8kIEBAlYrBAgaIDxNolQgGSDgNHRLNcdClTUkl4zA220qKEVya8mzIxA58PnT8CbR3iabMoI8gfBpPqXR8kvzN17n1TXn6dBb5UVGmgxQypwHqRxCJZnL03PNQhjLn+rQXAk4o7hawIJx3FpliM9vI6UE8nLqVHLUDgf9d7c4P8qe3G0sFSv7zn/aqe9ITK8iZ4JFO7RRZ48DkmWF7bNo+9azCfHjYgzJVpUkN+gC0RvrCi5Exy4vcem1HYE++mBhzLU0V1c7gGTL3D77GbG+wJ4sP/0WrCC+AN5K2cViWn70Kr6/mYcqXQ+PlWGPYzDABIkqyrGPpAVQkIy/NlpHQxlsKv1PK6y7M5ZHNd8t6x1b68NoyNwmCDObjMi1WZIyCe70ZnmK8DyWqwcQuBXQoZWIYd2DCT4PQ8ggGQnDY8IPZ3BVpICRBnKYABqhAQDM/zQsYDzDgx9yKbFpRIYF9CDVDNC0z04FJ5FEsjA1IgUloybNNkgtwLOPBwE+ZBIsWI1cW8iUIejrPTyZxmFkiy2fRzB+0o7o095wsEychXNiuNjLQ6dNeLVHs5yeTT/Ib7zT96OJ//HWqnV6U/5Ob4Qfb5dHT3F99Mn332zOBL5pEBdYF1F2vgNEwHLxQIhMp7hW3+fUcmPzywhKCkerxFDM+OYKr7OgYVN74+kZwetHzYaxJx6pJ6L5UYaGDef5q+cXdfblQ6nqS6KjHc8BY9mNoli8vD47mtbcvz+Gjl+fj3b+zEhzO6sdDqplrx/xrNBShXrywIRhWe9OWYpNe4ATQXsaNJ6lSA5k15lXXNM9a7luVfo9YdsdibzyvMKCYKPsHRJGfDT0OU6Fy8kbtOsgU7//4AO39onxnlbn6vdwNaN9GBjrBqe5KMV39WlNzCMVvmqPT+cnQ043R2CJJvv3ymUjTdoarV4jZXC1kwcOPXzbqS2LOenV8bqWLgDdfPT6nUtHFxAB0P/7i2Xe+dfXv3rhMN5c+/D/+aJKkTrPpFGjeDpY3qjt3bsBoyK+KAADNcpAAJGO7NVoEIH59q3hCYW1tFi1cHo8yEqQb5snJ4kp+5Ru/u2xMDj/42qUf/cVg3gMolLW9EKskIe1zGAVTqR4gMz+4w3FK30pgCSaTKSRY0UEptxuQ9oURmmvEHk2iEMSBvGU+VWnxXz37yfJs9dqyPsEtSMcdL9J0QKKizLMYgCiYB6ZpB2CxcHDIbNZEyw0kkbRDf2MlOz9V4bH/nXfKrzjgk1EqlY/u6hWUGFBBhU8JEtZnmh74ggBmeOAlARzrgKJokiQiL5chyCJUrVUYn8BdzuupcHecFJktkDmAoUahKsbEV52WY2PFdWp2BHkjxSvkLu9CNyNufKG2z7AcatbzwMvhqgpP48mv753VSuDoX3V+9T/8mz/4994DDAe0BRhoE2FjgZvrBVLf9ww1BFWAJHEeZye4rulwpRAyG5vrrPDybLR/v/uzHx7cvL3CLZcTmI7Vwlrj8tMXycsv2n1q8sffe3v08PnZOBr5Rv2m1P3MmJxHvtcTskK3rbPvrc2PzoIqn78hOQeLrEhV15iDj4f0ZvnOB/6f/agPrRkUHC0Wx+bT6n2F3ihltjcx7Yi8+7MTtijVdjMnr3rhsVkqkNyMxox0MECv1IMw25i2p25EBWFQvSxqKpYm4QpOmVR8+lL9G8vmr5UEStlsMU0zhHHWQ3J7lcERlKNHD099+zcnGeL2WtZTUdzMCrETWemjiykoZkWKRnshAS1dXCgo4Qmiv76VVbtU4+ulEZOBz9Q/+qMNtbqChfeelwQUCQ3Fab96svut93FqsTjUd68snw7xLKu5sXJ2EuVLHE7zcsmfI1arM8hi8Fil4QbW0a1tKMTtqep5Uv2yBKYClUkD6OI3H22h1kVKpqQCAu+Dgk2NDyjtIPHQcgQeFpajIIav/Z6GP+IMOTKjPkYTXDhBCjhHjoaR/eylBgnb79fwRrMDMGTfxOb68z97tr6YVF2yVqMsqYalBJqvUojPcAUotRw04TKROYG3mrXrCLogeCTnXAwsqlKFE19/dV5ZWsZ3eR4NiLJUgWH0ybmaxXXcNaYqhWNmmnRCi+7M8naQBEwCkJCCDZ+DEwi4MUkhOI4HIF7K823XgUJfxFGkjER2mk4ARhM4C6EWlLIFAoolYKIEzw/UCA9iHtZdV5aWM5OhEztxgntq6nqwb9gwXaIwPgxs3E1iG8aJCoewMBdpqovDfnrmEWvv5QaGnaozTyDIDIo6CPAjOGIQgA/PHG/hRm9Q8i7W/kTv/ewcuiG/+0ebx6dXLn/v2hQs9vfP8+8y+Q0nfe6qNjdLfcV2tDV+bBr6I6tuZfNaQuT9YYqvk3aKFVzGcWK5tmmO+qDdOu504T0BXlBYnsGzsZV2RzhcBSWSKThgPA8gDrFpcqWUzxGhVf38hX9zpaG1gttb1OrtFeVn4+OfDd9/p/nVl85WkC/Wg7P+YmOJ6ncgd6LkcgRTwCQZHSrIgFqowXhkOO8w4lYzKe5okHXo4WvSSq7UpCPIvdjvH311niGZnZs3Gt++KjH0SA/mY2/5TWZw8r666LQU/WuysMNA5DrOBmU8mQ+0CMqKqA0ToJDhic23OQwBQoz+5qNTE2hsXdY8adJVLNepF8gwDuaGZ0A4K4iOGSQDtEy5Bwvz/dfLcxN+47312UHv3/4/fnhuHb/ZEB06NRY6lcCoiBfKaGJS6OMF+CCFL77UUr22Wf3ORuH05flwtBDphAB6ElPjQRTYFi9HEzj65cPhtT2pPcP2HBpQwlhBchX20fNhIxvaWgKyHPBDI/ZWVjEAXCtw8JLMQAQDQomkGSN0EpMx7Q0p40MsChY4sBm58PoG/WiU/4ufnh1H2u+9K7Gcv3igoRCWKUsc7agzPzCgIo8lAURBMQ9HPAVBcBowmBfqkBcSdfFh19uSuOalDcc0B3PzvXdkSeIp05NZotcecTCSLTLWwLNckKUIaw6HIVYuZDEan4/ceQIRqGnEepZImQ0M5MowVCKS9roPzl+6X3SCtMwyDO6M9e0iqQPr1Yn2qB+N8lK9LpcQxlbC3niajKG11Yni9g9O79/3nnz+f3/1wevC5T/4bprMgx89J75TKkcWDoeQ5RVlcNICeYLBOAhF8bXlQgNX9vcn55+/PCYq73xzeedPlp599vnP/90XyJPGnTev+CMjwMBrS3wjKv3y04uf/PL5d79Du+fG3V8v6BkVhP7lHLO1kvGc+PMjLTxsPTxpwan98tkYCxAk8Mok34c8IgM/frkorCE2mKJ4WqmjuBwtMyC1x7+6p777ncy9u2b/XNdfLrYJiMeogwPkMr6aX4puxiBUX1bxSN6JUz28+8Vk1rdiIvL8ZDIAtbII+/DRoUNmCV+DGSwmUm/uGjuXXLBVfvjkSTs1e4t58GC+gV+ry3kzMtsDI7vJy0SojqcWtuKEaGYlO/RDb6acH2pmy77xeom/tAyl5fkmRsslDLjdUIWuwyJ4R7jzeP9HA2p1CeBwyFMuzoQ+WMSMOTJ3K4Xl7VUD+IevRmQxKRelVEt5QG7xyPPBnCtLqpJQ2mVQBz/8URwsdE0G42iW2RQDU5NislRkU2CNDoaN35efHHblvwbuiydH78hLd6y+42aV1itUnsQw4ng8g1vTEPhoYbW2ea1aMoK7v9mfKaAQY74XMSHUqsvy5XxUZJIjw7Eg0IrzgcBFNIoTPotzNk+yUYnJW27M5OnZAIbRQmMzOzuxpZq0+XZDh5E4j4libLxQtZZZeD13OlQnXbMkCVSgU7YR6K6wkXcibnTimkFAsRwMp07gkiSThmmkQkkKiSiF84ir+lgUZAgmoaEUR1IYQnEEgjI5NATKMMnkmTnkcQDwSAJlodjREcOhU3vBJLAHKkDPoZRMcamJrIWUguOJ6gaKKdFI1lfUs9nFhPG+dj0Yw+X2QTtKyaWM4AL20eDcC/id3QbNaBehZlXMU8W9vESv62I5l5G8MTaR/2mxd2JtfHGwsiogqvvZg2lVLhFusGnZryA/bUU318rJ0krwW2vKcldA9WR696GWYzBr056cmIBk03UW3n43+5d31Wfnvm5MlgvasFTYFKGDVmqzfqfn/ONv7z37sNdcyzwbB81oenmzMoMxB3Er1WznT39x5z9//cYfb5//pjOPSP76svb4pfQ7jf0g0BWDZzmWyLqQhVrKZOi4FOY57maO0ifeXJncn7rIYTe3d3l92Tm6XHnQjwpCmIvRxzghxK6COi/uPYT4gGOq44tELQvr7795xSidvTq6F7OX3tnpheC2BJNoOj6avTxyMoxHxFCunoUMV8P8tUvlN7M3Hz6ZeRCEkfjqWmV2Mi3fvuHizuKLJ+9d3TVC/cOO+U/+4eX5pdXzf318EzFadvjjH76Y7A80W6/DBX9hS7BDipLh2cdsbg8s+c8uzlfXqqE1iCjStmaPhylNFxmJDNBOfyajMSWD+l4+NbHTp+0b71U+fuLNjs8iK/zz/+mjrW/fgXSFldmyyCcQRrNIAocw4ksxtSIJLw+OsPIlkeFhsPBTI4ZWYZ6YtuZVcmkZYlIAYrAHgjEoQ8Bt/6f/ofxYNOwgO7QMd852baa5K8aEO50nRirTgE5wHCU9nEhybDZ0AoqVGY6eaMlFZ/61d2jPxs8OtY3t3NoKt5xJO1PUQjDd1BHHIQKYgjHNDCw3yW8xRx+fBK6z/f2itoozXSWNmQ1B0ixULKSALiTdKQyriT+ev5yOVdODcEiiTCmiuaw6g2pk3MWzb70hjcwItM8ez2iI9W5dqeVgcaa5lo6rRHTn+5uj2XD5n1X/7ve20dQGf/GXRJYDBTQZOTkmODaBZVvkmhihU4lDZ63koyMVuibf3MvndsTzp+ng5X6Ekc03c9g6Njxy91sdZ6GLcELAySQtvvZtBomiTx4DfRx8980NOgfha033wZmJE1/uT4S1zHgYblWxp6dSz8xWxSgxjC++HKoLMtuzCgUiVmH3vGNCAr201T/zqWUsVBVwpjx1rDAH37jFPPyi//j52XvfXMvloDdrWWY1l04XXo94NggHbEA66R+/UbGMw0+mgVAoeIs5lOUYH8M4mqZIe6gRVLb9WInhPKFiz7+g/um/pN+4FUTLIiBCow9DQgWglFBUQqNnecj+C+N2PTOezhR7uF1LO8vw17b2OiHBM8UYSGj/aYZiU22GUPlloHNgGwD/2W9Htz4oAqTfa70EQGi9CnFLRteFhkj1derok8MkJeTEEK4XAwx1J3A1vxIail8RHqleLoLae+AqAJu/i7TuSiiOwSCcnwxKTWmgT6EQA+PxYi9h00vfzxt/jibITu4ylhx39O2QyKPMOEhJLCwyEYeifYC7uVyEu5//xWFpEZ6KcDkDrCEuN/mQon0pdu8tTHi8UIitFSlxXdgMxicK0ciMKYEU8CXThH3GRP0Ghb/sTcp5YUTyGMXW36v7Ie981S2wGdoGggfga0uJ3h6fdQiGR1c4d6oAPZaQ/KlFZVcEzk/BKLBmekKiluqLUlYHMJpBQitBMrjt+BJD+L6HeR5CiBCDJWiEhgkOjGQ0DLIcj7MkNlRFDGHExKMWC2PMM5BjW4gFIgs718O1W3m9mQvtGO1CiJhAAtaeGRkPMYfqLEgadTZDLqb3FYDqxQZhKmn3hQ0sZvOtAlfkzIURYkSA+EdDOptvTMyRMtd5Kae0oBNWunOlOPvMrHYddwmWPN62InSWRDK8jGBnZ9H9v+rW9up7b28TZH4KEprN1YetNEUn6qK0Unn2REtclKKCnQw0HXghbHVfaLiqxWRaqtJXSzX7weOzx7MCkjAGVDC95lLQe3WCLiKHZb//7sYvJ6379y9SFN2WIMger2wR+0p0cNyvlKAkDszRaLKAKdmBQgvxbdYK20fdU0NJvGjY1R91JisC/6o/vRs++4//V+9ubBSePyPXSvQKP2q/PILPrLYO7g+D65vN5Wopfh60W+3eflL5WibzJqH68/J8dDz2Aw4SG0uXi/jopA1MBw5tYISJET9Qu7dvCZ0cASXhad+vC1hxbQ0hkL/8V/c5Xfv6H1VDDSnU6F/89Xh+L4Lmyo9fPDUo5KCj/8HrKwf3/JhOXSFjJ9G2hFoakFFV619gsFSD8/5onjeCxhLx1ZdzggtQH1nKpksMqlrpYmq11HivilNC9viFr3eUb3yjevt3Mv/m3zwYaP0qA4cJLDOeh6YCIA01EAtZMiP0Y/fC5L67VXl+oSyt5BPAYQmxUMHQzmw06BQAAAAKYDCfQizUHcD8QVMmQTmvvDoaOAZUyK+HTtjt6L4W0SV57zXJ9jxTT5+/cP7mH2+fHuj+wl+p45tNZvzTidvy1pdlG0BffPa0VhdydDA1WdhOy3gCdGtFSLWZEWJYkOGOH/UYm//WdysGBtrH524/2t2Q5wENM+xIN6H+IFL6RCjB9Uz+g2vwk1OKUNtPWy+fh9RVqUzjpobxHHJ4kVQbXHNrWQrYk0HaeTTzi/jm5aXzc7uSbazeZIcX8GgB/I8AuISBFAe3nBTQp2HMpQjJJVRBkDLC/hedIkBKObHpSDqUDGZUXk4urUMpgj9/NL34EOytMnWJDCGIKFZsDMrl5J/ePcTAfLcShQGWE9hLYqk7CxpN6tOupjjzxUIvNSo/fWm987Wq5S9m8wVNk2iee/RVeGs137bNHZx0E2g4hbkC1kih/alRyrGap29n5OHFIh4ln1razVs43oncsIeX2Cduhx8Zswe9anCYoKj+whJt8vMD79Y7TC1HZAko4iCr7/gEYbLw3PSgEqERfkBHb24Qi9ZiHtF//5/U/r//217mtlGFcnij0jogCyXQMhwOR2W5ZMDCq0/SS2tYaivxHObqHIzVljACJLThhZKAumY3MHIkovndZMB9Ca9HCc0W3twaKItDriljaOoBo2cPvrBXywxdx3wXzhTIQkoaKvr4wTQNhPU7YutC5e2FuVCKO9lFMvSgcuvLkYR4g4tTNI0NkPCqIXplaMgZHhp0B65sf/H/dJRejgONWMGNFguRIsHbGJJ05wszRqoSbsWROfchJEECtPFmmUy1yTA2SQKHsIwYs14AzcOgIqVl3kMCoztAQgjPYp6lcBQR9e3U1NQgCCr06YvEOZ+H2/nU9TY50Pmo7RhSZQujcMLW7UTGIxrtuvnc1wuXipR2Zs59ghSyzaKwvzAVxU9IKF8WMpE5010QRTk5dmHX9DyAQmGYpICMOThGE2WeLGXIEMAhDKFWGmcjAkZNOI2QqcYb6dYSHtiu7cZCHA1NJTEihE7nz030qtSX49Kzl4mDFpeXOlHEeFG+yXMTDZZh04FdenHxol9yaX9XcPf73mgGkiItg4hEwlj1F5EfYLkcK3nx6NwsrfN2a2bdP5Hx/B2vgx9PyTXqeKDQCnAWikBJ/Nq6oUOJW7x8vYxdz1JhMPHtx39693/zz+pVZqt6qXGw//JX0wuCguz1UiXBuJGC45hwk+6i6+rpaGG6vdQNX56ThrqyJkaERa5SL07Pl9eai0m3Z1VvNAXMnCpgVr2BhcejK8vcJy9bRSjffzGq8ahxYTknRoz7gRXDHqTPF65vTtrni+EcdicunCiq6PtetVmo8qlNIUo7/Jf/4qv/9f/p/cYb68HRaPXyzsGrV19cTJFz7+R8Uh1MLq7d5lOvli64fAWm6rmHapxl7uysuO7wvhpO7rY4mhBrUIAkMx2kMTLXtZ0tVglsJDWmZy0JIx49cpfW8qAfLR4/j2/VOon203/+qwCijOEUnk4x40RHgL1IrjY5kWAwXih+nX/6l48bAEFpEScA6HY+f6n+B//s72xdp1++mMFJWHBCJs/QImaN3KnpMbiLUwGW+LOR/2Tkv3W7SnIJxrEffz7Fn0Ovv7WNAOv0wOYoJEkJp2/lVkgscnMsms1iv/3oyZ2vXw6So3K9SIWIhcUYFKeakdsgRQA84AIwooCf6Ak8HAEoOvv8LkKAoXFqw9j29QoSJE/3e4wsFi6L6zkx9oxXh+Zrt3OTmUNI7KVN9PEj3VdCQATN3eaXh8YOGZVZmHutmRDxrG1V1qWLbiTFPgQlJ7MQBKwdxIZi09nmzoZ+fNqBIFYWC69fI8jKRtJuH96dzbfZ5YxUv8Kn0BYEV73Zz9F8jtLPxnOVqje+GCtwhnkrE4ZMMe2q+gj9qeXdXEYvLWcwKuwPTP1itJKphKijTwHq4ImBonmgdMZSBMcBiZyeyLGEC/G5jgpZtPW87dugsF5ULbSYgwLc7w1m2CiWshJ9vZiD8mVjv//g1cLx+2FWCozsVmMNd/7T/0X+o389VhG0vLdVmbe7j88bTQIyrDub/P/1z5+/891itgQ9ejilEmJrlf/xzw6uNrddGK4KEd8gj1+08lPBDBCrP6+9t/Ws5csZCa6gxZwwfqgNB9run2T7z6xPz+dcFeOWgxk0oy2aH9n1htRe4AJK10+Da2hTDgGBNmBmZi1OcImMrGB3gzMSu0AjHgQZs5hI4MkBYMSM+m5xbx2/fp8+/PAHd/7ebflKVTD4MPQkUvKS0InGzVXaVIyEjACGYyG1vL0E2lMQ4qA7O3ToKze9kQ746ejEK2dYcQtkg36+ubKO+IuZ7Lxd3AqheAHcKhZKOhYYo17b8yzLtsJRrNgAynOQ/K1dO7rgtEVJSEEVUSB6BzJ6i87X36l99v+7C9WAOXOyjJtEs4xlj4Gbqe5d6YLj/8Pxz838N6+XV29cpyLOPs2UJP5osg8LyEFKFMvs6lpTe3BYpVBdWaRw9PHZnO6olRsrq5vFxDas/kQznEqGxTaZSHGnpm2HrpwntGLk9yeVYawrYEC4HgSCjBgen+chQ6KLA4Sw7XYcocu3xXHcb33cId9qMAjqnRq17QK8VjaePx1/NNRcP2TjaFOkl1k+tKYkcja3OBuZ9iEqn59i2QqKoGRouUHkQgoS2SM3gbClhsQRnGbqgRuiIYT0DX86cq+sET7s26reDREYwwIEC2bK1PSENLFGkAbze9tse78lavrOjfycQxPPmyY4WmK7vYCOU0qxAhvjoBAjSH2wuDgeMkRQXfXxHJbJQoZp1NfwRS8Y9tRcntPPIyJD7+yu2Aszi2Gu7nTuj7VCENFoGAVZHLEjZTAZUvlCiGX7+57ET3BPz7LEEiq3LuyntCZe9Ndu5N/OXrr31Wmx38MoEmVx21NygZ8SWr2CApoxopx3PnnZ1T0lAjJe3461XDqdd07+9fHf/nvV5Tx49LNWeHZOhfGzVz0pqbC0Y4/G0+NTtMFiafTyQqFiQ7WiyDPGk9ZCNfXEzjEIHyYiBFAsSlicWhK6Kmz0/WtV9tNng//mv/vsn/4n8cJW10srv/9Hv/f8+fHLl597+uxnj09XUOPyO3dMyyTDed0ZQDpRzdIQxc/PjHUG+mo+nyWhFUMZlBJyHMsVvD4iL5WPXhylRpiTuLeuZfP9qeaFZ+fd0joHUfGzXz2czztre02hnuMl3p5ShQR9+Eh54736pXpuEUef3xsUZexQCc2DrsPGWBf+w3+0s/INdmI/mA+6b96mRmczDyCoS0nZIPKCseVESbpWr/zjLfr0cPThL46KZfp7f/uGNQm0ML546jQrXhL7E9cvFFhgBooFkSxsBA6xcGFRzkEbF/ERDXsUBBZeZ25xxbJU1vuIJCHhGEZcABMA9m0OzWXj+t+5oZ1NJIsjV9fVgXL0dEzDMkeWqDiw/GgxNvNCooxMAQ57z+bVTc5BfRAtem27eqnQmsYO4iUurPTnEI1mGObJ4269Ao0tD5rqUD2vakJgxmUxyZTx40/7Hsld+xvfWRaS46cnpXnvyf2H7/4H30iSWgQF+mygm/cEStQBWa9eVglfC76QIbbu8ILsO1OA1Q20wZuW1yjiwWIxnUzEMp/Po7pGy3kTGaIck4UYPlirTsO4uEKlYxu2UhAzGOw4XWZpmWVo5O580SwtnbXSKIxdJNnclWWWYVXz5dN5CWVIhp2jGWob/O7WGmBXj179+PzhxQvfurN6pVCUPAK/tpwkE/3hrGvFaa1PCW9cuqOm15ep3vnoSjHF1ICluEyA1Gn4qwfzzlNtadmNolCLfRYHuuaVAwD5k5Pn3Qev8FtvrV56gx9PTj//bMj5aOEW/PA30WNp3iiTZ+1H35JB65RBBcucY5uZQvAVLIFE+cwkrkQMztE1MGnZJDmdzQOCJxMDqZeE/gkIiGwpK5YCoviYXgppiPr+7bf/Jox1h+kCjkjTCAf9OM/L83M4m8mgmkuJOCgJznnaatl4Oq9sZaXj6YuHwyJPhQucdyI0JC1UzFzJ+joSEoQ+MD+JZxQHSQacc4iNDEqV8xdqZIYkwlrHT9zMnrixsp6Sav9Br+oiYQ/psvBUgG5ChYvewaq8JPJUf9JFiHyv1VrJuNkAKyHw4O5JKYNMtzY2vna9TJUuxrJxHNfrkBW5fKCrOpElC02cfPajs97pmNqFcAkRODe3U+KuNi0rePjJuYSFYmxBEA5q/KsvpxQFZUUvCc3siheyPiO7xw8NhueAIKZ6WA09JdKTNNV0iwvtT0NI/Jro662P/vlHN26WJU50p2g9R/tDe2KegLYNTE1xoWK9lG8QPuzMDk0v8ugExWKC8tESJTMe4WFQkOCeoTIUlKNxAQVzzcN12CumbgiSJEFTGsI0jZYcl0zsnsMwuEanmThF9Knu+5SEREfKIAxvXa+L0Ywej0vbbDsbLz59Dr9T5yUSnLtShPMFWaQYx451R9NwzzT84hZBV+WoyJQAyAwmsc4MoHApSUfrcsxbq3Nt/EA3dzMqiPvPW6WN7NXvr5wo7QPIGfVmv5svCsaov5a9KBrJXw38sWEgHDCc0u7Szn/2ezfB9sxRW1Pj3iuD5KnXmlnvSF3MjC9zcIRCe34kZyRfc2EYL7ogKTXWZPd4YPendiJMhe1cwlSvXFu7uZUH5mHFGbSeJ3IZyxfYn37UobaR9uAij/r3vzyzZ4mJ62joLSbjhWKgKKgWiSxJV3KFs57jA4+iMzQCB7ZWaBS51Sofm3+yt3fQmj5+5WIVqdsdVJja+29d+9q7Ivebwx/9vz/fv3/Yej5yMfG9S7Gn7r15+/UYwfoPF35PYbPk+vtFLoMZmrK4r2BC4qYRmU6Pf3meCFyGJ6cz42cfddoXbVu3O04IA+umGz99Ol16Zy/HBknXnRyPZ7zEitSOGDWgauH2jQ0t9+Sz//MxAAAAHCgzK16urWXeuqWmNDPwhDSEDQHykgIB9eeuR0MyzRQLyLg1Pzt1ZmP9rT/I/fu3yU8+bH362Xm0Snz9nfrHv5l4iFNfleZGDIc4AklMSoAooSTBVUC+jPaBRSNqDmQA1AlPvOLuFgqRJjYn4KwOD3JQAwDzWKPXiihMEYDtiMUYWNF0NI3mXb5AL1WyuSzdntILJcQwjEG9k6ej5atVRYvxCzeTY5QkEj0V8cmdErn/cvD26+ukqbXa5tL7uYnl0ZrZWF8+QjqwTIjKjGhyWdb7+GeHZODf/L0VH0zv/vxcVdBkhb3ytV0A/E//7cevseQCdxqvrULC+zy4sBWtczZrXuY1L0JDTXUxngwSKBUNj2WphI3YneUGvPcM6IXJhEC9T0eDDMLt0QBtBlkcI60kxF2XQumxGUBUDxH2mhLIU0d/9ms4kyFL2BhLkEDvXzj+i0EuL3M7+TzF9dv90lvc6NBXDoatg94H/6C8tfsBJD2z743GLzqUSEd24P71Zy8PFYvk23PvmFoJfjChEE3RopEWolkhJL2zgSYuoxiq1TeFAEIYd17LIC4t6IspyiD9g1ZO5t64LMUS8atHLUvDNy4JlEU9fbiwXyxurdbGJu71STkvGONT40m3eWfpL3+9j6+vroavXl9/Y+ZPHZPreKaM4HACEBilOAbDgBNDhBPQMjn2krwzuFMuQqnwwbbA/1FNfNMkX008mLJAsFCVhPaK21HcsvwcKW4KIIYDMt4/mzoRtlLlVZTIb9XIhd8swpAWAhxzA2rmp7JycH4mnp9/Zdtas5p6NMrnqdIKdXC/Z0qgB8nlmVuWq7//R+WvfPbe6YJWjzdlqHL7EmzgBTruQ5UXsy/YLVkLw0xt2f/5fvEafi7lRxNNjxCKD/NFdiJAmwXWtNzuRy+1OIfsrey7EPrsVV5QsHyVH0Dp+AIae+9959L4zqoVO6uvxh5Gv7g/9kyvSGNUgWJl1DfSAMBxjYepGIehzSxaTilj5Pmt7tEI7y7wG7gfUmh80k8sEJUzkO5tzRUMSywnfvyXnwGAeGLGTFFy7rRkQQoDPsGJ7SyVpiRgkr26PlH9ZzMIQTMSGxqhGWNUucLVqyTP0xzkhxEgXCVNPMVCcIhMYXg+TXNIklpRClCnp1AJRDCQ44VZKTa0wInDkGH81E58S/JjgaLUuTub+ESSojp/1vaGDzurm0gedzIMk+aQFGRjiHIRQV6myE4fp8MUckeKh+AYkqAT1D5VFis7LDGDB4ezSj48OlXE1K82imcHo61lekHBi8NxkC+eHfv5Vb6JQOSA8aBMKaCPXiwgz9n+99bL+SY8sRWbG7046lwSG1Q2+73sT3/eHXZcsc4QJTff5NaAi0M4OrAvXo0QLyFQSjBhiE2JEvHGbrGt+No0onD15jWJ/j6nPPziB//dD2/Uea7M3HuiyuuZkTXkBslFq3sQQkqrzwWoEjsYRHJYkM0JHE2yecn0iOMFqYjY9tU8EpuU4SRwkkP89mAwGIYShS5eLpJiT16Wn78c6dwgYPFKFf/Oa6u/s77266+OOs9bbM13QzMNeyi0ef74FcDkq9fEl51Z/FyJEiQnYEECMG2qHzlua5h3A5tGPjo3zcgcjROGDKosLuEeQSDaDKfEojoixxlkZZk0u93fubFM0oLPNG+8dcNBdrKVSAUCADoAoA2iSwC88fbtXFAJNaqQp7yLZDEOK8388bGxtSENe9psoJOpC1tBpk6198f/9p8fFVisUZU83zn45MnZ3ceVtYzCcqkBaU6SySchHjnzdHmJ9CfR1CTlatYP/IAoIyDfGkxyeBqlVjslxxZVFkEIBJBO/QWK8hLayIN0obZRa5GOXBpLwWTBrKwwIk5NVGT7auHwcKpPwmyVoQSEylEwHQ4XHl+DLNfSSUSfmZtLhfCzSe8ILWTh0TQ8Pj5/Y0O++1O7yevxhW5M9GyeSOJ2/2Ass9k3PngL0pRf//mXsoD87j96E+K54aQ7P/O4rSx15c1lEAAAjd2v2s/HNapMSsXu1HUIv7ac9k0TmsOsPWVIksovP7A4oiuvroJ3gWDQKokjVwlNc50pEtA0nkqJCyfjYZATGCCh7YkD02RiWuB4pntIvYYugsRlBC/Ur9xmuKz05c/H9zrq7729rKroYqi+c7UEWO5f/+Lln/5f7r/+zfxyBgc1sqsshhcKrEUQ7qkEJ6/J4/64tuTMXui9sX4PQ2eLIE5SSsQgKhWyzAiGJ4sFDFmqlUQ4sAxIBvBSVbDduJiEz88VLsetsvb+09k5x9zYzb2WYz55MDth7Y1lMty37Yx+DEJQz0abhfN83zP9gI0WykmmmIlTwlLx/GaGgV0zTuQsOjN9mmIthhS3aEezeig3PD1694Pv1P7e5dkiUmGbYXOoZdhWqkf4298g9dYhDYVsJvVmlo1T/VOFTanV5Rwg4enCZGipo+DtrrNSp6QiH9B8FhEB4LZvko0cfvSL3gpb0kJyca7ujwera/xqlRQWukDRzz88eaolxCbL4GgzyyxdrYXj4PiFufZB5SrA21q6LZhTBypffjPzrHfx41elpZjmkSA1+504RC0+QK12iopwrDM33gWvwpdPHvW+XktEge2brmdB3N6l+u8UoFEQPlSs9vT+QVdYLpXQuB8mdJ7MFcCso9mDWPESmQe1KgqNFTSKZzpisHwob+y9KbCrfOvZeG57dZlMeCaGuHHieR1Nc1yoczJvG3dev2zhSHeOLgsoBTtGROAp7btC6c62qGjnY9Ma66kfo0tcwiAL3S3l5CAkY5byITiwQ5YXQBJpvqF4gcGliRn2Lbs0VwIvRlMIpSXIh0PMMrohNLOQHT+qIEQo4XqQBBQaQQALfYxlS4LXT2G0iCClbC4yKcdMP5sj9uTpWnU7RcIA0Et1mCWXKqzd6gIf6pVQ1gAUg1YyDef3r5O6Obq/H5l+8XIjvquewfBUZvTHNo5qmVU+IlIjTccdFTuf8u9vHpzOUxALCJVHVHQ7jQni8y9Gsulmbb+yxzz54gIUlLReFr9Z8x+PXx3NNYDn+YB35gYkjBZGjubLdUJe2Yx8J5sGY81GCW+txNsu/+w3I6v/gN2RH/zq5GSkaN3B9VvbFwPzcOHY8aJ13EEZGOWJej2PGf51FscwAPiyHsc5oaCraYTR9ZqAyCyFqMMRslKSTy07hMNqSazfTHPskrCbc2bD0cMZV83J28K4p7VPtcuvb8YUcuvSTuOb1wMjHP/6cUZGScibH584fMkQK7qFF4anwAstGkYR8nSuGH2Xx9g08i0oocpBOStdTgRUoBN1qlsMUZKNMX75EuPOVXSzuZ6ln4f7Syuvu5p2SE3kS7sSh37Hhv7D//4f/aP/+K96oE2Uy3/7a1eWbm8RQqFGFcKuQuJwFEHG1LIGCy1LRRnyyhLotWILi6demNkkCtlmpM5Jmryak5fI2Z9+2kU5Ul4XKxU6mvs+Y8EEEiohRooS5oI1skixI+DbQLVAVh9OlphMmtowEqWicgCCBsAX0VDgczWbB0Zr7pgZDqFimUNIy/L3H3WiU+KNK7gxcpSLiQ6IBYyXHBswZGd/tNLgJ05IxzhgMesU8AGfsigl5i1fCAduknq+G6SBiVXgJ4v+yp1Cb6rs3ZJPX5rzCNn5ZqN3fqadTvMZ8tofVL6Y6+hBO2zrSzvV7SubMTh69svF5UoqwPBKlbZLxUxsMLPJ5witKXoqVFDBGnk4xQbt7my5WVrHFW/IkmVhQOfk8y5JycCJpjBYqttyBlwMHLGZl7L85HzsugFzKXM2HEMta/17mwZujNSp4AN/pF8cGpf/+Orbv7/+21988eju+drNXP9i/ETDmUh8848vAygLD9unD6eGqV0oYU7AXtuutQ9nhERMZjFLCMogjCFi9fLSbGrhXJLFoOOT+cLFeQY3tJSk6dlYx9JchgrHxzrDUTbpGoa329wSc9zcU3IV/PWsfz6M/92P+n/4wea1rSvHv3jRetbZXF058L3NNf6//d/v/35c+a/fh0bGCO9rfbjYSazHR8M338wusR4hipUqraWC2h9t17MiGmsAz/OURyDDcWzeaz3qpU3RzeGITfCPulPbGt34Rsb2zuc+2Fkp47ty2rVDgRjAsDZMD/anCRVRAmm7zsaNhhc6PIX2TZKZO6ruU7lJtlKL8sIIkZFxUl1h5Ry3tuR5tumPNZwoh2QgNXN+b1EmQVZ2Ku9tJgBBggl5BTPShZDCq3SRnGjo/vz4erz5B5soMfjiUM8snJDmjbnru+N6nSQLTYRDcqvEwcByeq82caBwcPc4CgnizZt1TEY/+6pDH13QEb+0CqmXmvGGHDlOtDAhhjo/H9O2SS1nUSaLqRNoZtqeJ98uhRD7eom3OepFx2iuM5StPX4RFCkrZnh77uWzdCtIoMQoZUlseUeWeUwJRUuF3IiUYdKzLRXCIrs3dqVoKkahicbcTk4BQTBckDFDMhABUNz3GDjxcdjUfd1NI4So5eG5ZkZoBAQECSIOie0AoAgD8Szh2VwW/P8Jwu8g2/LEMMz7nZzjzfl2Tu+9fnHmTdyZzbvAAguAARABEqRFl1wyZVdZVbLlcij7D5XLKpRZsiiakuWCARhCIACCXGya3Z2d8Ca8HDp33+6bw7kn5+zvg2plMlR8f+6vrJBmgPMx0ONQTQEPI/4MYRmkulXMvfTwLLLlPM9hqoKwSWwnVpmCSdd3gxDCGKRctlSc9fQmS3IoHIFoOuvhv8xaNH+OZK9+lpZgDnIsng4Twh8urBywGRooL639bdoO2WyEloKWoWoRnqa21yB8R7lCHfZygZhUhjf51RWh0+EXL54jTlpDQFyNExMsgVsmY9MIikzxtWtCnDD2Mg6k6uYqBUWaP9LJwCQpik3Sn/1Q3XE8us69+U7h5z+ceQMfI/Hj54PWBkhR4eaNwvEkZXGAIR5FcoCnLVic6n6FW9mrk8Mrv14iIDyy/HCtSsA5lqTK3laZkPL5C3NqDGGMb9yRVgq1w7559PE4j6F2p+soyQcfHGyv8+++efvhz5fHYeGLp2Nj/CUrpcZ8DgfOyv3XmmQrcRfO2Hvy8Dl9nSJZVpSzCOD1LXmFLsUuxPuIi0S1+g0YBSEh8JsogUBgkZavrV71Zxs3W9furGRTPR4HNW7+UjdfGZD1xuvf+eVuODTjE+Sre9XSZj0G+GTsEpkbQVa9yyAJzEhcYCBuEJgYDnCqsScYlpvhEM/BURxcnXmDZ/7t3dbv/WanXOdeXilXl5ckAzk+b6YkDGE6yDwkTBl3DkgTZHxGLOGTAmSCFpdm2efB7Gts92/C4x2iHWFM6Loktsi9WOQxiKqE+mI8CvkCXeu2rnrR05dWq5w/O5j7PENJ/NPD5Z03G8cvDMv2ZRaaz3y2RqQZRKfBwwcOK+AUnTtGkEEwjyOfPtdaXfbCctU0DFLn1Sc+EgcFkkZ9VZ2PQZHs1Ku6ijoDpUqjt96/R1SJH/7pJxxqrq4xw3qd5YoyLIfxKDjUly/VWo0qAOQomiVykYCiApr5WYBlzuWQrgnjwagPCFPg2SvbJTmIiw1t6XRqKFshIZzoXV1a/Uljq3J+ZshM2tjDIThcnjlzLxAorbtO2y+DX/zpJbdLvHV7XemNUDhsb1AXvfzVJ/P4FfGr/3HFiCACgXaTAnfmVAg4XIYgj1Ea5QIfipA8j8wwiScIV64YE+Rq4q60arBt9s4NJyW2m+Rg7N+kY7nFtXhsbU12td7RfO5hUoL4CWGOr9w6hdzd3YeQyZ/9q4etFfn1TfpwLBxN0cI+5jDO7/6L1U//cl7osa17FUpKpnZ2qWfXd9EmlQSaD9Okywm9ZxrDpPV9xjgJikQZCaCoP77Zih2zLxQLULGEFqLFq1Ey/mL3PQYWucklsvLaW6hAGGcRRnCBFbZKTBFGlCxV4tw20jJLwQgFU3IIaI7lhMiN3NGPP1zSjfBaGWpz0mCoDQ3jm2/WyJ0Gl2v28Vzvqe5xWK/LO7dF7u1aNlZnn/R6s7TWYVzCXSmR0XSwOASVEkpVJXgaDsycKRZu7yFGT/RoDCIRvtQYjEyBpLEcSS6XZ+MMguDmWqGXwmWheO92M1DCk4NJutTRRqF4pw3gYPFoUXJdJvKpgSWykuGhhW5rSYCZZnFRTMRQUKteru10/Oj5353zHUZcox7+4ELSrdc3imQBj1NQYzBH9TkCjytdp81XQcTCqCwkROiOoWioWAKLQRkWZQzs6GmFlCQsNELN9ZI85jm2WOQyBIIyYGceCiWubWrjtNRkPTtCMq+M+GYUYwwdY6nnh3GcoP5MX95t77Zj75mNSNi5ojQ2qo6X1zABpmNnMdHKUAxz/JHOVlvz1AN+0KxhTpKFNOojUN0EuB0crnNbKKIxAj5xsIUb4hGSxLHuPXnodWME+laVX2cWA7WIQ7plFXaE2djSU1+6U8dZ/ou/Huyv4jiFw2wBerdyeHBZPdKyTZ7eJBcfJ+L62gKsbK9yEmAnK6vHP7qKc2Fr+05Qj1Zd8zj2F/OsipETazGJgnyJt7fpyys9iawUJ5yB67+CmD1+dbtIngRS7Hzz+vW/jbgvr5ZbQh6Mou/tYD89WlJd4trdchTNWisFLC/yKAQnvsiKUFAqcRLKydw1Gu4NNrdW5S1oqTresMfzfEskHr2cvP/114f9q5MPFggkCwxZkKVXP7+ovUUPjo7cGWB4fHgRXF1o60WCqWC//B/+DrYyzDpc5S5imFKWaDJfM9SXp6bjSuTc8lEML93YEGs42RTODbhQ40PFM1IIF0UOTyBATU1cJvAyU6tCAURRwQaOozQbcBZb7lsMlS/y18rzeRLN2Upq5kGF1OB3W8mM4yI8uzA8mXJTTkzT0B1EpgNSJA8zZ61SAT3zyZMlmtg7dFPi8sGpcfYyssz5/Xfae025/+Ti8Ymxco4AiRRoig0sDeU4LIEimBB4BEcRugRl8QqEkfYc6ocg3QBZKR7ZJYIHzLISuxBxKoGiqQ7TcjXH9b7tiKPz2IHsiWo67LVteasRfP5wTiSEjqHtzQJFIdoQbXUr7tLrH41uf22XAeTRTCk2S9OLCV9CzHBp62uVmuT4yyzIru2XxpcXEiaPL9Vb90rDz0/lSicMUO/KqG90zSS9tGDWUCsQw0jwwVhvnPYbN6TtvZtwsoCRcpDL+ovPPQVbf21XEcrp8tCOkIxuC2ZsMCWVdnE7kdG4z4YNmRJLUYaWIkVZsEyK6NtRyNPIoQ5zPK5c5XrPvn1zi6qhF489tigdDywCTWhX6LRRhqei5ay5QVekwrOD5ShzERJ/9MplN4vyNe4bPDybjd1XrzydaDQQ/XObHEblG4XTiZJgmeOFDB5ZWk4TsUUxSJDc5KBJszwD7lQ1ZElo7+O6izw7G7kF7rltnz11uiIeBFknXegN5uAiX4GwOM1NHByH0tNP5v98+/Ir/3n9jz+kZkMLLzeWaFm5OvzgF+N/8Z9c27jt/vhHE6rvSlJIrpUZIiuySBbCQpnF2htxZKV+RleLQIEKOQ9dC2iaHv6PbK3IarZQKMlpieiplj5QpfJKuxg4QI+oMu0iSDGe++ZGGwe5A6Nkr5d7gauYJiy0SRQsBp7QpkpBAAPfhTG0WN7qxKobDEf5SoXZLSAQj16dLUuGU3hD4Hm4WGE1D364DGr5svaDWUQjldUa2xTpXGUTDMMKSn2RuNDJSb7TSG/euH3TMv72MJo4w1tvs5e9aZnnQ9G/vlM4H5qGm00TrMHjwarkoTjje2SVPjc9dBQDLN94q6bQ/HRkikUUQeIccQLbkISkKkeOxB0/6yMkssOvtOrVR7q+ESfzgmAkoTvDqGDO3umYTYGHORjhB0LsDMI2CmesuB3AKkVaObIO0yPc0xHE99GZj7Mu8BPfT5IOGdG0HMd578xKyFgsoqgLx2oEx5pF0ykFhWZkmguBhuQSZYW+B/tlPodiBFFDIok5itLdGIIAGuQ4F4ZWL8CSOOnktuonhKWaZCEiPR+mIIZLEiIIpZSoRbHqI44JOltFy8pNhGM7hBvkfdMoYTgCgXChcDrIp3NIQMa6TXZZoSwYE6pCy6CO+sZEP86UcVjj6I1uqz/zjcsA4dG9tW4KAhiPER2vgSZRDPhWOolgyEjvbrdybI3jC9Zh4J4P5sbVmiCX36xeACBIVUFk2YshjQajxUgijYLMETUxylnDUX03Lm13VziOiKDh0cg0sEo5GGsoUy3XbySLSYgKuA37MpOW64pIU80N+qiXmiG50e3IVRZxoOZW+eDpUg3C3//Hr01j+H96ph2ZUbsusuqytsnqdjpyE3Jzp9GoPvirs9vbr9XuXT97dcwQPvN6PYh1M3Rbu4V2F1fO1P5oSVfqWGAEs5kgmouLo/Zt0Gy1Kaw8xwoXV4O14Unj7qa+wK9Gzq5cMFKGmFF37pR7h3mpKyVuVpYJT08qFclO8djGFJPTU5qnU9cCpLpkZK8hUmJx/uiRvt6hoMDHHD9R1fpukxBEbZHiVKjkcbXGTdWIcF1dtxo1YrlclooMThOaaaxdZ0sZo15pnz5YyAWw2qBubFUuLqHZxFoMHTXUW69L5izJQxKLcuDGaClchkCUSJ1gGZZAU3qeERIm1PstfN4H6yWQhtCz2fXfeBeAJTbVQakNWN7W+KzBLfKAZTOZXwGLOQwXehOw6MXu3K5IjD03AOrmID45WOBIrkxtBoJygpz7MYRmw+NluoJIMm7TydGB+0//09bB55rloGK75Hrx0YH767++O1wC59xhAwz3cSgk+BYVZukXD65wXCpsS4oOSylE5Ybug/29gmlOBy9Gcba0XZZmS/e+fhdAJf38YSGhOCTO+4pE0z6WEzICIxBJhhLijQx1qsMcrZXDqIwoXh7aUZqGBF/jR0dLQSYCCXdj+PjEyxLIc2JrFL/93bKnxs8GnsigthsOtQCnC93rcuzBdBxXKzQpVVZ5TtoKcaKHxsbaLvT4hy8mD7Ga2H7+PJh6WKGM82I0HyYQyhpoOozjNk1djfQM5lot2jUgK3BQFNpakXpEWKtyoWcuj3UThRwjkxixlGKmlcysgBVoexkLhFfBvD/90+Ht32l+5Su16hw/mRGmI6w2bxQS9M/+tfq9t+vNf7Khx3DqL1w6FFGEr0kljjPDWKTVq4MZU+DZBoUi+GCktn/7NgDUxh5wvEDOfOiyp/bDqT/Jx+n6Xt2e2RGNvfn6Wnru2pdTD4l7eeykUZYESwf2olDiJYpB4IgSeYInMZgkwHyaO+SVApYmkMsEJ6CDx+a9/QIpw0f67PmV0vAtCvJyHKE17d52GWAFkE2jNJ0OVKkM63OvvlHOUhf1so1N0cCiqwF2+DPvN7++2QxezS6utl+jlWHmWc7JS2UKYoxDQU7JEF4XGVdJjk+tiow19onBeVReoSmMUhO76bnWwaywLUxUHSmRpJArFvh8FtRX87qU5h5lXUVkSeyuUXbP447jb2yvpf8YOjk4+eTvBkWGK3ZKUY6iTrRa4NyFT6B4jjNUShJUFnhpZqWoYZVxONGDcIlBfAyTiRFYoQ3RValULfqJpl85dI5JKBn4CVMjDC8slEnTwtQwIYCf5ylNpzmBwjxeq/PjvuPO/DyHSBJBGRwW+iFB5mOpYPxkWm9UuSCnk4Rh4osYpEjaRTMdZ9w1ynWhExBUVuhXEMIXqjsJGfnZRWYg5U7bYcoQ/hwFS3OcmDGKOkxKP/zheH2jVCoW3YQwf65AMWh2BY1CLvWk4qeCyAiYf6W7RZp5cjgVW/XN/ero8XKw8Lspvf3+9RUSHurg//vRYLusWCdgO1HvivK3/9nrlsAWHl7YMnqiKsvZQuBorkgDKTw69/HQZ8K+QQh3d/iEExIXQgROqtcmJ5c7NITz6OJS41dEhijDkUdyaaaqN1uwoqOTc6d5s+ko0Fq1dTRwzjy1033jduz/u4vwaepbVxmGR6EKadXCQ2N+u8GMz5dwt8htr33553/XWm9V364++NmXTVU7rBFvfv/2j//gP3zn7XpP16YR4Bpu5YYsBIvPHh94Gbibud97G1QY8PlI+U/vsOW7m/+rv1SMaPzCmgnff9P7+PjVhXbtOqstvII1ojcaBQpOETAxUAnSLpexXOS716uElaQRsNmIS1FOoFFOtLQZP4/eatIahuimbyJZ2MS1S3WTx2Z0ZqJU7MKkCUo8mbkuCjJeJFUl8Zf6zl79i48vL3oGVeA3rtXfrQmjvtkbz+NsGvm+G2a2GVUaqURAm41k4mPeEFSr1SUaTxybJ+uslZXJajCIu2X0MoilOYRv74BqCTx4QTAkgDAQPcM8Ewi7IECaG8MUKk2gHjTXVaBZoyQdqQsXkWuVxMZqTfjqy6iwKTEuZA9zjsUmcxN2/BCKnz4eCXRlt9NQL2bEO+ui5B2XyJkfdVrY0d+dNr+z8dlJb7MtRQZg4PT0bPLO9Up/FggF/PBoybP0u28UwhVCmdqbdCsnFouQ2OqQD//tq/Gp1nm3sHubouiGMZhNVKdeHBUYpIh5CYYKOS3I7cSYEnrgLuFYN67MfCXPUKmxGAFepOIglHAPkRldGdYFjismUjV11eCp5jbfbIOHQ8WM12+XIMw5OLwsdwpMos9ZgWMLWs9JVmlpTTYnpvvZkX1wtvXVHU/Tzp6ek11Cv3RPjqHb9dIKzfSDiNtDkzQPl34Ok1mERQJUr0oyS86PBimTPuvZRGqjqYszkTFxYdSNLNfxotpqIXQCdRk07zZiVdvskKeHHiTkcmZouEy7eeubWy9OhtHo8jka7d290aKk5dytVci3y/pgMYDpJppphSbd4bESIQVa6nAUW6Vdl8gyNvA8MRpHKdx6W84+mH5pBoUILyCkp1uX/UhedzIuvrEnF+9K0cybYqj/8qA31OPYpnebqUh0cPTsMqmst+AaTxvp+JXLVDkPBWc9r+4YNQTSoyjxfb7EZSxmDdVhz7khxwFRYtvFOEpWEQS+Xe+fLNEyNBwuczjhS1iZzCgUSUMXSSxOrHhfvgyQ6CLP1zZvi6v1HCAn817z2rX5yacP+7MrzHs+i4WvVfgXS7pM9B/nrS3seO5XiOzaXQ5rlsRphMq8lYA4iheTRePtTl8gYduoXy8qkF1H4x6s+TuN5eXhO1oI1tdefnn5CZmXXivcQHAdxtMkmpno6lpZ2KbsE/O5GlaEWFY0NiVHCVEXslgL8jLthA6FQEmJDVIfGuujoys9wdaLlEQlFCVQfoo6UeDGLIOZSwSJUK3JltfpaptNlMiOwsIam3px5rtkisBweOWmycQVcA6XcMTJYICSJI6a8xyiA58NKMSnKFCo8mqc2kGG4nmueAUBJnACi2M7RMwQWek2IMPFCxw5x0IkpRC0ixIRQgU59OWVwnI+v4ouJMyx05sdmUkM5WjsFdxr6+3qDXLhcrMLL+5b5RKPYERGZyGbVdZ5vQ/23lrFGmU8zbGxIRgAllgwzR8+WY5g+9ZWUZYYsUR211dRCXdF4+RnP71365bHNKji2v61av/o4upVb/JqmDsRTGNMTO++xvJwbtjLZcBGtkUxePlG4TlkbtwsKM/UBErKZUrIYp5kKQNVFkZ9rTXKKblDcoYepHCchDbCXqX54UlAof7Vo8sGRcUpxFaRua2Eif3oMo+FzsZm+cWXB+bA/4//87WnrxY//+n5/+Wf7V6MLOtgrjg+TTaBHzcbzOBoEpKjA0WLHfemBBsG4DOglJEltNm/fs/E6yt3w+L56GVvuK76VEGmlQIjCHUGJkM3GitonSfJkrzCZE5GY6lyoqnn+lZZJmUcDghIKpiwkFnpWqkVKAGS+4iNciV49OJAxB2BxRKIAgmGoLCAMhSNxoFumzqB4nMXvX2/8KO/fMkIzM23W+cDZHI1mfxUvdsu81he2RDnlj0dwDc3BF8Jzbn98Z8eVpoE1kFSjIRCz5tl20gmGTlW4gioRKSudhoLqSqgHCgVwTwG50vo+9dzoBs/ne5vcQAiwCefBrOXy++xDFhiKDADnOGyEQ4LNPvm++3FSf7007Hvo2WOw2HktbsVZ+66RkgRXJRFax3i+oY8H/koSV+rlr/4/PNV3EqnQ8iHETsU2FScTgJ9wd9uuAsk8AKkCHDdoCjkjXuysgQZox0f9sZ9ki4Ue+N4ZJhmja1I5d/4373tAyaO1exKH45U4falCugIS12mAmWOmOETxShAOY1FXBILTOw2Bb5OORptmuDCj2CcoqAUnNtYkc7cJEMS58VxAQYwjDP9y+HzfuftVaRFDJfjzF3ulFthQDTQyIMimYH0ZWSGbqONhzVo9mn/0Q+de01sFbgymf/wubba2nljtzR6Mat08tRb2DoWITTDSW6Sl8qC7sKmZXJI4OGJQAOaRM0FjHNsjpB5BIMsQQIIB5BhBhACOzPbUDXgeAD2tQCwGLR/LT/6sVu5TcirXLoPjV9oU92lStmNTv35k6DezBFG695jlieGzANr7thQyYBLgkR5/jzzbAGK7QyqtYuzz+etLgIU62aVm7wKBhGr9KyaFDm2XayihZ0SIFkVhWez6dA29lps5pEcij+61MeStLNaQUfk8KVmBuz1LssL2MgkMIhT9cs8nb3UM1Zg0DjHbaEpl9a/VpJyG1AZoiceBiwFLvDtDh8MWbtEwYYTNSiHpHHiTgN4hJlELkJxb7zBgkKSv/rsbz4OCfzNd65F5TYLNozuijI7akn5cpYxY3V56cQAFVq0acZYgjVXJU/IZJxOc6+MR4QTYkLWB/7ofFEV4blmldqsPTBTIkezpADFVhL0DhRvfq6ZyWtd4mxo8Agmo7Pnh2FTJK5m4iw0kBRZq8ULQ/cCLSQFIsPzLAzZBEb9wEYsGl32l+ZIwV5eaepEXOumjm2mdOCGquE2amwOY1TEtUqCAJFXXjB+EKTTqL5KnRqqMs/ZEoNSVBgljgMVSTIXM3Ue5FDGESDyIwAS1Cci1jdDLy/CUXW72R/NaC9Nb7UYBiX9lNwWphDGW+M8hrxlRO63ZRKENE+FBJQCrCmoU4AjiGZp2ijDPGNBRas3eCdFnv7ivPr96/B3vNkj9eLDYa1efImS73UqpGahNDowPXvp8iTDeGFLRJ0yRy+mB4cR7ePFvRZZglUCx1sMG+LbaHZy5OF4+NzJtOnClqh4f4ffKTfB6iW4On3icAy7fq2DjmJDVRm0JNMsgneX5nKIphGFy2EMDWL2df6J5oZZkrKVSJniLGXGsTZBSmyDqbAoV63ThBOhHZE9LRfsVk35b/689HtfT3imkc4xiu1NVaKInAWWMo3fRP3+HGE2xPHHV8Tx5be/uk/3Ve/h5dpO4XSqsC726P/3SWGldvnFnCmU5q+sQAdyaCcgG9uYYeYraBbk4ALdh7Zu/1KpKI9SHiabeGnTXVYrubsQNgE5OE4lkXvjvYIkh4abPA7Fkp9sBiwl09CblO94fk+17YAlEy+s7TGcJyARnzPAJUuxtjQMS4lJiCTZCGEGRJXsEHAYi76DkyDyYaEhp2qSJVEIuL03toefnZF7tVq7WWCI89OZ4iUwAavD+dY6+9nPX5EAF+T15hazf2NjGLIP9LRTQ/CJQ4XTLCzSq90UbVyce0OEvV/10/Fl3tqEbBmcfgL2MyCfDgA/XLPXOxtV8AmYHRCCTwq0nN+H4EzMXQw6r5ailxfQi49Ox6dnBIbtvVMT24i1DBIIdhMIj9xWh6frDWV0GUAozyUzfwJTN7tsyUhBi+TM6aRzuwSuhvdXqLgmACfgtGgFFtSY6hFZxTQZDo0m45cmaWb4r9/b8GBGni1vbYhSuVZFPOfi+R/8xLhXJ5pJWLhWekTvfQvAK/AiiS03QXIjARVZzxPS9QwVhlNWy7LR5IUx47fX7cs+zZP4yrXiMNHRGGZiNhNrF2d2bXeNG07ODuzKvS2hIiZX82cX+c3vvmahxKuJ02jQuZkUsJQLwUCxS9sCs0fBWWV84v/k0fjGnfbV+SDqq/fe38IXr4oSZPVtCMBtshKVqznVpN2jWRQIcThQDSRD4hQLoJxAye0ut3S0pYswFIYjNCFAWYIKqyUMIhTT2r7bnI8dhkEYAWcI6vyh/9brbTe58JCE44XmLfHxFcvZrs/JUqMyCaF799ZG58u5QYmAk0lRxkkKZlk06HkQDnNcibMmc90gm2stMFZgFj16PLA1cWu9uftmbLog2t2iKq51eNUPZxPG1pd+tUzYEpHbeF9gLZJwXNqIiIoGoZUGAtAJRx1OU2cawizDkVjFR5s3mKmdYXZIwUNQaUOK7QEItYPYia0oI0y3cDKCXc/3bVis8W00qBeMw4P2+Cy3GAKFGQTVPu6NwMGNd751/zd27LMfnT4N8Zs2AL3Xt6gffeEGHGbDQMzjt24JowBmRdITCyWaxbNMptLIgawmm2mzBzDEYNn9vdZiqXtleTg+2lxtwwhjPh+UK3h8PNwqF9A7WSI15aaLhEuyRvfGvjRkxVj5RIHLWIzAiUqFqAIte2q9ywuAiUBgQxAHp5iisREcqEF+vECBoZERdl1uN2gGQ5itYh5gYYzYOZBAwma4NUmRIn9tZcNJJpPBTFks0X0eZwly5mfhAmUJNgvSFNBYFq3ypp5xQQRyKM0StIbkeIJ/9Vvl08+mw9jvoekahksJdvzKQwPUNTFaIpQljhoht191JcLwaNEMjFnK3Sp5OBiopix5V4fD+3drk8dWNLVPzqYJjhAxan16HlTpJg0tIzA9D1oN2lzAOkuFXu77oFGi49DU51kFk6evZnJiQzkrt7ADU73+xu74SwMz81aZmp4sUCRjImyxNKowhRcZTM4unj4IzOcdkd2uNZceEhe4Vbo7n0IcWu2/IGq7omfl3jzlWE/O8XZHmPTdDONSSEI4G4+LYhNcXiYRX0yLhVcX1koNvTgdA8Dur4l/8eGzd769gldLV0eXNLOc9x1cmS3VjI2CUjpFVCeN3WKcdWOt92jWaBfgGPqTP3o1Cv3v/mb5xZldE0pnsXO7IMcY5an2VX+yUU0mp7QByXLIVRnMc8Zhg/ec8nqKOo9nuJG9/45ELxvPfu61T8LgCFmRihWm8PSV84GX7bwjcwjyXgGKR9b1Oj+9DH0CXWng/KaYA1ud65kxHRewEGQ2F68XLDha4rwPW1OJjGmMs+IwEjAEhwkii6NIWcYYiy0NUJMYtTe7fDjZWBfOEPTRudJsgRrFYanmRmG3K//gZ+HKXv23//nrP/r3R8PZvF6Az5yksop/pZosXi2BbvoiijYaGoNoSl7oykHqX+B4eUVWeT7Ns9Mi8c71dRNwM4Da28wS7BF5IP5DGeC1oodn2lJLTbJgnx5OWH85OQ83bxZW3q49eTgen/XtOWIv8kaDiaZhnGd9dOEnDvDicDxRZ/bRJN+dTE4mAxLJgJycf+GceGBfTCIbAomMtVvY4pwgJRKqXmv4uTUbH5lVCt+4s/751LqcQgdDo1XH7Jmpj6Onz06JhvD1rdaNe5JyNqtTzY1MD1JkMDTlUgFBPbIWI96MiKJIc7lOhVyp3gj64ecX9S51cTx7t7H9wRET5qlUZo1FLiEMjCGLJTvWcgEXpiOTQ5DzxXijIX/7bfrocDyP0xBjQ58ATn45idoVtLojvZhYZSLGKSBez/RHLFaWaQR7/XWxKpGwgYzM2TwmhJtrxdYbC0GCU8EfqeZ5aA41SRScnJY4TohNEaSTQ2VhOu1rrB9jOcgdJ/NmanmNpOj0ublETL6yzoWThEyClaIkd2hFhQOYSYvodJZWMJQtY47h39uwv5yMXh3OvvONbu90WqGSMA0YWT7u+ZuN2XSYDOdhd03UUjQlGJKmfTs//KGyssehcrJbJ9FYdQ3tYOLIlWQ0sSk9vH+zzhHEpwMUyILKryreEDwzBYpZaxWkAalNPeqGEJKIRcqKQ9EMKqG5NTVfaRFXIN0s2r2Wn/xgXFyTa9fLIFVPv7wkGUxVbLFV8MLEjGGU4Xna++xvrqQNNFACkIZoXLFQAZzoxVWyquS/ePUBC5AO3exsyTlTnUE+RezA0XFuxdfbVGC7hgl3GhxAkwIaRhFiLxwrCiPg1a+Rp4eTmzeqzz8bj3LM8OO7v9XSv8ztzy42qvDZDKFjVpTLgA/EVRQS4PKtzuBiJoEYqjAZk1qhXwB+MUiS6SRF8pmDACxaahkk+7kfwV46WCbNCpbbbpgT125VDJi/PJNXS1kKRBYB2YiAkIypYUvHM+lMQJmSzWhGhkuUtN4lNVdXAGEi5QoNyvlgAHAAHJgAObBNCIvDBou7qoPTeI4AlCgQ09lsPCAuMAifWlsU7Qx1/Ct7uZbXEk/PEENNJYL0cyhLMSZNii7AFrmwXZpYgBkM8PMw3sAkAv70hWpEAbMteYe6vNbptGFt6XNLPyzTUx9ehTMIS5aDPrVFEjiZw3mp5j2ep/ge//wKYu414zBgWUILYtxGFZcgRCLFskGOJTRstYvmgTqLJK6IjBdF94eunZ/sv32vIrsx3lpn8EOV6k2GrjqF2hR/vX708EWdhRC+1uD0uUIMFpH+/PLWVzZkinDmjp6CPI9mriO1SpVyxZlpUploscXPPxy9u36tfHmuPX1w93u3nvztx/u/tf2FtqwSGYenWoLaCLicuvwK2d4tfPjBI7JQy5AwNBdPfHfjRo2KPQpKzg+XaIg3QDxy3ZMDnWXJ6SzK8aIXJijJji2PrNR4Ic4fTportRgNvFSvIoUfn5/BK1XahLYS4o/chKkn7/1q9WAYKg+DqzRur6elIvhQD756t05dQXJOfbLI14u+pht1mbTMIGGQSqaZ82RMQYgdyD5CoQjlxyD2rheSgIOcBKWrpDEObRUUiYyvN03DmY2Wt3awWpeejdUkimI0qW2W+oO55xjrzdK/+YMvv/+P7nz1unTSsy+DDMMz4/iEjO0uj4nVQI1kvyMJhUmnXzazdXbhUJX6JEzX/FD3VbbFHwAGAZ4ENpc58Qpg3plR//IVUXI0nl+hJ+JqJZ4OZEC4AIkI+uzREqaLR0bcLNJ5ipK1AtSB6qJrLwy0wkMZlY0Ch2ASMV69RcW5Vy4wSIIAayi5OoG1td45Y5IJIIAzejDrf3XtLdCLk1y9KODiioAE6s8eTPoTpSbhv/5mB6LFXzwKeFZ87TtdG2Ydofb0cB4fLdXQJaVJLAf1xm61GT4/8Ao4JQ0hDmiGdg6Q5WKD1n55Zf3xorZ1BZ7F5fcG38w3huW9larxasmxu+Lzk1mdg2KatFOLkeKa7B+XG7qI9T45qPI1fbXUaFfFbOzzHEnDnc1rE33QN53q3t1m6dKcHGMlWejl3llcmqLJSL2c5q989tu398RaMYhXRqoJBr2Fhm60mSs3K63Ix0dXMMzOlhMNQVbWeCzlCi1yPJ8bmldqIQhOMAWextGNG3Lk2v0ntsQSKBX/4uG03SnmGEbSvG34dFZgGCClyJFvuHq22giHE2xwdLlDxj/9zCjfEqtlxJ9qesplLVkx1HsloA3nq0ImqJOzAzQtsmU/LlWJKz3Prb5pAYqNMd//yh1WeZVQMCWxMN0SUr46XTI0Xtvk+bpQeHEBal2k8DpxJaTnTq6n+UnK/mYh4Zx+uUlAaR6FCAkD5QSVGo3BY711QwoljK4VJShaWSfERJj2c1TAUhNZOinFlyUx5LtM9TUZApJwjF0tPDaF5XL1Hu9fqeFzK6oYsZBNoEqhhm9+7b23/92HP2bpXB0kcwe1mqK0nNGQqc2DG5VkWGIIXbVcN2Uo9tHyja2O2sHNXzydfvjLr/9a9+njg/KCQ5EYICXrcrxWLtlimD5+UvqNN9FbK+SzpQihiJcYYy2CwML1kCBZ8KlrR0QFQISaGdI0Dmm6UcmVLOBPkejeVjtKLBVmO2hizP08txEP44MMwrHTSpaSpiYTdqBsrQttwBMIO50uxXq9WAgO+kPYnBdXeHmrmEaZZ/jSGo24UDAKOAC5OAMBAKAEBWZYxtDB2KZEEligKpEvHATRPCZzXp4ub+zWlsNwOUkEWqZlfjbXgYZQJNSoIJHlxFm2240PFnOeRikM1GHc1SyoJvjqvKfiEEuAmLGv8nqlvNaV4DvVdDYcHo1DBGAV4lxJOFz2pjFC5jgaD0ZxESEmPsWVWJhM2NADgkMS3LlL+36clvlrEkEswiiKXFBd+SffKtbXQQ7USE97IeVNZdQsksiDB4e46IgwHGKsKBuKEUUcZYfxEkcqO9x8EMSxWW1jIa7jbKhngY1SaBFMhoYbWpniXB73G130h386/8/u58+dVP2i38WQZW9cyDE0951hvlmDTZg6PNNfLcJf+wYaJ8nLi9n91ymQBB/8Qg1irMiA/Y3SZO4dHsxgxK+0WpbBjfs2IaNSIY+9BCOzQLXurmPQyXFdJFVYV7SU7hS9SD41qJZM3He1Ud+ZE53NG6X8FF7bK/7kJ5dffUs4n+p5zlJUdV3e4EoRL6XoereAu8oPHkUcCIAfBhCNsCJM0SyN+FDo+xIPXwzOV8tNNyIsLeYAcPPQNizGgCti0h/lRzO8Vi9enI1rhDUZB1yZF2Tq6lT7td/ezwLl3/0PH3eq5N61GoHGCJGKkkDPc4wLdScBgCbxEu5zNq5q05HszFyC7muYz4WFStYU+BxA4xz23dHyAoED3yQBs9mk7nV4SD4DZDVHovU7YBSyVHel7qK6efipcvzEvfMtanppJDQVMGSEE0sbl6YRQYdJ6LgOSEA2USJumkMe3DtxdzqckTmE6FRaYgK7kACPlTM7noKbsPcFsDgSIVI7nCXR/Nb91ZvT5jjSH76YwijcJNjSanJ8sRgvVCgNMCZYF9jtX32HxnYhQJzOH9hDRBLK6hloEH7/VVihbSsyiyazuc6C/XVwlq/9PRm8ThYhSH7wKkkbIh5S+WBdcsIsl+q+ZeYsRyxCORklZAbPncLNX39dBPHDk7MsNd0oKkYUO/B0l1pprvhBdPL50LzUbmyT1AQUWRJkuDozUlf6+vu72Aw5+MNTvUkWvw+tvs4/vIIBjfvnfgLgAIk5YDXYULXCMIKkCvPhh71KI2UxHAY4gqKhgwAYHg7c2zcYqgq/PAkkgVq/L9lmFvpAgIA3sjtrwLciOFh2WBdOM0pkhTIytbNb1+vyGZwWxT4M2zLZ17LVzSiZ2WRalyk6HhiQkNQLSfFWDULC53992rPdb3+lsHw5FEW42WYODh1tHJZvkAvVqPuuPckzscTt1h6cabdqdcB7cwbWoskSEmcJHFNEMHIPtPFq3PvKjSoiMc9OQpZjOnBWrdWuNLV3ZiRCSGLoaBa091YYRuL72tXJSO+nO/sr5a+2+D3oyScX8Ifj3L0yo+LO7WLuY0YAS+VWI5qtVnLFyxnaX8zGA46UM1IdxBtvsckoUU2sw2VGQiUEnRNgFgS2OeJweNiPb92sfPwvD5ooKrYK19fEZ//qVeuNOrjRmX/qtaUCSXNJgMNx2L1WC4z86b9+Ity6SVYLSZDiRIzWxNgNYg7y1AjCvGKen/7i1DCh796v0gkQYJTC0QTN+VIhd6PlFzO4RPoUcWe3rCu5ceYxVcbHctj17NCCadZDIJW0aLwkSwHhR5O5VS7CQhPTDU+50MQCjoKUgyB3FHI8JawWXMOFkDhDcpBnqJGSiYPVUJkqsn1lmhPZyrXCmRZXEUBVWB2m6mXk6PE5dq9qhhj8YpLh9HmZv/zhAdstiytdXYOXMDDGFws7hqczn0eoaH4xyutV3mGQNi2V24Wwd+GBqnqDblUIrviW+qWyB9mPU+J+g5+cHu5+/fbo6Ippl3wRWgvpaJpXuRrIPUxIhws3BTEb02XYzUz/8vJifb36ZgNppvrJ6BCBdSgv6VyhyKX0JFRpuAvPBn2t0C4QDDh6PGZ9awB3GOeq+fbNiTJSR3xBkmIBeXbS96NULBVjqgnj7gpm6mHu35CfmQhK8zLDfXyOFr/yzsVPfvrb/+T2f/9XL3ZvyKeXmbi0sXLiT3VFCcolaXKuU1j0/AfztXuNRpso1Fh/pi/iSE35+eMhIgsru8I8JLQEjqW8WK0a2hRBoUqtrEwQqV6+eOLzfMTWyqOsjLbzy4dm4/6+rqhbmd1eI54vFjea5EsbtAhBqNLa7PT1dtXX5iYUPBpnWwSbBlittoUA5M1fef/yi399kKiEhm3nBpQAAKUJJkgFTCcYT+YMXCiQ0tBGejMQojCe+wuFgXMgsdZUURiaLZVYslgz1eFBP3zz9crTx+YP/uTsxtfrK1Xwy6Poo1Mjph1JDElNFyE2rvm/cON/+HWycxv6W8cWUHKqwVQi3rNyIdDsjZZcKibh4HKaRSCC6+i9G6u7sQewcg5KEGT8eDleLYYFqA0AaouRNurNPznEXu++9/t8wFx47pKvMhgWeDM/pROW0BNXySEOUJlr50iMliUWi00MABjGriZlQgDkFz3QbYyvlNZXKeOoD75TsMkBixWKOIx46HjCrmygIxN+/HR453udcoObHE+1pWYhMhQR1251yh3KIaRNPoGcxR9Z9O8VuI1yJQ+OLwDqw2CuBVEpmasRWXQffvT8/YkAoDp4XQX/m28C40fgIX9Qvv+JOXnr3bZy4nMlAmJg3LNBKRhnlkvUq2vk6Msvbnz13tOBmcyfX3nJV98prEHVyeXsA9NZp0zrJ17DuZRR6EaFdFRUzEWw9IDhhwOp3cJ6GPcJDQkSh1qXf/xnxL0m8s5XbpGuUZUD3czbTWkwMMmYb2wRly971sQulPlWNc0imK/gM1MLjKDCM/VulRKAsfRKRJVCIns6jjwcoLgJYFKiWb4EmZA9TwjeH0c0HetFiVo64U9eRiub1acT5d4bmyfTmN0va5h1+x154SX1EiniHPVat/8Trf8sNE71VqVWwtG5b9opX+Ux+nYJ+cODRnuFyHwyz7iyyMj0AEcMKy5hHKY7FFxaK0CB4pfN4SVXhtM5h0IR60mWr89iGSF5gj5TbJ7O6Czs3qjrgyQH+tK2fMeNFI91DRLJsBqfx7meDF78YFD4ZX+IlDr7NHazQ4lr0MR0ddSx5sXl/HiSFIUZst0uCm8XheST2fnd5utr3xx8/NmHbM53N8uzCEYVO8GDMk2jnqFTeJLS2cfKpHGt/ftvjP7dj+jfoIWyVFgXP/+L8c7/+bfN3seETvuRwKDq/JWN3RbKX9/lzfHy4cUpH+7fEsZ5gtqZpYcZDcen6rm50M9nwira/Dr9ACGFGKGKhWohmblE0bSUK913kxIWHqp+hYLLbFG4VXNNJw88YbVdHA/NNMNVHSKwaQbNruw0yScXU88h0S2yWCBtL6HpJFEyhsDMBYzpZiCDnMRygoSREIUJNPN8GkI9ze+GDLcqHj6fCU2RRzBnGq2UkasnFwKKhUaOeCEGUm2ZWFuuSkdthCXtBWK6w+VYZlmyWSzb+vFL3TdCQAn/4l/csuPy0y9OgOIDchaHMNeSI5Bf/jLm0OV3usLV8aJbLxgYPwoqHQevERU7jhCddBNYJJgCFmosB1HJPPNJKdnJg2Lqza2YfY1fX0koxr2caVoYRzstHg9ucejwjCsQsmV6Ms9xJe34o6fnp0leoK5VSsJeILeq5Vbeu3TrLAbSAPPROhn1r5BGkyBsQ7WzOcnOlsbEpKnVhHFTUYL0/tXa3sonNn7hZDKFanOzTuR2Dk7t3LESvkVIMvnsZM569u7dcnu/ahv+Ud8sMqjmuUWEoGSuuSXORppPInlEUXgWz00OAyQLu3OXwMrDKYrUa8eQe/OG/PEj/51CLVpkgsAc9idz198oNaILNR6Cd9d4kJf+k9/+9l999sOXr3rdDTKdX1LZMkPJwCw9uVSqt2orFHv5OL6/6lOYQxG4ZSM2yZMsgvJw6OU5Qk8VKhEYCHC5p7WqeBDzBMhSQiZbiQN8F3hayiA6zLHCbK5lhvO1d+lP/+LVyctspV2+c6dkzL00cjEtxhMo1qIR6VMbFfY21weHySsfNRpbeYLaq3FEpGrQbHhj25uNELpS6FbjFopGy5P8VS+jk0+WOX2HrVabcRJP1ReaTTmzDHAI3yrMF4mDkb/+lW9+/sXxajlHsCBD8quJiWCYVCYyGDfUFF/GgRYJBVTp+d0yf+MOA/yo26yrL/y80a4ysaHpo+HCYLHTfXVdIC0NWwzCnTvc6dCJU5Nx5s//rYcQnd23GKeS7d+S5j0yTaBHH08ZTDUymMPBjdvxJ48QCUI7XRL2RvXd5vPPg1KtrAUr4fywLTrJUdWwu8VZC/ymCP64+eHo1aPvXivVOkdDuoaJAsuzqE/XYgxKru+S4TDfhMIXC9s/uzRcr3mt/G6r6kyTgxNbxKkbotmiqOVrhd3uG08Mz1tc8foZyLI8NCGGE14jTYp51Nd00Tp69uXmfZkfE6rj/j8/H/ze727UN9LhE6dK+SIa5oprvLA2OCqpcXBNMif+8NItJzmDkStbJBKnbTE7PpyNRuCdb65CqO71cNj1UzQLkrzeYGd6ZBg8TuN64K6WSUfB9DCoSMxy7m1v4/bzwDgx2lU89UJVi27uVIZjC6ZhE817//bioOd+/9stbxwTdW6nSE5mi/deY2Msg57MqCjLsvTzz+ZSGTFMpMPABBbZS5dAk/k0I9dYN2P2SlgWZdHzJzpVFcTytVJYMT0OMpc22hKQNAjSPHSRhHICKE0IHMUjmJc5baJ6hI1W6b5FrjZ5NAbvv1lJJBZW4ADP477vnL1wZ0Ykt6LiKth6+w0Asvnp1XI8CR7XW4W3q+LYg1ts47jvF8QkRyE5ZHwWZ+DMm6tcITE1uFUEa3X04Q+//PY/2rrMMDyinFS7/Z7w5JD0Iom6e0N7ONviCeQYOA5cW2BBHFFrdLcoeIfg+IVauU4JDlqjUtVG47VqE5fGuHQ16Kmf+K1tO66VNHvyy57FQ7xqW1xBqt4oV4okiULDp8ooGFZqCApjYaM0xLD1nR3GNrIowQGcQo6qWHmCFWXfcn14TBFinlBJQOVskYQSslgnsIhQFJeviESRDJ0UgSE05bksg+bnVng23b4mwyIKtDm/woVVOfNMJwFRPF/7nZXxM4W68Bd5UqLLHTtucryuuq5m1FcK/RkcLt1stVp4b8PtL10T+rtfHPNBz2pWo0At2RR5XzQ6Asdz1L4ovcL4nBlCOg7jeYJXdmqDQ7PwevH5T493viKGJ3ElyREfhp3kapnCUb4qkogH3DRTMhohqIf9tI76uGt2ZAkiZm41nypeYiYHp0eLNIyMkFo44lv39r9Jt67VKrrx8TnMd2vwZcx6pMexcQahlsemeOQkaESjObFoyjZmiq6wKrqL06twlTsz/O1txh2esCX/4mqKY+h0olFQfHA1aTWRxM8GS4oqhhxThILcgeHMdJZDXbFxpixAHOIoakJAMA1fGEG5K3fa4umpUZXRmARwFkM0ltMJDNBaRE9MnyX5NmTkk+X33mstfnL82ib5hRLmBuC7O09eTtfWwIPjw3/GbX71/usHmBjos1QkrbExiRdgEyMQEjU/+6//5K9u2idF4IKjEah2xSHMrJOYSOlxwra65hUEm+ZMg6psZucApDFBEF5gIylYKl6pmftuBsKQzFNj4uE09vDxYXVbqr3Btv3MD72Dp1cEgKgytgwjsVSqlZHtr/FHFCIff3bwU/bFgNPDJZvrX3mD8WErhkiDorAi2cCzIsb4wfSLq+dAUTgWSFFys3UzrhTOgZiGY56KnMrm6+scyAHY3wPzH1797BXcMms7/NVk7k5MoYSkOOE67nKesoivY2TG4CUToRtEbug+4+aQN7laeOTGPJr/8t/zN+8zf/NXT35n7bOf337vENy8hdnC7p7qTI2x92gRf/U37+3tclPFrYv1z169NJzwcujkhhFTQpSEKylKlVda3NJf4usws4zZXA3FBI49pNEoq7oC8RwvpZmrG/LlM4denxPmt45Oi/HzX737rjVFJrwHhIZAGrrnB0DH7EMTu83hT4/1J0enLo6SsvTGjY25fjE8UDkC3n+jWohn+SIAvsJv/doTkGhsXL84KnCn+QUBSzDwdoRpZPlhNIz3v7b8b7e4/ufn7/eShb38wz/8z25fx//iv5+Wtrap4KmdJTt3qicLxUMIM3D0R8tqg17f53RThVEkQhjI954/XACM+d4/vQ4s45c/P2bInBEqDCAlYACikDop1iImnosuE4lij/y5mwdlgs9JP4Dt+7/answX33pv++rzgYxnIR2fP9Pu/8r2GVVx8P73f3+/wGnVGZAYAe0wvV6/wQqZj3z6sVe81UURyAictUJpY60wmExVP6Fp6uwy2f3eTQNrnebLm9kyjtL6ndvUheJptJcRqFDDxZLvuWPdbG7gekDiYAnIjCrkqZRg3ry9twWV2Uf/n2eZSZMkEWQOgWT8PpVlAjr07CydKGZXokvXWohcO76KtcUZokGEoK/uffUwv0CGcSA6+JK6v3f78f4mFo6zDtM3wrU40XOs3czM2QILuStPoEtom6Regsy8viX94HTn7t7Pj+0KSaA4ZknAqlO2JFc9WX/xOH4U43v87Bc9mufbbBly4rkWGxm6mrMoKy8ijKrDG01y4zIa9nTWio9CS5BxOM5pDvaaZEQSw7mtP5tL32zu/uamSuYnj4F0akmIKeYUKRhR4qYGiGCE2i7jK6T2XF30Q9L2yRIotMUBEi2ms1hns8hHmTISB2aeCRiEEYjj5lCaokwa+gbYfk2+nLoj2ytuC70nudEP2y1UT4n1FeKDn/e7WA9kECwgZZ5ngtw+t+ciS3VI732erCHXjoF96vf6S8Phbl0vE7T05aPPzh/1Myu//pXNvaL8NAzcBUBh3wsKPcSf2LFUJR0PWija1nb9Ry+vtvKM2pW8LEUt2+Vjg4CGFSehsgbCylIy1A3gJ6uCNF+I1kz1sxmerkE5rbFCeZNKEqxUQL++df106OZLyFIdnEKWHqT+dHx5pe2+WcuXQaDBxSqlLGy+gQSpn0BZgEZSVR6+mK3IvrF0ddeGcCjIrA4LXmWBHRgLCyrLuAycEIs0M/AYZO0rKzJACiTk2Kk5XWS6Xiui0zh4fjyY685Oo2g7cziPKQiuFvjJi1lR8a+1QaYaLTdjmRTmqDgnvdTHg4yiobmiVRrViwFP4TVLj263xeHckvalnTX54ZHz+r3daBtiC9kbMvbRf3jQud2RiyWoKhuWX63Y3pODg8emtM97Krcjnm0XMqBpgMfzMII6HFZi/FniVYXApTEWBFHERUtlGEJ5TCHQYuiwLSJHUhdEuywznmhsqOd+bGpueYWyQlwf6YwsqbZDc+hWtaQcT8QI5RiWlKWRSaszxxG4l2xh9x91NmR68oPRIGk333/78PJ5TYxiVBtfRDWCN5J0lipEnb11fbMPMXWocg6A64NQ9+7X1wFwNnIl9c9fvfhw3HO++xreveMMh7Pe1YzgTT7PjamnWSxMRanNhBCiW0m5nBljLClYS3U+12cFOHXE5K//Zry6s7qMi4dPtbJFHX0BXd/rt1cOgFpKPebgzPq191e/2RDaRP4H/+N5vUJdXxk+eTT85jvdie8nOihv0KX6OmZidi4vZGFqKQSM8XiyMLGO5F648xTOqAZhDoow3EIJIng3KU6vDt8Tjr90li2baiIaDXfQMpti8dL2ONZmKcfkK02a4Yl771qnF+TeBiOtlQMHIcLG7SYemfpyOHTVRZsPHYd+Yhw9i9C3i5CGLatrFoDSfOlBIQOiCuO0m1Xq5OnF4ov+3s0gPUtubWQb+6I+G0XmokHUZnMLz93hIsSw2LLzPEVaRd4KAs/3YZpoVOkoyXMUK3RXWrdWtKXyiz86KTXQnTvFJOGmIx5nCNNj5pq2XQkm8xleSGNiyfuhYyURHXJS9vBC//r3K3/yV9rNFWVlm3g016kWu3ODwUScG7mI4xcg9W//6MCfhG++yftBLzqdLgg8QGoaEW+UuUJdIIoyxDF5tXx84XqRfeeOVCDYhW0fJ/rBTz75b/7sB/+H//JW943ttuCii4iGMKhetmDoYjFH2XhipqtdOQpKbCl7+tMFHubxDIaqfJ43b9yeH1xYJAtjtExBfjbxTy/sXIPIDrO6zncbWLh0/LNhoyZFGRz4wYefTVfvRlxLLreLuh+GVVLI+Z1W/fDDwzKO1LxUojgws8euTyNEMc1Qf24jUQxjjp7/+p2N2Z/Mtlep0sb1T16axeezcofD+UhTwdbOZj71jGVfsKJWR1BmmW4suQKahxnNpS6O84TXSemJkl1KrbXbpWrbCK6WddXNg9iPzCVOYUVBliRqA691wwePlauf6VtvC++ursHX22ePTrKr+YJ1imU/jvPzl1buBeJOQ+pkFMfCkxRg+HKZhEicOTAEsgjDQydGgZ/H6ehyVhAxOMviPEWFohgtppNlRqWkfm60dsSVGjyxDNuBcRiOAiCwuDU2t3ZKrk2wrFiotzkwcvFK9U7ZnmvKR1YeQnoQLbE4dZRX52i7GNa+e3dlq+PlJHw2+0wl1n/z2pZm9Y5jOp4HnfLnjr2ZQK0sxFTXxmB+paE+6L/1VuXJuVckiyHsnEYIFSPFmKllydk8TEPb4GLHzrJXc2RlpVmKGGJta3W9VoM/E0oygJn5LHMSWI+NGIYpr//ydDHTa6tM9XanVod6h3HYKJbFmIQQmATG1bI3MjAcOXwxppK4hrO00ethnMmkYchcLHOCK6bn+sq7Nz/60usPtNZaHVcUca0qllHMyZMsXt/CoRZ0dBVDiL0ChyoSMgWxW4KG08QPcEjPIJZyjKTWbYgIGENxJvEAZdkC6I+WrQbF41x/5BTkNksklJ+RItY7Myd52n1388HhUPydu/AfP7EnB5OV1/ovP7293+5el2HEy+e5i6SOQ4/tWFxvNfdEiICCj34AmVYDDONpiNIrubCeo61IQ0kkk1HMSYGNMCCNFMdJUkBJCNkBjZpgnvRW6yszonl4eAHXijZhSGQmFDFlOHvr/WtfPj1nUdDstqIgN90rSpQ0U0MoLAuMEKMPjhCGRv5BFwHX3wJgw6z90erOuzWSuJxkMR0oTsyvYTim4jNkFzhphvV9AyDQZ+5kPOV30OT2Bj9Xp8HPPrfUp7OiXOo+QWuy5qny6karkA5G6fEkzmeOvCmgCYsijs+QTSouALiI+eEOl8EqXsKpQmO7Dknd6ExGN25uAtAK/yom3r0FAAUeDcA4+fw87n6ztvtWQ52Op6o7+fExlZBrVUL6+sZGDQuO1cL6NttQe6fh0cOje9eq1SbuOmGHZH2D8EKYRrCjV255ozR2ZsmUpD0Wq9RgwtDjdMm5WuzBeGC34BDhvDjgyYVpyC6WR5G2xNaKqCNDaT0Tx3v03Wu0AAovwaGZQFTqpMMEx51mAWb4AgABi4ZERL8nmbgWpmoZPB2Dj3xovwWIYDRF5lqv9kYZ7jr/p9WbfMP/g6ev9jtg/PlPB8dxpdJIXAdmIC6B44SQJQGTzYwiIQuCArQkCzlHwbrjAQjGEzxz5s8OFqPp+i1hcxcN7Gx04ZJk0WGrmg/JRMoswxomqPZSVgzazaWUQt18c4f/Qjd2YPf7v7aWanZto4BdIvo45Gl5eRWOT8LTXlh83mM3mMp6t9mtDDSF2lvd2pa1Oe2syzSVH51pJQ7j4TRZLqosCXnR6al7bbdGgfQnn9tf/Jd/wvnKn/xV+794fYLpU1C/JrGxazkFllkpcfwaNVMUaxk3WRGQSOvN9hcfvsIT7Mu/eHj3dzLsZmdXUp+/nA3Hy0pVMlQzRugCly60BDaBOvcEFpIEnhVIFkKCEvPNO5ujqujlaLIwpfK6n8EpUDdu3lkcfV6FLY1hzuPMK7KO7PJaZjlJgFXfXIPPTpJioR2z8EVtNf3Cbt9fWb2Gaj0l6ztiZHcJoGA2vkoHzxh94M49N6DpcptjYIzbEVXTlVKC8DIhwhkpnS1ms0sHyMRaq6RRHMjRNdSkGFkBaWQE3nAprNbb37rVPNPNA2Xy6NXgte7OdZGwkmQcz64cSpIjGYEuzDhG0ApT4uopaQ+GppihRJsmGaxMSIGVeIkJszyduK7qwXgSJAmgETR2wlqRtlO/WaXPhn5/CgQBZjPINIKawGQEfPfdlcGVFuUsjiKWGnC8Vl8VZwsI9rx86ZYTZ2Z4V8+mG1+RYZj+/LkyniSEbuAcu7tKotn6VZE9jQmkReGx6p4C7PGy06GJpqhrYQTpSa/3tRr84BcLIRQ6OQpyaBiFGOZkJAbozJoQJoEjWCGOZ0Uk4msgk+ZJmApc/1UvWZ5lkwqpIMzIjBkssae6jmRwMt97r7mFbeEYPn+lv/jS5SrF1R1SGwWAyYY9FbZdfeLjUIzTcKNJz3U7hygi8pwLuLpdUebqhkzaej689K+ti8ZVirlelqXdZjoe62d901ksoxwvItA7312HYdo0F1SZVBXgjm0mz3bW5MtL7/FzDSZ5plLOCvj5mSLzMsyxGA1TxQRBkdTL/H60sZc5Csrt8Moy5VJmY4XUw2A2Y+v8yq3fgX/0r37MBvFGG+s9mNUa2fZr3TzDCQ9uVelkYPTni7081i8ni6uLvToC0TKIiRjtXOJdKBIQAi7VxAkOBwnrTx2WDkUeVOu8H0SzuVbt0mdOLljZ9Q7/879FblQSG4KdGba6vnl+dIYxtFyghkez1n6hdqP0Vz+adtq028/TCAtjQML4t7r44On46nHYfWtsJ0HwzF32Jk8OZ+VatPL7awggUtsePz0rU1DMwEhW6Cu5DFIXRu7vMtjUmOvKmkyDrxSh9F2ZKkwjicnnT0cO9R+U27v0tR0SM8nJEvEmCAUVGa5EAyR23fZ6yV66cpOry9UrKy43qXiV9ZZ+tgm0/r785k3iX3wNAB2M7Oxv/xous+uLS2M6WZjwmTO9dYsj6K23VhsPfjb++V8vCMVDQzTLTZWCV2vynbVqfzi7/OgZTeZVJIIYwQzJ4lrLo2jUcnhnQWOcmgQxyqroSsS7WuhWuwKSxzkZ23GNJOfjSe4Dbb3LuKO8Jpr03MQ9tQfPJ4M0oDx8s0AmnpQYeOyKLBGAXDcD79IrJvHEIZA39f4rqxvp6xU+Wtaxf3DDnAv9wmLjH5LGc/inH33uWWDlrsCk1P/6/a9duxsLAmzlXv1WnDKJ9cSYDPI37jd9KPrsoyFXTJwk31xlXSMM1TA145jHiAIuCNgKnT4F/Mb15qUS5BgnvF7LY9bSg0BVyzi+VCYERiU6669QOWmbSQZ7gBunWzz+4YN5cav1/NAQl8H2Dju6iN6+Qz750BrrxLd+5bWVe/GruV/I6sMB9uKw4AncYo7HbiCXSEDiDGnktnmq6Y12E2XZQoGZj+xcV3W+/c07/KPXVn+ttEW8sX7hElGpAkS8H9pNL3D8JAwcJkU2C4w7CQM7Ov/5pNsUrzWwEiK43eBxP1wNs0xFtjuF5KXvTKYRym1v1LyxX+qwFx5Zafl0HtMEPLeCYgARjVqCCiJOUGw8D0xXfzF8AX3t3ZvbcjH8fe/zj39gDVVmYcOaB6iYyby73WogRf0+7MIwhUSf4OHu/3xz/ufa6KFZXcu2GokMB+qlPpizuT25tsagr5VSVzUPMVnAYRiOU6CrIYbRFotwVG4MFwsd4rE8zbHBJ8vyJityoYAj2iRp7eTABxAOlQXi4Yu5t4TBnljY5+1z37gaPL7Kyl1ypVtfhkQ/CfF6gUx4jEz9EBqfjZoCgRPRggUEj8QLCvBo5ga44QkYZsVhAMV+kIZZDCEY6iS+gLqeDU30uVyl0jRLUlRkwcyKbVN1AVVUYZSm8RaJZTxpZzTGun7KJX52Zod9B6+h0a0O0zfVmHQ8Zeu1jhxmJxkSF/FBkO7cljtr7OL5xfyq3xBKXJ0JJm7dVfG5OrWQIZJzmBrouVsNfqRojsO8SReKjr+eQxSjHmh2BYEjjCDxvNL3iE7NijIRZeDYG1xM/MgN9UXt/ub+GzU0YW2GPhjKrdQmzVz34xcvtUboJEPTu7NTXSt7z3qWTwo0irsOFMcNESzM+LNnR5M5sbW/s6ATKGHKsjm2lYCx3clFe3XleKKXWys9Ihl9evIr71fOe85inGtL9vW35DuN7GiajVx4eDAIPdSB8xs3Sms8Nu4bw7EhNvmG63UqKApraramIgadxKJMKku32q5AdlgismUTRFTSqpITzkpRqATjh8UuHqGt/KXwYlj+2v0b/9vNL/6LP8n/F9d7JSxRR8MH4Y1r3XiJv7hK39yvlX3DeXR0fHKxKnOdJgkGUwcVkyywtMGd/XqQYQHJurrPNR0iQT1XQwC5MBg0op5PbKbF3Lm38vEPjt7/e79OFS/0C+Mbv7r95d8e0OFo5zrZPx3eeevGSJ8rlgW7FaktzOeTouAygaMAGQntngrab7YuDuDpn8y088+SdSQan5euN+XfeHua5MkvT1MirG6VGlIcoG01FzdyaJmj2sni/MuD+loSJ5B9fsWkKLJdynwKRQguQzduNALCf3m+nKrG6iZS3Uun88Sz9SyNS0Ux0hw/pInMQHMS5Tfy6SzUsoU3Np9nXIGqESEYPQMX8ulffrS5tgV/YxPA82J37erB9ALFNlYwKgfaSP3pj61ffYNqiLjCUsEinthj3YviF/ndGyur91fE4XRp0rZp8xXegvnTsS5D0onu4TLQlnqEJpZpi2IsYjiEASHJobJcaWTRwIxkyjPC3Aumrh8vfMlNliyTwahoaw26hFHL4SuoU1pEGY+XyOl0sRZMggofCKUQztCipE2QFYmCdPrkB684QK01VgVs6YDZ3530bpL+9363/ixxrHTu+0m1W3PZ9A/+5cs3bu/ss2lfmV0tlPpWczAavTqZUDzc7aLVSuVyNEvhXNGy1Qpfut7lIOX0uXl2idJoqUuiLMoiFF6qClZPm7lZq8PprgnHdML6GCqcT1JCLFnoiGDxMPVxIUpSmMqTdqNy3jPf2RTOTf/BZ8NPD6N/uFtebRiPPh8fGpg07sF5lazmnXUqKFeM40WoWlYMGDhyoiiFuSAN7ThlJKHTYjlrCVXxjjv+P/53vzV9fvWawPeDXihnJOi4qHldwgOaIAPSCKN6gYS3RUUFrOlNL0fc3sbkKqvWKUED4dIAjnk5DSCWbb+7S8k7GAj0f/u0f3ZJFARlyvgXSo6yd1+rJAR+fjKrYb4/CYMCIOopLvAb794FEBhHQH2RFwFeu1F/9TfPWw1W4Z1UqMEQrYyJPILYxMDSoOxA9tBbfbP65Wcu6lzEB+aEoVs8mQQRjVuLkaEBSBLRzf2KHxoT3CknpJAxlAV0O1XTbArSiOf9sznE0kERfvK8t3e7Acts1KBt3RdJ3HWwXBDvbCVzIzAfTuYiu9ugqAbLWF56bLw6iTGGZlM/k7iL4SCbxHvXKmoUk2kk4j6w4RgXleUIHvl0q1KoYNYitDQbLvKuq3lpypAwKjNYMIU3Vqg05VTV9X2EpRAIzgQp9G0f0mxjDmyc4Oh6qEeAIQSUCDyfxkkntSEaJZAUH842dwWsS/aJVp2GgJFtsmSYI6GbXqYZ5AYQ4UA4e/DcyG9D6Jv0eAhKKsxTQFMspIJBDE3BOaI612G4EE0RJHcDTMFIiNczmkJnmpjTYmPzmZYKNMmxqLfIVMSsdvz6f3Sz3b0PMipwtdnzSTII+VIEoyCYemsgdFMYLTfuX+cnR73J8ayz050s0rVuKcPyIpKsJq49Hg0WCz2wF2aWhx6ADHw+Xb9FHgxFzULqNVEZgvd3az96fhCgYQiDald+/W3ahbxffjkeLxNPTmEzaMpSAyemPXfiBeVq0UqRSkm4hiSWH3sUkYkwAWfFKg7BVo6SUnctnFnTU2/tZuXJZ6fsPXlto/L4oZIz8k0/1YaqY1TW2ryQ8b9akZnfeV0/MXdYuNhiX16Gw8vo7mr59OFw1HMtXaUI8npHqtbRTx+ZLFI18TKOMps3qMDXrMBJYi9OE8UX6xv1QMcZH8xnniRXttuY8kinyGT+wopeG95ayUcjhJI7BqV8eAX+3j9Y/eD/8fn2rim6tpXAK9VrrEEgyxwDbJLELAnDYZimRv+5h9UQuEDLtxgUwre6Jb7UuVie/Pyv7Dfv4sVN2qWIq3Gi6el0DPJsiZei1/cEKvB9Emqx3JhEzj48jI+FKl0ubkKJQ/R9nIXR4iocwM6T54smLe9XKD2iU2AaelxplwyPigi3xICevkyg+PkktkBwY1PAEjflj2ZBjBDS5u8GBx/0e//3/jffKOAbCLLRrNjp2k4M87E9Qnc72McfLcsNgLOAQv1mNdvtlKog+9mfX0a2trNXnvTj5SCeDccGPK9tsUEOmUU1yo1CO0gnCBonbIZeHkYYXsz6WW4kbIBihk+k1NxFeRb3I1C+0cwSWqzw1SIKI8RCzRF4oyk405k/vpiDGsGCeFBiFiZJiITu5wzEc0Ga9ijfjLZX98XXUbAg/s2/P3nvt9Zq1vTwkxduiiKS0AV45mQLN40v6W/82t63fm9t9Gc/eXkymU+Wt2435pfx3a+trN9rLU+Uo5dOKqGFNaK8JxEZGdv+4+cKFvPr2xv7Xy3NFzN86tV43X0+rAE3WGJh4IIIwZviZJJKFLR0o706p6dxNNELrYLh0zLs+bEXhZk6tMc1CeWli0X87nfXy1Lyh3/43Cog33m3Nc+itbqY0fHI9/V+zxouO7fESKp4cxfiqP3NqjFzi4ofc9IqDU31kKXxQQfBe9nbO5ulBn8xQKyl36owm0g3Yw0/zgMLgjLfiELARqkPFbZKH/8k+NXG2nDkwoRHMn7OExSMwgqO8iK0SMaYXbQ8A077FtwhExEFe81toSkvYyiwtYIMDM8Wthmhcy/PHSjHcuAfxfYGXmm8cev/9r//ESYNGhwOnIiwCIHhhiwnrBbjiWv2EOtgUWsVln2XWC2Da6H7GN/GkuGrnl2AAeZ6bA5QNgL00MOcLEQ6GCbBEy+rijl8gXZwxFIMOLcMdSHJiZoE5Q7kc9iDV4tq7Le2qoYNwx5CE/h8mtF0ihYynoBd1fz0hUaskBlFr25S43F4+khFBIA1QiaHFwl+/NLmKDiMrf5wQm1WioivJZnqBKFj5VQGoCiCMjYPsygGWZgDBCVJPJLg0TQm2DSr8MQiBHSihBErCTRBqTRcFF13lC4OVDKGqDXZczGnIjIV2SvUSI3GZkvSULHYms0934NmCEEySLWWTRcRWcIuZ155Nm1kVlhukTB5cjYsYUU+1hcYFQjI9i4/hrWhG2NVuNTzEok6s1Scg86XckMWCTyFWJKi2dBx1CBsFFncd8cDM03p8nZLbCZ2mj1/8aIep0WpJjSRqsRO7PnxsRVDCCezTA1n5E6gW+bFU/TGXUrGazW5XFS1ceJDcDCyoaVr9D3EPSuvduZnY5jNV5vs6KVGhdSnWOmtuzt87/Lk4FTeJM6Ho3U0P4yxMzXBtJEYotttdhmDsMXUGUqCo6ZY/ODBGRJj612u//klTnOgI4HIgeZxvS4mGDxU4tZqEVbMIkbGZbjUBWthNcyDBolfLOBKs6zDPF4jmLIx+HC8Vjs81UqtJj69hBnGV6sIvUyVLyajUrG2UtaXh3gRHh3pIp9aVxHaIKUiAschjgDFMOwT5+5bTXAzRfP3PwX+BkQ8mCxLMy8JwLy/3NxlPvzps9J3dt/8zf3xdL5/U5pHc0tJ3t7Nnw2p6ORqdZU7nObX3rj+0Z9fvpPRhJfEBMajcQLjDIcMyYCro7yp4zVGzQMqDR3Oemn15r+Y0jm2s70hXefnp9OpC+c+grFctY5vd4SlF+CpLUpirjswiNbF77S+v/uy96jgEzyWqSndEhNv7oQAJdN0db949nhmOJU1uRoBhILQchVHqNbBBRVNou0G7/iAIz2YIHSHWRWZqQaHRdJM4PwU7auTRYSu9qztSq15txoh8ak6IJUMR2MotDvXYMLzbUAZDNYfpMsPB7/3u/vf+weNzz44G52NPJelUHup2EJNNH0yVIYJTMQwEflplqK6i+BhpIcUG0MEGzA8YUuw36g4WgIYlnuNvMGkDMVaGuRcOYvZDHACwfNFFIV9r35/Y+PNFfdocHkFUbmlZpx9rMyH3Bo8Xi8I1P76zEG8yyeLL/NNYXm3lW1Q+s9cHPrau63BIjBh7dycLqGVrrB5TSg0uNOPLv74J04kYHJbrvhBLFd0z3j6rDcdBEVcuPfu1rMvHs/nMVPkagyOYtjdW53WBgls79mHWiEuCpljM1WbX25di/KDID/0fb5RKoucpU1MTF26pWLh4c9GdHxJCiVYYtN2PXccicYQDxYpbBror20xn3/wZMkS/8vf3Q9mera75YyBpy4mNsRKYmNvDZSR+RIppZC0umbKqKdMCcz+pZabWKFSEF/Zbpbh7xJpblGAOzMaxTsIXgAwDTQAqjLixStyqmuLmZKIKAtxOBbIBDoZW5XX9xLvYDwgMNcxLufMO69123Xv6WPv0D0Oo9Yacf83vqHF88VVqizwaDl0MA7n0EIZK5ANNy/kmXN4PNrb4GG0lAHDyisFqPSV33v/07/4NyN9jkD8zu1rfFnOqvL5mXH6hcJQs2qBoBViduFZ8mRVYptlVumlQwS+GLrcrpjJJBWQRCIgTb7sKcvZnHLjUuIGGJdisOKjS92my2zg4kHq+56XZGpQK5Yw4uSLY3Se1d9bH7qhoJplhlSQGKSwculeX2txqJ3D3Ph0doGH1EYdbPKZcxaeK+Q623yvE04U2cvmphIRKDo3jL5Z+Mbt2pVrHE1TL1oYQCxhum4GlgNIJIlCFA2g0ENWZCgIAA7gJU0hUVCAcmBEMIQJUR5AdHWLyhCU4qFFgBICknmm9cLJcBzKqH7stDYRbYiIMIGMA5HK0xZ7RFvFm5jjIOGVGyqRn8X9vnJtvV0tFplc1BcGz4aRGSMI3iExTIBzCk0SCGfgoeI2Mj/57Ex/JG292Wit0Wd5WpKpWDUAFU29pF6jaZjPE9U/1RZLRUzr6Pqq1OY0QDw/n8lFcWNfkEV60J8tLgxnrqGI7RrRuzUvs1KWsx3LwWAkjwNegAt8AyPzH/7i5SYe1lnqchJ99KWNtaXC/rV9iUqnbrPiRVJ8dhEODiy5THdq8XoTQVQQW7BjGhWBoMscnedPHs7rq1CbgyU+GL9SZ2OnscKzsQfFQa2csgm6XNilKsmDxFZsmGOwEj01w/vf2vuzPx/UzTDicmFDWF0lnnxwVb/O+IJ/DnLXn5XLpe07roZpeQnoSVAv0YvRsFj0e9q4HNl7lXS/xJ8uFRPCno7dlCZKcsqgyN1/vD7rodUfh0nhIjpzRzuZGGSCzOIZdvBUydxstYrbff3Nb+0eP3vhxikFmMROZ+O8S4dDM969WfnJo+W1N4sLU0n0iKLgeYyysqRM0bJAdrjUiWyZJ4eKyZcdM41IWJr76cZ+LYIwlMYvDvrOaVTYKZL1piQVrs50nKXhJHjwk6OVClEoCsdWKm8hC8RplFYkNAQuMoa85yPKnoXNdSEJU39urcmFOCEGalpplJlWZUFCGlk6nSvk8aB5H9VPdbIKE5UCy/tOkClsdNlzWzQHkHiQLCFOBDe60EZxaMdnS8ONLDaw21UuZ0kWYPDCJ8gECvBdAZsj6Sd/dvb6vUZjk6pUkK4kLeet2Yny7Kl5XSZ2V9izWcQVeNOySRQRCrg1RWiAZ3qWusSF4ks75FJP5BIW2Pajv346Dyy+wCNUYX+7tV4XJ5cxn2qoiSFwuujn03Z1f2+XJ5fz6ehmBUsavHVXzOZ6//lFBYl++sFh/paXd6Gf/tj91j7vk4IyPIKp1Ivh3KE5pnptpVjakIeXw+PR4c9fTjZqubDG5AN4MQp7vney1G5fJ1aaRchGzUsL8pjVDVFq8IMTja0QzBr1ZKQox2GV8Nc347kXdr4m/79/aV4HQfNddrVV+8Xf9JrdUk1EvGXoq/ENnm/fbZ4p46qAm3b07/79sMCK3/zGqqbr9kK/vLK/fHA0CPzv/OPdCK799WdEs131ZiOOkSnIbjeKkFi5GpkJBFkoyRD8pTuZHy0oGluXmVaeBlYaXoRkU7yKhOFlvt9tN5BSCxQVcOqlaD61i3UeYhF9lsl5YIcIVSgksJO4wYvjyTea64qe1Bgw8+3xNNpRwlTGBqZU38GAQAgSOZhof/c/9e/tF1gsOpxqK68JLAumrxwnnFHN9GK5uHFTngRLPY2uiVsZAACArtwd37lhOIOkXJwIq0uOYaCEpKiGBDnzpfmp2r6etSn27IiAqghB5XAcIAykjBAcMAglEqgMpQwUUwGIRBH3zZlB+dHxMWvQVVHOQ280Bome22nA4LkboRJPwqXK5j4cz2JnEOF4NrNsuUvYSiKJXBjYylhzl0muIJU6fXkSczRa7VQSLwxxJS8xOc8iE9PWzAiluZVykaMqUpdJ4ZcfvIiW+s57lYAFkIhTCI7kFMqhCYKgCQBW6EEThgkQ1g/dPU718JXAcgMqCSPHjdIQgdAQdQwoZJwE4yMXXSbxMGc50Qs1+cZq4llr6/xo6aS4PdPsu/WwyxX1NOt9ZtwuVEMM56pkweVGS41FYCTzy/dvZucX67cay8uJNlis1cQTLYt4XpIY6PF87fsb3fVw+LMD99x8NtRbNxtl3/pERg1Dr/ghqDPGVQj7cB+Cd3YkGpB9KZaKpTp0Hu7AVkiPF8rixdQeakKbT4G1nLtElb7S6Tou5V6QQJhE0/FsqOv2z59rYnvaKPkPnw4bnSKBVW6+RfvXS4oBGGyBY8bF+UW3g+3cXC8K5ONXtvvg6va7vKUHFZqeZXDF86xzfbiAgzBDBtbqSiXCnZ8fj290S+sdIggTl2BjjELzpNhm+RKaeiBPCd0IVwuFKx3KHPLa7fr50Zke4up0TDxMC0R2Ng9kA0bSkXfuam+UFAeNWXtpRCZN1hGoQLmx75XXHNRPIDx50JvAWGCiRYwHEhYxEZLxfCahi8wRLtFikb7eTgKcv7abTS4dBHcLnWhcZNrf3bVfzkBg227moGirCS/Pj1ubzGFvqmMbWwsf8nPTS6kucqIucJSkUJKwDBnDYt0ZLtLueracqpUSE6SeVKUfejkSea6uQ9IGHuE8t7b5VXzhG8sCdjVUSD/qf3bWvMd89++/oYHp1KD8PKqfnQlofLbQnryc7lbBN35rC2wi6kUwmnqlApxkzHjm05AHU55iZcwA40q4F7plNBC6ZSWwKy0UrBRsiI7y6GCcdTqRwLC5FXA0cue7K4QPTnNWsbOq5HY3oWXCdxwst0Es4KNJVsvyKHDHF3nGIus7kp7DGQVSD758Pr21PRkqVI2TppJ3daZiZ6RfRfKJptFo3bMNDKM6+OKTCZUA+Y5YZNxLTWPoBmbhCAG9u11obFGWyicn5PzoIq0ynZb0goqAyt1IEmrSm10spmBRv7nV6orwhakQaJoktevlSokzzg+Ix1/OvvfN36h/t/VPv0CP9c9+8jwOsndvE31AQVwEc5y4Bj/84sMIomgCbqwQpSIR5aFim0ouXG+xTLfY6cCGkkS49Pnn80JHsjF8djKjl/Cduw3f4uZa2BDrTb3HPDktNYQP/rsnfgX8mz8//49+q/rVO9femBHjifFqHjol3A205dSvMhRKohnrtzolnG9cnDiffTxMKJIsk7/9f/1uYXjCzUqL03AymQQeClKcqncCXO9WOSRH7Znijv1KBVSbcjFz7Z46NLJEVRtCuVTf0CWSm78IlvNxbe/6m1sFEpJyCUCglG1E4G+vLCjDfbiwqTVocUa7mrN5XQzyol1B+XQ2e/yBzUoIhEYUv/3rb8OWlU0Wcp0z7BzYsTE1588mt9uV2qa4fHlCrlYRJnXcvLjeQGYRdjVk7rdgtNwgT2SkCgAI87MwdFvl3XqX9E5YGEJIgHtPp8dwUeQztog2i4J/eMkj2Gqza17RXsxpzlyisRSNhbUGWSRgO49yV2RZFM6MCA5IKIVRmSAwmQDT/lzVEZBDOM6UZD7jNIAaU1D0IapOssLO6bOPgr12rRErvZ6aFs6XXsdwGlVKPR9v3lw/uUzS2IJR38jzTlFyJ5MM53AMHcx0lEikIkUKOLVRCI/n7kc9jIFCU5drTFgm5RB6emjttthIJoIY4CiG6oopkKSMEwSeKUE06S/LLBsTMIRjEKDpJKEy1JwYooe4UAawLM8xRswqWyspQZVZYh5RuhaTDARLUO013O5BLz4aIu/4UoO+viuGboxy2OkC6a7QMxgwGTIehl+7xn/4cVpZIdh6DYKB1CQrkeKkUb0p9ddWFZMqpqFBYN/8euUnH1y9/H8df+2rNLbFlrCUYfMTz6IAVpLEbqOjGS4LzzMvGWhLNbbyJOJwvJNHRJ3HNoVnl6q18CPLFPIYM628Wcd92Nfto7Mp7vUZmah0US9JNjpELlsFDGPlztzpsjZShNJKOyXZvAfov/zBxMmzG2Vy/2Y8ZQLH8kS8CqGeVBCiyJupQXGd293sTC6c4SQaK+aGwHTqxVlAsJhMc2jg4LSckzVqMPOB76dxBOPB1MBxQnzy2N68B33x11fdncLaXvvgcFmXWTH2RSJfKyZH04VtIBlNzOapbSh8jKEC8DJYWXiNdlaVIADiSBAgmEEwMY1NyA+RJm/FwscXse4w3W+0cM5pwIUvjuPpYw8nQSImKOPUpZnzUjWVcPDUqorceG6FFUznybU6c/qDg299zS74yYIPPYRdaSBNwXQtG8YxNy+7CFQrYRibEuiYIAMiJSANtSBaR2u3Gq0NuOAGqG9RjgF/eexI1YjJLBr2tvZZNsZ++tPzlNebLL1/G515FsRSNA7evfXuu9+mgPXFX/63X3Y2ktvXEZ3GBmeAZJENTF5aocdRRY6czea2T0VGWPbwerEp8jqORhczGxZJRXdqsmiafgoSPRGzFFC08Pa3OxMH6vXUoenES40mQtQL7ZnHeUziUaYCcqh07dbq1dXly4NZXhCErcqNbSS1EajiF2DHuVLf7xK7a+Xz5/npwqh0iXikIpBXkEk7LX71twof/M3w4YH+xorEE3gW+6HrVCl5s4CEE7uUIdkKe2pHs5nS068wgpC44jIn+SL5dsnXl9FPXzwoYs0NipmrWM6kV0fR9HQ6gqb1pjv50ec/DWxkbhIwJ6zVWwZjeCkjIYBENN1NzJmzvJRaBTQl60yap7IxTTc3VxMKsCXcJahj1YQTuEbH5UZaqea6ZfAcuVERXIxaLtyVQrRiqfOHveKak2nKf/UvP/v45HL/je31xh3CP6hwJFbHJIiFc9W7WDICmiZUjWeml3pwnpVbcLdCpRl297Xqwy8n44ezoyMPQ3myhJCNbH8D1FaZq6sQ5LSPIJNJ2uGp73y94uvqRHUGQd6ha1tvtH/y4mwYUit8ocSVE9JWLgemp2vRRZLDuF2UBRZAoRUUuJr56oXRII/2OjBJ4mOm6EO3aQj/+i3y8BkOEBQETAxTO9t8KJQSw83NsAwl9sJAIng88QsCV9vir6amMifff014+mwhp2nAJ+0bLXhbhOHmi8MDJnTWbnIAgMiZidwWAEFpl78aatOh2+36cIxvU0FgJvKuWM8EkPMyDyE0W2SJWQAtZjFaoIu7dUPlVJytScJVbEvYoLLEigRYaAEJ0nSZ6UYaIkStlmM8KYisNY0cFdQKTLLgEIdCXPp6jXxhBdhkQTRkc5DttCAxphYJdK0hn/1iXq1nLIcv9JwiKQQndc2/PLSIMCkU+EIRiVK0sY6enUycV144jGpImkHUzt1u9V35oRIVMoqthD0tojgEgQEOAzRjkSgPxlZUInKCRAoQ6Y2ciAMYhOcl2MGiEuKTLcof41KF8DKAyFSi6BwPTXI48ZPAdnM0nXmx6i4gVy/u0yyf+54RTRAo1iZ2WijSjkzF9piuSbUSrS/GprpsrtWC40l9nf0EARBZYXhzhaSmJ1eCqePErmknQcm9gnxZtqkubyQh+jyWKnBRRlBcQGUaKIbz4dPnCrhk8xI1vra3LxbuqKEGHU4ErOxl2b99cpoGxiaHnb5yaiWhLIhZlk1nquPY9TsiYiXq6Vm9u3m49OI0fXBiycZk/5sbzA4eX/QFWBhdxfMzbf8688//0ebnPfPiwYmq6+0qm3vBBZyWQ79GZmaAbaxU4zw6+mJSF4lKU1osXEikID5LXQeH8+UEXa0VpAI5CiJFDVZr/J3dugJoQw9XqqK1dFmClNb29WnPHPpaiDKj/pLf1BVr9uNepYvrmpOoag4SFmYLBXq9zM00HcUymucoNvzsx88RYrXaZMgQx6FyFif2OC4XyVIOP3XTIOMnj84MTcmE2utvMyWpcjbvX74wuDvcgvX29tzh1RDubFQF9Ozl6NpmY/BCbdSrWcKPXWWhaGW0EIrMvO+GEZc4icDaBBYhLgaSnGAFoYNni5Dab2OZ9PczCV+AyUxTJcHwshsywu0yYTm3koxYwkc/6d36/le2f6UEvzi9MrJQdRr126fRJOpFs8tzQwdv3o1/43/2+uFng8ueW2oi1DY4G5sp7mgQVmIoQeDbra4Z20dDFSKlqizokBMlHhoTnmUvFz4LmKLEBBAIKBInSJrAXzyYjBSsDyPbG+W6DqG2WisQZwoyPsgyXDEhzHZ7Fca7+2aqn8erO+zjgyHk0jRYAAeqlsrKY734fkdYX7nzOqP84Diyxh4q1AQsyMOrx/0a0li/X7+YmyeDKVRjc8ST6GI8UB5+chlSWqssIZv4jfu124m4/GIC1Uqnh8mEtieOH7+c1N5u5DdL3IX9rLShpjA9TLc2Er+2+WAmOXoiOgvrhx/BXNdFidIuu16JfOVkucwXs4CX+fnpBDA1siLOBpbtoW/sMP2JkQskIlEnZ4fw+mZ4Mdu70e4v/PKmPD2byVkCAR5iOdTPRYqbjy1i5jTLJTeG/qu/OLj19+/dfbJSDLV92+u+mi7JHGZKfu6rtpd6NM/C80EiE/n6huio2XxudCqIYmSTS68mUCdfGixf5HF8dbUWBcFMj+cvBwstzypsoEBxhH85gPQsYmGU6aylUMys8ADhdze5y5eT/vlZcQNxVwtog2AMRyRHPlw6vBjeWVGAiSuV+PQybYsF/1w5xdh2iYBaNS93aCj1CujOa+RYS3EXz3AGEfBUzzus52SYBWFSuxa7uZkyO/doIaCPfnBQffNa/+EgPJuC33kfIpBZXz/VzG2m9/9n0a+brdsTxDDvt5hx77UZD5/znpfp8u0LjdM4mh5ZUiSLRqo4iuOSK7ZTcaqiVLniJJVIcSWSLUdJWdJwD2h6emYaLtPLeJg382LG/JE8n+O5UvBB5V0AoCz9KGQlACZGOvAgM++50CoxnA9uzHRXV4J85CvGeeasbvEuwAY6pOZRzaIEml8RgyAnVXgK5RDqZFwhMT+KkdS0bLrARqGpR3hsy4zkk4UNwTYz3PMkPBxKMcykVBaVK/KFNn2EMvI3ryeODmVU67ISxeyt5fzuo+ORL1d+8Kbz1UHtP3n7k4dR9dxKQ1tfdPEyXsFZUsGzPK73jVT361m4cOLqrS1xbGJp0F0RoSjWPhmLskRsLiueDuU4yA0pNURlgYZhFM5Fi5HrFXEfAAQmaRQn0sRdaFgesyGEZwmuwYZxkjlmYERQiBx0tIjwBZzLY8lAdX3cbPDg4MyZICqdZfkbAkqKiyBLL1wNjRs5hNPj3s5IaNrNgm4Q3dar8ud/tKjkiP2/Mq3RpIVDxTUcQjy5CSlNrBdh8uZSLBR0eHH7rfL9IzVUM0gEp8/N2LfBTCtsU0YK3/02Do/D4Qv35Gdnr/6ozSdNi2eNwHS8Wb3M5AnPfHBa9mFeKuhG4iDp9mulLz6fzs1UYqh7Z05BH+BEIEX4D5eFL3+eyLCPECnXVlA9Ukd4GZU/+L0RnQOrV+TXf/POf/h39/vP7eYalUf4aBLEjFmTqcHQpiigoHh3x1A1Q2nxxaViBiUY6nsJ196uLy+znePFeTcqVwq5Ant05kMCCJLg5Gyel9j+Kfveu2999TP43k9HWQPqAztfqsA8n+mL6dhXFxq0UDGeXLmcN6aOamMZyDg04lF+tG8Nh8zduwrOBZPDuMyI3dPwnfcbj577J5CLoyjxNk7JKxws5iPy/PTwy+7Je5dLJMNBYwLLVR92E58JtytovG94mivnS9O+Q2QM8COBw8qNQmgim2zROTYQmg4h4IFMVDRWZo1BvHCdzpQq8OR43wQIyesoycgYyTVzDJdPnIFvXESzfSe/ydQE7MXUfrq/BxisvlReEbif/8mza1dP7Km+sZ0NU2u02//939FuXZYbLTTwqcEwUc0sX5S8wCtxMRRawRTpqBCFEzlAi7l8JyZgquhoaQYBdWivlDkigegFgLCEIgGs0ASKW9oCJEkFS4QuJAX26MzboZP2Sn0xcjvDeRqP4FJyooYbbzfNfoxE+sZKhmZR6mGRkRE1hiwHjhfEnz1B7pQKmznJSaeOc6EJV+tYlOwMf97nV4urEtBgItAighC2mlzgmJu/+YpRodXe9NHL+ZdP1GtL5XqxcWSiRB2DIyfynaIInX059Ni2A0g7s0oCD9ey011/eNJ7fQkKmrkM5T03fOfNjV/94c7uLzRki3y608FiuLop6ep8akFXN9cmXU8fkDfeYiAKsQXOKJUR1r34Mrx7law21xANFVM9T+CLkHzn+yvPxuBiFoPUFROiyYFID10JfvBhhC6UbwS169+6ROqdqIrASeKhUYrFEA5NB0iT5k67dlPGZiPXB2ihwehmkJi6pAfPPlFv3ipfuS60m3VjEmgaUCeWKFI87VVzxDzzJh7U3OJ6w+BsHmQJXIAChorN47kazr72jXxjS7n/8IxgLkaJZVtsUQm+/Jl+5/vVVivePQyi/twvU4xH1bao6ZhzEPblhKo0ISFQR+TCCLJgnmQO1qpJVshkIVqmIwhHOcryEfTMIEkTFBHM7Xkoj7ZuyEqVe7mD1Ta3eQDO7/XLXFzIcXSrAEjKBlFiLbix7zEDu7RJO7dXWCVF/yidDkkSsJoWza2tTfaDvU5ukzjDa1oqJRIXsDI7S4u1MiLEgCQwArZjnyJ1VIdoCJl5ARG6tmGFTkDV8zAvCSKZX5MYEA6enttGKBcAQYVJHvVQCKbptIlVS/T4VOK3tt1YHp4YhUYJ6vtsubRMuI+/4K5JtQk/wQtQbjm30LwyxXpu6ng+oYZ0APqn9upWQXXivMgQARj2NIYQnJkm8gx8s9wfO3mAlUQGdRGCRNEAJCIUxnOoIjAdg+Qsj5aRKHHhJEqLfOr4qIyYELaEEJ6MMSI5Op3ZUIYitk4kCfDcgEAFJKCIVHSXroiAGPVFiBGmI5lzzgY7gLlDhO0sjq8WVsg8OpxpfSuqs3yJwfmz0xurRL7Y+ry79mb542fhJTiWCcI3576IYyfByQfPjFfvqj1Tqeaqy9nA8Ks1zucKRT8K2GS5ae9MLMLzjHr9fHCU/6rdvKlkedOfo4RhN/0A8qi9BcytlpubBHR6+CRdfJjId680C2r2CJDKjZKCpt8so1/83gnx3o0xrY1s7HWkTdcj8H3lYN+hn+2uVsDjA8v66LOdivj1HyAf/1x/8SK8vOxdWgfPjgZkroQIGZXoqhqvVOLFzeUgwdIcMVioySS4tMoXOfTzD8+cjJFzxPXrQtdCE3Wx/3RO03Ea0NRGSVDIoqV+/794Y2Gbez/9ZHye5nTDcQalljjozzCKLl2WUJxOzYBfln2PpF2NJjKETs/PzugGlVBRFKoRGhs4Ut2ix9GkD2Hvf3dto8o++XAmbm9d9DqGjjTAWL6DspVfm0MHn372V9/58Wv+BBrP9aG+LcQ84Q4IikQXRo5NfYzvz4btFfbZw/0rVaVrh+dDN6rVkUKMwNPQ8q0AZhWW5VPg+VVRwCj+qNyCQBrNvMWzo0I5pzSUhBGWY9foqXa5rGzX/TBCrNH94/4rr71zrRpmIz2fyx/sqFttibluYMvNTz96NhmOeZ7erJQqDLS/CCEigEu8PcMwJJwZXpsNeJKNnYXhR1mkGt7CtRCeICBFwazYybKYpmsMHsT+xI9ggsQhf6Sn7mQOCxhbTPc6Pjo9E5ZymxtyNgsiLFAXVnxvtvzdfLZj0T7jBewv7w2vteHyaNAsCKod9M6ps4upwji6EES29frf3KJyXJgsOrsXGQIyHCVWud7nF1ffqYz5AnDnX3TPxnvm+hJx912R6qgvHxwMDwh0rc6lVoLAmEIlsJdPGDp1WMvQtEwLzhf1+itvSxSNXHQmTsQFiDX3Pe+neyQNm8dDuyJdvn4lHE6JSnU0GMnMDNuGYBWpzjsl5Y45GS+V8uGDA+Tqdr7d0i7SAkE+2e/9+Dcb456ztqR4C6LiJkSOt2M9GXhwFlsLo7CK2tZUUSjWPHz2iHu3cQaYpW6P6lIBUibRycwMDJwAvhQYcEZXMMvCEihiZdSConpbYGV2YAS8bd///Xt5ljExrtUS+DxDCgUWhi2VKvAUcjFhBQG6hGO9+cWD0dVLIhtjNUx+8bs77Coj5+RcaDsEY517S9e46A775Z98+a13t8Ar20tZfDDRWYzpTlN5qRgms1ojwyU2BRGUTnNkyaCClXIzowpMRsPAj7IgTmFnwumYdfHgcHN1Q1mqj+798vJ7VzyZPfv8pXD3FZS29MNzRFOFjbbMYxjBHidYtfMSg/Ozkldmb2BQC3AAA599ZEPA+eyKi4S1IoWJO+cq3CT3GWwYoXQbE7rMEhvBOQwNWMslCggfZMBVfQLnQ3gAwaEX2lgC2xGiUsgyBVI8K0o58/4CZ6rkrW9m9scsNY/m0YUi8iHKwUggt5X+TJ0M66VafHyMvF6x8myNzSiCTyy1IPjjucV5ESVRUBphvklwtMdiuOUEESUwRCIyoFjk5po+iZosj8SLPEU+fGYq6yXNCKrjQEginGbxIAiiFEX9LIyhusQEenitwKoSPJ/GrEgBGE0dNM/xpECnAaNrSUxBdhQLLNZ5MQrFZE5BwxBaqrAIIKgmLbiJroWcHwAJDlyETPwQzsp4lgtJZhI/DINWsVoU09San5mj/c9mc3147fDwO0rhjw499m/KZ8fzQider4bHuzOY5amQS91wGwovTgccA4s5QTVCsVw2TRfy3CCEPhygSl2S8nkcCi9+cnL7zjWCMmErFZRIi8hAFyQRWVmP6nVyqnuJb9crEeHQm5uXpvv2J799UilVjoPZBooSgoBc+D++u0lYuUUPfnmvi7zYKW2Wswq7dZVrvpKpz9w/+Be/nL8MmpXm6rb38vlRSFGcIg9MzEdpmvbQyEz4zBxOXAg9Pw8ay/lWu1KSst3dU8/HVi/Vfc/0x8b03MuD9Jtv5wBJTHsGIxq8zFMseu+T2epr9R+//87jx/OYtjujQe/E5YQoC2HLwWQAIygX++jQDJaKqZPFFOnhrJRnICold3cWRBJs3JCDOTwLoBt3c7A9+vB/Op5b5NduVr0A27hZWeNKH+w+m7DPh7v7j/bPNualUint3oubJT/vUacnEAhD2E/ixFz+NnHykwBF6NV12fEysUpaIMHzxAieB14UOzYXyZNRJpCoi6D2KHV8w7TH7XX0SkvsSOzpsbOwEpHP8jkY1WJfV2EE31gt8HSx/yt3nkV4KTd5ab62WZpOomkn6h7oZYzMy5VSCx1N54vUqea4Bo7NVG5ybmcRmWAcnVdgjoLYFC2l2Hze60VNGQYEVc0VRg52PoaxIomQOITDUjLPMghNAieAJYGCATU80TDaQSlk3wnuriFGF3EWhbe+gXW6849OvVrmrNaIMImOhgPVdYeh0N2Hl9pJkAaLkqNwWLEMW/MYLrBEscIBzGqVqvWqPYhRYiZCUA/nOEloVd09RweUA9nm6dyP9qjXLyvv/sdbJ18OdLDQbT+KCBCHmAFRODY48wqrXH0L01S8+6zzP/2/krc28imBUgE1Hs8kBmyvUTcvt2f2JKHl2zeaf/DF/JWblEVxPla7un714f/4JX7mvgshj09QppmDOb8CS1K99fzh4PYPC5pteBIf+oRrQM86gcAGaxKMDc4PzqDCVv656ZdELndLfvqT0Wgsl+skQPnxxaAPuFwegyBNM8ZCpAGVXC8iZ2eZXIGJDDhGwsu0nSWHFzZuueqRf+XVSj7HV5eUpizhNPnRI02pUEtVucSXYogyjqH1XPT0dFyvZauv8zTGK60m8MAIUfumNp1YDYp45Vv15MXUOaVeXbkx4tEnP+15kgfVlWJTdhCpJIUyjkMLV5sby6KNQwIHY6MgztNbgNoAINCzSR5mTnsLTIfsIRSWOJShyTLlO+EcqWt0sXuyOD8AP3gb/fmfneXR6NWvXYlR0D11KOKQTWLNCvkGx3MtFLTA/98Pr/2NH559rNO/+sQoWv7cGdCpDy2FhdbCKeoG5dthqhqVbIHlYjcOUJ3V/axIMogZUDyuO1EAMhhFoWmwuloVi8x5aMelReAN5346c0i2lu/EUTNEW2OYD2CIiJsQEgP4cIQqgdlb+NBiKvicHapZ0h8m7mlI3mEcP/LCwKMZjOAgywsJmIJgnEWzSAc8z+JQmhgRzsbATw9nCYvgJAmxoWsHSTryoDIXzPQUhaAMoFHkTq7zeEBC4wCdBhGWFavk+WSe4gQSpWqMEDZeYjBYQCax3h9NKiAaXvRzLTFCKGs6G4wdf+4Jl8T2a7hAQUKKMjzEUNhXTtaIG1tAJE4u5AJe6Dqe3rvA51kta97Njs4mUEFHnpns5FleIZ5p1t0inh5pcbtdvor6Zz5uLdINxZmYl9b4VNP3IiORS/3zwRSKa/3+lbeXZiQDE/7Z80nxjRXlVerii2ltO2HFDTSaUth47OJeCVVR4lY+E4vI4y+yK9vN6Mj47f/sD0RJqYydWzcU4faN2R9/uvT25ee/fA4Xyq7A3LxC16qMvqt1/vz5DMcPj/wSBPK/jv6n/8fXfvZH6mjgxmiwfrtFnL4MGqumHbRYsng4OA9yVqt89tC8sSHc/eEyI7Cd+/5Rx+dkolBiUEOrLRG7vdkiJnoa1tubI2aENJaLi/lv/87Or70uaXj+7J//vFhD3v3h14a9njDV+n5UVEje8eVCydAtgud1zS9wuf7UE0gQdOYEbfcnJD2dBGjxylWaxphBf1zI8Ytnz8N4PhPw2ntvQbywthpe3OuVtsjwxD04HeY2bvyXb4aOHaKBRMhoXBY9L4uukY86kbje0s+60TjzqFq/M19fkQ6OF51dA+O43CX2GpVfUxLziafF+MNeag7mqACoklDMwfkZsIbTR08nxe8UN24I3TNidNDntsj8MjLtnFC5tRcns/WVpbrAVvvHydr683NcOz1oNiQ3dYdBZnz8mF4qIVDcBgSqMX0zMY3IJGESx1aLpJ6Qvs4w5SpWCI5Odg4GTlGiEzNAMtlXqVmYQTQ1zaJzmr0eeaKWSRLrhqFXSyE7IAfu2NA0M3v9zbIHDw9mPRil15ZiKgn74ORzAAEAAElEQVRKEgQKS1/8qpNsyNtXkPpIA+sC6UfndqIhCEzGxqlNX2lMdc6Z+zlAmg8vUgLyTxeNIksnhOoiCzPYeOPWUonKofna1hJei238YkEAKA4//mDB17u5gkj5Kb/KeLrha+lgEDWLYbTZ3psY2M/UwtX2176/fv/D3vGDU7zKJBnTqmeZPnsRyp3HRwnroaF9olNZkYpJcWUTefzlfiMDv3Gz8biIjPJLrIzxVJJb4j/86uTv/i/eCk/dp44H2ry2t1dubXz1cPjmpXzlkjT/uDPQE9ulD48XhECfHfirRfgTWMslYzRsTgsrez31SgXTMOJ8CKGcjKLosxeTNy/JlJl6UihKDBQDw7QbZVinQCZLay1y0ZmtXr+xezTkRilaRJfLfESwO4epcNeHI6P6LV478rZWKs+G+lKIn1v+O5fiw0Oz9jrnHfhXivz80DVtQG0p5mLnfAXJlBtLf//S+eFAH7g4hk6A2S4kAyMoLeOmyqNgljkhTRMtrIkROQCAB4YoELysl4ZuBgialTk6yS/zKOSiflJ+u/ZEI4swVb60NvA86WrVns12CCpahDqMkFh+uZKNxVoZEiEAIAAAsB7+irj+Pv4+AP/2bQFPlrcV5OPxcfu1VfutTZxDgUoOJ15pbJeaIT7umGeWVSrKsg4yGXE9xAtiJBlDSCCJxSSiakvbBX5y0uGFRPnk5ecTs5CWevfV37iBBVuKEnHW2cR1k4dVdi3v9x8N8Ap4fjgJGXQ5lpsZ+oQCdKEwnmtbrejxZKIxkQTDXl6wex5HRlwA4VmU+gCB8Ygj/IXJw1GRiked6WqDzaZGpZ0fHhwKYg2CIg5QWRDAERzgKOqbHjrBnChsb+aMkB5oIWlmLBolEOz5sLnISBzChUiS3Zh20UqqPZ/GvRmTJ3EId1WYadLL27V+kn5yYNZSKEwVKopVjMIgPhwVaay4wGCQ2pgTIa6a8XoIg50XboDypbX1LYLtnju5g27p3ZrxlZlbwCCuu09NNIWYDWjncPra12lyETx5brFC4QaJnKnwd765Yr9ZePqrrjnwN5b5kOHiJ4NKSshh7J/o1ZvNiQlns9zqUl7l5uAy/enoAgBUeVeMlEzZzoPEghjuO9+pny0MuzPTSLB5TZk+zUvlOi2wBhbJTO7aHbs4RxzXDdvFL345+93/S+/6TeqtmysO0/2Lz87pJSa00RsUhjh4MsRSf4VCOHPMr2003/7hymPV331sCx4uF1Ehc91x6DEozwoxkSHpooqAvWe9Gok1i+LllgB/3frjf/7p1/7utR98o/Dv//Q4++xFuUw0KjSiW8ncT8lMBRBVzUUQWeRTMvQbJWDMbAincCVHomzQp1blZDElra42fTxpfJeDaLZxo1Jp1vU+fvDwBZ5ABAIcZYm4VNxY2uqegpf3JBFzFCx7d53qjtRZhjBs3Zj7jmXnS8KDZ4tgZvYJu1xH5prFgvTv/qg4rwv4SNPPcNWTuxb8+t2GiESnB+d7Z3MdzWQ6Kq7mR5F+//N5g8w3bhD6uT82o/L2avfpYksIKjTATKvNKB/89sM3/uPqd65Xnv3lmeA7Xhz+2nfbj7+w7n1wXniTRsIEUUDM5Fo1AC8WloGe71suK8ZFwK2DveNOf2pt1CkS4EKIJQEaBs7Y9pKEgkrA7psuC8FcJkFmpM9mAH9xEvAsrpF0tS3YgA1DAmfhCE8tMzVnRKSUgiHKOxEBR9EcTnk8hdlBR29XiVoFC1wISFRF4ePE3Ds9FyghnsdRBZ9djDikyTaVzEyPP7ioN4zjFPPqqpWEyTkLe6sr6ygoevXS7N5n04uDGRPRHhvmCGJzPYdg3LivjRfz61d4QkfPXgxHffiN16r3VNPHI993ZzNz+mz29p1lGKHOLsRiswYFvWXWQPsnpQI39KDdmeuTVnbaMVn+2rXyyyfD67fYPGVCyfTu1Wj/xUuUw06n1voquHaLf/jZyaVBBsIMShyxCplR2KoyH3+sFW8pr725dakMfbmrB3OLk8ghmT45s/ECzkeImBfRw9Afk6t59mwynPimVMWUEgc80BQYjmfrdeXzUYaRRBrAlTy2szPHFKi04ToRjuPaONEwCB+ixO31ysgetHKcgsVpguMK6k6ANU0pGiOUvB4GpWIy0cD9f71jl/ZqvLKxVcAZ3vTiehmnAMZJMg74y3kIABBQcwISBqmLppoMESEEfF81HYNxPARQeIWc9OZXt8jOYJF5oFFBd3V1aKitFeTeub5a5dDYmn++S2Lc0lapmpf80Jt9fPqg3FzaGJ4+Xrx6a+P2+2QfAAqAcHT9k6F6g0FKb4leu8hyS97FqHA6KSCUQvveXPMsWyyFWDYzuyYRzdyUoMtYFOA5wEBZAKepRCZPny40TCfL0Zm1aInqSUnItcWDC5y68F+5vvbi5cnACxqrhZe93sCaNVot7bBbW8d2eqHluGYSlc1B2hvYYcy6Rhl1Jz27gGPYwncgAGEIS6OeEeYKAMr80VjjSHjqhFMvWm/ktYEdMiEs5jgExGiSphAt00kCsjSDISKAR/PKxfT5iwXkYlsYk/kSwhbgAMEUmL/ErMipknlzN5hNtARFipuysiHgEHl3WSlSDCGTMe9r75DOt3hTIWTZgYDbInLXULnZCSQI5Rj2l2dGtwp80UEUpLGGbFmT9l2GuiV8enhU+a/+Afzd7b8WHOWU4OTjT65WkO6jj4uwzeIUczHWg7wTljeXWN+BJ8MwjfP3/t3To09H7W25uZKPZ1nEipNGfkjFIokfa/BLa8TlaO4VRrheti46o4fTFp7lDWulc/Lv7z1FrmS33wqlWvTxo93UsqSp+s3rS8/+7bRALrmd8dHO1DrwyGnyzM+fsvLJzDhJw40flv/Lf3qJ0rof/9HHNgzf+sEWw8MWgwx7x634lCFUAiXKdEk69SS0NguY4afDNlrOE0WYxCcpsNJoY4WfH44x1VxeZ1Z/a+Nb/83b5dt14rVbOl9+5be+fukfff2f/7OP9vvm1e9dgUy7JCmLedhsiQUeU1CJ0FRmpFLn/SaWkm5axkVMKMWaQIxiBoMANDi5ODo5v+h0p833NonNxvqlkjkCx78cpC9OOVvlatJr797s7y7UERJ4hoyp83AQruQP+FgTGHd4RDinjNBRye5QDh71/TPbWLuMI3nrfNQDMHT7R9fMATj7/enRl+D0nAzmDITlTncnf/ThhV6Gb9/iRF7vz7tHe+PNzVxxue4Y/anrr2xByVStUBWmyIDZXJKCx8Mzay0jX1/qfvCMZ+3abzYHPvn5X3Z3/uqiva1U1jAAkIUZFitpIHoDY4F05qGVRRmxvlaoNeTes/7pM7u11ha5fBxzIM1bXnFkFyhSlqPQ2DmGD7tON0jQghcogSvrvTjPpSzB22aUR7MyE8spjEkcLbQCrdT9XM8h1DpNK/VybJLTDuTN480Koh+PS6JMyuLhYEoouXDhkeFwxAZulRBLlOnGhEzncuJaDid8bfONWmVL3n8xGA2CfEMg+CxAwoPD8WS+wPLF7/3G0pvfunTzjXxBASeD4cPdY0bKtt+s1Gn90dPzmTOV66hvzrrPB1QB0frTME00E25ebcPXrtgAQwZdiIf3FxAcJfhSoxOElzepL748Dqjq1tbVnYOELZczwzjFks3f3Oj+/PnWGpdP3a9vVB2Re/lVZ32VvFmCng9d+JXmAoL3O7bugP7EuftW/dnB/DwQfufZBVAiBJ1OHefUJEtc8Qor0gBIcPz6tdzxwYhl09UtYWULNwPPtfyxFS+c6PGD8e7LRUKlsD9bvdy66I3ab7bYaqYFKVPg5wMTycQZ0Xr91Zt/etQyhNbOsT/aMZ583l2q13Mr1a3tQhD2t7fkYD4hoTAQ5M1Lze/cLi1j85f3T0+fHZ2fD0Z7RzuzqeZ2j4CTAAABYMJ5CGAUIgBoolqqCMwihAlomVNquFxgUAxTcg4uJnwR4eksRq6yiMLSbVqo0XgzV7m63kBxSp9PTk7nn//Jg+5Ho9U6X4HMGAqFWzfugwgAtAY6iQ+W8mRF3njw58PTITS0WXucVTVQLDQ1BOl5ruHZJhfsQFPz7EKbdSDXCKF4FKI+RuYSoV4Sc0UkAQElIjI1g/q7/QdPoMFZ8f4XxItTqHu8ip1l0b2ZlALYSiaJvn/BlBClqT/vnfToxGDI8cKWN5cOTq2CzMOYu4hIzMd4isGLkiQTRTbwlhiTS90UmLHnDhfRYqpxmcaHXD61A8PEMAShFL486UQJw+eWRH6rABQKgyF0emi1lsjYt4tdY/HJguRBc7M9NyEB9UMOdlUX4wQfpQCHU1h9bEUkw7ReyyJMHFbzGytltMANZiNIT4utAlZ3+wemGAYaGiNs7AjU2Ym+tSVJM5qYR4sFbOxE3AaaPQ6ZxdFvrK381a/03dd3Du8q/zsIfe/6ne4juCIsWZT48tmIazP2xF7xzEcfjFMiwTbZSYJu3ZA7Xefxo8G5FbQIDEMiJ6PyOuL5rCnHjBDTnEiDXAKwpztPIVy6tFp0jw9XfbeU6n9P7OYe9T99PL797ltoBIsk46OUOec//8JufyNPKVKBTJxp7wSR7IxkG3FcF7onx73n2GuX5d/81vaDh/jzoyFNOcsS0rwk3P9oWr4mChSjdsWRm+PRwpXWUti3iwi40eI+/aAPo2G+XThczPrTcUpBRYmf7Dt+HF5ryi9PnWtFLpagi1+l//jXb9Ldk73n08o8SXGszKBzku6cBzyGxrEt5zNNjYU88WRnQRKckbjCqtCQSRVFRRomYjWMGQvwLQ4vFvJmP+i/sAgSFCR4vIhe2QZqqn3y+fOKjH27Ro8s3wIe987qgi9pkBMdDCAywXiQrxYSFZoEqRHL65uoM53QS22Z5UE3OF6IxkFQUTiIQQjKhRKLD2cwBmq55PDEnPM4fS1f6kLDfvbwQVjdpArrpcFpt92mDxbZmWnlKel457R5RSGTKDkxfvB18ef/xvr//La6tSG/c525xCa/93++dyUS1pbLy21obz+86JloHeMIUoNJkKTVYtbfOx2nKk6Q1XZOhPL9ziDP53wodfUIBjE1nrmHRzZqX3prrVHkhwu4LBKaA4KUUCrKak6sRdRs3ntxMai2UCkKa67GE9HO+RQ6p+YuJUI+EiByO/dyRxUwgwYpKaIjfXIyMV/7du7Dj2dgYSgKLdFZDLu97pghktYqao1H9z/v3v7a2o1bVQIlcTg62B8MJ8FKg3FcKDPjl385Kq7I9XKBJqRNSaw3w4ve4lefqdWhd60JJ0SSJqFua8tX2nFAVHPVg32/mKA4DNfaIjwcnXzRJQBdJjFAOj6KVVn4k11Crt1Ii5XUiEsF6vyTmZ95bDH56P+9+/6Pr/zxL1Ss2YTXVy9M5vvfrH/87/unX86W3monv//k2cfHb1yREn8O6YtnB/473xPFlToUCbsH/bPBNCniMI6xnsGC0AngVCxoU3epKSwM6OmTSWGJU6r5pmRlLhBWyAxBhQoa6+HOrnp6DP/WP7m88+EF3nFEgo4M6NbrpZMDcmqjS+MyeBf85iXw8yEi3iqPDzswyD07n1293CoJsX00R2y7mRf9kM5z7MD2E5Kg2tSd15TZDKCu5hxaRGDZIN5VH+/Km9+/Th5p40Riy6ACIMXjRxlA5qNQbAg2FNBwEqt+VaLNDIVRuFyD1DGVZG4twFACly0IASgGivXrbku5e5qCchqQwxgpFVAcALCEL9KGuHNwoK9sXi6Q4D3wvYvaX/zfz3/pY8H2HQ6DxaO5A7jAsX2KzQSLHvuBZBHJ/gy5CbtpCBEgonkoDBGS9NIo8mG1b5dyoDfyakK0lqfv/5717W+60/PegrMjDvmDeW9erU862bUMYVtMPJnuHGkbyzLh6YxLBrGXOIg+t30I82OxEiKmmUahTwNnPPcRAs0iKrId1vYgGEQxBGiazfOoH1q2AWiWlHDIikAYCjyJVqhAyBKQpH4CoAxFmyWE1cYj6UYWUcXySRQj/TBkGZn2woVxCXV6y4zPlPp2iRaQckOmkYBZwBQgwgTPHHUPjtqROT2myG6nirI0lYvnI36UZKsMkTdOOuZcZTaauaGqetfXzw4GhbkjL68NuhfTwJydR+m/fvaNX19X//wR+Fvy5hL2X3/6ResfvoZ+fl5dbbw4NvTFQN7wjh89Vbhfs54PJ48usO9LG7W1yVcDgia7aIDDHBwM6qvgxQv1O1u4B3FTAElARxOAiaLl4SBMhiemTKhuN2SM0d13r5pn0Z5YyMEIbgG117v+9dY01iIUKufIkgwg2w48KDyPc6X8WpH/+D/sP3wwxDNQ+dqSUmHQ0+7RybC1ncutOefnM+lvSdK7Vfw/7L++UmgqykfHPWqNHMx7uQKAFax/McGJRGIcFeS7AUkGp+Kxr05Ob9+9SpgXbTbdI9CPvjQ23pYunuqVlnzh2M/vdVer1O6xVynxO0c2htggAlCgSdk0okXXxdJdRCByIknLkBFv4HpSKh2ORE5yRnM4UAcJYIi8zHDlOuclAZJCE0ba2hIH93r4EghJQKPz3jiiA88dGYX3N0BBAO5LHDeQuCKKJQ6JMcRHPD09PczHQle3V1caNBWcO0Yv8MUURv1MwFAvztYZdODY2qEuAW7tau34kXU6B3wRQ5SCCoFrd1N0MHrrSuPPH/U6B0hOqd7/7AG0eu3G94XKo0wwvL/6g3s3v8d885+2Pv9suEynn56qr9xQyL463EccqsQKkIugmunBqV/c5i+XGAHHe4Zda+UjUj3e9SkpSzwogkODpG5+76qI+bZmz2is0w+An2F5OmZ8FTeqRYevUv2eo+p++gLQFMbjBN8qPh4515vJR088rlXgnDi/1d7r96RrzcTwMxgHgLE/31niSp/8YvLtH68nbP7FvW652lI19aAPX5yit964JCDI84/7p4vkW+9tNQvE1MnOezpGk1iQ4pHX3RmNnowZkVDKkEJGl26LzBK7OB2NhxEhQGaaXewNoR6QN0S3lvJbEnakzXCZi6HrRnKzyPucb52YBMMOUmI6yzAWJ2I/7xtHs4SoQT94E3/+wYevbTechbfcrP5H/5gaHbqvvFv/4g/3C0T59u3ar/7isTkuvrUh/dVH2v0IZ4zFpcvF0yy49/DwN9YVoV2rclu79wBZcqJ4kvGy7gYlf6EgdVcpON5i6+2SN4x682CueYVGPvH0FHfdQGwsVx79snfllarWg54/6l1+u6klsBNiiIsmlsUse5dTwa8Gi5jIocArl8CpP5StCorML3SwPAgpWXyliXEYBIjJ8VCDqzgCuy4ppe7ZkS2T+SKcRy5Jc5iHMa/mjI1592g/PPr04vPTB1fef2vnpvIPcmth1MFpFkHloegI2lBAUX3uUpVaBAfm1PAZpchDXsbBRBDxZnd3VmACMiaNnC2AOIgoN0PmuzM7HtcpBGRwLieuX9qyQMoDBAD7yyejO1dFba0FQtjbGUiUEwFyI4PchIvg1DJpmqQjEqMJDjBUgiZinHhpxqSmFk0028o1icXeuZtinx1BP/3YatigWCk8OZxUS0UaoVDAX2KYQnGB9C4SySiyUXjWNwqoM9IRQyJIH8nYFm9itGIvMiaJLD/1cPikp5GhBdmWWMkxOKSOnDhj6BwZYygI0UzXEST2cYCbIaI6KEYQDdlTEHPnXJQ4xI8jhkbbOU4I8ZvvsYNfqTSMsAlBcOIiAwnJyEwYgcBQ1QsjTqiUmSn+PkLmsfkiJjGEWJgYD7UrKCjUC0Wmyq1rT0dJZiDR3LN8yUoCBl1vWlPTk+t5aCEXgLLUyoHp3BTVlyFQGdB6l3YbRDJzj47ovf/ts3ff2dr5s25eg3a7kCRZv/63Vp71ZhuXJSFuVvK4WcsWJ+MnPzG5av6VMuW6KRGEOB/SUOoez9mVVv/6+uuAAADoaXFl2UOj+N7ehGYUmPVGrHeiW13ay51oQ99263Rty49CaDgfL6+ssYz8xcNFJYfUl9n+IHDHrh/Cp5+mb71RfP/t9OXh4PPfPu2DAM0jTYWvYO7zo2B7e3sEng+fauK1YzTnPnTDWGieoca1IjK/cLOQ1MeUbeEspJO2xRHcicXe/NHb7tTePxlv1J1sjB/rM5SrJV7o6vmvfbe5e2Hk+AyNzSCI7dFCTyjBcUgpuxgbQYhXBMida4yATzLyxQCEmFPOQTHhL0Neahg7RxejBbK6xn/nWpti6NEMTg3vwyfGO29XoYW9uMhiMgYpxjL4ZMpIntdShEURgElqWnGRjvL6lATcQIthEswXCD2Gr/suCUJUSwQuPfIFehnzEtYZ6rjrGVOQIVhBIUM7QXEss2GyM1xn4DBx46AoxcjJPvL6nfanu+MmEF9rEff6+q27NfhJofPcucyBvtG//OMrwwz9w9/Zv7km37qTz9FsqNJfHSMbIl5dzTkIOTgBqAKwLKWEFGcX9sBP6vTUoswgUJOU4WHIs+2hwXNAo3Fs7CIEnFha0Bmj9Woiszzn5SBITHV3fKYDDA5QEgmQuR/XxJMJXv/B7X+1NwwhxEjHWCRrITs2+7/6ePGP/qPrR71oNndeW1K684hu0za74hebSZzOBtqNjTYMcoHlI6hH0IQ6jQGR5fjAMKbDUViqSu06MOdGFMDtkkjfkLWpag5np8f2MPX9F7NKrdGo86aP8ji51iqtlbTu49Gzh2cVa369XhniYI3zvHE2sAEfw2FWOBym196AC7bop1Gby3oHGjCH1ZZ8uhtt1sCsDzIX21zLDV50rr/C7X3ZhWGmfRX/7OGFJPnf+yb8iw9OMIxn5cieJL0L9dyACmuFeGp1hlS+Xrr91laWinsXU9PTVmuo4fhrm8WsJKjTBLLC2YnHszzbgNxp8GJn4o0tsUjarkemjsJQNEGvbeMnU49CCYzkKiUu0GJtYcY1opuHp4fnEMP8iqzvPAX55WSzhRGmtXJpbmqcrgh19vJp1o0NJwkhUk4pDAnBdNDTAgTKt3OPn+lLy00mtPWBUaL59hqmQ+mtd1Y+Y/nzP49bCvGv/u0X/+nXyvzVFRiAnI9FBmqjHg7D+vkkr4S6Yxv+S9uAMYScWFaSEPk68BZxaEeHJ3N42k9ZprFaam4VdZ8po2UfpI8HaqEKGgABAADAVgpYLDeR9ZbtRTwVDikMZCosQrCPub6Qy5GB6gv5kELZmM/jCB0YCCZjRpJ2QjglKTQjU45oFCR0yM2gubJc+6yHv9hD13iu95dh4bJ8YdJeFvGTuUzCNCMa6mT9KuNnietHpaI49IhgBGGxWS0g7tzITLuwIbJFHEGgxZic7kcUFgNO5BUx9LIsSVKb8hZIYsVJFAAa12wAI+lWg0wi92ThwlEKcDRNYrSUgnMM3BuEUhLjOZgAKiS7mFGgMtTU6Qz3GiVmhRLPex6eTBKVY3qmIZFQilA2QtBcNkFTmCPyjZocgde5w4tRyXFQXZ8tYlrko1E6PD/PalUGhgYXHvf6uuVFCNLj3qwF2tRf0IOFWd6o/PV/uPHlrzx8vVYK3fP7h2yd/fgvT/Nba739bvVa2iIJ4t5p8cZ2lKHf5mN/rs1VE0KwWgHTRj7T4sbDvdJ3hC1S+P/lpu6JXo4jMzqJSQ6yxuc9f9zyfDrhHBWgJEELMuRRIfv4zN6ssQ2RN9FEwK0nX8xHAbZ6t1GTqcUs1qbGl5+fNCrI1ivrES8cvuwwqtk1FzWgXyH8iy9nyxWfj/2rL2ePYuS1BkfToEzyn0mzEVj/nxfQCUUIADl45nz2fHH3b275D9SdT87LolhSpOMdHUJazVwBTyICyopLnD5WH35wdvlyS9qo92cX1Vsl9fxk7hCZRUtl1POTg6dGqcbWMscqkvK1sjMPSSvWLTj23YRSXv9h/mTkO4vwbN+QmfmxzyAAKIkIIWYVE2RX9/O5cWeM3V3z5sMSQUaCgtVkPnFGXc+gMwXFON01cpGpFKAY20h6lOYFL3jezYTV0S0jGWHlaWTnWSieBSiGLhbBsYWTZZrPfJLGZAkeJ26IZIk0p0pFJYU8etR4e/WXf3V09269E86RQXTnCkccD/K/sZQL6f/+X3xx5xV6+7XL+ulh1A8nTufazdL2BhWP+eF5xiIhnTITVeAwOwyyaATkSfiCZ0Yht1ZShMihPOvcQlpVwiT5EqF5oUVS2G5vUFxSckLgsWyZl7iZbmiGR4vHPQMhspWaUlA1IxgdOmHK8chy4XB6tHbTtU8eZPW75iLCeCzJUTLmffFnu9g/e+NJP1rZHyhFwe/bg4GLVUonM4+Es4nhL10vvfj8ol1QQphC3Wx8Bscotb87xXEPJwAvsTiLDHoAwfEYZsQVgogdzEVc1c9mU2S97kfik68Gr/5oZeWbrYNnp5NP9hKcXrld6YydzDHOp8blNsy1JUJPhlPVwHF0NF+X6bSe0xbWjcty9HxSb/JOSTlz+ev57C+eBbUlR2jLn9/fXSrQt9fJn30y11REvLN9dHgYMJzu6RRtd4dsyMOtau7E5JIsphc6DYOlfBLQLEg0Oc87IZr6gBGqGJyyuCnL4flQ1RO/tSzn19EEZAzTyHwwvnBHE5v81hpcQWkLjk2bAASpsIkJBAZuHdj3cjUxiCcLNxH6A1/LvXQBgZkBtLXl1aeTacW0YKIKq/N65lNuch7jdRSL5bcbrT6A1tbwAUnibdl3opWrBVXXHASXp9GNH3xn/W/CUgJ+8m8++eTvXf8eDAA4U/Yot+D3Q6Ngo5nHP9O1XIkgXHdh+lKxitB0svCKSAJtrT/5/BnRzFdfy3mziPcobqwadUYNU9WetMtzx7FSupNA33YB2PjmhrcwnmMU0b9I8/komQDTy+GED3OwDaAMeEy5eIWl8xSCUVGGYRgOgDvydAJz5TROTDhGodnZeJ31tv83tw//ePxvf/p44/YroOwSqznpGvzlL8YS7url1CQg9NB1Tcu6D0WoZpgQzBPRYiA041MHWG6YzCNPi8pFgDrqyTOjdvk6YaUUy/mviq7mww/7eJ5jOCZUeQZO8JxkhS7qLfi3Vk0kNffneYmJF0aAoBEOUJDJq0U4Nvy46ncpq6Mbsg95qUmTCAzYKPWSjmmjXomC4QYNrxN0IiHDyNUDKMLUvudMMXpGhcL8goKjzIbtZGci1fMkoyIx7yVMJOdzi6PgrWoD2bVz3Tmcww974l7frr5awTYF4ytVHym7O/G5K8xV4pXb2IefEVdarCgisYenQd0IVhvr4qc7B+KaMSZMY+pXmMQQ8XmMea6/qkDjR4Spbr6tcxIAAIDeaffymgCyGgiwqu/w6Q0nGe2ovu/ZsBwcXpAZUmQNHOHxOAFOyp+aaHOJ314P3WKiJ5B/Ftk5vCDRxTeah/u9vZ1B0rFZjru9JE4O5yIbTA7NDQFfUmR1OKeL4DgGWEGZofL5KDUJ6q+vrWiqPLgH758aIze7ea02+ezo6LMD0YFtPHoydmtb0EaVs71p7FkO4DCSUPth78VRngC1Js6xk8VQH5+by2iAsNnxDgQy89IqSyrAsLSXA5Nz/dEwKtdFrl0Qii17jFzszA8eRZUqiQrBbAoDiWiWedyH8ricehlOOQdq5PionGc5z3ItTC7TdidBHVSFRaREBr6fhSmg6QblzebqSMtcnZYThlAz4lIOfPNV6IMuRvCUbzIUlRSoiwsTC30SC61jT6XxhGEbFUrh4aGpjY8DAuYKFeYvfnn4478tU9uJ5Sy2aN0dHBdb/CGIrWfqrTVuvOSfdWdGFq8yfKWA02HY6Tua6xdxHIYw14MwgqYRWu05rMLZcEJKST4dSJxAY8R8SsdwIpQFKp3M+nh9STk/13w3EGVyvcma81kNy1iHDD3rxJ7GRjaHs1DPGiWPL/FmDsd86c++mPMsj9DB7PCEWLhtvbX70emrhfzpV0cCYSGYXk/jo5PTJKLWLtFHB3GEZgQaDfXZ3cv5Zw80517s2ElSAhITGWqaQEEIfFZJ0ADMdG3koQRJmJqniJE/cQgCOp8SLEuyGUww0HxoV6uS0MIf/vK0djlfo6VLN9v75w6wPMrPKlXpcDp3UTLJ7IiAM9hvcFw4dPsX9soq8bzvggiwZfnCRQGS5CQH51Ol4D74anGpTe8+micJg7aLNzfgg6f9RXdYFchaNXk5TDcubclMCxb5rXXs+akX81MDZwGHhibpjOdyHpUV3FiYgZG9cqd6cS/RPX9v4NeXmFYzn1Pon/7CPHnkRO70hz9exhQIzAPUjoQsZADKlkTXJESB2unbm6I0g+OlChS9fAFPRtTTo+M4+uUL4e//5yt8IX/RVTfXZBRicBB14rgqxSPIjClGwfO7J3Yjh3784WSLxGvbwUiHgmrl0E+7Z0lzveLgEPU0kl7DkyRd++HXroptAAAASyi5myQeJRCqD9EKnbd93PEVPDu6121+U6RXhItpOPWgMmwvyaGl+5oaKBDJitLp7vD00Zm43qO90IYYSULHp3Scn2oCFk3jzhGE8OMEeJ1JD/CxwMI2jLtza2lZGo2TkqQQHFhocYVFEycKQt82zRDYjYKQzQORoXw4RpH8hTaGDjESy7/y1vXmD27s+5qyXDrZ9/GhxeWCFE7cgOMu31qoAYE2AamztIdnsHV+fHQYUK3yaAjly4Xv/FB6+ElnPo9WWoVWjT0+cBEU5VHCNizfjUQII2DSzagYc2OQIZlPsUld8KadCPNTkSUXEzVLswQgaGBlAYE3BHraD1uuVlqRaYydqTiKYxMSi1BmPfb1eQKT+DxI8HjmEHTUlvIuDqf4IuRWCBA5aRQjVGofsbjEp3KMpqpGdMn8CjEGWB7z3K7VwR034CZHY+Uy38hBUr5iPDjWSjLzjiyVZXYVXVmgC1LEIpiz2IlRgykEviuUfk3BPh3mIGj5b6xonQlXlxJ5foAK4XRGS9DBiz5iFm4V8bpITu9uAwBMcIbJKchouztF7ZkfO5ijXq4mDT7445fOiR11euDGpsoWhLEFlipKThYhLqfzaDfQcoi3JsIakpg2CkVZkMCVlXwMZRIxu7jowWSaSRkXsFmldH9f54rM5au1TSYwF4RQaE2sS1ffuHH47OLTf70bnD0PNu8odzBqikVokEjM8XmQu1Js321fx7J7Txx8SmdWutTKGxnim1kAUrIkoiiiDn1T45uU0D00LyiA82rzBj8993onfn1LxuR6UZh6cxc3J/sXibSKRl1mtVb6xnv0i6dTz5+zLLNxnco4CkqhgMdpmt07HLIkQ9VkCUOaLD7aCzdzJZWgagU/RrNp4E2GOuUuwtDDYAy3tKtSTOKA51HwLAQcDrZKwBfm69++CNRjIFiDvfUsWVfojumeWQGCs2QOcZKwC8RCuyYevowRdHAWfW0rXtkqPnmwt5JvXjxXt1+Bd5/39LiKff/q8/s6fsjDjXX8bIfWYoPyR/vzZlkMELQi0HRAsyjO0lHfAQ4KxcUIQP6zkC9H8DcigcvJ7txoEcJZRpIG5QNjSaAkfzFKgDmwhWU2diDGw+kAQcOYBCxFy65m1QUhw3Vk7uFF5KwXLESveBWDzrrkrJOXqON+iGCAIgHMQgGeJJhx562C8fnZOobpOZJgBEub0DUxsRCSTA6PZ4upKxLIq792pbtzEcdsBFDbspHUSKcBnsJahFXWq6iZ1iTagC028FOerGEZCrIk80MOp1IQzhMxz7+yKbphGJ6b84CWWrhPJADgF+PQyug2iTiqm+CQrgUChUY4pRCIVczZQ3TswpWN+uTekDRSl0r2K0nGMJPj0SU6u7xUfHk23Z2NvcAo5InjubPftzdKtO6ZBi5UWiU0svovg9pq8/i0q2wIzjQRJQKVJSy2gwworbzlIta57iV4ucSJiUBk+pe/GMD2HDSkv/t3Vk4OMSsL9NCBYG9yjCgl3papkMCwOBUVUi5lT44u/mdvvP4XT8Lf/9Px7/3Ln/y125nw136D3h77sb+Be6OMmxioyBNmvyfjRAWwbi+cDbkIdZZEyyPsr3//zvTJo2rpxvSDF7VLRZXm164hNDmtixsnqgohxQ+eweGdzQqYKBkMIH8RFXPUCIvtkBdODsccHkSpm7E1RtFPd+avtMRbd1odUhoenCMjc+L60zBzkqPX17/REJeX2PaFQXP5BYgnPNzmV0gAyETt/vTzIaLuCZIQM7qAlzli4IwgiUhoRJieqm7Kx2IGfDADgIRwXzcTO0gkMYWxOIVs1/ID4sg0pcrKe68WRg/Vw5ZevFSZIZ7kspWhau16NI+RMbI3HZevLaEcU/7+lfeVa1Nq9BRgS2DfFyPyL15aPVVOYZynQHnp+q/LB3/6WeESfxi5kBgi0HR0MQpnFo8nAeK7BkSQQRbBJJSqWcoUSOPQL8XkwqW5PDTjSQIl4ShDPQSP1BSvsLW79UjnBobunWZ5PkEjJg4SNMOOEYKX4ix0U8iTQDpdWGYEoJhgBKmAezgBMzSDkuQ8CvJi6hw78EIPMMadxWlDhCv04XFclqGeMy0sIb2xqtjIyV769W+xp37r8MkEv1z41ImYdL7se0QBIcmWgm6RcbNRzkzMuFvM7bxm/axzLEmhQ0SWm/mRPxs4hO3Wi+w7/+Ra8IR/+acqcwz9UxAAYGUpWpIKAJChc4wWSSxQByG/sCgT3G1e9ux4hkkZgicpyWY2cBLYHBmAsskcJSX2yfkgXhZr1RwIUTiGXd8M4gTAmIvR9aVsPLZkTgzSkG2gaxX+4LPZX47tiZJburSiWvm339ocelTUvNJ694Zx1Ofk3OnF4ex8ZOWx5fWlm2Xw7Kvh7v94fyEJcklBSkWWCUdTz0wgDMogOKQIgsAROqHKOBpPIsqOrYm/253ItfTdTcXw0e5j/EJiLyl4o6CWyzIdQGbkaGfTz45npbr83l+7PN0fmLMgSYBuYHoGcAkb7GkeguIUgqhJaMy7OGMi/FpdTC0vCZ35YODEMOVnTJjl2AbsxRDlJWYc6hZUlty1pXiK8WvLB7vxH/cvlCvrm+urgRmdPPwij+MFALuAmPrE0ShBSgxiCjZKZ1J+0Zvj+fjzr2bFVen+CXRlkzoxXHxYyPPl2IBmC+2dDeXoL08Ovth7bQUnE1+4zDs4McgIEXd5yPYtYJD04TSB6lyABcHLi+GUM9HanWsNqJ5ODlRrfJrhNFFPeLqsW1qrQYe6SaZwcTuPKYhrozFhnT4/LgsVDBUiHS8o+YII2zGlTcwzj0vkeOdJd/syWicD1IxhwMUKvYtVgrJpGOErr4jHB0r+ammU8kQW8BZM0XhNRjQbYpQKx7tqf/Dm5SWaUYSQ6Z3FzeXIc0OSjPlYCxPItAmhwMe4xCskhSOJC6wAxzSY8CBCRrMiR9eRkZ0EBjbsaGxLRhm6UKL7j8YXQSYt0RQL+WpyZVUyZ0YQQaWKUCoK3sKnEEKpi/MMJ3PiuWl7Dy4aEYpM7aOzrlAOrtwqNBuspelzBO05iYhbk64+0Eh+mfQAlYb0nTtrnXP3WXd/syLBXrws6yvFjAB+pcXsTeOZ5jZb4kKLF2FE0xgo5qu1le5gdnQyePe95vtNguH1P78/GxiJORlwVFot0THETmzCQ6lFR5NDBIG4LEK/+V77T/7ds9nU+OGNOHeSPEaCW5X36su5oVIiK+jBOCGZOC9FpmdCbiSzJS+BGJiaZQgUUtu3sB3/QoKgL0y8AGXcG1Ji+ctI2UicgU0uMfhxyi3F4M5W//lZP7e1lD47gK+3IR5RU8WNICRyC5TRnyQQztKotr7EH488GEmhnkoCm4yDtJpekht3yhX14quf/sEvajllba3e2mTtUPHPjYW/N4KES43tttz87o8bj1/6lH7StXjMT90TnE+ShA6DeZdjKDI1U1SeORhDE4ud4fxEbW9xPATPXWKqxSTHwDVyyY3TgXp0gpb58narpF3Y0FyTGPDV2ai0Wp+p/sUUUlZfadSXYhjhAXFmLfpz16plexAtXG+ub7Qff+XiAG3cXDoLtMnn++pRl5MSOBuGPqYxcZzCZD6AojjEpgyRD3wrSZzYw2kvpmjEQ1NRpsDUHi4cThLSBCYiBNUc2xyn85FeLBMomcaXJV/h5h2H1AwBZf0wirBwBOzaEhFi0YHn4wK57seGHQDN8pF4l8ySUKqkjYzxfRUqV/CRRZIjzRQL5hDJO3FBFtKj4wux5R8bjm9/2Fp25OyXj41Lb2zk72uX/fjlMnswKFwe9VWbDHPW6pu1mWq+XUb3z3fXzz7t5GtxbEV7vVKCjXqgJpqEJK7lwdGL0aVBkH/lnVgs6l/69wD8NYAfpxYG04HxVBzP/BhPAC2goh4PfRdlKFIkEUDDjOC5gWW7iUQAK8wKGEGm5Py4jyr8TAfeo0G94dlMRSAymqRyKPy84/iojYUp5IdBBDQPtgR+6W0ksZk3lmq5jJt6XGjAh2c2Mj5aAgrPuxe7o8Savf62YgbQ7gdj82kEsuTaZg7YUGkpe3l60SywCxQrEgCMA0tCIxhe9AMCUuVGA/bsYmFGQK5jX5y8GP7On8/e+V/941be+MYS+OILlSqlpmlduZyDikSGKqlV+uxi9tP/5+O17VKCk7bjkDJTQqPBuZMGcO5qg3JH5ztBI7WcVqZu59Xkgui5GRkVqhjrZQImJWYCjSxkAZMQa82xG1srsDFJSoLRqjky82AX6WIyFnjnH502cej115uBGj19NLALCIl7ZgpnmZ/5BhtFugxZU6buJw4Sd9WIDOePnxGvviH+4b/68rt/59tLTZl6ZggSdkFFjqjS19b2n7u24ZbEHDHReJG/OAsFE445pNauz33nYGfRvz9/427t+y0kCsb9HSzYmSxdgT/aVyfD8NtbmBOxqhWcX7gYJipyVsimpxb0eOhEfogXEw4gIWd1PN1bINtLTAEjw1l69TX6/mHiPuwKm23m2rWFit+t6ztPzlqrlWw0JVzaD42YIOmYREcLOS9ddL2M4tZa9SyKRvOA4xSIYWZT7eXLMUdhuuclgT+NIWfmErSEx1RTlGUQCBzshCEMBwiFQiSWJIEPsSCIaynNF+ATIEggsUYaVcMsvEjnhAa1gBlk4TiOeZIucsOXdgm24euk29xUjoYnXxw9CnVEZCHNsFxbzNGNq0rCZsMoTBn44nxWpJixR4xcJ2O04dwt8uHqNjWdmQimvHjYu3Wr1X5/aQkVlIyODOhw4MlNfrjjb8gILaGhZqVU/VIunc0RppCDxhMHdho1GkHps+e7FFS8dDMR8GR4YG6u1yIJfvxgmgXx61+/NR1HtIcFuh9maK9/DLiwfEPef34aN0rjd8rv/fCV6Z00LJFLaAedKiyLdScZc4nwgBUzDCLiD/WF4Ma8EYFr7TSFzj64/9Z3tr72doGKHxaIV59NHlMZG6aRbFgZC1Zy9DQLFFy+sezDkAOuFwFwpGIVgDk0wFCvV8yliB8agRnaPCum5USxDi8ITyi90wC72m7Pix6fHE72fvyfFd78Xo3Mqk8+2kl3RkZJeu+WRwgFFuLnni9ShGv6ieWcjF05M0oKoWJYqlsLP7QivCTqHZzLcpRjwaR5ru8b1VsVeJM3XowKQkNrog5DkGc9DsJqJPwQCdXDWbhnlFGxgHC+C/BizfY9ozOrbG6XBAl+OjcYBOD2JHOavJqbY2HVGeu6gPIr7UsYk3XGYUyoYRbV20k4mYlVooNDOAjQGLe00I44PHRpW8siG2NTDMdZi5iNtXyDOT2eAAjieBTFaQTAEMWiajYP03Dq+d0P/VILNbt4uVXOtQu4icOmxyRk6qUWQh47kGqHSpmPzCi0gwQBvIJ40JyCPDexoEzyxJwe8W5vlrcmiBU5iVEocARFVaTQZHyE8jWXRKnCVy+g99dK+zvH0KrZaMTac32JEe6KQktwCCcefjW5/B4/Lzk/h6ZyufdLLRj5abzGwHNIjYhmThZRJZ5GluMWhOzwifPzn/281mrd3Kzy6vjEfnSrwYycw6k1Lt0mh2cGZ0MhcCg55WG5u3DUWS7UNZCBFINgIlFNM0yykUkUCVQHVCEnYEkYhHbop6LIVArJrKPjcbS9xUwmPBT7nUWcyyOR6xp27Aa5yBP2vBbUBaU29uZrxUoST6bS7/z2V2hLvNOuIjK7mBuT07hKiEvrZFBWBBztPBm6k6iUQ2DESw3DgWjOhWU57I0sJAHbd5UUn3qI7xGIo2dvvHPlH/6d1Qef7f7RT4/iU/Uf/DjcrnGwkFkAuaBZ8jzGCkL+8tLXmszJ/nwR+TMz3qghnm3bnaBCKrVVbmhYZJjc3KDtHrBJoA/PBwKZEyJeyWlpKmdxONWRgMoEjmSY0PEpHBm+HCiN+MzIcGo+et7fKBYe/Onpl1+4y5YjXafOy1FsBW6cUCnhWFGOC3GGhEFM4DTomC0c9dR5hWd37dlGDd5fzFYqVUHgeAF++nhGJ6nRn1LwoL2M750Y194RdvbnXBzbLoaOySRAUYQhldQ0LH3aLxluu0680fKnk+PdvjdwmXbJJqdaXQrdFxZRU3F+42IGItIuV/OwQPZGYOhZDkyKDQeSKG0RRiBN7UCSkUkvuLJcOTdDbUa8//7q3sf4w45YqxSfnEbvXMpjUbfJEkMYHE0CB3CmyaBmNh/RiY2kAKFwWO+7o5GeQrBcoc5O3BzlKAVYZNC+FiJk4gaByLKKwhmTzBpEFEAujhcYGaAY7NohSCM8hWmAZijjjbBSm2VlXgWuG3up5aTxEIMT1YDYFEOY8Hz3/Cadriul1LVmDztlCJ2pQQ/YBZEt5cJobAaGTaOBNnA5DhfWqc4Agn2bsLWCSKQQ/OjMzAlYFkOAId9fuR7R6yeDzsnJcP8DtX5VKK9JCAkJGSRyLpx3j89NLXbXC3Q1Dw3PUzqPOnGA0VnqWv5QIwNt5vu+o/3u7zp3btWQ65yPgvFkslGHjs7RL7+yrl0tFiTy5MKrVfAEgJfPJ1+7u25i1vk0qqy9+n/6b0t/9Rcf4/4Fk5LWfL77OPnWjxWYhs2X/o112bXswct5fXspSFMvNaae42ONeUoUoMrHX51/900gaBaUM6tsCd1bZHi2pUDP718oN3gbAB54QRLhSB6CI4hQ5KICOuak32s0c46O+Ui8iKBL10gg58GBD/xh6No8C8tVdvaTF0/+QMPyNKxkxTbVvrN1FotPuy+kgRbX2CYVnWWRHOgSCiMiji1w7cReRCZTAP4sSEn2mLRam0hgzrxHB/MLZ+Nqgyqpn3x03iiWyDLLlyP96XFJjiMs2XW8hIXl2wzLKckgnOxZvuFCmh86YaHEikJ2fjovEAScwWSKcewIsdC5NaItcoNLPQMHXldFyJBN8tcqAMv2jjrF0CKbHpj6YYwHkJi6JA0xsJ+GSYhklm8nQQz5OtM3Y1hwKBYsJoEoShTPwTGaRhiKF3get6iY9Bu+3FK8X5zaqp+U0vMgI2VxCUc/etGVt+UmlQE1QyYqYOnjAo45Nhx4apoxM22KwL4502GLry+LJRheRF4adQLDsuu0ShbStJxTAlOdWhbdrBUm3UHUTsjykz8b3/77N//kX96rfXwBX22MztN8HvSrGQJPa6/V64e7XxVZhxVLDjvaOdbCCPfUCgadn4+BDwU4TjHU8rvt5jAOHpz8IrTzCPEb33wLAIb+8hEqFvuwN8ryZBof703nqnFls3z5lvjkKxfGIRR3bNvP5YSjp5PS7Tbk+QCGrm8WnZzkuzaSkKc9jRiHTtkrbl8KZ1rg9W0Hw0JLrOCTk1ME0ssEv/+yT4d8yGSUJxzWa1lgdx7hr7+V+2f/4scPf7n37AGqNWoiNY8yg6rQaZHcO3VnFF6WRDu1R0FIhpFA4wUaDiDSgqOYyqiCMB14sa2qo0k97HmzFM/Mnz2xfnO9c/VH21LE/95TK0oTBUWuL4sHY7cqKsHIIc+ejRBA5CgUhfNsZMxM16cihmReaZ71gnyVfmSxdCEn02w1ei4yFDqG3coyw4oEFBljtU1mhge7gRUNIj8NK3TKhyJMMZRntPp7dg5WNtaW/sll/nc/b6hOCtPtKuObyMGFGHNYbGu2hcQAkTwukUg7KFOQPqsguhbZPtEdOnC19OzhAG4UXWeoGiRSoeXMYTO4vM7tfLE426OhUNZ6SAkRjARlESQrUyaGQFPD8ogERa0xoe4P/urBwV+/trxYryZQdPrSRory0PU//0z93j+SPvnV6azJLbFEeD6DGNnT3VUCKq8QyGSkS7XuKE4QBmQ6DYBCBmld+himUyfLKaTRYE3F4dIun/EzZ/z4S3PKcYjrJhmAhyPKx2BCu8hqVJqwBO72jGpDSKGYi31EoaEQioE7CBajFOeHsUsSAgfO7SgEFuo78yOElyUSyUU0hQsC4OLE8jI0Iyg4IqjTqX+zRFbz2QuLVefI9VUGQaBppPO0QJzu8KKooPDVt0lvwc+00Hx2AAC8UkYWfS1ZFA9PoHahuiKiFIijxayO2oZsi6GLDYykJ8Mx88aKks48tEqdmbaaBOQdWVhNmjWB7ujnzyZMhjeqFZ6TRweaS8aVVVI7d3uqtnjgZBF2RSQgDi7nC301yzAF5ifSjfUVvPDW2ecfPZuYfrW9zGCwJZSiVqG8+7n5xSdWu5RVNq+hcDrrahSUPH7oiG0lr5mdPz2j76S3ti+Fc/OZbfXOnPfeX2WX8/1jQ0RkP1Rm95/fWBckiTsfhA0y/URF3/96/aODZz/YVFZurX4x9i/l3+kGw3WOK2yBBTRTQEG5UZsf/2Vl68fPwfwqgprpxJlclBM0M/IOwNDVKuBRupinNAdyJoCTgT8BopDtGZjMHv7ioHKpcftH7ScPo6Va63huA9uFnzyqL19b2VwCqXv/BPcabAWC/ALXf9wd/8XnWz/aOJHB3LBfJSJU0Yjrgof6wuSk+5VOjGHhco1oiLOHHwhcu7HKeRBVwkgiJMohfJ9mU80te4J/OvIqoTe1QJMJfIyC+FDiZGOeJFBUhMc4t+4l4SyJ6fgMIuN2AfP8YuCjPlKBaByFn8SF9KI/AdNgjaUtMNrpZJFIiFyQkjxJZwGTJs7A0GHSjhdWRhAcSooVTlONTMmNhzNsrN36mhjCuJMBVEGy8wu7fJfBl3KqbiOb3OV87sVHnanmlu+0ZxiytYzFXjibZcWy6LmAoFHVR9xpmJRpBiHMALgYRdYpBROHc3eriYwGbv/cqG5T2ELNFwhGIidUo9l0yv7J4DzLBVT/EFpZqT3/2O8+di9t5dKLCa2OZUmGt8VvvfnuATj8l7/8+a9BCxulvRTKhp6XAhTn3bOITEC5WgdwRkTx6d7koWWyDH7tjcIGJCs8QQBzEvC9C3H727We289j9v1TvbhMOAx+vJjJJVHaULR5GFp+aHrJAmLCQLZpNsems4xsVJQi9+LZIY2Z+XWZRgjbBWLoJ3gyMRBKYgImsCdeaUtSu1ZRgVZeUxqlTerGKu5Dp9fY97mV3753///2X3x+/dXqRpW7c7P8wg5PXyQrdSLQ7clBtlxV5ByBT1wrQmEER+h0NA1CLyqSqKM5LMNyXkJ7aRC64dCDYBw2g7MzxmVzP/mTBbvs3b69+qPfau+PPdd1jg5jeikJeQgrC6cGRPJQZxhCkCspVL+bclVq7XJ5MY/GnaAM0eU0zU2MdhufHqCDU+N6c0VP8MkULTWEkIGB6yKJRfoTrCTFBB+8SFkFnB3pZmokKFnig4ODztWNMvftnDgg1zZ57czYPZljBTolCWvqizxMFwUohiZGBhNUFKd8Sgwta7kE2y5ZUBg7Ztcq8Wg8Hur2ZAJ6PUZB2dGZ3Cxx7iAQBQJRzQAxmTJnGFCajH0UI7yEzKLQTqp5ceYsRB6Xy1WEoUh+6fkzX4jpGp7ANOiYxmzW17rhoj9kyUC1gOKk8rJcauVNlPIscGmtDnA2MM7gYrAP0w/G8SjAhRN1vG+01+Hh6RjuLB6cR9sY0guzUmnL8zXMZ6olcrGvHR7PV9/IJYto0QGVEsVRkTqDtSBJ4oymUwpEmmHnQjFK+NUCbzkuwWmj0YwUyNAguJqIk+l8BGr1QhwEdDkLQttLYIYGEAFOXL9YzPAMpT1k/8DfWqfDSQyMyDrDNwBf8gS7j4ymYPnN3NDDtFOTxojIiRTC1zF8RalnSm42HJGWkaHZOhup/UXEI1DChZq8jEmqkOJlJzgdDlAnfXKEZtlWW6y3eHs8Gh57wBlvrTGMjP3y2dTCSKWFLZUqyy3mYsgYgbv7LLi5gWwsyRnDd0/mR3/l3wfO335rBSGOf+37zRcH6kUvPtgdywpyp0GmFL17uGi0J71Rwhbo0I+nk65UDKFE2K6jjx9YMMUuUFBrQhmKLH3tsgvQQW9483rxq0/PQ67+ZvM1CoDTYae1VZSg8HwRthQYBURjMtlcWskSMJ1hCwg4DpQm4wDgZTjr2zQMQAEEiQGzngipY30Ea56NbxGQzAyMiCIzWcZkkQG6GQ0dbLsWOypCJDeuisOZZtPFjWsYYLhbOTKCqfmuuvfZoHG3AFPozbaoWgAmAAqBQvVaSHyx+8HOAs+JbEens9WbouYlxkw9nzsaQMo3qqLCHz/RDx8Tmz+qOTPOnk19JGfJbLdAutM5H/K4QLO31gBq07SJwT527mQu7PQn94615kbiI6jM+04CQzgJZWhm9KEYywA+Zwk9dvAwhc2ctIF1bHa0YK/d3kYPxmo3YTPGVBnACGyOW3gRRuAUCi0mDmEhtQbjhLmiSER+lBNRkzUcDVcNGmFJlIZQ17ELm0JoweEX2sVTtbmdtwT7tHco1MpZZv7s3zxcvrPGv71BXM5pxxccQFWLEgFB8bDD8qm2iBlEuKJItmEHSV7G905dCMGVepsOAcflHVG5GKhXb90+GY0U6txM0qCJabZ7iYjZS/zwaLS67MeUO3s+uPa3b7+ktIcvn36zdPrWprREal981nlrmbjIy0g69/Zsmcp6HGZUKV/JiRnNXN9edWzo2Xxviqb9uV2W66+Aq/vTrlz0bXd4MsJlHKuVETD62vXc6Vi898WDzVsrKEjFPFyWcNdHeVhg2xLJoFNjKF3KjWchn0n709322tJxJ3ivjfQ0S8yxuDljg5iBYiKHjyYMxhYXJ7PjHjSOHI6Erysk00GfFM9PytKP/psrGoRNuvH8S2OUOukVVM8RK6NIXr4EyGjy5bG7yPLr4jD1A8spJUFGF3wWJUkuj4T9rpuXsO6FYSBQhLqVHL58vnd/WSx+4240HBx83kVm9HKdK76x1D3jRc7QA54vckMnDpxIrJFpHC+6YXtzSQqi3vOgL0Ewmz06f9Hjb2a//5O/+Xd/ULjV3HnmnltuK8VlEflkZ75thd7BgJwZqGgBQXYiZC4U7K5BMhFRENhwkgRwyenORnCMO8S2ko6Ogk7C5WWIAqOuRsVxmArcIDQzb6cuNxC/mlE5hWoI+Cx1KhwHAwSTeBeNvalGimI41UanwQQ4EE6KZUTFXRzAGWrisK2d6GKVRyCUdCiSCQ0bwqzh46jw5VfWr7++fDDW6zWEGHfnJ85oYYrijJJFe9CBSNAA2aCj1reos7lG5WqOrR/98TQFCkQLX7suJDm3IzJhRf7pA70752q1aDE+CCsegMeoGdcuM2UkUIehy1FQqIb2Ig29R19pcEQu36iV+KR74i+XqvW6ZAx6To/IbUhICNORZatxGc7BAEsK4jTODM8f2D7DU3MDXS2WaI73jFTJE3ksckkAGa7hmSJD9U4yIV8IswAgcTGfluJgiAKNyBF4Vs/nsFzBRQGGwLvHHZwITQ9p8CZ0BTrrEbkS4AL47ab88gJHBDkez1sGGs5PIfzsur+YXV9bZLktsdxm0KUKD/CnnCK/hOXJdEimvAoxEQVVr5UQAkc8v+cjBMvdvsPRpD8aeaf7xmLCF6u1a69uXrmLhQa6/3w8He/7EvaN5QFgCYhaHIwMaccYPJ+srja2v80FR37vLJEatNedzufBtJ+okUHhFIPRk9MJAaErl1hmg8r60WWaiW04f2lz4tj+/WOFES4WoyKTtL721ggAenCwjqNISm+AuG9al5Zuh8Bc6DhmmlYYHBx+9oPiXytUFxcj2ANVBCH1yXHDvVciyqYn0+wwkSAYRViyrpSGdhgydAZsNfGzmIpQyxz0tKbTwyiic6o/OOlXKiVitGsT1Q8/7dzkvTffX5ZfXzqci6fGIEfQ3syLYyovoQQA+JWCcrvED6Zl1BcvU4VVdpWenB0EIpypUUbKoVtLRmYXPvWbv9ZwBhY4nJauXPZ3xycUseoE0gy5c3er44aRM50sXMhPGcfjEdaNnROfBHmBlBWywNYB+hBJ4UhYS1JWljeSWaoFJEdoESFRCKGloBO72qKEZkWfdoWCwpuolNMYpc5RgEZaV5j56RmPIjkMI+DOIsX56w31+DisFhZDlXWj1qvL8lJO14wkRlEvY9haDnJcIkNqLa7ekOZlSPzu1is38ucvnaTFGUgYWkPy1IEO1bffbOSU8sUwmZzARYxxIg8lUimKw5mRspqAJt3jkNJ5OMxcHF9fQfqQWU7ZLVqs8dPnXnR1Hd4djrMB5ClSkYhmY9vum03a01YJZoulTHvdm0+feO0q8ZLK1bboM3M2fHYh89Flll6UAgZ1n3SGiA2jU8Sa6dHC1Hb7frl0d6MV5CUeInYzkyzh52YIxQiDQDKYffzxzk/Og//6f//22U9nUIFSaoXTEyMwomtXyy93Ej6ElHZpeKiBSNsu5PaA13808Dsap0iPB9Kt77ZTAp2NcLksne8MCcTwzdA2CJ6TeBycTyxy5/F5g1vf4F/9T7737nuVZdB49tGD/r7q6dTabQ5sFBcHQzTF7LP+f/ffvfzhm9zNNzeMICIdSMBJZ6RJlEfxeJRAZkTJJXKuWoMZhKSYRdROjyNuaXMUMDhMhKwgKpjm6tqzgPPp0tW8RkNICJwUQJBHod7OwaRI+nIuPxy5BkGrmOAQMI70X2rC13+w8mcfrv3sQacx5+oENbOj4dXFfOK0CriEENAqCb26AkQPlNbM/2GXCBwGdiLX80LXCOFAi2vLfENxuuqEZ8S+H7k+RNWg8dgkYoAgDOyhiiKYHbeYc2mIQEqSNYpQNxEp3HI9hfddI0KhrCKUYc1kRIhvCS/2TVmJISiSBI/B0HHswgzFUhkCY84ZyKLMI6BZ4lAs/8l9hxfzV1aaH3z8xWuEFjsuncMDMSlIPJ4FHOrTkAsCiGJD082SUIM8ejFWEcAjbG+4d3x/KJFlpnY9d/Ii3H/Uv7y9BnTN1t1v/RqDTV2ChFVdsOLkBKNIlqoUlEEInJR+9bvrlpeXaXvn+UmM4+Ub4uTpwjweX7lcX8Qze0HEUJaGAqlA3ixj6JSKIRCgZYbDYdwIQzgRohmhmtGlZRAaMy+AcIqkadxUJyRUtlWnXA/7c51h8pATmzBroETINRcZjbNrSAlJcjEsBxs3cy97To2kXnQ9d2xew7BEd0IBzZRois7QJOwvwhygMD8bLiDVJmGKo69vZkqt83DYWlJyVI+2cDkwrUwOaK62TNM0lMQZggOKw8cX9kXHYmm3vszzJQVn0YM9Z6AeiTjSuLqivNpcga8fIukX9x9fb3Mdy7l7K1eQ4trVvAO5H/6FCWvWZOZCusfk2cZKVcKs/fPOwtHjnIHxXOMa9eHnx/VXlctbRefYosNAvejtdr066ylVagFDr7376lnivfjpTotMV27nvTAr4O5k6EFL4OX5cZTNBYbIZfFSiwaQfRF2A4YIQBmAuvTKuw966Fa7KRRBClUSzONbOGYHmpEweQpMXShzAsO1vFjh5frNinf0FNJORqauayHJU7MT6O3v0f/5m5fOHu/94b87kzfUtdVVhA1RAo+DRaPOG5BnAKwdxONSldwo7xiAf8s8OD85f+Eaxx7WqmB8XniTtxMOejDJr3GLFHv0IL11B3dZbzEOWNtNmrXS99Ztlxl/9EJkQgENAQ6jBGvoHsviK6mJpdgq7jy8b81ARuTgFpKhFITGqIbpiYnJFMoxfkD4xqldROcKnlQz254wTE2ECwqNQlUhRHgyyMW+EEVThAwQAaNSJg/jrNQiVR2t11GRYgYLhhJgHQvN2M6iAMVxGnejXZfgpqbQ5HaThFOT+tql/he78yeT5d/4Zj0JnZPj05fnwtXmyVDvfN5DN7cjmcAcn+GREo4LmbFThBxWQD45IxGWK9BCADdrjKEnVAldqhPq0bDapkKIxoFUlxcQjO0hAX+JhAc+5rvZ13NYaenMhvK6XyySoZ0EY42z7VIL1VwjabNo2dBY0Hm+KFyRSi6b/PmRE+d1I83VEOYfv0570dljV0Ks6fmFH6HFgijOQrWfpFfql5Z90h/+5XwSWphUET59Fv+D9zZf3F8IBD+IRWU90OM0RyWVSrU4iMQ7pWEuKG+i3UOfgqNaRVxf2Xr5tK+Pg3aOBVQuSgNWQqcSd3EWKwTzVl3Y/GE5fP8aQ9zkMvKLf/v0pPcxgw3ufGv9K7dt2Gfeh0fGKCZLytnB4zZhpFfWknyld+9gbbuK6PGeO0FyFZwGQLcoiKJE5KAzT0UQeoEV+I4eP6/yYktYJ4b7LFe/sx5q2fKKfP8i8fq+6oDVK7iO57LENDXv8nZBHXV8mVE9yKcDtkJ++ZMvv/7OLcubPv9f/h9u/b3/yt6aUOMB+kKTmGijjCcvzn1QhfoZhMmAxcF4lGk/DS2Iqq/2Oxol0yRJ+PacTdH0eGzk83JBNFQsjDhIomIB9nx4TkChgXIOpgDHpy0B4CsCcoQIqexVRVrhXTBjIMi0CSYizc6e5bshGgeAnyEIDAHa8JyJn4l+CAsFbeDpU133jJYMU0xIMOZsAQmpACEjeGqXVOzmW9uDxbCy3Dy7v1OFUSGXTbOyaVFBCjwokLMIcXAtI2slOiIxBvAH54NcRdFF+as/Of6xnnEFpYIIIhW7un/7BqON3POdSbHE+vNFbFt8SwEtwXY1JIM337heXhH7f3A+HhligbmxxC92ZqPTcHlVMg3dDx1h0hRSrLq0eRC9KG2w9sIgvCjwQqHNJEZCFfl8OZqYSUnmxjFBiOSnI/4KUcitUUl/n3C9yLYm86DvEZureSwxywvjoNGM6sCJnLXr+PgoGi60QCTPDTT27K/+5Pg0IcopkbvOhCytJ0QYa95ALflhaMUqA2UgK4gsCdJtNoHyEqjebVXhi09+0TlbZLUoKEuIzvsIFQBFRIPIDhZ6CM/N2f4ALQa5Bk358WTsXbkuct9rrVJF/HDmzGDfpfHszDsxNttSi6P3Px36rgYtlTov5v2xDYvYapXd2CqMo6izQM6f9mATYQsoCMDA9oGXlXT4/XdvdDudB0/vPzwz6gpcqhFNGCU2ltAMr7eKn5ydhC/N9VvrsOwHEGPPIaFQdNB5El8gwF9pIAJS/8rbvVYZDLNfJUJ7KbTRwOv0P8FKxXhu7n7yfPu9qwwECKIJAwthPZEy5laG2oYgsnNveNKPN+SscuMShq4YT7H1rdI4tap8oX251nty8XC3W/nepR9uKHvnjh9ExXWKSxC9Ii+GhspiZQE9mbnTsTbtjH08T//q44PpoLakLL29zQbEVFpFhBrx8sLtX4hLeTv2Lzfw6qu1Z09eSheLxu0rPp90DoPg8FzO56qk9cTHEzKuTAcuCw+OLQfremPyrMcfBzx5q7pO49s8/ROZbdsOKlc9Dx77PgPp5/my1EoPQkxuQ+G+gfqRWmzjV23m1KFnWr+2RjHxJ1+6WwUunWU8jM8RIq9Ah5Mx0s4o4CvxxBYzf6HGUIjG1sKx0SSNTStZbTN9B3ii327l4Kmx+3svXc1drhZLMYLooDPD+LXyldui/3QweTJsU3JeoVMyY2VkNvOjCA50UnaTNAMUB3lmGqGoRXHFEjUfjwxcGmbT+SmGoWQqcG6gihTpgKCdIf0ySsgMVMmHPH5+gVypNfYXfSwAkGXzNfLJjicphdikuy88xYULV6qoR98ptuKN+TSUOYBnd6NhC6vvO44QkFHqnS/kdsXlsrM9QDGiepTCbaQML//tH7VcLSnwvBlQ074LAaLSZGem016WPjlIuJRvb0lHvzgkNtHGasXW3hCxmT3LlV75OoCV3c93V1+rDtMgX2eMC9kfzNZrRDWXtyO5DNpi+1V4Qn6UDvEpdZn1bvzW+52g8/QksKcJl4Rg6r16rUkQ4txcfGejGtPE+eezQqkM5HzvpOM7RMNyBBSCvAiDbE8HUgHjazmzlxXT4hLPZpz/0ZGZBRBO+c9fLBCPXhjT/EpZhrkwLx32CX3mlinaNmfZ2J9OkWI1Oz6dbWyiV2AYOe81Nyv/8g8f/e7H//2/QlfUjerlK1FtNXxwOD0/FwkCFiqUSdPDYVrqmNlZT5LQGaMnBdm46HFopduzG0Way8FkhUTx1tFBPO+QPEBWK3Efd1QIMWOnTWc5xQ0Xjo2lJEddGChij0kZtwn0dOSwYpaECApsS42rVVqmqUUHOZt1YQg6nLgcxWQJOu3rtITzKLmyuZZK7LA7uDgYV1Yg39RaNW4JxRObObZql65KHxoLyAiX+Py7t+QQdcMB6QQ5igNiKbO63mRIlArF0Ged4ciOw3qOCzGaSpMKBhCYsVyxVkwKkKONxsEgDCsooK66CYyQeLUAQ6zgmNHiIA5NyCjGv3x6IWvm1dU8y0GpDvUmbvVqDkNHkergwG3mIhQKsX+wgf1cmw1M1B+Es/RmUThZmLl2vtvXYBTn81kSBADhLDVt0zzO8/DEW+wn1QJez4f9TpguPHgxp2N7Zgp5QIRZMgmnkJHGOdeI08koOLYvqsmiO5xdv35pfYMHbtKP1GnsT/QBinkwWtExNMw30ByB14jy2pI/bZKUfzbZXyoUKjeVBNYeaUOSk0MXW6pSBAxDAREFEM0mYppwfKgFPm4gEJpVCrlRFzs6S3Bh3rQWNgtLVWl/H6CzVLcXv/0fHmy9Iecv3dx7YiBM7hu/eYusiO75TtAb6GqMQlhKmojhd16qnhnwFYJllEdfPTvY06p30JQN3/1GkSHlNHK4Mu+ytBUjkG3FXf36lTWhKn718IyoZSiNPe8uhHrrlzvON64u93rP0+QMRuzpmEEUsEyKO5+fX/pRo6Eg02hBERh5ldlXg9t5XM/gQQqniUVCEAu5aA7VHBhi+DwGFYv48UeHS818nL9szFSphHWPVcsxym2sjUZP/vzMJ8TieiVGmb1P5kqeIktsIVckCAyOPBEjJa54MlUTAe3DS/kyw+Wl4SHIu0j53QICcQ/OkvNY5Ddy2heTm7Vs96Pn0zm2dFN2FH/EWs44ZgqQhHmzUDSiILEiCUVjPlY3BL2Lqp/Ob/765fr1O2/clc52Dw7MQNRUyIy6TlSgaVqAB73IzYX1Cml7Vm8R4krUpWzIHbHZIkBmXDW09IuRgUAQhmFFMg+8hAwzOI2hRhLbGRhZqmYFFIWOrYAz0wQEQeijAobEtj995G4tgykDa50hHUYNmg638uSKMPzV3nqjCC3nrcu5Z/YAPD2sbLZUxGRABEncOE0GARcygHExPDSOHbxSIHI0ohqA42NnqDXyoQ17tI/Rk1GIpaoL0Iu4DgfFq/TOqcPfLhZycQEKiGej4UvgPR0pm5RIWn/hzqQpjJAEQTjYdLg4vbCE5lvFksaXYBdN/sbl8sJ7YKSqNcr/oVtyoucxQjPxy6+ev7bCIAggMuNMIY1pf2XhYRy0cb11cq9vAPzaW0v9zkG+STip4aSCGUdbDUbqHQuX3zPXPdgdSHnk8m3GvDcivvtWYRXvf/4hz3mszCw0u5xw2kJbv1RkIUdvL0FYAe25o/2nxtPT5Gubrk6hrGTQ5uDAUMaWN9ekSjRWOF9G5iMP++4dCErFbkDUyNLb1b39efd4Km23gcQylGdKWP94msw1cY1III6K58OBYZ1ZmzfId+4W7YUmK7CA4VhBiC3XGQf6wFxbqt64LT6nCsjsuOLRdmIIdeViYgoSKbnzhZ6+9r++8kf/w2GhGcvQOzY//evrsmdOvppRXHu1mU+nhsQscujyJaFFAGe3j+FkpGWmnBl+7aoMsdgGwkmxnzpQrNvIxCDP1PbVK0xVyfqWPj3vkbxCpOu2uTONqsWMJDEo9FI4TWhF8DQZJ1AcIWxnGkM8ystCajr2wal3dYUu3tzQh1P2PKkVqVE/hVpQP3ASP7BGk+A4h5E6jWamj510zTnUswLq4MUXfIP48DxdX7LPU2Zrkxgjwd7BmEfLNc9ECfvgQivgeJF3qDw26BoNKTa9LC3C0WgxT0i3IEwIMJmMsZLr2ojvxKU63kn91RI6jyOYjH0Ei1Nt71hfgoP85VZ5OZBAJLSLbhhNZxap0ogfpT78Jw/T13jIOItubR6CzdfA/+OTesHoxaSp4BKFSBi8oMtwArHUKhRbE5cSmBAydT6FkGrr5cu93/u//sFv/ebKx1F+yx9/9Ry7fq06x/IsOpdJuqOlC8dh2IRJgkodb+rgbBieLWb92cVmG91anQqL2f69kIFEgpfIgYktIWoMgoStFnKExMoQcHbswiJJnurLWwKwHLzAAuFWwf0UvVXxBoDToMB1OprhWdOkLma4jYspR6YB9v8lCD+AbU0MwzDv773/p/dze329bV9gFwuAIEGCpNhUKVGylUTKWDO202aSySQjJ57JyEksR5IlUiRFUyRBFAILLLbX1+u9791+T+/n/L3XfJ9uQADDMdQ8WWIyFY60C2y63Mwky0+Gf7aIaLmCF4StK1fX+uOFp0TN9UJnNDx/eCpF0zLlIRHGyCQIkDc3U2sPef7UGg4HxSLKXtvJOOawj0kkHNgx609sECJkwlD8OKSc8wVH0niTOjztQYhm5ORscJDa6srylf3BAgQTqlB59ov3y9/++13gx+utedqg6FcLYPveNFPqJEEOgZXFodsI9gCpBmYh8LmFsnlgmEBUCsVRnA8Q6MJGFdDbtTw2u/3sw2E6VNRvfJOt/YNdzArcFw4a4a9cXZuNxocHw4LIl18tUYYNFeDTkZkpqhnUkvLpEZXGNKSnbja2BEBbnIPRAquKkDZf1O0+Mlg0tpcxnGDtoYtCOhiWsunjcMY8SSlkFjtLYQM+iSEYT+AwFbx2bzqaM/Frq+VHdo3ejORL2w9PndeNrtxA+0YIzF0u8BZVSu2MVghqtSkcweHi7kheFoRkql8w56N2fM4VobE/i3DE8cBQUiCB2rSeqTIhjS1Vh1IeT6GBW+BdiwCmKJbiaYKQydxFKACDYUSWKTqLgK2YgxGBYM7nzqxtVUv8HHE4MXnh+Ts1IjexWx8NYkgTM6WrX2vujwIvCJkwxm1yiSYCg3BMH6AjJKKpFGGRaObrAEjGRGACQd820Rhoq2pxFzOsGY0ETkyIsCdTqWdGe3ONBUw8gzZWQ0eL+ud9Go5vLueHA08bxKYmlaTK5f/Tq2dm9kdP97cz3MHHHSsZ7coMiKNIF3CfBx0IgfFAZv0Q82ANACA8W8c5UO+THgHjMYw9Oh25jlkoiSACaIugsQ2enntra5Q3CaolftYGixeHWF3XQogRkOETObPMX71YPP/iycNnw7d+Y0UP/TVc7rSjiJRyK5mjx91sFnzywV6viV58JZdcZl5eIiggf+eF994HfdQMLl9tKHc7HkGhy9LZLJ1+PgpbfnmzVGGz+TXi7M7i+Sd72TrNXqwoqUt4cTaTIk749MQIp+LuZm6Ui5bypMMbNOl5DnJpo2iDHgfKoyC7fYlS0bS5uzqcJ94MLy1HvfM57vVejK3L37p69PPzK2zq97SuA66/2ngiQtdXuX/1R7/+o49bpx6MpjTF4PqRNfMEkqEsOJ48m61fqBhIMdmA2ifJpavEeDLTlCCFJi9ORoCBsgmDVEjOxCtXduF8xbeB7pGlzuntLZBbmJHlLRXToRYEEQrmcv2JmSe0iWcEri/RpQTCMwRlJ3rqB3kexCLufBFzdijiREKHTJa51SzHaPSmjJ0dmPcf7UGoYZsWri5Ign2zxqckQl3NcxR978UnxZB459WNCJir51Zv7ARhXJQV34tGw5FEQ7DuR3Gq6imOCLDvg44OUQJIEDSLbq4zk7PRalUwfQjUBBzyYyzkI8hoqXAugULBDdyJjlSXxYvrXAIAx08GoeoVN30XAKvrGfvIJTwjtOVXb6xInYnvOsAvTEB/FJ4andeteWPLELhsObd37OfQjLFwczAMZTPCPEyCAIIQjwGl7B7/6OmJ+mSvCzRvJMsl4vTMFisEkNqDUZSv0J5r5HMQFnv6eJH2hkgsEz51KVM0GazZMBUL7B3pPCXqih+gGouEqYX4Vrp6owhj8UyNMFXlASMyWuqgU6oshQ6J1i6QoFJuXFYJlMTUsoQNTJtOwRhK9UHbknwGAqiGCNISk41aZ3FtO5+jhZJYGw97/fsnOjysr+Yub65BcQRD8NGd9v/n//35a7+WkzDqoN9fo4AQDBdqmGnmdZ8KLLDtwuXqipzBT17s9Q+GlPq0VslWGAIJqdA3I5ohqNRUjHDhwmh+Q+RhmdAXykLRLtzIHgyn555S28Ym0CgHzB4cGtc36mhBLAPT27ZyqSHseydFoaRojhQ5hDqjEQT21GTuRdVGAORK0K0jYJAA2xo4Ub2EBRGc4VMA1VM8oqBPvCjhnY0LztMfPRZOTvPyapWL1RS12lqeFS/cAl/saUA7piXBGqvrpRoKZGMA/LJ3xtnALoIPohTas3d2yQc6ZYM5vSgzGWjfjCZcJG/Vbu9p9SCXZKkiGioBGUwsaB55gJhr0CEGl/i4PzZVYwZfhmNPXgK8F//64cmMWNm8enmF6wXqyIpP/mZ/57u3iGvlgh9gtBs90gZ37h91csU36uISCpU0FBpODh4FoV25sj1sRSWMOyWAVNdFhsMm/RAWgxBTwnARJXqaZvkECmIKDYUYnE09RgYACowxgIBwJJ6m93akpJTCz23RcTnL9gUo4SGBgqiBwhTYT0wE86zdFMgJ4kkVeaKaWY/0dUDPMyyL8XMUiYyAxHt+WGlwCI+ntpVt8GcTdYkAVdjX9UxOc4EEgRPPMXHMDWMcfOAGEBehdpD1YhQja0j8MQxIHELXCoPvP9FjjCyIedTgRSmzJYk5KVBT7GEH79tId6biHKEYIimzMHSMQSmXrHF8itlxgo56i+zqBSQ2oba3BKUTJaBJb+rjMZbMk4ToLzZWizNXZ1nRj8gjEynkK8H4xXvPiJcvXTdPz3gnV7Geh2/vziIrmNprv3zVAWDYaT+87+MC5hCVECDk6uD+/WcvXiBX13e6mabAEl/c9tbMHkjmrl+uLYbp+FF76Ii1pbw4XoTBnFtCppFlHg2/SgbuIiAY8PplAXi1pD1382mcws54Goqwl0NB2CXgBYIlqA7HXglLCVtTPO9A8yOk2cgCBKroakQIDj1PRUIb9keHBg3ZmTKWjbkYADU8Hc0HtWvck/1pozX91a9nkzr2fqu/v9ddRhAgK5WXiznJV0yfQUxW5rNR6+Mvh5exea1esDPEnbuqbUE8Sul2SF/MVWXJ2lzdAQgKgAHgxf6w5RwrvSF75Tp/7iS67tLZYDjrdwUG95GCGSYzgtX9ccBEHOJBqApnl9P5dpWe6t4X5hyrFpCnc23uB7QLIOKL57O1mtcZadsXpXFv/u1LnCCxP37sEiJuWipPxkPVDc2j/9f/fvfLz2Z7n8zcbqy6rmZH/RCXBBin0cCDqKKEkUk4TTMkGWIoQmGaohYLlBVECRAs7EBA1GKBxDfTqe6nM83kExCJ1nYzRzPNRLG47W4JSamZD2jqi/dPYJ8L8tyNS81Lleis7SVJsv9MuyjL4NR5fOhzlvft3Rrw44+Td5dMURjq4Va+fD/gaUrO1lMtUPA8qitpHqQkjnx8Piokpo2Gc8M+RMLf/1f/bCcAWIj+yz/8MBF2wYIUTs3ITW0C9i2Qa88cKqZDQhK5dGQvEohlUqrA9UfdmRfFSS7rJQEssxADiywMYGxTppZri357MEix1av1jBV9dK4UVu/+P55c+7XV6juaWIS80EHQDM9FqE8UaLC1l67XcCiWVTDqHw6ICRyHPTat5FGu5kMffzXWnEM99Td+Yx3nL/PAycFX3dG0E3tG+Sb9L//se4dH9+5+dKqWirRIVvgAKAfB2FXOHAdjjcFwc0lYLmFUqdh/5i+M8Iuj9vqNLTQJcwLrh4ANEy6P0Fk57xZA0AX8IMsDQ4rsHPbYgGAryBJSfvbs4EI2g9Urth5s49YifvQ23YSHrTotAYbBUH5KeoTjwRLBmcq5zu5j6Sv5S1kAWQeqSep1MALGAhCBSTTWZwOlBwzbaghmeeBxprz+8j/lvXkymlHnesyu83KcjJ51bZyXXrt4NrF5iQan4fNPH156swYDq40q++WjoT+4W5YEpCZ/oe0DaHUnDXgfoUe2cuSu3KyWMbYynUbLdbrOSK1hhhOZQ1XvqND1lcwK0Ztqk7s6jvobObh/Yvr6vL9EZXao5Bya+7P//G9+cu1CAX3zVn1dxntm53jWViyZ8F7lEuZ3r/7kBTw9sWWS0Oezq/jgOaVtbkgeOratuevCRStgPXsL8OY+HBZzh5MxCAMiiUUYSwdhkMJAPMeBCACi6EyTKpU4hiOKQAIIThNgO0NHBf35vlZvklEEFjeJwXH00U/Ud7673PfwqUmV1lECQ2ETmHa8SkVubMnP+iqSJihgD6d+No8KISGwuTi2uvthRIBchh4jruVFTOgnul+8CJ4OlCpBqSPbzcEoRB/7fkYiEgVD4ICcOriugnNnNZMvrjSmfG4+CmSECQ0I1dHJB7aupptvXLj95cmMyWdrlfAaB4e42VYLsRr61sPbLbQU47AM11d5lKnUd+cZRD22yxgzPdd2M8EEjMkSqrUcGcH0jktQuMiSMkNoi4GURefeJE5IliSc2IRz/AVx4277ObUsX6rX9r5oqwuYqmUoEbeDGK1gxr47OEnffOvilc2L54dQgaOuNpPBCx2Ko2REgcoE1ZgNCYpPI+XcF3MEWgVnw37c9pEVVM4w2QwiULTRD2Q/QFGzfzqkeTaS5Y3NjOEhMRgDqb83mWNOoHlmAS+ThdJKOTufxp6lzvTYD05vE0hlM1vdzdWnkLUAT6ZA6OPP7trfupn//N/24vrcs8JHd/dlh6FXOaZB/a3Xc3VeeHBXdY8MKZPiOTiK1dHc2d1oXC94Xlf6+Ok4J4v5EoInIuIgAU0k9Vw8g8xj7xMLSJAU90gGnRBZubEiYDI5+rK/tV1K9NBvoas1EnOIHEeyMAmHLjoKnCGYE+g2jTRffvvg9EMsjapptBhNODH2gjiw3XyRPTidpYCmDuamtbBnVjqQYR7lhawjAMVMMQ7D+i4e6+b+X1vFndfpl7WTk46DcK6dYBSAA1h/YBmGH8is7/BXNoXZyOFrvAkkUiVvjBYsmMS6laVJRTV2VtOTnoJF4sXXr3x67+HZk446mJTzWUbGXxiELabHz1sJwTE+e+vtC9BSjoyD73/wMEGlXNXXfMPwdYop6Sq+udV495P2a07zkZLOAmrTdxhaGBgsBGHLGWt45ObzKeRiEAwmVHr5tXL/y5k3GMqS/bfy1NM7RuPmSmqD11auvPRKfWRZoKYtrdFddV4vYZriRSFigcAEQGgGwfxwYaFnTw3TZdAcnSvSYytGIhD3U90CsYIMlLKzSD/WY6q2tXV96fmPRr17wY8/Vf/xdyrVWx4AL+BMY6ytygBO0KFrq7Qkr90ktXNAGwVkPpUlOKJcrROfPk1jLTIIZKkmLb98iUxLLjD9+eETa7yAY+bW9956eD7ew6Z3ft6t5vPNV+uvb/GPbk8WPX/Qs/X2ormafWkXdE5G6mn3wX60dG25lGXEivX8MYGDEYFw9tynMRKKIq0X25Fix6ZU4qV1udNznNEck6mlVeGwPe0AYz8IYi41FL11f7ori+odc+uW2D3GpDrF56T7n06vbzttVXGfoAATMhKdP0Hd03Z/tSTlqLmDA1itlAM9Y2g7vDGOQATmGuArl0G4vT7qpzNqE6fjJIcTVmgPrUs3uGWZe/yo0x6ds3ncEuhqfT1XWXv0yQfMqywLhQU8e9+VXzwwbv0ajGlkww6YOgnjkAQhQnYBZ4AiGPQPg/V/uDsJ/IUyu1KEZ5GwtJMprBGddh9wfLoCloscpJtVm4k94+4vBgSRufGGIDdWfVdpf7Q/Ptcur7MpTUkVKM0ilOP9+V+O6pcz0lphACcjS5Ey6ZSjSUBEjdy9h1omXyK5Ipr6SKq0QnSKwIPRGPGIIgL5LogrIUxAGm/roBcIrq74gsuKFAUkMIFBiKMulgdk0B1Dp4vyzQv8W8udH/3Mu9tbakrmGrG5jrd+3uexiOHg4cF4ea1IXc4poTjvjmnFs9seCCPI+lKuLGDdVms4MSUkotAKg7Go6yUAbMO+5jEVyHM1n3BUDH1hwnk3zl7Av3tB+kqC7Bdq9sQIk3adSoI0/OIDVVyjauv1CRCeAAg4GYzb5A7ttgBu5MZmQVx99WYDiBUvsQfKMgwOiND1A3M9o4YOPQNGY/rKPGULyYxe6xR6bgvgq0wQ+hU6sTwVqlWfn4yrN5udkznsmKsQQqLsyA0yYmho0zwDRVrMr4SA3cFiqnThwsAdpY0ckAEu4NBwNsgR0slnD/7wj55t/a2rK28u7//wiRplt35788nhAgYDJpuHzAHoe+4mTuBoOJ2bnoFnRTRCghG19k4T9SELduCSAMxtxINIwj9szTAYmgzHFTFPN2t4InHREHSsFRqlgDEaMBlSMiNy3Lb5KtWsCH4ACkhRWKY8yJ8NdHVkulEMwUnjUq49smaat7wEtIOouok//zReKpGoadZacZsj9+04A7plLIHBKI0Qlq9bBnnwwYmHspculy9uZmYdx8GgUol0+2kKJGf3Z9655cv5jXqsVpZrvHGCy5GI5hfs4qzN0PAqoX70ixPcm+WtYLlSxG37mRImTRZq24kOaKPRzWtVABA3V753/P7/ZZukzuxo5Mdokhi6zbHm9ia/XqDpb6z+9bsn9XoxsBIjDrWToxSHl+Xo0Pc+eeFuIsG9zz1+BN9cDwERmFuhXE6pNIA8x0izm+s0KOArF/jAgNTABxPrRLdRnHEME0UZxIYEgV3EIbzAmyH65GR28A1TvZXZbEBYV0kRutX1hBQahmh9AxdguVQqJq766Z9PCig6iOi/8w223Mw4HXPc1bYSXeufL8LC+217Epo724U6ebz58vWHj1otLFvbXFEemZkMPLM9AILjOIZQOOrZlTWOvVgez+53jZC8IjlUqnYVbFVW3AG6CHBB6I40J3aSJIhFILHg85Mx2iAdc+6asOfOGUaPiiCKqP2OwaGEVPLMJMYVsyjmSuKLBw67lMtBS8RExp9DoP6idRyenTR/c/PldVe7d9BypEKq6E6ijPQ5EjotSxD4HQHPOtPevAo6ihuTRUIi+fVLdAoIXQTtjJTORMnOpi/5MPHLLx8DvDX8DGlHr3+dfnFgFnMNtaWc/PFzG/SLUijBi+y3qrTEff/7H1REn8XyUgOjknQR6gOaIXHVUC15o6i9mIklfNxRsQiqL62VCwjIIAscUI+nTcpPL2wsTIOHowYqIAJ31h6yWYLKgvRuPvelmz7X01sXTn/wsyu/+bXujdzyoJcnudMMQV//+uLQWGazmVgLjvJn9x2nRDG1WJNjczFH4cSyNYkhEN+KfBwses5hPDx5DnDc5Uvy0E4Tq/eTf3v+9t+9KH+nZj0FZZmGxp3O09vVN3/vytdWowSye+1fuc5tly59+LCFHR9QFB2APH9FDDpB+3Scvbqp9Regr4lXmHwRffSwX5CTORT7ayVmiaUpljuZJRJbQQEPSPd7iqm5tZQWdzfhyfDJqbOpTCtblVd/T1IW+c5PH5hlfzCGCyRwbYmqbe+8eDFF5gfxJUofuzvNPAZypMtQ3ESrA+cnypuuXC8hBk8MQnTEEaFlbIJ4EmkJg9s0J9h+EBuaFaLjOWnx1y+K2JXMomUmeoj0nLQS2FrgsiTQFLSv/uzTLch3QS+i5Oxrjc8P/VjTaitYFPv8jmgz4slE4+PRfDQtEnDfBukVsbSMPrvd31yhOa9YLaNQGtGwdfS8B2IBFvkcDScENrJsSSbNVkIgIn6h0MlnFpGJHvTW5pE5UqdxEoOQdGPt0ksrvc7ZuKWVQDv0kO6hqgGL+0yYWaGoBMzKRLiYTCPYbjkMFYQoypfFZGtZhtnsuQK8MHkcRCD98SM9kMPNAq0Zvu9R1ixyCGFwvqhswEqIFa00XdhQQY4FfDoLfRuluERxVUJovHDtN0sNN7CWGzgcTVgCHCHRdrnsnA8JX5yO4d4p+d13bq6usl/+xy/chHrtH1+771nnx5Pdi5nO8RGDujkq4mRyNgtTXFnOxmo/yqytJBmApylHB1GEQhTGdqEA8ABPxy0dtByxnks2tyc2FM4CmGBK1RIyDrUZGE78gzDIViUiCwVRenI6qWRzcwCD9aQ3X7TPXRkBCrscGFOdoSKlkUBCaJHuzCwsKLx9zXn4WdtBACxbI8RIKvN24EOpL4jEfBZJYLRcl+YBZHfnP/uTRxcuUigDG6721Rcw5aIEy3MycPW/uAwzWR5MvpoOoJja4NE4IhDfUp1gezt5dH8ch+721ay4hJze63M4iRMliAK4FQriUB+Ax+bYzxI8ADgFfjINoQzAO4mjuQIB+LoC4sTZsXu9mVuuiIgTmMGCDqKEcOc6Yojw3qNWfhNdzxMkA5wN5gfHgwLLbGzw5hwMFXh7LY9GCIJg00W698xxJnG9EnqWJ8B+asI8zyN2ihDgXImFWtVHSJEAOofTi/GsotvKcZyFsMAE8uVMqSCRZXt64IFJePy4bwBepimuNAsvZZgXH3Tb56oXabmcP3TjnasEBKLD0ejG2/zWK9BHn0Xvd92ZEW5vLvgOEXiaO8BBjiZTFIBsxPQ5Poxw6UizAX45zro5jhHTwLQtIIlOp46qmHWc5FF46zL9aP+w89TeWsrakje3AxR3K3VOtxnLCrzRQgsChKECOndqKPQsKLEkL6lkHq1SeXVBEiiTGtI3v5d7zJi/9dXub/w3W1rgDM+83R2/1dd4x2YJjKywPkh8/NM2OZmVBeTSVgINQiR0QlTOsN7RuU1mwjlGUQpSCcJc4zK1IsUpvn94UlOSywXEuAtVfBY4dKfHQxNGay8vGYMzqRC65vzhnSO2EHD14qIXlMtiEsBZjmZ4eFKmwDCcjwNaxLXYFkp0czWXFZl7H3dTJEl4Jl/OcRQLW9rJnYEzTdouTKSE2VIub+EhDk/OFzGPjEKQTdNUxN1F7zUKcJyytF4+G+LhHh6fEJt5WBZjSgDMGHRQ37OH5yfdfDzmN9DiOhEpffVoFlhhDIMvXcz7tcxssQg6rdoKXiznhk3vB3/yHFvObDSWZYlF6ytp1vmb9/68eaF0ofgqX782m531XxwhrQkEautFhOXCF1/MQB/PSTSCwvNZrGkWU8YW5iKZDdca/CCNa03EgB3oNOH4NFeklPHccGMmj4NgzACi5aTVOpYJ2TpfmB1N7566O8vc9VebU0uNNR2fO/MI3ajgEkndPp0ACy/LRo5rpQUqXWTff+wCNHYBqGzxJc0dTFJgMXaNEMGCZJEkQA6gPRt0py4BR57FoxQtlHwabydpyVEBw/ecFGlmBDbBBlNv+5Z4aQV6/989Rn/98vJW8bOPDuqvyaMH5/AWM87A04V9qQkn0zgf2pyTaIg/D0BCFAEqTsdGg0sEQSYCcDKezzujZykSoI4loes+rsNQ24oy8ymkE8eHEi7w/reXS6tF9j/8ZWhA9nTuZRi5gNuYD6yVwlBe09SHTxXetbrHvtDIrTTzuqE6agBLhE+hXsv2eTJHqkdu4LjSRk7O4QWIkMduSsCRw5Mf7T/7VRZENbL79MjMlXGWJwgmguFMZtmfIavVQji3X7m08tm9h1/7zV8ZfXJYE9IRRSdRmGl1gcaOSy/unsZlkZEXRTULQGaYCKkOdyJAX8y1k2RWwCDzrL2YKu/8wXemnfkHv+itNlGW1GetIXUz09KgMkLTvr/o2s2NWjr2eSTwCoimunI+h3XmHEQucBwGE7WVNvLLpqlytEh5SR0ETU9RNH3qht22owYWiNEb6xzupyc2sPRabpuMUh0ZKdiTz7palK5czDelQAuCUS+AmMJUUUg6ToGQZaAXo9k3vrvz7br70RcakoUdO8VdlStQaWjPRh6MIkEAPf2822zmrv3y1vCzxexseKpOjSK2VCZkIgcpuIynB3stPT2v0gkolENZIme+IXiRQLhB6Hn2vHsWzFwUQE6/OG44Pn9h7e7TL+dC7vr21fNBF6XhaGYI0w9/HPG8iFM+AVkOFCc+HMUIEEVInKLIwvrZn3zOc3BhTZBsw2P5WcpSiAHT/OrStogae3uz7asURKOP+w1PUbacyFjYRQwazXXFQJNU3VoVX3Tmu+VlfR4bGiLV+QjmSASDcl4ME2QEOQg0eOLiWHqrkgWeTZcF9AU0wVMGXthM2d8/mSH7wFgxa6RlQfmVtzLlkmh/On40JlZebZZzMH54ZA3V47Eao7zjzi7/Mn6l0PqTO+PDxHkJOLdkfoNknj8MCkt1MF6YHtQCw0aYRn5AWL7p8SRDYjrgHEXzccciLNsPCdSyMG9jsylZwXEbwqEgJwqoh3b3ZkwxlPJ5dJFMBkGmRr14/mIheRGdu5Av7D/wcZlHBIKuVkG+YJiIDHBTfu1K7WLqjFKw3/hHjfu/j0uemjk4lUVOm0NIP6g1pDhA4QVAFbBcRSxlAUKbTQ7DSeds61Jhrqo9K3EyBUHHMJqCshgFE+gS9uGLADef8ET5wiu1GQB/9MH4ratVp/+suuGWN+uHz6KzBylzPQdBWnXFK18uqJ1uETKZ/qxuQqMlxDPp+djJliq5HDWdzhCIzSLJk8+fRqEY6OblX38VlPKMY7ZbYx2LExK5/ApPkQgcEA1k6fTJKCQSY2S++kYhWEThaSf7zfr5F5NqnXvMYof3rOWIWaR4sgpnJB4I+icjNc3UCMk8HQekwIupCkahYypBKqJFuDOYx4mjhXEBtPjVqjHsDlXy2f3x62+tffMdqavTKGjNHtp29mh148qvvkXF5kOg9ZPhlBt6AgZbAduo4sH5YlTMUMSYYIuUXy77H50nDOszKJUVtReLcj7vgnEXyfBeIFmgP4yEYvO84wmFRvvx/doam+A5/5EpX2q64bGtOrwMDKqkhKOftifbBEpyVMkx4Txp9qcd1QYw+tqr1dMApVhELjMoGozicHMpaQcpbxOLYQJkKBSn6htutQ+oEUhL/FiFt0M/pbExFtpp2iCRiaZzFGCcj1AzYSIkJWUERXiEZX/ll0oaav3RXbfy6zeDEv/goTLTwCUEpEpkDghVCy4Wyt1upA9mDIpAoBPZWMJgqzkytlwMpoksB/iYbYeRCxBIzAG2D+HwxIVhyrfMlGAntjiG4KVbG6Xa+rOhcdjpfGOtmtfVLiKop1HsJtVV+UUrHE6U1ysSs5RoX8ESYBWEHNpoYBMi9ZzZwMAoEvZ9ozf3OYPMk8AyagmCeIoDY1WaJnyNwQrTUWSpUi3LEnMdhZxemliBkqAiC6BwYjnZbHracYFG4fDH6TuAn1Du3HZ5a4aE0OTEKVat0bMwk0JLShaGIHQcW7QXYOAiSo8WQdfRGdqHhkmchEtb5YPT4WmbuLgbN2Pt7IOuZEBMl5y3Q5mTnh07PFniKstzY/DkXHntSunRvW5W8mxkHlj+vB3WCxRkBRVcMsS85xBBhFISkVhqQpAck5NuJicPSZYNZofDtUZ2tVCYHlsnz61iAUkprbRMSxFGErQSEHuP5kDCXl0RH58atEjz+SXueHY5z/c+dGoZ+u23+JEWBg6CgiSAASEkml6yVs3mOPZsMl905ve/7N1Yr+5eLa+k0lf9WWIC9476AifmQnyemBcrkKpAJBr0dBwyUxwCtGm/VrDt3gls6Cs10fddx4XTOqp64w8PH3gjfHULUyyddILrjXzsyasJGduZWdInJQpOgQKYHt+ewwSMc8QUjkkcRot830s0V9y6VID7HuXoKAdyZarTAgIL+PIDXZ3pr//mRnsPVk4tmhZSCjgeBdfeLPRmXsIBFy+S+sCa2tjqbs2FPRkhlZGnAGFSJIkELDIwtiDySGq68A9//vwf/VeXcjED6RkN8RV7tP+Lo3/yT18RV+u+gj/TIShafPLHj69la9cuCAlODvWU4ZcgYMICYNyzzxVtWSZvj4y/ebe1+ZuNpSVxfzy5/SwqV/HhdObPFYFN8pvsk0d6MQ+CgzlT96eDZPEEY83UjxbFrLtzpVloyhQVzT1QEJMGEj3da6eJfqtMYkF8fNu57ynrq+jcTd+4kmNXNpFCkNpyd8bCNEOJQKiCJiPNmLKYJFKArwQkAAAgVdx73GFkJyfbuingHBzYhLmIbjRX4GoeDNzDTufkI1MxwHduZnCVJGTvhe2Mn4cxINZWwL6NyCI7cfEkJaMM/uxcSRyEqm3Xi/m2N5zM1V99e4MB4C5NRwYTRBRw5P6T37vZsiLNe8FE/uO/OjPGs7duoQUJtkGALNC0T603ufYIVmgwgdmFaqU0bC+itesgmsiUben9ZOEbJOYhHEtlWFPzlPE8CIIYwJ00ee1WuY17mh+SOfLZ3qxZkHWWbI/coiCmOA5DBGhbtRyO2F1KSOA1vYXagReStewtCQUVtzcbGHMI9n19ENsJShQq5zPDQSD8ZIy6MM9hEFv44fcXuaq09jLKJJE993U/vN1tyxaSKVyUm5lSE8gBgJriz9/7tNNJMIEYTQjARKKG79uDUXfU2CZHC0MoYQEIxF6qhfQ6i4wGgcNESEHWwXDcUspZnHMA8/Hs1q3SSRrUWX2B4saJuej1clLQG9grHEXhZOrYs7mOQm6oqCDJ4+VStZHLepQfWU6UjJwkUrFAE2E/ZXJcuSSaXmoNHEMPRCuWKxTvYDzEajBnLNyQjPIimYwggMMAFmw/chhtRq0VMYxElnD48ToLrpMP7mlniprBkuGxvS4Ufuk3hL4C6NMIJZkmB5k4tcCNFCUyUHzSCiGRlsoS5CUTP0HqZSgOkal3ZoQqjEcIGMQ+NvRsih5EymRm8GiSkcnSZj7CvFF7ynrPkEkCf7fYO+k4EBuw0KKnZkRhLSJkipu++3QOQfnLjRKVJElwZE5owCPQgCcRY6q0WrOkyFU26pU1Vjdh4myBjPX5OQStp8Eb7PzjuzZbP6jR7gLbbOTs/rQ71wWRAjPgaKRFkxlWa5L+6LGnVb5dmd75srl7/fDhA9ISUNlSBKiq9E+6dvGlN/RFP1aBY06gufTkTrv3QrPBlGEjMELzcGKgtGcgdLkgWwOMhhQDwCqFOiVjtrG1hIkyxNcgCgFDLBElNFHgdW3QTVwtsM/601LWAotCGE4aNSrxjKXlzIkWofNQM+IizFY08ySlT02/1Q2yjDkxXGwe1WiK6LZsiASvbb+yDLSfHbtR0jqYg4HbvL4d+kH/pAPifm5JSkYzEjcblyvdUXL6tF3OMREMwaQUx/6i6++ulC0ECOZWfxDmSwmVJ3wxeP7oYA+AyjtoYHsRgG/XYLiKKp0FV5Ef6dROGcVNf5ESpSK7WcFPitGDZ9PCHLUxOOTAJ3e6jfXMyTSwDP/aleojd2oZU1miWy9MI6JGTwd4qRwZluN7iR3phrdZDUM49Nzw5mVoViu32ucQ7O19dF6vrZ23Ym/ipV4qA25kTt1h+vY7DRRI/uTH/Q/vjut5cdrp//7/+lvHw4lmL55NPCqMjvupxKK9Tli62BDKGDGyrKfjAKIy5TjHR9MJ2RsEsA4ctqJ3fiN/S1LbB3NzFk3ufP763/uGhqkv/+3yGRwc/scvg2fGr/1vfmOlAoY7rLRVipWw9+VjF+cyscVXxHfWwPdUhriSO/jZJ/OM/Or/buPKNn7wsNe3bUIoaIfTHAUDGHllhxqet2SUmR8lK0VqNIsY2ygV5e3XYQgpSFdgCMKHxxrhWvMTz2Bi003WtsBnz833Pt7/xjeuvHopMObAyGezsb4rOxde3/7RSJ8woH6OfefWkgOqBjrKFqCJ7Rh8HkGhIJz1j9FCzcc8OhoNLb0MlVnVQwcIlMiMQ0SDlMJdg7vG8pVkp2c9OxmxRTEzi32fdAS8WctN5uOMRAEjfamMGgimDxczgy2jRTNCO10XslVcFptp8d9/cE8uUI0lbvq0h27XoRRaPB88GJzMksm1LfjlbWgrmMALChGLBxA2lgvz/ZGTAEkANYRoGMFImoirpVGSppqP4laO4noghIOyjIKOPZ4NF4BPXHqt3m1pYpU+VYaTccc2yBTNEyj75CtQqso0H4aWF2MCmhGoWEVALRZZQHKnx0dCTawXiyQA2WAn0E07sPppiAR+MQOBKcvVoIhgMwzkzFM0Ts/nyCu7VPEK1xoYSW+u+W7lynXa0rM5EfB8Ew8TAAAB4Oe3R6j5/pNnFrW3t7JJJHmNZGqmG3cPNPlWnWflZBRg3YSj4lHXKW4XzvdTYj5Wr5U8cBzc7ZOby3/5rPfWNRk7VWixqF1G5uOBuA5jO8LRp92Va1d7vee7+ugcB02AXRJ8KwbpPJolOHSNmAIJwVIgQYK+FtkaBZAZFioWSRihfZsSRDf0MB1xYlgPTFCf+DSPWGogZnEv0hMncgAPBtLByKFWs9Mj3RrkKk0I8S0gN7HTsY12rNcreJVnHD4yTLx3BCyeLEyYzL7V0IPg6MmESuNgpKYFhoKgapGHSMYYm0WZI91g0J9XMbBJ2fDEGVk2gyIZgjZ0k0NDOi+RYFaU4JW1zPEQKV2U5weZ4vOD3t8YkWMIG8LOzTVEgjSYoThcmGmPuy5fIma2boHEyxukMVLxGtGeulU/tU8VLIrypWwU4+E85T0PVp2WB5dvSF1J9b1FltVvVSxn1l04cLVUhEEwI3uWGiBCxOeYwQg1vQTjibv/ofXP/3nzxz8aXJQXHMmP53YOJQaBx3jWTCYBa3IeaMt1Zi03HZ+3Qd/KsYhhhD4oUAAce3EFZbCc5Iz8kp/kCWoQCQzDjl0aA+mrr1Yf7XW9xMqLmedfKbvL6GDU/mgBwHX48XlvKxfbiQ4BIQKjCEsFumVSUwHFNFVHqATDDMdCqUJaSGhyVUoglBSppXz5/vOgRjf+2z/YcfH4D//oi8icbqxn166x1ozNMu6zp6cyg9VX6IVp8Hl58FRHFlEmD1E7RQCPaIyG4zgOkTmIE6ystyfNGhyxoRsao4l94UIhqSR3P+p89L7VKBcKK4Q2XugLAwoQJiZgC/b8ZJR4F7++iUS4qk3Q8aBsRq7DnvfJhZqsF/PPTn0cNjazXLedrlWqbs8jCN73LdQ3OXkBQz6EEULkmQ6YKRZdyCcp2bXN/smUzGuj0/PGKytra5WlBv7V/a6uqCzgVFeyQkFYY9FniutMlJcviliOBUzySMNOZ8GjfSWemqWLEoNkuv3J3I4q12R5gzneH0uOkUC0VCJDCerjYaYp9O/NANSja+m9444oOGGc+/V/tPN/fqb/8b/5FCmkL7/O6L76zZfle2l8tPf0zgONAbx5fvjVZ+2vf/PS5Z3Ln/98/9MnI2+gqTMLWRZf2y5mb+Z/cT++9xPLNRiQheoCzJKRwIL1rPnk3eORYgsX1n/97UZOYpIs3p47MOyZMx0I9EdPsUKJg1mgdw5GtgfCAOpNIZ9/9WuNpxnw3388ZTLWS9/OwCQFB/GDp9F8Zn88Jl9+vXJxCTM7SmuhkSK5mGMYAgQLrUPipJ+tlVwEpKxkMrYLOxtla+7QqQVoSaYAd3shiqv+RO21Dqub4vKbjcOZ4WSKvhWgVAO0Zt2BbrsIi/rtwGNtcjDxWReUqrJmh67PnZ3ozdWyY3jvEsBEkf7g7ZUjezRO4suvZA6/mEUueOGVLV1sXkD6pTMPX8OVg1CKg0RLNhvsvRqOzwlKQuc6yOJQt+3GABYpIY4SAILOPQMnUsf1oTjUx0Z9KRempAcAz5+1V6VinIRTK929vvz4kdEUyEoztzBwz45DFOZgujP0CzLIizACR/N+qhCFDa4SAAAcj9vtAeJEeLNEqlCe9MmeXYZRluFYV3FhqLaC61qK+/azfeuVb9GOHi8tk0f3EhLRjloKNWdoLKXX6BQAJsB0Qzj885+fFyGQuCa+0Pyju8GbVXA2pXttHGVyXxyrMs6hxVxP9Y601FEIBIpMDKXGU4SK23OtIkXaedw5drazzJefn2xdph5+1JkcwKtrmXefdKFVrFnElVks0EyNxoz9lBOYEBZTmp9NoOUGGSaOYqViU1KmACfbdJnU1EAW4BBJe67kQJGVgtlsoUAUQaGJJU561AM5DDQ9jIIQFAamII8TBAqkmMIjTBAkSBYmc8dUOtinv7k6RTuLLATq86zqp6ofCazJQr4aIifHgcXHLEETabelF66XIgqEJ3ocJoEJMfpcRSA3iaUZDEPMdrGpQikhalUTx1lbGZKZjdJGXsos1FKV3b/CkVt1QwrAVhseAgAHAHOPcjO9KQCyI0eGcrfIzCX+2Ez4WaI8H1dIHvb9MZGxMlGam+GFjJtD6lBKtxZKbMYgRMhUUEol58g7nF5cWbGG41VNf8hxk1QIYYrGBl4YJRRgDseZDO+qfl4q0mef7hlr6RsvP3vQuvEHF//mf1GWCySoB2hs+SAxPh7uLEnjUQrNIj8VkgZ/bKl2xJdA10Q4P5cmvneUxQ0Dfm27OgOBrKMxlYxURTofP1ovVY4+M/IEuESmmjIrVaEBmHz00eS//D+8tn/vMbTK0IGZ92zaD5bWs8+neuDBW+Xqk3bkA1IYpREm5EDERZCFDS0S0zBV8Mbqty9u6vvP/s2/+nm0SKFV5sav3vAPzxQdowno6af9taXCInaEUu7sq7Obr9Z3L/Mo5JAkZ6QRDxOLsVdrcngKArbuBnOPsHooDoEhGQbADPr8YLJ9g/naOytiE9h70P74L4Zilr58lS5KmX5EFwCotbnycrGGRy8+/mxRwsUQK1UU56fP7ZVtabx3PJYCN3KZOtINDGZiVctckFRHk2mKYuPhXMVkHAFDFQ5syAeQYBgonsFmOdNKPnsw39yR2Fy59WKxtrpdawCqgqb1bHWVdUfGF593ECi5+tbFT07REzvIE549djbfEWhOo/BoeUNCQvD8vCfifK1Aqq7WnT5HSYgGbZdhWiixK1pUrPSfpLFCNnjgwiXk8MTRp6il2U/Bx//s1+KQYGKg8Vf/831os5bG+s5vrK9kiNNPADXGhkMCp5lMAL/7pwc3L6Mbu+v3jyqkqxw9nU/mzo/++09ZUa5eYJYy9IHvz9wpwCGBEZ9OTOVc+cY/fR34+vXscAF+ZTDzZG8+0PyIgyfXKEnzicVQzaJifSfnEeGdtpk4QHc441Vvezv7y2+K91/Mzh6NlK5O0WDlxvW1t+u/cv8xksAwAetRiu3W+Fzx+POz7SKkEkArBgoNqqX6KyucUSIn7x4XVjbJPH36cat0tRYTaAZaoDOLR4KpAXnH0yOvmAqsM1QUFvAEusDCBw9M6mp26kY+RZsA7fCUzOatBEdco9goDe9Mo2p5Zw1/8e7TrdWVFqC/uDta2oqMaHTeH3CS1Mjw2rT39HDwzBh/D/Kl7y4BR/72MNv60Exh6uViurQSHo+hg/MEihFjYiQiLJZYII48HJn39CJFg+UcI3m2GTuqMz1zdm5t2D4uolpm9wpPV7Cwp9kmzIJzxypT4BGXQ3x8W0pAGUpsBUUx2yMaW0t9AMimrZHaTwlrp5nxtJE7cqGM6CAsk6auDvT7mrhcSuEYQN0cLytnveCZpgHAAJconlUOlUs3lgEw0g41LL5PwF+zk/29w3Mi72mxtfZfyDz2S0zyU+kvHBJgM5yEMgS0mp2dDpqpf9ZTmoCiTyaIgzCJZ0xQAjE2mlioLf5JHv3g+YL5la8d/rc/gH2kRRcr+4fxa5de+X3m+KdPf+u/euWHT4cXQTLB8DgvsLbeyhIihbgY7BPm3ACS1L6zH9XYsLFR08KQqVBenHIICZOhHkW67yB0DGEwt5yDPcXXGLpZGENYdGwEHWypLmW3ONt1W86hT5Bp4iJaTs7V5M/MiHQJDy1SUHp6ml7jQEMQsBwxGqpi4koZIW0pSEjksoRPcDkJ6w/dcBGzcqooihmDMYjkmhmYpVAEBzSLwWHDwV1GQyrll2q8FaF+z15M+qADLoiFsQLFv7Tu/6HX4MkIzKJMDpjH8IsuKlnU1GISM5nK7AJEDS5s8mApb6txwdYVJWxcWZYzser65okxmzmejEIpXrDi7mcjfpdmhXyvhAkoEtPU4qDH50UryRCxDOMmEfrayZyAoXHEraxGl2+IgyfT77x07YdPnsAj80LVjBIS5/wkQdkohpJ40PHSeln0IMjVAH2e9dSLPOK1IZQFoyzkxTFGJCsE4nT8QoN3sHgxsHeb8Md9I7UmAmyd3Guvvo3FVvfRXWjzmvzsEzuamFXAPftQu3KFKe02B13t0AipbfZoT1mvIXkZj2LwyZzIZDEgdBGCZ/DIjcnxYPHpX3ylHDyuXyIIGnnzt2+B1YbSmn7wgydv/O0igcITJXrtm4XR/ggMoDlC3B25AoeMTowbtdxMwAw1ZWlkPouZMnOuKU1xTggBJeD796e3LjSq2YL1zPrFjyab2xyBYtcK8M213MkIQw1s3HFqr+SwGnOtGB0ef6W254V8qZwVQ4qorYnJwee3Pz1bTfVP9vuDg2TrJbgAgH//1RJQKHd0DNLxPATPdQIphGMDLROEM4ZABONKhfnADhZpTmD5RpjPkERC3/985ILOycgOI6S6XH0BoTvN4i9/k7xzd6yMg2oNTxIsNAN9lER6cE5OszQowog/1TDdKZQzpgsVKhTsqT6JzGhwUSeJixtfPTt9EzL9MC5TMQHB92ZRs4rnXNCsifsRqPmZH33/7j/+e1eufufr8hry43/7fm/vbL+UI7HypdevFlfYj37qFeXmkdu++0FSuEpxRdYd6sVlFkmFV//5y/jNW+pw/2f//umFNysEzp8fHS3ThJ3NXv6nb9Wul/bn6bOHY/DB80UAFdeSV24WarUyeIpzCQMGyMHD9l/+/07KrM6WKVdNECYeDMG7XzqbO/HGS/JGDsu+VQkwvprnzp+emSr45lvLXz0Zyhu1BE+MbthocPrA02FDXCrV8OlRa5i/7EfmXAsCyvfhidoZ20soEsHRcct1p0e1ZV68VnVHUar5cJQmmgKXsTGIVQV5fTMaL2yUQjMA/+I8ZNiko43kKhOR5OBFG1a6y0hAnBrpYnH1ndX33u9vFkjf91F1nmkmFAfVgVgeRbvX60fH+F88eLACgK8VKkY+Y4FCdDr8/IH/6HS+cbEg8jAGJiXZVGOq0MikqRXqjqdEYk6KTL/VtjiMbjTyp6czFqNizfaUQBnHf/1F/8IqlnqkZtm0QCV+yEymPMakcRgpSZBQUKbcA5FmwpDp3Ei7cG9SaggowMAiScc6i2FOhJ0vvGI157FOTuBG8/H5MHlpU7iwCp6ceKVdYf44urjBn/bT5+3opYrIb2THQVtZnIgyTRBeYWeZzpP/yT66gckNaFu//nRlgbOJ0HHCQo4+WSA4qa0vWdqpVakBwyPPSfDG9dx4X1nirJPToHmlpH4w/MV/vo3k0/2W943vccMjenCv9fY3ywc/TGASF4ssmZNU3MeYWFdsIoZRPGgWgie92fbbq51xqp5M3L4LMjOIQDEs9RTAg5PEw1kCXa6VMMqfPdZM8ymBpE5sZ7k4VUkaRbEahBDM+YldyGEJRoKo76cRMktdCxFv/Fpy2ouvJKPbP3nS3LmZkjDRn96GoeUVGnr01JOXcIiHKTCKcZYj3HMDdiE7hcYnCliAsGaRPze9thUFgYXwLBxnMnyFhc91I+lPpx2NFCXPgIwAgWZejjdYYPRFM7/8NqZ/AOimgeMhELGVal01WyefH1B1nHGnls4uVUpUsfbxidZImTLAMQLRphzCSuqaaUVeWqXHrJEzE9AbGhtLdmY2z4MsBr2x70zPnqSFS+d7i93f+SWZ3gFTfb7/4PLFHIYwbQw6XSSrl3P8DLGN4dpv1PH5qRjR8zLEYqWzxYjkmKKtTO3Ys0SZS0WY9kmdQ4v+sN9aLkHAqOx4HoKTvX6ytKwLNDZ1S3X2zlfj+GvZ7W+s/PX/+NXVb7/x9PD0TA23ri/97K9629v8q6/Xj+7ee30Xu6ONylNcdqXKK9lUMYZgcc8z9x6O880sgwLBugwooSfTWuCDJd5uu2iOyLx+a/fvsn5kNWaOLwF3P3y6+Lx967cubd2QPv+PdwyIWhhRpph2DvdvvnRprCWwOgRixPeQ5Spw95P227904emjF3USIq5wylzZYqn+QX+tVHt61qN04+bOqoikx64ty4z6VEfWV0ySrQMRclVw6uzzT2Z+PDUeqtHLBYSEQXPU/6qHvin+9v9296d/fJyq3BuX68pNn7MHCCzmq8y9c0X1ETILqUhkOQse5fzYTnwQoa1Oa5xpgLIMLKYO1Ww8fTH0KJLE7KUdfnWJHx1NZvqg/cMTslxTPSVza+XCby33brcsxZNyAEOxWRlWTLdv6i99fef4sx6MxLM8nhFsz7Q5gz/di5bWhAaKAVfr34ebRXFYQwr1HSh+huCx/852+Sc/3kNIRCqy56dzr0EWNhqf/cc72WuvGM/PD5H422+tbBWST38yOng++uLdu9bU+BlsS0sZPrRb93rn3QFJk1YaSyxhh6Mv/qf36aC13MjkSvSL5wMV4jFAq+CSoI+yX7TsL8ONG/ns641x3+4po8OfHz5l3F8bAdJrV5mlteo/psIPRtOjAzWBinnKAQyqSb9+pTx+NLr7c4WsMuV8PDcU6R1qdTVLkyQeQ0EYx/lMbFmO4bz8vTfP7j1wng9vLnFn+2PO8WGoer8/EHJmkTIGx4NaUZirYalEeV6wELDpYvFmfUlYwvttEE7sXi/JtIYvrYTtLo4FcrRQYZKVMzlo/5jJEHSNeR7OM3h+ODmSWQ1cu3D6aDxAXNDwLmZSN1AoxpsYWpAhZwfatWamZPggaN8q+Dcl/gEfP4eJysrF2qnjQqhV0v1E6z45NM3YhwEIiFmhOBt2sRCpFFnqyqrlW4sTuyQzdKkAI7EZArOppgXE9KT/tbd2P5v5RBUcP9BNFxUvlAJHbyCEb4AhBgFoRNAYeOLWqwUMoBLVYpMcxrOLxTwWDBgUAmSMFWBkZDmy3QHg0kre1WO8JoDaDKuTCzQWwohBPDRCk2F7dVPyTmdK6zy38S0OE/sc8OPFJ/owSIrW1DJKdz+d5IZfv+TPwTwWQ5/2LGGLBad6cS36Ah1lNhheRzmWobnxzIcjmnHOg+nrEl2Wur39tddrtXhy+ZsX/vUffXa2F62/UWp/6Q9xJ3qV3fvss0JjZdw9i6VMJolSCXFcww2aslehjp60sGlaApGdAjY1khlqhUm4ACk4suw4secThAA8az1Pra8mI9PpabYIk/ZpB50nOJEjaYZIHQ4KPE1zgRBPoxSKkDmZOqL3+KElxDrFUyzIVRj84Gge4She4VZo/+xwVLxayeREjEURL42RyLEsB6QKRdxDjP077Rmr4TCWK0hCiVjKEilFCgGwuD/XdB3BzYTiIjASVmicyqDr9SMbWN5ZngILtD3O6JG/mDvILPEinybYap6Pq4GegD2vUsjlt5aPF5GtO4U8rytKQULKfpDqIBajQF30TAXpeAWM5BGBFzJPlPO5D3MIiMzghzKWeRObnUOPPz26+kviy+BLexWfnVmm6gEj7XJTsE0cRNGzRZIpCBifsjAGeBESO+tlYTzUq2S4UCzKMi2QcMRUc1jbdSSSzVCuMfYEiojgWBLp/aERoQhNBJDrZ1Dk/ofaSin3xfDskqMRTNzqD996Z+ej986/+Pnh5ZfIBx8fluXVjTd2p+funedufYxjqLXyUuHqJjU6M71uxMsUxYgwZIpWisJZTyDYRJILwjICP/l0ZqiLlCbiRZ+149LFbG2jeO/j48M747/zL26MZ5bUAPdt65Uipqiol8psFnSgdDoyLSKYgvY4SsJze+0y8f676sb3KiFEW4axJgSt/kjTKTBJOk+MIgPnquRHH3URophv4E9+2so3UDHBJ225uLMirdHPHpzzBby+DD5+3NuoMVvr62l8EwX6WtmGZootiIus1T44IiSHSePEnjVX8sOFhblgkk3nHu1xWTMNu91Ai5Hv1PBPXaBWIQEA/OSudvEilrtelRB+vZTFOeHoq/0f/vnzVCS+vittX+JPpnMYCO2Os31NTl2hP4x0AKPpoCwwKEvoMPJoyK+/vHHlV5a+uN37T3F5r23+JiwfHfV0T7i4Lp0/mq9FDptDJAEKU62eAbaWMOly4aez44++uH31Zf4PfvNiQkDvfTISs/Jbr7F/fKZfuMQ1LoX68ZwWqdJFdvs71/YOwqYYgrPJB+/tr2UlVKALRXzRt1oH4Xffkdx7amHRu7A50/awEo7YCtK4hnL2opyaZjDygFAFE2P//qA9Yi9ktzlrqWZPDbA/IjiBmUxiugxduy4AlNhRkjAK39wiJz1LTUE9Emc9oLKUbz2abl7OujTCx7EzwTQb1VP6dApUKsXhNNb3IlzKzslw33FZhNRP5g2xVM5xBSbcaweninMpU3KpaCVHnc36neeQdjQMsQpQZxAq07MSScJzHKufGiCucAXMsCYxPMZWMgeHLev5AoXifmq0gvmg7/zu1eazuUnDFMMjSJCAOzlgRAPrAihMzaF2msIuCq9xTNEef/DgOEaGgBz4QaCmFjYFCit2EhY4khvMXUoYz3ozgmQImm2fdEE/kkgkWMRXLmJqksMwALST/qlPkIQXQr7hHXW15TLnh16RhDU/hiN/nZMQJyIMIFYFmmQ1WM02lx7p8zxgg6GYaATA4i4EP3q0+Advi4MZPD80CAgKJzMOAbtycA8IX92RD75Y7KxL2ysgtACff/ku+1Jtnbg0QvmzJfJ8NIMCrLj6TT4A3x2ZycPesoxxfkxPkOf7i3rDTRDDGJnVAtkglAUwhy9woKwxAuyu5koK9NXHzu/8P4s//rPhWjr5+nfYc6Af5CjvYyu1K99+qXH/P7+4UvGYQgrQie/hHgkbXVOWUJhm8pXC7Q8Gjm8QBaKQY6EyoYwiGAkV12ECj2XtLBYrY/N8Ri14LJsDcpkUtr2pmWJg6qHqQJvxEgVJMIzhGEr5ipnGERSmCmxOAn6MFfG2NrzHlU6DMMOQPB1t0kjvB08wmgVZPLMqEcHU0+djzdTQBEyM/p32ngojPFOp5eM1HtmsZSpF1HDaHfXJsXI8NFOOydXwjVW5uEyKEo6iiLNIFZibAtPfBoJXTx4ik/HpPKIEEK0mwBVGvlopvb4tlCRMQO2d4p3WfHTYv8TZiDYJZUTVFu0TJzaAdkroFAomQBlxBsNThQm0WSsHed/ZoLYWLpUGzWLBj8y1tQiW9fnRRwvgpCwSCpSrb8nNulCCsKxczDEI2lJJD+0DIgfQOQPMpog2gopg/LiUxy7nmw7GR37J0HezIE3QGYxsulg2I6ilZmfmt8tbHgBdBD1UEH4a4vRLWerspIBym5u1Rz+fvLJ9ARile3/eri1zAelmUOMbrzfsM10SawAnf/2t3UwxN9LBo3sq4qb1ehGmBDxJ45GKeazfIjMBIjAUGquTifL+e8+6L1pyJkJrcjUPqOcTZEcYmpoWOuKvrw9Wl967b2oexrDcs3NNbPp4YkgUJdKMB2N5JirKicTQ9Szm+8jKrQvH7TBXywynLAmI05bTPe2Sy/kimRztn4ESsl4mLd0fnI9ulolYASScFHYaAM/0nyjZGPSnEkxm3/n624hUWwwUtNt9eKf7wS9OOiOvyYZ7T6e6Q20JxHhgkRip6ZE1hw4NKMyIRECYcy8A0MaySACEMtGkhtTVU832cTjqdCZqN1DOks9ve49+0i/JxX/5f73xFhf+/AcnP/vhaZYjOBEfuuBXn05ZK3E7s/WGqJ378ZgY3Dd9F9tYKrycbTy4C41uT5C+jp346Q9/4d99XhyBFw7Gr2bpdKgXivLYs3p7Zyuk94sfnv5//23/O19n/2//zdJCT979i71PfjocdcHD9/YXsfH2P/16muA+mA8B7MvPFkEgFqNR7LrPn9tnHz3OXLjM/so1cEMK/VRX0S5dOLCy/QlG8JXFadDrUxcvbI1P2n826fwAd9FceiM7rSdzuZptYkzdS48+2os/fHL8YA6zsJuGdA65sMVovvPzg1BxtFe/trbx6iVMbOJs3VvZXHu16SRxgYvX5YiiKVyCjx6ev2jGzy7SOhLZAOlCVPeFisdW8aX6UF8UMDid9j0GejK2WxN/MSculTLWQW84mEgh3+lp+Ru75YsVjKSs3tn5hx84onsQz8bqCfS1nLpMOPosTzOUogxeJGKSut2QxGlOQKPOQdaJLsTQtB/zYDbjQUucqk9cgEmAMh/CHMCdwxtAaXNcSWd0Ge3KmVvXS9sXijcv5uu3KlSFhQnovDt9dDg/HjrnU3Nv6sUEgLHCOIY6duhhxO61EhiCz17EnUj88sfDkoYVcD7DElxBRHkke7lC50EAwjEjIQgIJ+w0tblqDCaArUz1F9N8tsSAAqMzlkUItzY/2+9K0mY+ci54Eyvk8lvL5Mz92itXW/tzqXjTDdKbYAzFkC6vDO62D0cWkN3ZuPlNeGCctveMF6eR2S29ghZeQoJSokNQnqzUblwFfFB3IQtzeSHiXeVqmSOsVNHx4dAYOqNMomPtRY02QwdJwzheFXr0arp6+cvvD5aYNeqTwPNlfpdBj7rwEEFurd4/AatixR0DAJnMnbhCImiKe8OAQvHGxfzKEiMGrn867h6OF9H83FK6iUtQAIiBTOqKyzSSozXLen4Q6tPETiANCC3GnEWmEdjqbKyczWjLIgUUYbAkhRGHgs5nw2KR73UMp2NcvEjDpD7znQhKyNbxfKqurwukBLXOVHPhrJVJBoln3TkagQGYbm+wcYaPx/O0D0Xz/gMfzECejQa5Wq70WpkrEjFUHg0XlmHCRJwmvhsbG1nk3u0B1OoFraiK4BvvNMPGlpt1fTh91J1ZmsMkUKEoArF19zh6+ZtQOm8jWAYzyLEKeSKPlvIEZFkEMJp5GTW7ezEbrSLtRVhfEoznaryncWWSaWYDhFQwnGhgx8cIhA6XmoWW7iFS6uKuv4hKBRTPMIZrWkaEJ6BUEE8XYTHP8AFUxvihla4UMmbkeD4y1lUqZnJ4MrdJn8RikQ0spCoXaRByHeLJ1P7dlzJP3t/nAJfNTDHr/PXN5IcnPbG4CivO6joJu7MPXgyGQnH3nZ2ffKziQrXLwe3DRSmb1JpR/3wKoQiKxXCKeSmGAVEIJkKGP1Hx2pJgwXkBcq9+pwpmagXW+ZP/4blfJm680lzZyi80hLCZ+4+nwNhaKfCdJ4ttUZxNjP7RVIigymX+bGTTHNk5oGc9lJc4beZ0z6yb37zxxfefC6R+YxNCTXipzvkJCloug6TnA59dZlgO+vZrQqcLxTK7uk0DFjQ7fqac6VKR4Shp+yJz/0mcH08OxgdhyJiugS3zr61AAIzvd1J9AG4uS4qRWBGNgkTEUrCj06rPKBCMwo6NmA7Yn5luij49iykSn8+d5W3OK6fVAv/hk+7Rw/7Vr69L48F//5+c1761duPbl6P6FKKIvdPxaDZ9/XJB02ELJ1M6ofGUAHEORUGah2gympmPnPNnLwb14OBNI9HedX/y3t03auF//Q/XwGfnMVM+QvHMtoijJBDGYglYAyj4IP2LvxxK+YRs0OIivvJGBhdKeUD+d//de7Usn4yCTDXvucHXvlF4+tXkT37yvD+za2+vwGLxm1fon304DNsTmGf81H6ZltY56JTITA3IVBNTm1XB7LGxsD73p4o6PrPerFlso4HDASR4eybFX85FZjYLEx0lmpvR6cOgusX+/d+/fj3KvP/9z5/9x70rV1c5kb1UFw5nfvfI1E/nsSyyDKYMZqsSdXim8Aj20nru/NjI0x6XgAZk57cpe+76cwsH47GHyJFVxuCIhg8GCglQaIZ3Zgkd9UbHi2jJ267wKg+cImbng340iAmMndo2rcZMpEOUIYEKAoYXQIi0gZXUP3xhZBuIg7reLERs2j6Nyhl6cjKuNSyY8AAbAa5dHgPlKnAdA1QLOOtMx4sclL1RqSnQ6RNNaeljJSjiIlmux6k1NcFsYJUoFmTc0HTNqeJh1IrILK1lv3xvfnjf+NbfKetJ5BYzjdfXOidjr2fY2SQGQcVUwTRIPVBLGS/ReCKwKqckjEBSlMtqvsWCCBACgDmO4AZTRjjdZUCARS2o4vk5ddGb+toiBBH+8Dy5kk4vl/Bn9yc3thhmK4ONvdiw/9I7ura0Xqu8YQOj7gtiuq8yMeTZqRUpOCXlZNzFWJ/BBCyAEIfxp0rHJlfyjINAakTvar04euJM+MQ/71uZc1UhrIvLwA//6L0mLbZVb+wA+DV+0tJECVQWSSzYO0vY/a8m+I2tNHRIAM8zgKvHGQpEWCCOZVmkoiXaH85g3dd6WuIBIBjn86yqe0kSIhCyGIKejdQaZdCGLdO3DJ9EEjADcLgp0xgZx4aZBAAOpikDoTgpIqv8bLpVPX54p2zzlR2rD08ZI9JiOtsexjBVkOVCgTJxQnFdcpk40VJx1G0ZcVxf2lwTz1tnZz/enyz05mox8vR6bXNjLafWqWmMxt2B/XyGVWAGDOYsNFLmEgaJ69WBCm0ETso5rQyCIS5WIhapX3GN3pm7BulxOTsbdHoxN5/aFpSbzkJpQBoyOzubOAy/uZKNj122wQFnA2nhCRdLqoRCLa1EcO9Mu+8+GLlgJq26l62uvciwIXW0KhSqce9gj8XnK8ubcNgSOVJQdegUEDfFfLaOdDpIjp16E7IkxTy+iuWAsPeaBAE9rdiQEtv/7IGZQ4Zyge0AyfNTp75bpnFKudPJAyhQBm53PDKd/eYS8eD2AMqL7SFZJUOAEIOeklvL/uSgdatsF5bLIUb323GhxHmdY8pyaJnhTBhxGCaB+2NNZg0iI6EYggqQ68NYivGJZ0/s7aul6bMX3fb5gz/cW3+rlCdc7PrL9AXivf/p+zsrL+suWlrYczSz8dvZn/93P5TefqNWc+d/+r5yZTPAoKPv37/xX11t1FcOfnZw/a0bT6cHHBRI3lAg/MiPMjm2P1PSCql055IpL9XRMyMyF+3TFrxyRXXKuWN7Or4Nb3JgSmTf+LqcLWbbp+bJwYmQp8iU+ebXy2GcTRDqxz/68PPHs0qdWAycqztUI5M+eTYLCmUMBVAoVhYeFxGaAwyMqLaaQYMIJwB+CXccn0Kh7nE8meMHrTnHTL7zd16u7N5+pyAKsHmuJnqV+Jc/PiUeab/+v9q9con66LZsedh8bJRZPuTE9z87YGA80DxaYEUeVv3Ax8HsCiuApVZ7zKyDN77xBvfMPfpRd3PB5C6S74/N78gebqcfngGXzKS4VbjUML84oBwrzlDkEZw8+Pzgs8+e/t5v1zbfrONQpVs2h2KB1MP53qnJSPr3fvmX+RFCCYPC6P0f3UmH1M7bywLrAD0rw6bqGIC4+OFJ53ev+kMB1Z3+W+vguz84WQINjZQ6dJGacCpBvvManEvQJ58N9Fm4/MaF9Yq/tcorHnB6lvwP//Av3/79b13/7dfMuQq0iJmNx+f6rGcxkLJySRCXhK8eA922urQs253FJa5YwYA//Ohx7Xe+PnGsL/9q/x/+3esDBWdop2sY+QLXbp/UCpeDXGT1Iz2JBVu1MCKm89vXiZ/++GCRKsIbK/XvXhz4yMO7X776yqo7Gk8NlSIEDGT7HccWiQhCRu02nVmXChSfIzr62E02bjZk1QGtJPSW5BizpDUZOKz2lMbnEPh7ApAFijDAlvLPj/7oAIaKA0bbqpBSNi+G2Gxkp0Fq+XZVopx5erg/D5DFzArWqyDJQeP9Dg/Aa5sgQxSlKju8+5QpNvrDeRjO89eQ9jTBvWliiKX63MWE3SIX2Z1ZchKK+eO05gD7K1lJHQ0G97HCOrdao0/joQYQl99cPhtN5PKmZUJws5S4X9HXuSBxX3snO/7gi+I3Xlc4f3HeQggdg/BaIS1L0sHpmb4caGAR3lpqfZWRn334zd3GUU1iXAJ6fjpTeFz1K4IG5jhsiRjfbU+2XzEptPGne+dLW66BY+7CwYgLVUeGgYdPND+erG81ve60sQxrvblMp70nE7lAEjJpKa4bAFWEbi0Co8JZ82kJiU84EfNC3rPGKdgLHCWIIxjcvcwGZRKJbXKG8RQKaqaKAHAaimlKxbbT1yCKW6oQlotq88g1QiCxshJkuU4kEJquUi5LQDiCYcicSW+ixKNGAYzZmU/DKKdLZjib+gYmUVgxy3I5ftBLccOAEqd15gg5dKMipyg4u9fr7rcIPfrl37mKXNyFAM9sw/vPXFZb5AoUjlhO6Jw99dDYxxkE4WOiztuUC4B4usj4M4+bhxJtGecaIKgRGqKEqzCcfqqqEzBTR0CfB1ienDnJjO5YKcLTGZ5IVJsDraMf3TN9W9zCgRx4Nncul4sMTv/sy8FiRFZ+N2Os4fdCZ+yLZ0dcOEFVL8L1kOSLOAoFJkikBoakIaQZXZgoYuW8GMAJGPug5CceC6A6cIYAdR1wSaA7g3Z2bl6ZD5T42TStV0TxaGzse/IuQYnlkWeAsF8ClS8+SK5+q6wfuIQ5LksgZBic1g/OqGqW+NP3BlvNNbfcHAq0E6H1LYIRnCefad5py/SgHAGVaRKX8MHAj+xobRnXjYDPEF0QBAGeAxGJwX7xYCqui8tZejFWdtexFO3/9Z8F9n74+q3EPYMuffdCX0ree3+s4GvJpVdagwd+Chbx9PSLUQKE2tFUXso8nOBXE4JMyenMSs3FlfXg8IXWrOJiQXKGGpzEq+u0MePquoviEMPZ+p1zsqgV89TWRp7gyvU6v/fZyfMnU5Jg4CyGo0Fh2XPHkwfvD3WX5hL32yuYDSXl3WK9jr77aReObAEZMSzmBxxEMLTMW6DvR1aBE3tdCKSo/khBkoDFgmaBFnJCc9d+1DeiR6eBGZ8iQcAUoGbuV1/Oz1rtKFEOnp2Mx0FtKwcBqDtL+F0kSnx8Ya+s8ywmDIb2LHTSTJUS4k05JMbetXoOzDSakYf1ou7pAxg3aZ3iBSXSsoYFJYaNMtbjn3uFIu8ncaFGIQzC5LIoBr58Kdh7sri1dnElSx2+/yJJOYjhuh6ceUkWEcQ68A6ePHlxrF74Wv7W99anhvP+L1qlKj45nOUrSa8zqefmU9SAC+g8djNohPj4qzsXgmIuwbzjOW2i3pfnbnOt9MuN3eet+S/uTkoVjMkx+WLu7//t1c725Oxhy9STi5eybCGb0DKZAeNay/YJhMK/vK8CVrxdYhHPhvNUZos5PrMKMtdM3Ci1G4SZBi4cuYd7qgNZ/BaluMFQj1nIQ7yROmScaBImKZg4TAN4+x9Ie5/Yj/9qT1wqX1omjaOYTq2UYD1wYJ67laAUIkPf5fkARuyEkHSgCStT1dyfVlZzoykYIG62SrGR113AjcsX764UMkLqnQF/+BCA32rvAmcVYB5fJ25/BXW10MdJEIFkjnE5wTJcCMej0EMFuJ7BXB+rYAQYQetbqCbneklsnboCjfSHU89165gzHzpwvAi1SMbZ1Wa2Wo8A25sfHf/ovXT5lSnaQEcAjIFkE5AgCkuvesjRiTq2JZ+kOGD/KL3QqH6yr16qE0m+eaAps34/k29+8UHr1hZzMIOlxbSCOt05tHohAcnc8UJx23gdpHsH2saWEIP05uV636mdtODeWVeiAQkjd4pQ9Q3y458+9mB2g8uoJrxzHj/7ZP7oobG566cAN+FAOMZiRHz+kzNpjY/J4iygcg0RnvAIJnlQkJBeeEkaLFxHiDA/qixDpu5JFVxJBSBUc35AOHO6HiVnoX6m0xXahYCzvhbiMc9AKBKjXhxFNAsHYQJkGCpDIn1bG86nJ3NCkASWYzULSkFwrvlAGoO677o+yREgRyBAiiy3xj9bXl0uGINZTI50Do8RvhE5roKGgCwEqfHioD07dXwa86OwyMdJKZtAkLNwgT4Ab1xqXFCtDApYMT+cleFCihCpFs1OzkwSWF6L2BKdtBc8FMuFbJ7C5oeWnilTHKh6kaMa512lC57HJUCGRT8LnrXb0JACasQyALQpm8d0IIHmAIPEtotBkOcXp91j3V105uU1Ef2dTWw+2mIwyhje9kkBg9Lv7lqiOBuacBvGKyUxR0ww1tsfATjl2VAlDBgKH+5r3Y5XupKzEmdh2khgVImsjYGxE4tOZwCxHGTSe31oLgKBAOxoRGmtef7FPk0QKVIXhYWlgkIlLnjevXmewoo4DsTp0b6PMXQwxfbmWBEi7bzw2bG5usUvv7k2ApPv/dbynQ+fAkzu9NkwIwKXt6RHt90Fibhxqp1pV1+pogV2ZiGqF/IMGZsAhYUcRFB26ovslV+9iNXZMkX96MO+JabTu4Piwtj8178GAqhzeHAK4ag2uy72BtuSFFp3331e/qWbQCV3/kefr2xf7v6sTVwjMiVp/4v2ZjETmcF5N+ZpEUhwCEYEU4Ep4AWf9I/38s11vkY+15zCzcZ6h1XG8fQkxn3vyy+erwkFFLI3rxU+HgC/u4s8/miy1y3aKdjYJXCxOJ519z9zshl6dYU0gQSkoGyhRjowkniaqRMgzbN4Z6qEAGGiKS3gQGrRhYziGr6uKLY3fmxcuiGgEPTg3ZkbhqQ4MZZy4+5s7/34bzeJ+P94pWtSg1/cPtxH1gswXsMtH9R0B5EQR5/ZKB3BcYkVlUAb9JNVHkRcwdU17Piv58BSZ8DWt8tSxCreKIbpHE1Bc+jSpeV6QYX19OTINWzvrK/tXEErOVCP0MKrhZPD6dMPXhyFQhxE8fRMuPLGNgP99IPzhSYixlG5UbpxiXdJ/+HPT7p9r15hE5b00U6Y28Yyi6czTMjKa9lQ0ZyMuM4uA6DoMoi2VUm2quLDHpmjoA9/oGTK4dKtzPeIcUcn/BhCFs7f/OmL6//4gny5infGp5qFsAo9owtq1DkaHo/8d36l2B5Pllg+SeHW3mn19e27He/J3dbNl7dAJzp9crb0xuU/e3Z6c6cUOTqTh3JVmjjjzf0ReqGKBOniYJEITtju1cTRD57iPJAIBPTOt9ee3ekZez7BkvvH/VvlrU7/qN8foxIp5VJPjxkI932wPdCamQLA2iHjq9opXE/CRAPmaBMIORzXdSh7aPRGnP46cKPVSv/N87XNp/PXS5Xt37qyDQrzU+b4oaaa54sADxKZoVIHAgDAjoGZ6o19u0hbXTW+f9xdb9Zu3qxoJA8CRpJQYiZvejSYBNWNFYyD1LH74CttpGAxA37rBre1RYuZ4PPe0RsZZwB0i8BkACIlFHVrAg7R4yOPjox1KshH5BILPEux36it68Nnll/c3L5m6f0ZBjYvVT9s2UsXrpqPlPOzAItVYDkXIugCwxlwflcZqMz4ap7Aq9J07LoJqpmL5tWAevtGPznjSju9RQD+ydxFcPNv9tbFzPSNZZDg6TUwjLDVlPKOg8W/qAlrK3w4ZP9oLycKk1UJ1RwAQJZWK/TQRU+8pVXaoqHUVpEYqmAYxIf2o0W0tHmoAPtKnG9IyYAU+mPQirEKiLGo5hoAwIexk2bYWEYQxbAQGCUAqSKX5eTgke74CQDAGCl4GLywpiiCUkbAEASVhv7CiCAIGQYyZQye352z9nxraYXwocWBUsgTewE7HtsAY4TTIcogHCSkAF1aYzgRV0dWq63TQvbS1WKkSWenrVwyMX2P4Mzaq7VIEHwXS0bTF60ugQdbuwUXIcE19nQA4znZUwIcpPLFzHIB01tEPGrPD89G54gJpplmMVMtQhISTMMibqWD7sJKcakMRmE01M25Hc8NIEUIBs6Kvns0jRYGm5f7BHc8Da8T2Qtyzh7HowFVCbleS/ckX5DcyRTKX6y8lL86P1MePlysykInHZ63+nBWypeQScuFiNiOkSUWDdwhOjoObR0EXIAUAEIMHhaCcpG6dVn+7K6YwBhkTU8X0bMjGUpOQzNIoJCiJ30grwdsGGlhhKHTaQgpRhh4orRc8VYzj1+Ml2jIFtCXtvzhefDx+4MrOzkMR/QkRWAkhrgHHUCuElyOChLAxnE7IgGc8V2qSEO8xO1sLr/33ukwcNd49EK9ameNxz+ZPPvBfj/FlzPj1ZWodajZR4ObG1eWoMVTALHhHIdk0fzGzo3tEVV8dNp67WXo4L4pXF4i54HqeqZJKj437qeMBROcuyaC05FTe4OTTtV1EDj7okNkRWGZRgYJwIC3LmZXt3OelZFT8ioH33t3wtDkrW9sdcxAUVt3+lMqyWUyLgAHj0ex3BAihB7PWdKicdxKEy203CSw7YXf3Bbap26pAB3uuYVa4pg+OLJWLlf3B9CXPxu+fgX/rd9pKCZ+pUoMSfJOlhhbsx/+u73lq+tXv4FduchoCXP6yKD4xDRmfhhIBJUvYsd93zS8vBTgILNaQmMnBlnydKyLkNvH4B6Qyb2U+ZO/HG7sZNcvJl+angqGbDn/Vd+v1tEtnJvNrNsfLoan1oUd+NndiUdaJgGpCPq9v7dVo7i9u+c//bCbQyKJ8P/Bv7gV+/zJnvvl7bblAhWeF8oChoFD01qrZpLFNOu4ERrd/TScN+LqxRK9VhTGwF8/7lXLcDIB0ELaHlmrlwq3fqPx+fuDzo9mb74iblbpuQNKMgMg6Ac/PBFvrd64vBQCQgDgRms8HgVIEnzrTTGamXU57p2Mmjdr7Ri9UUcnn2meYywIQB25X34++79/d/f7f9pxEqdUCU9PVaXAFSFsMu7HgBcoMzfWY1I6Oxy1H80CERnPLKa65sHQ5jfZ44dmHmXHA92LPAmpL4L94eAJKa3MtPE0CbJ8UY2NeAZnqgi1Ttx92s5T0OYN2PDDcxeR0xLgL3hSU84r2xtVOZfTo9Gjbhn78XhE/yDMre5mQJitcukCJefz/kJkovHcgeA08VJft1k+/vznnZVLQgMgkvbo7jxYv5k3TD+O7dALpfUCU2BmRtx9YuCwv7aZW7vJHqnB+XThd9VrF9C6zZs+a+IuADQY0FUBahwwAoGAvB86WJFPOk+P5RgO9ASF0v5i0Q5zF8Fcmp7Pu3jj6k380zvpyCnKQbZKPns0XA7lwJl5LNBgQTx1TjQMNSWSaIivIq9B89N7B08Ozpu5R/3P9lavrHEGoXcl+o0LvUamuQ5gZ+eQixA+ncfg8y9GRbeyvXrldWD1CzQKWTQB0utXy7Nns7kb0CWud3fkzyn59YYym3ArOaUXaFBMVqrjgwhehHISAhAiIbnS29UiunP+aBwX44XZZVMc83EPNL3AMA/8MsWhUKr5nnVqrFwXilk41KM4iFAQz2flOQITmOctvCSOgQQHYi+OAAScmyuP0aUcPIaZ6ulIyZQESYLHTjOLeAnFAHqQzcNeDAe0WK8AxXR478QaRflGvbFcnbPh0Uf7hYa8XC2zLC6j8diyHs80GzRu5JDteja2ExhLVNuG7ozbLrssQKsBnoWRzgJ1Geo8EuDtKptzMpojk2hubTdORRhjUllf6GlI625xlfX5WYrWljPzznMDjpL5UNWo2tQ5+cvzr32jfDgjo3CYJzYrRuB8oeQoEcbZghjLN9aip8eDmVcrSfZhp186ay5fnRXNsD09/fh4Q6BX8PjkzhMALUMNSKKR4emUGczo6YCxxsduavIFwPTKwN1pTthZAl8GI7t7qsqCtEPHkYP7MQjEpwMzK6CBOrUhPUDC2AUjKA18OFcgDR4dDFUuj1IxjHkLjgA+/+hhYe2CuENOLT2foTzFsNxgZ7dw/NTH0lSdWSLBYBiEITSNA1aQ2GPl/BTwTC1TgIgsC2L6wfE4zWdXfv2tzrM2HyWOIM193p4b2dVm5Ubzr/6Xh9dfpo9TPwfSHRZ56nCF1dUGDsyA2dU31sE8kHxuUg1BpmnfmQXBgNpIjadPcutbPkBHL5z81McTnKf91sJdpGAlT6Y4Fc86Dz6Phoqzbi5+/Z/9rdBwWgvr9rvd50etC98OXmqKnpu5fXdACFGejfyjsUiRfJZ2x2kMwLYHwxiUJWKlIicR4DoRlgTiEnd+pm69kX8+HRgxfKVOBHzRhcx231GnY8KRBknkxQaFpVd/5bWDx8bHfznTQOBvfVtcodPnXTNILHcUJHP7JCwv9IgEQ2+W0ua83TXPc/mlqF+4Bk0ApzdQVKHx7v/887IrRN/OU3Ts2yKE+urjSbYolSrx6dM9ex797X906/548nTY93ncSkFmtRJD5OHnZ3/zyezV7279i3+wdXvfh5TW//hvHz1+0nq5DmPVeqWMzw4UxHaTuXeOkITuZjiaKniuZYpF5pNHs5KTjs6cK5dquSodOvFHZ34lTVdfWnv/k5Pfeouq1mR3Eb14Pl3dYgqVTD8tCDvIG7I514a9Ew5IykUZtGvrnnfXXeapXP6Tv3q0ksfVImU4KSbFH/348OJy5T893Pu9P/huXznfXeG/eKrmK/zTU/Wf/dev3/kv/+xBiam9fGXwx+8lYQnG4+HzU3p3O/fqdfQXv5hoNn4xya+w3Lz15Hl11A+s8SjWnBPkTNNiigYP9o8jcBZK/GI+S/QJvlkwphqrA4RFFxFbmZ8EA6Q1sRhasCVodMKs4JFvd5r3tvJvVW2BIQfFSMdfEpwHygzXzBhG0iS0F17MZIamAZAoDCZOGklNcqKe4Qxuo2Ds2RZIXGxQk5C0Jx630dhciqKpsxhNaR9f3eUdIYf5YPdsxlMwCk35euADGorpbHsfXr+xAEwJiDiACAUaQ5CQhnEq6GqLcSiJfJK6Fgx6kJchzdlDAwVWS/s//fjapRsBhUNn/UGjOJmpuLPAsNjzlDpbHYLpEllTwqe6F8fmfPjxqQ4bKaS8/arg4IC4xp0+XNBrNxtfR2u7mSPDt3sR5yLjIZEeTpZuNmwMrRrDfpfE6i+6I32bKYj1qtJmSEiPY49CQinHAnz+8pJoZ8KzR8MCQdWEZQ8xBwg/vz/BkXilJl3AoKjEEXIurFXHamfZARJTXwwjLHaxwEkELvVIYDFxcixIst2BSZFUZDrNLKMmMJ0CdibSx24lL5l2als2iDN4GiGnGsIw/swMhWQwxLmE9uBUCYSUZTNVIdV6WZmniIXNwLTEErcfdB0dzW9UxWLWT205xNaurHEYDQbU8MHoo9nsW1+XrgW2g4V6G7CJIENSipekGBFjMIegvuLHzsA2ZDEGcIyTV0uODsBZTt6iGZzDhFUwBFwDoWi8LjEHo4gOGK/l2dmsfrmR28xsPfjkkx8Pmk2m9GubvZ7/OUyHIaliqwLApRtyiUFeHAOqqf3p0Tg39Nc0CJIIkg+XasHs9MSkCkTKICxUuVZWEG+WOpEyr10uY0jUP1cbRSywAQYGAI+UEqS+DI7jFLS0Ujpyz1B6jWod9lEubulRQ8jMNKNGWZ1AS0GUEC0cMGAj5AFkME4tIpsVswkUQQVWrIJvlqHJww4YpglZncyTjIA8b4UJQZWK0vmDWe+pJjEsCsI0GktYqpkmwgGdiUmIIkNChQY3H4eGgkdKYNrA8jLTGZnyaxXHglIPkPKgs/DUkEk4EQJhl3IgWWR0GwQ6KDCUaHUxBtgC72BOZx7mdTDOCBDNirXUWkwnqEoF8rDZaOU38QQqcqx4dWXv9ihlchAJUCza6cxzawIDwysX2VfF9cWz7s9+dDjwFls7xI1f29rW5BeHnaPn0zwfXKtxRyMbiykKMAXMnZymMwvBWCRMyZwAtcaL7pCAabGWpWajRWOlePBotmjpmIPngLR3PoZKoR9aGJmtl9iQxVYlIj13KAxPM1j+uyXY11488e9/NcjKCMmhsAMt1WQyy5wOAZ6EoQA7fOKsy1SuHGLCSG233J5ugCmLTal7j4Z29/Xv5vNrECIQUuKmo/FYR8Ii8+57Z1yo2WNa3qnXeeL2HRVM7fGLSZMitl69QASAivUe3mllBDIWIZifvHxrefNrm9jcbO8Z3Yemo7hLHIx6CJA1kwxWrcbzfTIvcZBDfvNmNlLSw9stx4GXG+xo4fMCeDawKKaH+X73dJbNNdE6H3l4gpEREMrFeOigKCFkymkUldIIsLqgftBpSHGYh561emlEOiBWL5LjwKwJgB96BOLHc7kucB8/aAHWYu+h+43r9J/8cPTl570zxStOp7nT24ujverLph8LeBkKkvif/Zr04Qt2fH9gHofT8V6hSUAL1tIUQ7VzecxBI50OYozhNnJjw0Knqm/YSYYBNdMPtO44IYgSiaMl2Q/bRhOP7ZYRTy0YVfQS51LO0NHbj+bZjfLOK9sPzxbnhF7wgjiG/LEKYCkOkbELyBKrBbrvJdk8FiyUw0+dne3m8hb20V8cXrhKXd2pD9s2K8jLeeD+h6csHUslHANEOkminmeDCYfGPO16cSqyEE/HZkp3Bji1rpMAn7okgPMl21Yx2qZYEodN3ROrYOSn4ABMO8oaBWhAuBy3E6djOkQKA8s83pvhNxpSvxVxGcCxZnwSWdHCQUUYAEpc2UVPZDrM0TGaegvF97qlBwOCpTfEJV4ulOA09tsAEdqVDGgq0FJBhJHYd+HmxVxnkdaw5NN9VP1cPSyWLhVWS0B8+t5RUsZnOA5CHuDPvzqOll6qde4vQs0Jp+pA0cIwcpIE2OD9emGvjQhmJ04VT0ApCohAASSFXA1QFrjvwASIujqUoDSBCpUV8vj5NEthJm7MdTciPS0EqxUGnGGukuIw4MYYHkIRECFskeL9kW3rWc7OacYdEyhY3bRAwXOSqZeQCpzVDYQDEVtVDkIajgqXqj4ugRjaZPBHx3PjsPcMLWazuogPS79/HWXV0/9w1LwiOIGVmtGciKEN2XccCEgxHyBRAqCwftfK5yg3GHJKSCczBQODoY4t4wkJcrHpK5EVavh6JX9pRRui/eczWAyuXsDIltORpW/+xiseRAGclF1FYjyeZso7Zs92q++fD89N2I0TuEh95zuN4rHz7O5hp2/fz2d2t/JrV9ZeSrzEG83GVmFdOOjPu08VIGEBjPE6qoAy+iLqnwfGTG8aRDBPPOUI2GmGMDa0PbBtXyJhQMxMFra8mu8FQOTFxjwJAU6ZzSYG5CRusZA5PNWgkhYy5eFMXV2r0zx13p1dutn8+W34gjQ3otjoe2syWd0sqoOxR9NLO7Q6AkGYenaiZgsoxYYRzEIEIfElkvH6TtK0/dh1gQTPZ3NpPArCsMCYe/f2QJDMw0UpDT7+9FF4dS3BEPnwxUvLYLc7aG5e5rtTGSZIAvYBvd07BddpqEEeBSEIhZELYzG8jOHlSPjZR1a6kVu7vGLHKmoyMJ4Wad3A/LahCYyQL4MJIh72wwIVaWnLv9n4dvnaydmnJy8mD3/0fKoEF94Rr64vPzvpuD4AptEkSBQNFi0MLgolgrLt2eQ0knOUxkNCvhpNYTueOSEwdSNht3DQOVhqcicqkskWiELkzLvOzPlgb3GjTt3aKqIQNhzN5kcJwmcbdXB7I7tw4ElvjiBgrxNeWsJLTAo7Q80iQTFPyMjnU88c4f+EHMar/F4IUcls1h4MDvrZS9cLIsqfzd2Z04+x28/637rUrG/xj3+mHveD9cL/nyT8jrU9TwzDvu+v9356v72++/q86TO7s53bSIpVCgUlkmJBqbDjIEjkwIgAI0EiATZiBQklUY5IiRbLcsnllpnd6TOvl/tuL+ee3s/59d7yRz4f8KM/eh9G3DQWucYqGjxRccv84JmyShR/b7vVj0ySjE120nIKrjNrmzHNlxHfIZC3fpDvdjzKFEf2AgqC7qUDozEZgsuOjyzxEJ5kX0H2z+f1WiQIEJbH9XPMdNzcqnzQN1bWBCRA8/na1GAkgu4OXL4hROoYXi1pVg1oXM6F85dxSSAxd5a+aOOv30rTPqk7y47XPbqA7n7ts87oxo3oF+cXfRGlptDLQrG46CtL/k/+9lcb15Jes/XszCMKwoMByNSEV1jl+KPgJTpkv7fWevyJlALJxQcjSlVhKnbTFCFm5k/vD7bvVedns2WZWTDh1ZOploS5FbFz8JIMSFMP2GyIxNi2hsVk3HNtQwMSP5XXWRoijQTXXC83H8yfd4MbRYzMQQQ6PRvoN5mJHa3bqBliehCls7ZQ413bcTRz1vbeeW9dkcnjo9P69WLtbvHJpyezWSqs5MeH/SKFKsvrSR2j8Rw8tIqJfobzOBvNWlFelg8PRwgL8hlpXbEvQOtsmttJX0UcgMpSdqaKDN7V3Ay6NJqjXhQXbgRfdrqVWnHMWWsoi5GBLTqt8YUeIxxDJ+fThasV1rITL/ZQQ58AujxMQT0CibhayA7gz1Jrt0GxwJss8B05C3PeAUAGn51cE/MertysVGe+GQrswog4OPS8+TDHecpJbl/aJAwPIacudP7lSCU8B8cETR1kQCqLDYfpHoYGbK3g/HG4aE6t4TUGiDj4TJ30o92JmfVDhxCgcd+ZQPwK69kJbcU2zCG0UoBRM7DmaFJpFDgMyxEcsUYbD1+gAfBpIK4qkenrz8z1WnFiwYnruymaQlHkpqgSh+bALbyGe9PoceyrYkBmUmcxqqIZ57IPR0iAIHM9QFwz4Lk0R0uFrGEjLAz7posEUHkl9/bX99KMeP/pw645qetWvRGfXU5iwAg4rTWjei6tVoTWscvjmAu76twNM4iF+0bqJCApbIq6as8PvO0tiNUG5x+/yK7lO3CIz7G8omTXGILyD9ZQk4rVWt4NMHSMhOeRdRKhsEdlWSahRl9Qe7cgSlBS3r9WQk4fD2MbHx+N+LXiXoVny9Pur67SytjnA5iX0+EoMkx3FPAEiyO0d+nFIGB46GpuL1XZ/gy1B9HDZ9bb+ezkeE4QISrBSQzOukSuwrx4pN5ZidQwhRMEkaUbbHL/pxfLWfSkR8Iw1ItkFvZZyOJELE1tJoZFJTt4EcYBo5MQxPUZPJjNbbFGhySgUXQ+x6t1YaTiq9cYiUeGcy2gKc+iw1mCIJ60hhF4kGUREcM6VxpZFO0crjh42XbSKVKpIv0T3Tm13nkrncwcP1CXd+TzScBLae9kzBOBQs0ieG4YVpHm/XR+cm7ckHi2woLQdQI0Ait3Xi1WbuVtII2duDl1t/cybBmbzPs5YqEbDoHLBTzBc1IQJIuW+fD407PCk/UlKr+0tJophC/HnX3Q0YeZBgzKMqo6SkZSMDeP223NP9m3JcaSooDBUVoU2/2wfaka0/kP38sctye3lpVPTpDIkVbWMmaCAicsCUIiYux2SoXYk6ENl+BsjKSb6KBndQchBftynZfKtN5LioK0XOePHpwxGZRWkkmgFgiIYfz2hf/0kZPnaxmY0c6d8Dz+g1tba7eZJPYXA/XomS7dVKQsPmWYOJ/Z/rV3uJ8+daJxSfBIAek80SIIxQkGRpNajXzcmmYQdnJpfH5g/s7v3KjvSfuPDO1yhjNp7RrGc+D4Sfd5046jpfVbLKx6vBylqjSYERsFnE6hFAkcc7pcxY9HWC4v5SkhR0UhBEPZXBJ0owBgccDznNoEue381eOuhYRMNqN2cBxB7SC8UpnMqjwgIcZ1QSnDzSbPm1eOh+7Vkf7Y+PaK9y//4nl6MKN//mVj3AOzxd4d+uDzniCan1zNmaqbmBMvTW/XijOfni/gzlHq6qO/vnj/f/pfybUVfWK4vdEMtcqZTDyxDQUnbNX47lekk6vRk6fHRz5Gc4h9lX7171Q811pMp9ECilKkP3MNl+2gwnKJuOxNIEooZxIbG2CbuTgJJkN94yYlkbCxsIYh9OZ7ZQ+KDG/hOQ6icB7E5Vf86VNWG9qIFZUkRl7j8xx+cmmRqJIti6MD7ehB9xs/XHFQg5HYtWu7gUn1R37f1KbdTqMgkrRXAJzJAYaWiFt1ze4NBsNmiq3sFGS+mLopBEjQDQCXgyM3neF4NS8zhBuGVxPdp+Fea1xbhiJy+uH7C3l5berGJdq0/SmGyNdyuJrAQ4JApTqVYLk0cYHOgzAmRVaW1/v04rMLa5LU3uYdPjMfTykiLtSU3I7IQRw+d4/2bXIto6wYVN+M3c7iA48IwjNvlMpyeYsjSbl1ps2AwSmpLpIUiBgkxRC0XpOHkxgz+Fdf30UKkC5ElQAvX+daEmGRJCjBDBvSBOMuxnAIzIlKRAzHg9jHwwCOHFpgAAyhbhJNYgRBAw1I5WXJTa3YjDiS1UFoJKiPRKjAMGwa2REKQaiSQXtQjJxomBD7CVcJMVy3phJe8jwQoISPRDiMUjgdMWvV7EkQKBNdnwTgev386QiGZXmLWQTYkz/6DKT9vZyizYZXl37hnbp/U9KeDkXdPX65uJ3IvKBYMyeX4/CsezmHCBfCeRZNHdRNNAHliknzoJNJZi0i6fmDbp26zj7dHyKYIuILZqVnW1kuS0L8IFw5nvzyS5rbQHa/Xo1G40ESvr6xrKDO+u3cr6xuFMayzLDrq4XbGbEY9fC19tgswCdXlFiZh5nI1CdmpSxzUTzvT5eVrDVIxCqVeCMaxCjl/HSqxYet77/T2Kw/bsG5sZ5mZ9kKjbsuHC6ggiQnZlJflj/9fFIqFM8X85ZuWguto5KNKnPru1sJPMUTgS9KVtvycHJg+/F8yEjSmT7LQjHHYg7uoEGoYThsBCkEJlHskqhhON1Lj6lk/RiucwGdp3nGVU1/ROLnRphJDJjLprR0NWHTK6/M4em6ZFXy+tU5dX3HHGhGS0s3S7wGuoBYgR0ntOr5jI7QZ/p8uwGHgYsO5/HcPTgal0PWqshUKc9l62V5B4rO2u8/LfK8GqNGT89n07FHTDQHBkxz5unHp9u7dVjJ2CWs4JPfXcUz+WKnFfuFOE1zOcvM++koNBMnpZIkuhr4OP3RwWVxpbq6QTo4ZDYNnCPOD0wGioUNqvTe7eHFYArQ/rPOyDb5ZWeeYPVcedpsh7IkiElYipIg/70q+7Q/b83GnEBfpxhWUnrnl4Nzk6aDSdd+53YpAELfQuuZDAgSSI/NefBuGZrUCzC3HrvcSXso0xv1V9FNmr+88g3XZjN8tsGkDLld5g/i8t2BWcyz6N3cxbGDSenEiSPXysuVvuG2DnRuHWdZwTiZv761khe4BBOah87KPYx4i5gM8c/vfzH0tUmMvv5ORQiICPaOu2G5wAxUVcbUoUZE+dwSBboWvLWScxFIQHFF4LC65VvDm5sbhwJuLRJOloEeYJgy2Z+vFIWffNasvsqdTKcbGQmfoO0mi91kY9XXAwJvQvhMLfNctIYYMGySYrc7phvIOxVW/v1V8wVy9j8s/IPTX3xxJq9imdQ8eOzurot4mR+ZyeTi8cdHMAXl3y0wK/jk6k/1NyatPzIBtpY9fzEMSHcQgzKQztxE0OaPJ+1731CuFdij5zasoAQff/rBOVuUV2sIIARCSubzBJkO9RG0tsr6ccTw4UhXK/NekOFSOv7j/9eHe9/ZVvbYGBn95Y96m5miLEMVBO4b4Vi7GkzBNYVAGNSfp9O+BQfQfDohwpQq8vdf9gSHfP2tlZWN7BcfjcpFaD4Ztn9hFZazqWe9u0dR5VzscglpkErRPBrHk3j1h1vw7haXTkaaVpCr4ys3m/UTWUFIIxjo6Ho+oggpnvdQRYUAqYgQDFf70+YBsrH32rWi0Ot4QuyX1kW3BxuqXNutgHg8n7WGLbd6N6JTcHR6nFrYrD+B+0GEsd2bdFQN4/YlQYWbDMwVsO5ILUZ21DGxMhwuRSzb3X/0hACdWhcjs5j9g28OgYw8TMfHQylGxhgZZQkRmegnuoySxhSsXpdwhnb+5OfNAuZ8qGVvZCrby9PlRWZr1QIJZMRTHbUXxvHIFOBYQ+PYNWq02JqoNAP7AsCt1LI9PkMjptk8mlZ3imidMc5tqzMFeYUsZI2hoSMQX0hRlIaQOAgx1IiSd9/Z9JrdaW+eo8oolhkPk+08EbZtCbXFGHhBmJD8AiIcO4CmNlmAFNFS+30ml9J2gFqpwBFv7mIzM5NC1tT0LCeeng5tw1yRgbKOwjTx8tTxYaSay5we26ltjGK2UMoCy6LghAbkqozPTMinMDXSq7pLALc3DJM3Etj1LT6L87Kxb2XQZkWStOZ8MGC59WwYOLMnve1KFgLoD29k/9N/+AA5xeW8K8NcHSYVOvzkRA0/mBNK1O9o775SbGQgVLVSCrp2KzduegqGHvfQQokMAlu1VIZykchs2sjOt7e869z7n7UPPx00w/lKtZQURINjAomGY4QG0bTjlZeikJWgRr47jOKSOHOlez+4ndtWXA+JHU4kyW4roEJhmoZJFMRstILOp5HBoujcGEkI1epaFCcbUUB6bmdEJyQSp2xGaTgeTaF004pS7IqUpWw2w5UpaOGEdrBdCp93XUPnYKUeM/rZHMlm7B5DkQz9LIJMw8sYeNRMJFnqHEbAoy9sqkF4NOM5QeRoQUlBux+2DwdotGzBdaKlRzIcrPPG0V8b1kgtXGcofH42iyjMHieQ1KiXZSly2UCbPfhoP1Of3/76DRQIC90YPh+eny7sz9spn83fQdrmNKRgSlSWtuCPf/ry2k34jVuFiQ6Nhq4LgtCK50nKLRdXSmVo5Fxfrz65cMtZyRb12vry7qv1dUEwLcxsJkcfn1G8jleSIMge3SrFSMxG6cWDVmTi7307DahEIXkCIkbGucBYg6sFT6R8gu2fBG/uLkMKVJ8l5+deaVlJxYi4wBvXFGgw75hzuojn1+kYISKMs+MkhFEKNn7141MlT4mhX1tiHRAs+ubtN+t+nDoO/53f3W31yUKkruygbIB3TppOxz2bRl3DChL9xo0qzNJb26W9xobkOMNnw1nT3VgSor4Px75YyLUAvrxbtadzlo1jQKQx7EJYLp9PEsIMydNF/PY31v7dPz9cu5HYiCdkg18+Hq+s1+SaMpiB+nLckOc2rN+6I33ZHtkGrEj8VC/tbcK1WvS3T5+2zhb5AjkH9sZdxTjyrz46ePbh2f2fPnr9+43NtUTMo2whZy0SlgT3H2nTWfDaO4yxf3UxP+8Mwf/2N6seT5Ak4O4DxpwKBTlB3CRklboIa0FGZL739bWa7//Zn15eWuAbr64cnIYZkvjKdtk3fJcAroxtVtAskev2bZhgZ0MLSgMWpvrTMRJD9etyIUGOnz9VF807e7XV1dLZpcpgcoBVivm4VMbnZ53uFzquAF5hCgIHp3hiBiLu9EPv+q3SjiKOBs6jjybWyKNs0zYnbJHf+F4FggQABQBQh5daCkeno/GbjeJSQwAH9IPR9N71XOfDKbSaYhINCWRPt7gUEutSOSF07ZDt6mWs/vVr5UcaEGE2x0ZwCmwIfnpk128Xp4PEUdXcRiUMsilDIb52i3k1cM+Gnx8D8XJjSiCi+LJNfPTQLW+w5bsbTOBOpp3lV3g0hgzcdUdYO8QzCibWmfNBX2/vh8eHSUyV8mvqbSEGAHan/bGBGOjGskRb/mSKwThqjex4w8eWmC5k9VsjyDdeW8rH2eLpKPxsMSLsoKw6MEJALgqimWkbDO6kIVzIMtbM9GA9KwFjsYASACIEkKi1QOcJwEpZpcidPGoDLC3eKMZ6iM11xE/YMhHFIMBiDEVTkkDDsTP5VNuM6YmtKqQVWWGJodIzr+GgLZ7VEYhxNQgloIIQxzCaEp0Q1zIZp2UVZZ4u0QcSkugR/sJsi0h+ehXUZfEd1n/YQeNA3dh9dP/pK1/L8q/lp/enmGvb1xuJNZEfd1ieiVA2iNPe06YsxYIsGctsMk6IRSAQVqViSdYizGYTM8zlMuuFUsaNbrVf/pyAxxybgqkf9B2Inhjek2cpp096kLxxOtt9c4WI1Cd/1ZO7fRuAZcbtJWBdcjvPhnA988qdvIHKo16A+h6XheUC7IQaJ0Ch5xsewcMILirNo+7V87O//VK/DTdZVpYzGEkVbTz0yZRHKGPq4FlCm7h5MQpjDY6nnELv1bJrG9kI9+SFczaeTXDcGEC1cpbCgk4UDrs2EmNBjnEcV3cxhgQ0g5ERBvMJnOMljHMseZOIwrJIgridWM6dmgKW14HxyMPKw88bcUKU7dHzM558jYodwLIWIlPTJ9g8k3+TZjElnsMGw+KZ8si1N6OZ8clJ/GoeXs6effQ4E8Tk+lq1cRMDJ4uq8417ok5g+v7pxAlxkv7VRxMtQlcaXFjDi9m1+dXs8jBpNtVcZjZ1Hc+j3/nt3D+4t3X8GXX1y6vjXqRs8K9vEpubVUdMXx47vDpx2c2qlE7aC7xUuvND6uWHD2AUiBJE55BB08krtLJXWELdia5XsuXxVf9CJkUHAQOVFtnH/+HxoJSdTJx6XXjz3VxXF9XIKsH8eIj2R9PQ8mu1CgDQ1EuHmpmELorIN+/lJI47dBw4ZXK4v3WLMSDzwZPBWghbeBFPB41K7SNCY0q5RknszMN5gExcg4Ad0ou9Jel8rDkgdPzJtJnsysTzIx6bLJpDlboZdrpBmYfia0VHQKbNdHjUpCzUiWZffRv94bvFsaueTSK40y3UJD0UR/fDZntUVKjcGqmU2UdfdDZXczPNic2ENVE+xroYVq4VUpLhU2IG0cUtcdq+TzpFCL7tb3UftMav7Spdx9v+9dXL817tN17TwtMlBHLNsUNR/e4XrSVOKUJPpgPqFfAfvuhnvzw7HSLFFRbFgoO/ePDiSZeOgwTNFDDz7rtbCI1jdLIYxbqrtuZwqlqJgCyVYnuLfX0r+uhTsL5F2cTiw1/Y794Fd3Ng/yUIhQVBoDrgnr2cEFCNOZt/8qD72XBhMshv/2AZodKu5TRu5q9Mq39hkiQW9MJXNhoiqpc46kTGLHqpPJt0LdxESGkxvXwBfDmvrJppx/vg4JD4IdFLIQqCJpezMiSosbu5VFzNlmaOqnl+iFGQmUQwS0YugSCXzXnz8wGG4e+8mWeyxZBmgI9vvbc1B3QGsprnC1rDOYRZWkGXru8QUJAOYfXo87gJB7PerrRFJd10eWMetGZcDaAo4+EIPvENVC8VG4UyAOCuCAwTdzKrINaKXLRSSKZaZy4Qy0jmhdEwEXB9CirFBgApXrtTr/IgzCA7CwByv3tjf6b00MGEmdgsNDO3MIyDu+NwNsCZM7+6hg9rWGqPhL6dZNRbf3elTgY//cAh58Hlzw/ehsDCFvy75UcdVybDpOtowNhczqURjGZp4AwhOy4IcLdvMOWVpRo+PV0Mx/YV0Eq7RQyOGB30LntJLUfbaS6KQohfoJJEaf6hniEStSYhikLaqNVf8DdKLgqycUKx6KmagHm4CmMICbthApnzhBN9O4YhAh1PPVzwZ3hYoN3T1EdiSAxLcMr2knhqQ6xCeB7Ks1HiBictp1RRnDSynBBOIxAGdhIKZTYxzen5JVEgDRvMpGgAoe/t1i4/7WfedIy3q4cfTzdX8XoRvng4Cuu08rowfobydrz8A2jQXvT2Z1RFhiB08tJcrSOwQk17TKGiPLpMbizh+T5s69Y8wXGaGdzLE7XEfxZDc9tBQ4DoaobNVDOX32LYWTL6QzOLBJ8/6SMNgv7GCjPV9TmRJBzrQ4jhz91Z/+XFrXsboECczxYmlIh18mJsVCUyBUEWp3rDJIcne3nFqWx85z0j44hyQa5s8n2En5pIBgSW5sISjcOsrnsw7NQk7FHHDbmMHYgwCiAChzGzJABM15dWWDRxrvZ1Q43Xl7PawErG6dyRlYw1bLsi5knoxHMCLcVs4EoFOhWU5mkslfHyFqnb7kFwBkQpJEtO6bWm3V9mtc3caOoNAzKaX9EHakrIhN2fsbKUCmEMEjyXLazm8qmHdhCeiXwLzjnh/sjoDJw9pLzbSOY9MZCvNW7U5nEw7yfh09bB4cAdWMU9PiwJCeE9P438AAg0xuAR6oD1JW52bH76J485Ed197RaRKy/9Wm6ESYapzQb22uaGI/r7T4+DFiLWIcQJ3auJa5qVKgNoUjXNwamJB+h6tX53o7j/xYXjeTCq+kS4pmhQRNgwYsz0Yd82HZBFvPbLhc7Gjb0MBGOpTY557rV3N4+f9p2Ry2WcEMNTXUvc9MXLaSODX4xnMoUWyHQB0gUWa67lZiJEyW8vF1/ZYz49N/n1+gQnNQdUb5dFGLl4fkXDqGFZdnuOQ/EqYlfeIl+2Z90X3tZNaTLLvXsDUzW7WIz9YfrlT+9bFnTrVjXk3e27jfK9G+cnrcNBz3c9BUogDA4ieDaIVzPJiCWlIlBd67CveaTtJrDl0ns35VJFtGapfwiL1frFsetB6LVdadjVCLS2vNUYXR0WQ11awpE8Mx4ulrI8QjIp8DR/dGK7veF4Z7P+UhvJjWsuGJ59/PStVUF5g7MPiVyJkrlw/+OWwBFvvlVweY6uZoYH02Lodlpalga46cJxLGAxWyVGc3N2PpYzwu26Ao16P/gvv0u5z67IC6wA1AEIeFCQhcUwXNlQFmZ60jXNlNzYXdn4O1+DswSZmn/z0dn6tmKN1WbLxBJy/XqeYiDzClNlMDPwCI+WOAJPZM5N9DAsFSQdCnoa6Nno1q6s3oc+P9MymziIoAwfJNYiCJORz/cNkpMUiPK8NGKzZIxB455DEckqnQo3s/RSPgkTTSMaAs3hkDnWh73Fhe0WRRwWWYkK9e5gPv9M0/1EVcTrO+I3Ns6M6XaJdMddZN40ZzaZD3CUCAAz0FGTgao87wJAAnCVAhZJkSTheRpOF7BuFOI+ISpdCecYIAHAAdC5TJSiytAyAOsAhsLQDzHrCmyk3/DzthX3wl4LCmO3dxQCNXE9gs4XjGIyGfXLgC7dWnm2Sn5heIsvZuOXEYWSy0usZtHVPfHUd8ajrm0DnitwNSkhMd3WQnU+0k0GQ9N1xQZh58rK4BwpIFKBK13nHcTrjDQOT7Q0lFmcjBnIxq9tZB5c6AHQiDAJSbwks6mbFgiYhMHVwxa9zlc32OEgxKYhRwu5qji3rZntqXoEZnMcx2QRQ02asiKv6XkgMWq4TMlMaCcBh/gIqMsEGkcaCenTJEcFLAmHtgMhUEZCArqQ6rBHMTlHdKiJWRfGJcVvBvxwkN1bJgWevbKiz/o3f7M+8Mjg0sy8vnbhEZsvJ1g5XP7N1eMfn5v3LfS1BiiOsKnnLqdw3jUFyjrQc4t208vIfI59fn4dJp7kVLi4ijf1Lx5AvVpShXR1h+OrYtTUQm+uHOsOlqOzxLj5sLX2zQNfuPdQa73Uupcq2r3ILF3/+P1TbDlLbZVycDxVnRT2UN60JxCGRXIWwlhXXRhG25co3oe0pTp8U9jov7iUYyyZu8OW/Uz1G9U8ZzqejYrLillC/L4mN6hhiojrso9InhCpiCWQmD73rNAhgUUyBMPGdSaZTfBR/wIiQ4uiPR2KTU1qEIYeGwzuOuENgYtWlzwIUTLzmixXUPv9Fl1eXPJjuhWcpq9Xf7C9i6/tnk7/lbiEjp5jNBQiLJTA5ErNXbvHPHt2tuge196qKauZ+eMP53Ohem91+xXeHTf7F64s8HRFSnz/r//oZzcrFrG9qXHDcRsVEK8kkkI+jzZ4V1c9bdFhq5sbWy/uPz29tEGS0kRodObsbqmwfI2+ODk5iaBtUlDbF/eP9xrESBfm/+7P1+9er72WCfvz1pmOVngKxO6R3e1NHJhLkDiAwY1vrV1X/P1fTptTWL19+5/efvfZ8V+UfUYbWVoBSmlu63fuXn60jy4MmfEjyxsejhyGrF0rYSk86cz01B4EGqsGWRofW04jx6y8Ucoy7PHBOELcUZig7uLBIVVl9NdfUd5j9TaIn5jCf/zoimD6lZjLkeyg5+aJ/BJBuyRL5yhK0k3VjFL8QQ9h7m1pYysk6VRYpCLlG5YoMLjncXkhH/rD0QLG4ec9uzN9srCir/zG+v0PW8+nzrfeWUVaMwLBL8ajMEsXvEUYBSwZZDazYW9u2Jllkr8aRKLAVwoeZs4zpXI8aC1v3ulh4PNnz7edzenL9hxQMZsTbbTKZKORBTHx4cWDl80Hr9CCr0b3F9BWJTvXXKX7+Q/hzMG/+9Tycuw9si8wJ8fHEeCRkD67MC9Oj9mCUOeIOgtvwl7HtY8W4G6eSlxr5vgzxCo14CTifvlM+4evAPno8Uf3r2AYfA0Fj4ZAqoGTsT41wd7EXt8WGUnc8OP0ljRG6NHfXunzYxATw+fJaGDeuZHd2ORpM/yVS1P5vOxOXN3VT/yF6oSKXL0pWEP3xYW9VSPXy+zx8/bViOcapLvQi3RtdDC8sVd6eWUKEm9P1e1qbuFpOEbFMZaENkmiOyvF064WImivP9dftG6uZ/NLm+2Wp8gcPLNrMsU3WMsAfgxZFoyxcr4krIirIHIBmgWAdbO6D4jZ8KzEYY1MNhXo1I8RdgGNo+tKNfQiL4ENGJYgaEJBsRlLnqWg9hyN9AEesIEUh/AxWIRg0gBoDVp0P70oIzyKIcP3qPKCAOs74KCNucRiNLlKSvUyAsyry4EIkDqNl1cxPtAC4EOlSmwGq48sz5who1GoOFDKwsvXqz63zBZnl89wGdWq1uTckkX8vBtQtG3RlE/OllEFS3AxZus03Izi+dzIZhALQ0gsRlTj9GyBiWQpx3mjoJskKQtYyJqN9dyarE90zo2dqXXueCsCrJflIA5PJ1YCJfyKEDvJ1Yu+T6e8iAkF/GxoAgJCoAhNPNcKUmo5RnAaBdHgSGNhDAuCJDXHnovYAUpiOZxa6JExj/lNUua4s8GghLNOigocNDk+/+iXD/cKxNe38U+1XDqPiSdh912y8L+6Pfg35/v/cfz7/+DOL5vt55BZ/mqI/ozuN5Ov3QEW1GxfKdVXaxArH/eMqKcnqi1XQ5sySYpkWkC5B8YQM6cgezhMBna0oOXvFZoEkg6Nyz98ii8TUco4AYC+XERga+217EvDrD+b7EXe5f5kp85lKeQZYAo5ufj9tck05ZQURfEgCQI/naphRcBGMy/Ak6vQZyQchsFoNj098XsLsbG6R+ShBOB9qD0Yp/IyemOjpDf7mBNYCyfOkgej4M21nK/CfAqsAC6mXhH3ey99d+QrKwJLZiCWTAVu2LKKBARnSYyE7LFTLYlXk3hyZiolXJ+DQqWAyfLcCXyAfTjWA02/lBkdD/KbG8uv0zmu/PCF+iMj/D2+GnWdEWIjikstaAFYGULPaiZ9FK73F7EVoiP8KoqfjjrSxvUlAZkEQKluMGNjejXm7ihvvLn+8enJrw514bxpOkKqUQLui66Z8XEIYRxRhtezl+e0G3psyGxt4pjNElGEWvDhOVrFQY3Jlvj4+PxcqnO/9ZUSCthduWpMxaf3F5/ta19dceo7+Yczm4nhBEq1sbx9o3rV67x6k2WQ5M9/NsOyRGmv+HdvvwsAoJlrXeJQaihvVcSpkTxwnK9dv6U1j48uhpOJT0kCu5qBmTiXak9fTmNPJ2TUM+CzOYA90ohh3zXHMWRA5NKdYvNItyfc175avX6P0iLiw54xydQaa9nsX/4MkWyZRea2DYg8nYQ8w4IIM9wxWcvqAZHBhSiCJ59P316T5vN5QSB0M15eKquaj4QEGhKsgA2Nxd5SBgH+/ERf2WY/+uOHJ6f9FGXD5aJlptDcLRMBEVuJv0hSP0DQRT/KEaBesMXSYhLEFxaz9Grp/uPe7/zDjZ/9e2N62d8sCIIjQJTJrvIZi0QgahhFqcuUMySfgdqty+Da9kFC7dws3f9zsASKP1wH5vAV8i6fINBf/dvn4NMLiomM5rkVF4OioAbkW98sLdX48yfz8/YMB0jCBusbaBoaUuTRaJp10+5BlE8Fd1zSXwo/fdIX1+Bv3UmSAIQ0wAHIYFJuJTk/XLTODFzRa5uZ1nGn2YLR5uWM9Oq82yhl3t64WRCjv/hxq0jYHgty1yKDgxCBurmdhRZpq+u3L6d8ka9modnVXKKoO6viR09GxY2qg0IAJuk8ezXtRxHtewkSwseX01oBTjRDwIEfh3gCTQOkkOFsP0yhsCQSvu0ZrXG1mCC+hxECnCX1ZqqskmlOBjALIwXw/4cu/vzi7ITwb1fct6HFaB5Vl5lAR30/JqkUjC6XGwXPsVycZpK4C0N8GlsTPye4KGX63gLhWQQlluiAPO3Mnvt5btV12OAuoSzfRkFXSwRK9pF0p5eCZWirrh4YZvzaFv383H3yYEwUU7FKLGddp2tEFFJdzsYYl5zMeUbmIUS8LavgRCWSNSGxD4yzK+DihMbGozDBI/T5gcPhhJ8wegT7KBZAiUtiAUUPNC90phTu81XpeDpOPEQmSUHEMF5MHSx1/VqRbB/2cN+oKinqBbO5MZL4+kbOWcDtrpakASOTMY3HlgGmCyTB+BzmwdCwa5FMzCY+mWJwGqMoy7hXg7N9AgrMpFgQJNZzKcOyEzFyEvSVDP6LWZDiiU4xaM0DGIJ58yhLLYbagFyq16UlI2ptkq0TLXj4bGstf4Ik47ZReNCcK6vQvRL4q2Z/6t382vrBn/9C/7t3aXl008SeN9n+tVeyZ4+3LjsJ0C/ysUAxNO9aHuSgaGfqILTQb6aWvBPvX8Q/vN2+SN+OE9ESVuVIymVWWLP1YXv7t7kLuFJRVpa+tR6vLNaXVO9xe/3GFr6TYBBeWgmEXSXEknAYMEy+JAjnf3Wk/L139m6uPBkNIssmUCZx3BUKjlzzi7aPQHzixllZ6o1JBFFinhmcadx1SeTcoWGObKxxS4plnrBGVcjzTXaNkQAURFKG1Y5ZazS1sJgoBpOJu8tkbaS5cAKEonWst8CJyAcpM9JntbvF48vJDCQsQogccRbAVyfmtUwxX5eDIre8mlBkxYKtv/zl/A5r59d3R5fzzwVH3lkVJmb6EmoiBYbVUTjEE/P0x5eb18ugEKbDTuyiBl1AxDyiTqKAHVkzfQonUtbO5OdpFBgoP/GIGUTBTGkvk6VTw+PC0CquKacjBwZEPidcPvPhDGGNzqcL9d4yXFhJh03DHHUOIGK3TN2obBJc/PTQyymRMyBzr6/cW7k1NR6Hx/qDZ+cXhfLWjcKgPwthf6VE+KrvB6nRJKv1fOXr2zksBWB48bmQQmyNKZTgGD4Jq+aL/9SDuFX43Rvj1xTn/2yUZlliLUBbczM2FrgfX468XCUTQnKRtspkLrLGWS7sjc3Xd6UQwdcFDH/93iuvyy/+w4OxJomF1UzCms/nr7yx9def/CqFbGhvDQu5IEVgYKecpCrVVZZIb987+qL9aqLLq6hPGrqHZGEMRbFUn0oCimFhXAKFGpPWkFZT3c4AirOTufHJJ71SgX7tt7b9OOVEdHrkrCzTF2rgp0EiiFyGyeY453QGhIZBFK1+UMGZvXr9+IOLZz2c3a19+qPe1761nuU4FMV6B12RRxmBacv28CIme0xUcBd++A+3WNKbWvpZZ+12uXv/5Z8Mdsqehihf+easleTOvji5OhxeuOjN369KarypN3Ac//kfn7JseX23iiBQpxnV+GSxmPc8oKkpVTTOGKS1f6V4S0PNcynxf54n/N6kG4IsDWyWPniuVraZlVsKHAIPZe8/VRmRygNssrX0zXswu1jotvfzT87UuYEWRZHjXylziqLAEp2JoHMYYUrwXggX3PDJoQMXJaHMm1Zooyi3khjTh6Xtb04v1Dfe4awI9V3T1o31fGqOF0S2PDMQmXZ7vfB6hTfmfgBTkIeGQTBZqBUlt9HAKDIi+JDUQJuG6N0lRSFg0DqJGovOlduJOXIqQMhXb+VpUJTBifV8ls0KUMrgnnhI5yM0WuJtHs7QKHUFeWxApU4wsyOJoYMhQASYh/BR5Og27pjhL//kyvpJ5z//P2ZzDVIP3GhxMWf7OX8tGo8jSd5SpBDklfo986BpOhFdIPKrWHEV7fL6kQ4rRWGMA3GCwNP5YAEJsBzu8skaXNLHxkfTmTwkAtEiBDsbXzohrGnSbOD6ikHq9l4uG0dZlE5YcWKG1EInkyCQKVjgRxQFUMt6oekEU7m+BBDSGDmmnSo1juwYVhgxEhaPrI2lSlpXCAKfayY0m4tLS3HoqQjE4zDrAi0Vpq7HQ2Gjys8tQ4kQBqfCNEQLLPr8MnnrnygvPndb3ZCsszUUHxxN125gHhU9boZuQkEYb4SBUCBAPrnqOERKGQZe/wpZgqFnf3MBM0jjzuaf/dmzdzUiuSXhW2775w7888m7v73aXPJ+9vPHv/u9GwDmXl3O/uLDzvDhAfIbNlokjr5E0v0hlnHCp1PD16AGyd4R4hJvKrCvpXQeqSSsqiH4iZczghRD2/vTaY+H0OLeO+LWb7wF35Ia/fnlT89H563zKYV/5x0TExQWr6xnxBI9mo767QgixupJSyCmxrBHzxfjx8zH03C5lsU8Nmo7KCmJfHh2rvt+UKgAH+U2X6t1T9yDJ4MMTIglNwjSyzNtRmFKhrK5pD9fhFOnzlGwS6i4KEiiQ0JpupSB7NUiP1MYO43kMssfpspxi6d4zYpEMuP66LIIPv35WWzHkpyBFRinmZ4K9+bJ67u7nAMBN52noX5FPZrr+XT4P8Os0yethe5ef+s1PfadaWm8OLNRBWfZIAUeHWmasLq3AQpZkGqg780pRJZFPIAwD5EsVYsoJE/d2Fo1OejpvqpENC405BwLClSS4q6LzIHvs/TxAJWWMwvVJwNTDxawu7i8bNHZtNXHjjWj/trWSj3b1wqHD5xIpSQHwXKIUOYv9tXHHxwxCv/qGrJ8r74YhQqXmyz0i2fjDMXDPOFqULWEZlfoSFEm7X5Cha0pe65Cv/m113onU3aD13/5Pr4CVa32Lz+bvvhA/b2b9bxjoBHmC3QERuPj1mp9afP6dr1aPh+hNQYjlBQ2c2Ey++FXZV23hhFFFrMLBPy3//zztGW8/hWulMeHoZbSvlii8ZX6+cRZcylymiTZmYp51QY2WKR2k71xt7x2ix88OjRNL3GN7VzothyBDREmDTg/JJGLTtdPCiADkwR3cDHLRzHC0z/89k5GgW0neP6yV8hn8RL9Utd1Gy2syaQWamfzWIYf9ZPvvyJ3297i1P3N3yIv77dEMxLmo9Us+hEwyQ0s/Dnoz0xuA6SOPQX9WdtcYwuVMFwRsdzMufbyqP3k57Mwqv4awb9G/Whk3E+KlM5qXZ5/h23+xeHmBvXm7nKLrSTH02bbb02MvU1h9XqhPRhODGgB+w8/m2adOZ/NLqbxNGL2lkWyjI2fGbwYbq/zQxcch9U0RFxbN6ZENS/MZyFV5TNs2qgyaZ5jBBSLpJM5CC+s++ezgRU3ROT6t3dzext5Ux12pmPXHF0ZXOg+vuxZIL8uV1//zlJDYDvHi2Q2RcucJwmMiDz/H6f/m/9M/Mv/mDSb7NI3K2LYM/YvD7uxPrX8PM6IEpYJWw+bO+s8FNuLOZktr3JLsv309HIO3K5TLSC8CHNUUMlhuE5rX0yFwQJaPsvWN1ffhCGwejUfXwS4H7Zus5m+cS4GKRBYaAxQy6KSkMsj3hjYuI+GqcB5R2bIoqoxYassjqsxrsCYzZw/N2/l+N/62tvKbQu95aeT88kUKq6IZU3n0dgONdoVYwB8QODIHdB42X3yZStYSDwDLyy8NZUU1kByUxuaagkqJFyG2EbKwRp79PDF4leuMSCswLx1W/aTtJHkLJeY78+9g8X6HTnOS/JyXrc9er6wOxoRgITDYQrJEGk8t9VhxBNkTpYRNknUYG76UQCzbDw47qVWsLKeD0ITyXDlUuFqluqLmEmxOSFWCrw1i3BjnobwbB4TjD7qqfMYqd7MhHiSupDpBqbtoprp0Xmm8zRmHWh21Mx9txZFNHHKdU4H3D/aiAVv+YTEhvPtGmd2575NCTSDQ9kJgLILfGG5Y5ucTfXV2PreH6yddVzoqb7yncqt7yL0ZfLlLyf1rWp02G2ZUJqSlz9/ufMH15+0WuH9/Y3f3RZeJz4/n258ZS+j82g7YinS+Vnf9rHl5bB1CNiUIa6G1SJHD9Q08j6Do3Qwu3Znu5bLHD5fxC/7vb9NBCpoLRa3cDsHl8oiGU6AMHF/6g+HLSvaH5SWc8ucl5eD508tg/A2bzNaXuelGVHMTM6ttr7Ivv2aas7av7iosXTq+zOYffL5XMQyO2vU477DpqgX+EIpgyewUxGdAEU0z8cJNwyzhB+afZxx5oC+hli0YS8BgqNMHPdHT5r8Y/+G6y9yQBTRkWU9M/13GuDmdwvH0zhLggDEluOSLLS+IXeHaqAiJSXOUUkz6J8EDXiV4rNM849+RtyVrOBFMkW+yYyS4SCv2xHK66xlyyDmgO8R6GkfiCIUlxWaTBCCc10sDUIiWL8mtxNi4ltX++wObJIcLS0LsJj3ZMRdpBSVOBBl6XHoM2kmXXRHV5qx89pyFtLtMbRewyg/cTz75Iv91ofTt//uH4ivIt2pFA+cjWX5UE1v3xa3idDAOaU9vIhXpptYGZa8/ftI+yi3x036B5UVHyIjtT/OStmls/EICtjf2b0+GcePDi6m0+WkLVZdcPTJl3+KffxoP7vCU5Tw9u9+NTaunLe3v/jSu3OP3Nm9ZUzjvO2uLSOLyVydU/VG7qCpPfqgNVl4oFCdNts6bPSHzlduFdaL0oxMWzM/i7nOxFzd3Tz+/NPHnw1/750NnBS8qd47mN/eqlH6TDl6oRSoowbGOQ7v8K6mZir02cDfWeavxqmiRIVKkRpeIAuiwnDnadLuO3Eyc+PkdDSwodr6ch1C4dzxBYNlTot5NXSTKFqW82pzSpClDVoNLbirpw9K5a6A+S9OLxkRhPqR3fv0pZrbzLaNWaCUxg9av/eeAoLhowePHwpf237yo8wiBtZ/7B64NR78N5WFBvCgcO+z9Nk3LaKyLCtzIPiddsCcvLz4/EjfgcBbGyult5aAO/zFj09izdcTjrxB/Z+uO4dy7aVT++1y64PW+MXz8V2xsJrhy24/nMQGmpWwAOEiVwYObOcEzArAbDoMAkCAKsoEo+Ys7sXdLjHH45tvbL5Tj1JvcdDvf/Qv+yUh3bpW2sHcZ0Vhiu3+ox0x4rjP9Npnz1vbjaUbb1TdePrpxXDLUgSZ6W0Ljyzl1X9Se/STR1/NfnXWiAoutF2RPvz4UTjr5eRIX7CVeua8bd+9WTy9WnAkfNWxmRQLkTCKIgKltDOfZ+L5L3owhFXKJeg71zaoIoDAKQAFAICYizqjkmbMq5IL5SB3Jk015yrd2pBQIT0Z9Xk8MaFsN0mwRYQ4jFiC5jYFiwjDwaqLGuiVMRoc6v53dlikUExPPEjEBCWGnQDtmwlMaY7Hcen4CHDZqUuouZyCbuZP940nJ+N6Hp9VheJKvXBV4CGSZI1FOneCxf77B+xxqbaKqNc3+aVpnF3leLqW0hRkewmEI8VwVwK3Nke+M/5yxiLzlHNCmQlwkKDYHLjM1K0iQGPKI9W6UyqQhHb5YphwMiFgMu31z0ZoqcgvZ4e9OLKs4ciBUgQNbaxaqig48FLrchLpVn4lf5ZYQXdRKRJotpDWMedUTbwwil3HdlBfTa9vFAEBcrXq/UvDeuLc+mGudRM5+AtveRxOR45Po0DhqWU47bAzLcpShGMFk5TGSDRfY9+4Q372JPfF4wU/nF9bZ0mYPf3Q1yHi7Tpa4FPTT9c4zjTjV97NffR//ZWMzQu/i7f+O2t6QF1r1E8/P5kcdHgWTFTt1c11warkZ4t+FI4OxhXBd1Rz6sHW3F75fo4tCFOALDAG7Uwq+OzseFgEcLLGvvEbu7YBZ0hhcqie/fhlbV1hG8GdksT9WnHa9Z4/6DJodDW1KD7NEHIhK+CxbwxszPewJCEx9P7PerDuyYQUQwlKxDkmHbeai/Ggup06/WhqJsDLZFlB0MI4TdWRlxMQkQW+NsNFSJ8YuRwMj7RU60CNGPFsYEaFZEEwVupr2tROKCkRiJVa3fL17U1mnpoEILnUzhLxwg20sx4pS4WdMk5C/Z61oHMkkXUp7t//6eklkvzwVrRMj1qIarV77KCfrsImMVyUoIteGohYgGI5hWOgrM0EaeTOr0Kmrs2MyHZ013U8HsbKNYzHXUwIvDnpC3yYZHz2sjnpIXBMQoFHFjJM5DgbUrjvTTN11n76cil04xFyOZe33127nmF97Xz/37+v9eQ33pX9kBstHIElfBpuMNcA4H2yNlvwxn5bsx9nQ1UW4QQNU447Go8yLBE65qI1eZMljLNL/sNPKXyGXD39ioRCHg94G8TFX/uBULtdv/HuWm2JgurZ5z91SjDx5tbNtbJ48tQcD6YLFneaY4plAmM0P/FHvTlCQ+UbymxiFGlbroq737uWRzAnTdvDiYzPsIQYLZI8YRE4XdvEHj0Z3dyJZJrUMWIa+eeOXddPYI/BwsgwU5aibT/WgzC3LHWj4M5u5tlHg+vXWL5YHR3PU88toAyX8QJ/0R043HWMI2l90c2iehJ6Ay2280wqcyACV4NEd4lCFb7y7bG+EPNMDRo9/WW7mrNZcwwTxLrkidEYSbvqwMEjwNWp55eDc2s0LdDZ2978gnnx375MFyhJAvi/Ajtp+EfpL34N/uY3IXmsmuZawea5i+w9IYPYkfnODa7kOq2D57DKjzVHEokbr8pMpvBpy75qc6pPH57o1qhfv1F8CxMVH7PclAbLBC+4UxRiY2NiexKyVhabxxZYBBvLggBDth8etodLWyn1Rv7b5Sq+8P78X7dpzvEcjdsmfvBrjfOL7ud/cT7bkr7ye3s4mx/2klbTyWackoL1Pzlo8sT224W7y6XwjOwPwHptrZ6JOMqUKJ4B6RtL1c8ObD4FpQzdbZuZFNqs0Fa/O+kl4tc3JHZepO1CQxyNIMSdnR1OKxguKGTM+bgAFW9X7TjjRanbD5jhs3ZJ+XRo7eFYrVqQBMXCQp8nQ4OviiREJEYy6S9AINIcgxDHbQFPUoZEUGc2cT0aO4cFVfPWxYwJuDJ68Tc/6kweVt9Sxmv1CtB13p5Yeirt0rDrVxI/RWd4ZAQxJfJ8O+FMRSzt1Gp5uUAm+37YfwbG/UDMIbY9dfleC2gr313O3l5NUc/4w+H4mVG5Z08yMc7hl2kaZMTMHh+2rO7LbrgIKG6CUI5BJ3gBXliYH2KEzGIc1hEiXsDqZGh1jOfdYTZD713P/Oovr0ANK9xcra6UmkfGxfFob5tj8gkS4qHD0Dw+vhiZ7UGcBh4GzfwUhQGr0Os7ynjgtR6NgOlAfpRRFI9iUbGieHDQ7oxfdt3Nu6XHc3s4JgEuckLyaGitLjUKIdTuW+mpRXBKpoIRPB9MDLSQpRnh9MOz6f6JvNf4+/+49PHRSkUtDQABAABJREFU3O95DOcgZCZRsJ921MYdJpina2uFhx88wf/+W/J/9uvjf/3TjX9ahXMqdNHsOIWqXB4cG0KNXgD9QHM3y2IJ84Mo6v/ujRHuMisNSWbEQwNM+9EYUYp5OEcZY0x7a6X+T68bY+/5cdfqh9qjIVYWNv7ht27cXIM/epHZrY5bbmdfVyFm6509d6EFbyUMiQSLwuj+Ynh66n399Xu3yn56ukqYo0gzFFHPEKQErLnawphJGBeyUnuy6D8fLXDhzr1sKSO7i0EvwHUbFFYxN4Kaz1urXyvGjrM1SRkrThMOTJyXHsjGLhGY5dOnC8PZFuDzpd2BU0YgyiYyp2cnC80TxCQChDaN65sMi8LjxHr45cGdPeT1O0s/b4+docj+9OVf/l9+yf8fvnlFF0P3IfZZ6OXp4yD/QzQyOMaadzMcjVeURdsistm2l5GUkQNJIWwdg0jAByoNBabGJCyEIVs5fjGdcVpMiMHFlcfh9sxC9k+7m9slYmk5NtUgtLXuKKLdo9NjSpvckXmRd5QsjA9bf+Ji//SV0tpWzVW77v/Y1HeuHbrWVwX3aB8ZU60JJEsFVtut/ACVPnvmNArcPhL5o/SNd/j2gPOG82ubmcdnQ2spD2XynqfPCPPaQL38PLskc4gHeRt7EpZ+613VXcmXvcHzH59uxQmZIO357OELKHoS4GtLXF2K2iqJUPki6J8Hy6srsoLGInupLX7wzdpQQJ9dLlIBOfeEuW0jFiXXyXliHKooI2YaK8U4MjTPJzJwu2nqNUR5u3b4QG/go+ruUinWA83OlqX58VQBVJNwqUopWzZ6h/OddwSPSCpoHLm2HzA8q8SMsTdd/NwMia1v53JoGNvtkEklj/U8IkycaNUrEmWF6VE7J85+IUXQg7QSZILUd/W0aY6ICvI3j9Xrd4r+fACZ0bwb36lgqIxj1pD8xY+HS8TRDXYyNv/e26AMgJB+9N+Bq381+Nl7xV/LjJmVR2rbt79+LdtvtlMYXLWSN9cg+06h5UFCSZDYuNOyVoiREkWfDjl3kP5P2N7iDjWlUNTnTYSQWIBFQHM5HIcLBdaCUB+D7KkOMYmnAaNvMRkRNv1NmoN4zPWRX/7J4fqy2NhiAS7amsJhiw9+cv6b/2Tze9+KFg+jx382PH92mt3BGlUeCvXrX81yrxZaFjK93xG30KAoUPvQvW810r3Viy86tZtLz41gj1+991b42ScvCjubaeeD0xMQcZmkQFPTQeusVd7km+eTgkQZ/kTJ+3SIP+s7ezhOsyHkQWefPWY92qL5pFDKKdCbCEbeQKOheDwMdUEYXkUjN7mWZ4E5COqFiZR4OozPiti8mQnVmciZuhGhAksRjgchCdiSoSIE0Uxh85VXv/l9sfW4Yz7WH/XU9QzDiXTmNhVh6tVDDb+1UbYNolby2AAEtp3CoRbEQ3DxfP7ZfHJ9W1aqhFvAfMNWc6RQkhtwMD/UZ1qHx63ixL1zI3dMumiRPPEcJoAQLz5CACHB1uV0Q6SJ3bJV9BnPHtFZLsXQ7Abm66SH2/MF0VQrr1SMKUyCSX+qe7+0qSgEeNGoV8zEIi2vmGWT0LWmiEa4dhiY/RDWxjIK53YKpz3dtwyKJbIoevpwDGMwGnkUiFBRETneAyhKCNTAZ+V73MHH/ZfjeRbyNd8W6sLQ5qsTnBNdnaMiImqfjSkJiBu5eOYGqoEAAkt5pWQkk3ja6/71v4xL38wFq4VA5chJtL0mDYIkK5Evj8xNhvn1rfz/4xfPf/uf3OufFedhBt1iUg+3KLrxXu3soInBkPImFEIYBaOsvPr8i8sxlRYlVOsaYLqQlvgin9f9+PJgWExoIZPpxdhf/mGTdHTWMC47Sa2GBY4Ld5rNxy7lIhVaLCxlKC61SYjl0WBFmodIkSDJmIQpi2sbW5VKtXHz00MnPR3MDIckMcM2WkGqoADRpuHIDUS4fTWkaPrGWobBsYkXMlXueoE7/HJ0dLCgcCTDOYSu+o4zTYDmwAwGJnGiljjf15DHC3GZdGfe0HLd8zaH+01fe+3bNxdkeQxmsRWJGF6oSYPpDMbTwmZGuEf2np9aT07tTHRL9B+3B8VXr//2N9ezl9b+x06NJqd2JsjVur5VC6PTA3/1FRkNoKMLwG2LLkKMZrBUSwKA8MDVY3sxsVACRa1Y5AgkvaAXDqL6zV40daEIoP1TH5foQkPI8N7psyuxFkUUvbpE+mGMwUxnGk8tBM/y5XL9PZh6/PP2KM9+s17DrmeINQKZBX1L3H2tEgBkCfCz1DDSiQ4tmh+dLr0i37tZ/fzJ9MFTMGkmFYq6fDrOF8THap+uF71JdyewxmMokDLnVIYV40m5MFecTz93FT6Kac6EJmRVOvrg5VSLShJxfVsJ0kHvSZsg5saEytTpHAf7CRm2fWzs1BfG4gIXsoVdhO8faDPL3K7LA92hNGh3A6WmZu9LdzQ8311bOZyOr7/GK7R58Pwp5/mb5QJi508vpnfrzH5vXAw5RcZSEMBB1O70quvWJ592WTnEYvyDkzmP4aFOv5ZZT0h08MxF8Nz1O9A//8+/2GyQt3/3FsEj0UA7e4qSNTwOfR9xSPVsJbEBmf2i5+GyqV+O5FDy41iYo2siGZ1aUAhb05RACSdMnx3Hb/3g3noUHppzDxgHHvijl6tHF8swsq3+r/lc+/hBnM3i6XgmCKl3c2Xl8pMLiEpSG/54X89WiFiQwFxvnRuo5u5fErxCvvtf3HzwXIXpfG/Yuzx3rhtpAsAIBRZBRRkxJ5G9RJ35VL6Kjk9jOAXbdc7su1HX4Dmslue+ODW8OOIQeDKCGyW6lFeOjpxaRiEi7af/2pWXpbdvL33n9+oETV8d92gYO/u8+9P/NPErXHanWFirz3GcjNPSneuFv/vrFZA13N72uthWDZK3WblO1UawwEm7G+Go5xqzSpmdzNn96aC+UibrIkTzbJQELkksVCeMHh70gsCqV/ilPRqhYSSzHsM5DMo9iwIykQvAgUDshUlp3Tv5pVp/TwIwDmZ9yTHSkYTnG24sBrZGUwYNsOakF8s1A1WTCJmmbgnKFCDMQm8OrbmZcXf/2UZo4BhlBs/b4dEkmbumxVTycIym3dAowNLYN8AiSTue1lFLJYZfrxmmGNihNhvA87mUQdXjuW5M5Xw1EJAYFyFB61lxhYdrqN8f22xGshOqsiKYlM/EOa0Z8wzn0MVuarHZLBaENA68LoxGYKdeu7SDj+5rsg3cEKqVORqD0RLCSpLnR0cvFlk0zdU4YxrCUEzxjJLlQUwhlG8PF4fHI933kSwgecSO05BMEZGiERryEYmVMAjzTReVQggR6JiNt1/Zkfp966oVt1zxzXVqCZ0ctzK7X6U2JMxEuWkiSsQiEwoxQS4IOi/NaWmhDdQwLdwojw8n8smsUvSX7ko/2Y+sjy6wCUzlsTffzD4+8ahAWZrYi//+iyyJ+0/6HBaYOB4OCPWyy/E4jVimvmBj6soez0wnT4r1U991B9xSCfjRs+6oIxud5e2GnGoakowXaUy8qxOTmWhEGtoopTiSxEnQW1yMrfcaKIpKWCGXp2DDNAZTrSLHrzCYD1PVa7nmUJDpYUyxGGgs7chPf9rzaLC7jcxcS7VxHA2svjq2EyeKvNgXiBR2XHc2h8XCUFef7A9JCxTKSWhpXsKeXJm+EWxnvN2ONitk472NMF0MeqqEp64WwRDJpIRqUDBZKFe4UdcZjOBcFQZJdH40xZNwZDpRHBrd4avv3Nz+9caDmNIXi9IyUrj2+hsIvuo7h//Dr9bybG41f7I/36zFAeOI8Ww9GWNry5OH/eqC3THG+0/6ha/Lw/IW4b30XsS8mQohCjIyKGWYitg+S3kf2BEMG/a1urDPosgyW80XzgbWuLkg4XS8cOgVBUEMqjtLvVT3fRgMbdudXWhbtQ22JIGxPRzx5ma1oHobqbw6nZ4VG4eUUAQqnzy7HlWnR18wsHc+nFpMENnDobYy78wIwK7lHb4On7oFRSCcGaybIHJiiYIRdGHM0RRtSxF/7ybVnHitz0/GXXlsxloUVkWsQOGYBXALBdPAqZFBzJ4+X2zcXJ60RiAGU4hPAfLgZI5eLVY3hcoNOu4gA3UQKejIcSInjXMiKM8+fXq881q2JuAPP27tLBdqs7TeP2sSOqdIRtPEBQQTge3FKI0ONFcgQ+hiZGXUDBk8vn/wO7c5jY00G5632387MTCKvrkGuKn2/n/9p/Na+e//76nF1O+eYh/O3ep7W0umMZb4mIs646BEkk0jqaPQF9O4LmbOr+alXPp8Nq8iDOIDVo4vnqg7m3nY8pMMe3Le+0snfPVtYr2q0V8R531naF/gZsVcQKJB5F8MBnR95EX1FEITd+NOeahGyxz05FkyOgF0OoI4HFMyOSG6e6fUjYMHf7k/b9HxNiQvIhqvluo2GxlRAhl0+bU3WMv2Wyifn6WJM+KWK2xkzUYRpZA+wrYMz5g5AsPUOSy9GT89WLxs9/bPC2GA718Nv/LG0upe4eTSfPDL9qoph0THQTyFozdv0FGuqjXjZIxShWJx3CXBCP6HN2wAzY8ebPLkyKZZJ+ioRynurxeI8XCg8Rl9ARYnugDTIGEhK7x4ZOeVyCZ8HsdSOpvEsOZ5Ect3tVC1Q49dRHywPr9qXvTb+Qwg3W1Wu7QzG9vgD4fphm6VvYDxBZBcab2QJqlsHF8uiDu3pA/Vi5x5+YjBscryq9FiLiolmo6hBQYAAIDZIuhOGJGVNPXieRoEbhz5cyJLiFbhep4ClNb0s1lO66sPl9PHi6hmwElNGhnOrVpIA2wUscB0Ri7ePTCW6+VKQWHk7McwrwRhRsqN94/x25lnLZW6ssiVIsQQBVU9OYqJcgneo1bFsMuEq0KOTR3npG/78xJKDzg5I9E+x3X6E7Jv2J5G5IhOzycgnBtj9Zg2InM60wQgRFTIURQLE2IS6+pMnU6jIGI43GJxmEPmY3sxjWCGRzw7TIjtFUIpyZEXmKaDRnoi497xczy3Xa8tVR+RLEYn1sHQ0JJihibQqT+KKTNEUMwyPB6jMhiigySc2oo5efarZpEGFjCLyyQwuU43WqxQ0itU6nvCZPLlB/7OD5apJe9kv73BgsSApSXiaD/gcwgKQpLCEJBaI2OaehDI5XiiEybCSsoyPKsEKlUICDRICHwe4ddgMAYIg8O1oiiwhBmoQzHELVxYbVRLo/MORbvju/WtbynZM6PzcWt41RVsjFlT5GvKy6shEunFRqxfDJHzRSN1cob9bNh/o/EKuI5/ND758NFVTGDL20taM+xdDoStgusgkYuICk1CSYYBKWnQjEsSEMHAi7kzPHe3dzgaooyxakfmHIXQHIsDP2jrUILkqpV4o0wA/uppnzCiLFsg81mbDs+APWpN9krYch1NNPje3VLCc9ZA++iByUHYztf5V8vZdt/KfPQcSNLpwkcddKUgPjoPkKrgk+lItYpJ4i7C5ZEePxtfw6Ok4+fpAQlxTyYjySNyIudGbHYdQUUy5qVpbwa6I5KiTBhefmV9boJ6IRHimEUNDUqjJBBkBPYRZB5HJlZGsxZMv/QXxDYOGLrjYlQMe4sSRFFGVMxo1iIQyJWyD0jrbPD2XhCCpoRAQtL/0aPJ1m6jLRuASd/8g+8o/B03zzz707MZmgbddG85ar/oMKFlaGiQRoDXvMAvFGQN5rSUFGMCHV35F/O3l0U7Q/lL21jsBxP1xVATAzNf42gOQbnw4NI7ezbx0dDnsHwDH/V1zHHIInne6ukqqOXZNqZHMMTBikIyrOUtbefN3tWXn7dv3ZasCKWggGTg07bBrocBCKQy/eBswCvQ1BinKcJlIl+zY8TRW97tN9iff+n1AkDlEmee7NwiJlTm+Cg+eQh4PMGXxX/5X/+BPT/6L/7F81fvrX/1dtYAnrowgKVDDiv7c4HGc5DNyGjRGiVqRArYAkUyRVydq40CktgOBPkRLww0r1wknY62VUA3QFn+Pv/BXGwDOMHhV/HNpyed7Y0c+WSa7V4wy0oM+OPHizvL5enZhRvAb+6ygeOHDgsxuKF6JJ08+vPzuehmPO6775Vgd9p5qN54o64NYBLApmsz2aDnmpdH6jBN397hVZ3yHcJLUzP1fJC4kANQvbeAZEzhEydumWTi3/t+g2Jkb0K1uv7Hn3VLRe3du6V+bCPdF3/z5Owb366MXfrpEV67HhJGysv5zrFTqBde+d2v0opgapdJMveLaAJMnIu8OZLhIxUSbT6zsUrblmpgqAa7PoVzTM7u6yQSzYZOIQ8FdgzSCAmgyEmVvJBCdBxB3kj1i+z2tTpBxTbAFjPIsWcf/cT5/jurnfuDEpciGRhchSLkEqQ3N0FvEWQceun166Oj8LqSUikGJk4W4l01pig65C4xeQWCAFT/IfPR086fP1c42knMSW/G5NGLKcOQgpBRCMnhEQiFkc1FJNGw1SibkHXla7980bmmuJWGkPXjJikhe3kYeK4KBWmaW4bLGHz+K81f4Lu+cDVL5CKbMMmLo/O8l6uSku4weIVXICs36+VJf//xEz9BM69uFYQC6TrTy7GQJ7cDOfJGSYzBKBY4o0yhmi8pphGINBbMXSmXMWReFAR4AR7db5L+gskIjd0Khqbd/dHoyqTQOIkSOIRYXEBZZmJCuO6iMUKSDAqHCKLRjcDqf9gMG9TW1lqidqZHxsq1Eht6vQW7WyC1JHQdTsInBgk/2ddQK7LXldtLjbt/Pw0HvaNeF5iBUikRGcb8+ELY4r2VrLfE8U/bZ//m8+CtgsIHdhD3JbHB0/mGCCF+vJ0JfBB0LE+U16mIkKsxFd0jmGk87J8aOSoihel8plKwSUoCZYebw6ByK9dexFN16s6SGKWl7cYSH7RaM3GvfIb52AB1ftZDDo4zciKKfJJnhTVK3sreuJ1/0msfnw3hw3H/k95v/t6d4h79+Ccv5ddzcAzfeXVFYaT7Hx0tzmcXZzEUCvOThbXwuYwgM5AhUq2F5c+NbIFeReNFFA5gun4bap2N521nqUrBeEKxxfQkwHocMMt7d1Y5Xu+1NWOGrG/d5ixTn8PdnpEUzUyJoa10mNoOLgHHsV4i4/POrd+79ub3WPCk3f+zA50FY5jaoWIXwAs/YfLKYqk4HKu7UGCYvsTIsDZiCJofYTk6b6RZEExMTxfsIR/w9Mx2HK66WqNqsgdeTB7O1SurWkE2hpdnwu60cXdNnyBpn4oX9mzBwzKBwd54wbAQFUS+Q4Mcl3pGtoaRIqaPOjQmTQmOyQlmWl5PoCctslZFA0PlN/feAPB52iJBvas/vXrSdudAB/N4ATM5vXMxdjBibxmH32s8uyIVwlaHbAkn4Cz89OVRdX2JXeeNph26kZGMDhhWmvSXErdyq5xzUEpE3x+ljZyA00EgZ6aX/clYQ2ggBogg0OJCtW6sTN3YGU9FVBiHYRJ6/F6h3XMLfS2/XH7xi0OI1Dk2Z/MkLPK5tap32RrD1YRNvjiyvnkj+/Jpu0HRw0eL9RUR1tKcFPj5ijoY85MIioQsiTwYOd5wskwQrbGQr5CGOkQFbgQZhVe44q6cRexvGtrLf/dn/2qf/I3f36vS2fx42tJwcYuJ1nltEveOFjfkysQOOxezYlkKr9p4PtM8nK+t8ILVG1HK4fMjGc7TYia4dLbQEOj6EOJPj3WkQDek0KpnBq533gwdMgv2z16eXL2yvZR67tnLmCnkcRlBK1KrP0e8QJ7MQwLD9WAwiHa+WgWEKwbxCrHonvbHCyyzsr5Zgp719CCTS3XU64afpouqSL4HxVIogDDK+VE/gVhOjAL1aIEU83wfxKmaHs/hcjZXFbmf/H9PUCaMVOvud6u3vsa1e8FHT7XNVVH53vr1rzScp8PlzRqVILU8FlYdyXe3STL7D165XGDI/tX1ytZMyC7UlKSxR8EUTdLUl/FgVsIIP0UJREhjLzYxex5P/UAhMX1sAADNPaSShQgYx/vRzFT9OIVZ3hNKoI68tKS6FzR1Mx+ApCG8vbv5qXs0PujOn3bv/t1NAMqGSc1nrn0965go/Kun7/9Z/J3fX1PYbLxwHJ1t5HFcovaPNepmiSQhNNk3zpaFtY60VZUrgXc+Hzpax+NyMYHs5FevNzB9egar903h3SXpBteYdKD3n/bFzPB7t1afTZiBbqYmk+1ZWt/QtMU4AGWYS9eoXCfg86FLjrC71BgKEovRa2z/QMMQqXa7ZPs5GGAeSI9H7vqIeNh68aPno+ydcl2kk7OeNggcH62uEjxLCHVC0NLLNLz77nUEsuzQGEzN2obE0lJlVerVq4uDvvVSnaiBDACGEK2pOW1pi1HiRoi8yqWYS5Qy9CymBcqDYXu6wFM0SVHUc2EAxxib3sypT7sdkS8INT69xkYvMTjxZJwbjWKZE0eJgWM8laT5DZq9xaU4O3o4aJ00Ncd99StLoROikdt5cB7MLRSDLQY5b42UmU3AVP/AXhI5scQALm8sUhITVwuincD6SLcibKuxivPg+k7u4+eXNZpnXaOxzk/685mFFCvV1FHTIEgNtLCOJxQR7s9cFvYcpLbGkjJo9wOxwZGQPT9s51Cy9XBgE17xRmHr6xuLUPafW5O2w0pEhiTkdVFh8QmXYjnywYXB31EWMVbEk6un0RCF6FIeCh3RhHhMshI2u8nGgbPoq/rYF5ayqxl8ONbOF7CiCHtV6ODxiKfigEWXciSMCm6cjMdjTsxXUDLuJE0OTAwG5yWTplGY8NFgNFItNQTWWOJiJIIo2AWSVCoJmQg6/fAcYslXtrPvrBf6MR6ZKAnHLz642trlMCZ9vj/nKMqduAhKgUBSTSRAw6MxvPBQMgGZxEBd6/zS9SiVpLnitiJ4hNocjU89NJgu1RVAgCtIThKo/6uXt9bCCApmdrCsKGbTVlIrShezfhoWeZaTFpjsUjlpudGZHLlju1TLQCrm9tBUIajKeuMai8Xa5KT3IqC/srG6BjVavhG2laP909c35MhLIEpLPWwhMWftWcxl+UIGC+EJZACfXM3gz7qByq288vbqxw8DjhHEqmw+H1+XwvaV0XXNSqXqi5QzShBL611qvOLmsqgA4Rf25PkTfS2/QimF2h7857860QJgKdDNBrKWgR9/uZi0ortvlqen9iZb4N7NtA7Ont/vFu8okY3KNDH0Mbut5wm+49lXkNBJPVENAjNJtNjtmX/zdPKP/lnj//krtQ4Rb3937/D+JVMKdCtOgjTAaR9NVRzRNddQcLeP7i6XPUP9D1MtzrJLv1na3i4eHliDNi6s4ohvvvilCkGZpRKPCRTrmKTrJhceg2BqExKxitWzzr5ooqjN1QjUN06f9laKpCuhDimHrn00tlMTWXJYDrEkDA4187VtxpvzxXU5tGatkT5Xl68t514cuvVq6aLTDRbzLJtOPddH6NuvYS8eX4ZBguAS9kr24qnqLvx721kzhJ0UssMAwtEbe1yGda4eqaAUDiaBh4QIRAEYsbQ4taOVJXmysHDP3agKL4daZwaXGXRwjv3eH+SnF8SnP5tXNpWN66u+zqinvUf/cf53vrN5xmdyEK9zdhDFZMHn4OxyfRNNZDhh6g3lycfdlSXksq+i2SlCOe5ksYxkrwbjF4D6/uoNj3PGbU2oKrFv6urMBC7kQWJF7LrBdAAlMHS9mqNA/PTSbp+MlxcJfkjfu01CWJKNkVo2a7n2f/Pf/zhTd5NxDEtmUFcAWNiIwi9Bv/xPl6UNKNAGPoacfh6mKGBIQUKhy2Gc9CZkgq8Hi9ZJs7bN2ubx4Mfo5tt0yu6QtW7NRE3EkhvSGJKHTVu/GoZVKCGR/aH0NY7I1a69+lu1sy/+yr9/0kBCmkbSJFFpuvLqCjdrXKvy1hVDCUZw2lVVn6Eju2x05hdrMDTsZpdlnhNluucP1bFUyA69FLejue9/eKU1r7TGXaE2X6Dno3jucjxvdGMahonEbqASoDMrCd8czuNogHlB4KCwQpydOqA7sXq2a/lLikj6bOJTfkgQIlxCsY0cBwmpa+g4RNAkSgHCA5A+N1E0iNMUrYioNp39xSy6uYJj21yPTA+ftu/V1us7FYYOL7pGaKQ4yQdgAeNgFaYDm/rkM/XaLSH0UJm0K1lCfXGqBUhjMxPfU1S6nvRD4uF5CXIGtLi0TZeiSCFcmpOCIEpwYWe1IAlKpzlhXaKxKqSun5NlBYJEHx582dVcO1MnaJpx5zpNYU4I7yrZDiS5dDy8bEO6cRHCDpVNfDu3rx3wMq2I/K8+LKmxkocH27UkB5/rzvjB2OlOxJmTXS0kJhoqBIISqodjhYgiqbicXcutx51phERbG/JoFvmK2LkKbr+yUpLCs1HEuMjJpV0q0f4UGR5oNqWx1Qq7Rh8ezcSp45Bh+ZUlpqWeH41zu9W2naQST+PR1EOGc3NmBZlVZYOBn/7V/lKpULnF3S4pvUnSPnMtk1QwDBHJWRB1R26K+W+8kkNxn9FmFxemllO6Z1MFib/7w0ZnOkLdgI6w7TXoaU/gAp/jMD2k0sjVZ3NWYpjA7V16b96tlpeEXLXemuf0sbdlz9jWqCjS+1EZ0fAzK+2x63dXNtauZi9e6rduqGepOHRAsrBmCwubLTQM5FCCaYQzREfsZBYmAIUVSXD1+HqRaMNSpx+Y3iVWv2Gz8ObtNwNKbWsgghIm4Ws7yy+1xSTWMmlMoYwfa7c2inrGsi1gJKaywpljLVklWyJLa22qRlIc1jf6199Z9xkUnU3hJGXcpBeCoyjeRqzO0ZQpFWJENew4njqdITQea6gsJjD29MvFF+PeDE7rFYTN0R3DqObqb/xWZjT2Ls7mhgnO//2zV9+70fjanXKd/tnhWNBMRYHFDXH/cPxrbwEaw5BJf6kqXV0cFaWSbo3Fbb551Z8/fLCq0H/83zz+9a9ueAFCmLgx9w1rdjOnH0wDkC9ho9kqNmsutI6fsfwkk3k71qDCreyf/ETbQnkcw3tzk3SjFdy1wWSi6/fPrq5fWyItY64KuWVaKq1li4jEIeXb3IP395dJAt0qRkQmhsOgINpQHO8fFFx1aibVWwFiA3FgfLmUHTFxJAM88A6OmuzmK+W3GyLOAj3NVStj+fiLi9aJ5ebfKD25NJZ9FzBUul6+LmIvLvXWpf/D1+iVrP/Tk9hOcIVgl7PYyMZGh2MFlvshxSyQ8ZLiyHnoopurKOPTIFGdCwt5e4sGWjJX3XyJ8zKIssn11TRT5paKKEuZT3789NbtvZ1v5D5uGj/94BCzkAUulDcrFAQYhaHO7TADIU/aVayaq+0CemKYU7IMBsNJOU/rpufmUxuJUya4AtPTeETPDycg0wQwtcmdH1mbUfjB/b6M8gnizUKp9aj1+raUvSNrZ/Z4NsXn5FNb3dkRHCb7J88N3lqUp22cKMpl0PjWV2SQTo7PzaFf3BXfK1JJ//wxPd185dbWtfTygznkhX6VDFlfXrjFbN6JLAiFLByOd+jNm2iCeGlCoBwnxVVy+km3b5y5Xll3796oEatUkCI5Ovji4MHNpWCFfnP5K9/+LKmjf3MfE0KVrZW9rlfi3K5jJkQ1x9OFMrvBH5/M+/f7+AudMLzhsmLO0FKVVNtxxAYKQaHmIhrFZkTujyUfJ6R88ZMv2kTMXsuUllgyNJLxCq27kKSBGKJKCMNc+rcQNPbdT2PI6c80FBP9MK1xeIGgajk2QlgviFcztbI8m0x6V67r+W4UlXIsFZMRDtwU9q0oTgACwUkaQ/f/5v/+5MUQ2coMLM+bXe6t5vRZQAvZYdsmOZdjqGyRyBHY+3+7T4mMUGEALOoLUNhcqy7JQTRBI611dHl1PKNIqvHOJl5JX/z54fRBf+9OkdgRm/sWKxClnIywdLfvyqhUk0UM5aazSUohIRoVBCoB2uBct+aDnb3yxRUwfbpRYa9UjZFQyokaUDwNkQ8emAw3z0d2IMIQXXLCgE2YzDX6i1/15GBy59U1TFAObFpMNH44fHo6dq7Ca1+rle7mZJKbuIivg5wXD6bW9m/vNnL1o5dXiOHefj3fusBPno3zFZPBFkUEOnp0OVmg5jSBeQjgUIznYQ42Z1pzaFEUslajbCPEyDT0w+ZnJwKDvf7e7vH5QOZhEquPF1K5jPGM32kOkMsmQ1Fbr98Ggnva0rIkwsChH0BDlSZLmA6D21vUoD2j82xbd9aqAomQKcdiujFrj5AgbWmqqGQhJPLGSf5m7ukDa2M7c3ZsFbNpXiESDyV8EcBOSqcJThzG+dVypeBZvBmCRac3yZF13F7QHYyBs2K1SIIhbFMu6WpAdEOQap3LLKl7B6edKNp+ayORQF9j6hQduXBDQmPHNy0NIpEZVR11IGuKi6vLpQ2uOyDWNnK3BZAC8PxBb2oMoyIkWjNfX2A+PDHC2LGXt4goTZJwEhHQoK9ya4oDuM6nF3fXCqkVD3vqta+vH/c8ehpmBEYdGB6SYlmIBfbZY6NUzJKiP9PUyILYFAEOLt5Y78601vl5r2e+92trJOWrsXfwtNc79Ms3ylmexaj03XeXnrx/8eL+VEWJr79BXoUOTRKtVmc7Ix20em7bvPNWxZ6Fm6+Kf/Rvjt/YK1Yq0v5Vr3cyf/NdKo3Y//KfPfzR//vdweXI0ECpwn/yuPebX6+8GHgzDUWnJpfMoBjnlpYezuB6bvmyG2I0ASfwsoyitKsyFKClGDZ9zY/RYVZ3E4gXRGFrb1WTierqdjP0NsQah0Zn9580//SD04Bc+cZWlQ4cL8aKMeP3xzO2NSHja2OpSCERMqWVZGrr+9NsHG3VaBhhn3XJOpSzZsm11cLh8/uff/bCcR0lk+SLuHU1f9wCpVtSOe4PjtmVldpqzfn4cbsNJ6/fK3IYxveBmsYwG6aIBMpL9DAcBiogedgaRxZhqiqFIIksGseDJIYdN7336tKT1hWF2TfvrOw/dKhGzAkAT9FJC5tD0d5dpbq0hkVKf9TpN83lpTxdyrGw3PjW7be47ZMnPUB57vRFGEwvdZdjqMAOvfFCp2ADS/0aTa+nzFmv0ppuvJM5HC0MgIuAESJif27Pp3kTDddWlufHx+YXzdJbDYUuFop8VpSas8XZ4RywbDWfCyHw9s0MKUizy3BkplwE7yyRKIo8mumsgJrWITm3Ri6eXMXVLJLbqLZDbKJGSwBerog+yaiWm7KoxFCx5pRXaiCljcSloXhyfNzFU01ZEYfT2cMBks/nbkmVgpIF0pn54fv/thnVpb3bItV8acPay2Z4edAryYKYRHSuhGAiKZNpAXh+cNzsbK3U4jbU8dXYdc00YVKmuiQzSOibEAEcx6yed6Nw/BwK1JfjM9n2b5QLDRhZyaTebYxABYaltSEMYURZh5a2NLBXP34wPVw4iZClGM5nRRIDMYhpzdIvxhYrKCtVNHLdhQlowiUdCA5lgon8NAlhkCQgCGACTwCEJjxJ/PodKW75IzluGKcfP2WFZYzGaAHq9jRM9NsqVkjhXrnCFRAmimMD26nIahoPNV87uvDaLeqNlVf/ceHBHz9r/fVRYqs4TZZe25iNjaKNEVsiEcftsVpIYZGjOFlSNYxPYV6R1DgxTo7Trdyj+yO03TnPppndG9evQfZTe8yxjB+DaSjeaAyOTyTejFBbvsGIGmzGKG0iLcuNmBS5aLOX4/K7G/aWyCDSllRP+hdQaO9AUPAGj+VFEANM1xEHpeeL8alW+3t3ohy3aH0wvUSO7AgqYTuZgp5DPMM/uRhX+aTTAWslFpAMq8SaOkdcf3pqLm1gDZw4bWuDfc1HEtj3Zh6ESfh2IeEqEQNTtG5vw9rS19YeTdX46hTiwvM34K9U8myBtjCEVJ0UheHIj1iKlhBCDPVJ+uSlud0okDkx5OKF5xUJ5uLjrgP0zW9vnz4coqQepGnMlh1vnC9kGNZP4zQnJsyqyLDU458MuWouPBohm6y9lWVU9hqHRbgA5ofhkiwDZB8BGhLupcOOdvLlg/massRW6nmSQt0Egc1PHNNyIhZn+FUBVajJ0XmuXBY91AjxNsotreQZmwjTROpphCh4wAkuDgeZ8op8yzOmJ0R2kwT2bSb6i7N5k8rdAA4KgGqvZcSjuTEznYhE4iTL2KiAE0oqQLHlJmRRUb48n17fK4+eHCBkeW+T6zfJyEFevZXRzbP9q2lDxGoycW6rzaaLItCdm+wbvtq8OOBxrPq1+uGFpb9cfDlVIyTcfm91Y8sy5yTsYmmV/tmPzm6+XfRubl08vtw/ODFycR1QdCnfN2KXldVkGsN8t92/WeptF/GT/enbb2afNZ38pmBEJDQYv/2NtQ8+6ly7vuzMe4gBTAt/8VlfbPDT6YRczRh2eTiMEs96FpWkYrC2ttI/be+uycMYxipZkoCipktj/HoJwXPLihjAjjeEoEUtw5IK35mRi8lcsw9TtPK9ylbxvcKHL18+OX31h3dlhQ26Dz8bD+fehuIirftN6HYGa9m1Cg9lcnSD9vrTebNj9HBm5dZyjXdTe86Pl3/n9k8f/zlHCs9UDxlGOZj62rYSIdDIpTEqLBW894/1OBLXr7NZbn51IVkmgtoepHABLeitdNSMr5X53qVZXMtGqNtYyY7GAz5xXnp+sSaIcQQgF4VMQohoELtqKK0XrakWkfDut1bOv+jMD4P4yUF0vSa/urHBTCTbkihMVvA+QwIANm9X9q8+CaCpaU5HRmKetfCQtxBA4mD3XkGlU2ryolD3s4Ldn3l3C/yTMfSVPPbpXC0XPVuK45Mo/dx45/evne/UMMNfHGBWArWfDHZ+c2vt2t2zL9U85J64yk9ODd7TCpHVYFmTzAVIipFMBfbg7qhlZh0zLYnZFB6pCAPUSCDhRaejrm5Sa9mzX13msw0ihFgjtn0qTXA7UCcIXMFoYWX987/42DYmmX/8m6tbpehgjJrEbDRVrjkb3O7G//LO53/9/zHedz55cixmOTyHb1/LU2acTj2CDk1ndjmCFkMqn5M36V14DkOsI9YypcS5vPQ9BJ/ObYFCBik6snDoxdFmhopeycdD985X76i94fgjOy3lRTmYiBjFiXMrMAGEmfGzY91xox3IqizVhW9XRTT74v7RGBagSbPXaqI+pI8teROYiIzHRuQlBAyY0A6w0EggPEraC1hggEygMAaDNEWxhnC32vj5T0PFZeS3torLO8QgHj1vpoS49zpzeenGegzF0Q7nwB3PDxxZiS46KVRCN7aotEMyodf5rHf4Ai9yNDaZtscWk8+/ckMYndNOmAiAss0IkFIAkYbrAjPMFGBftdlY5Wk0XcIWTpxXxKzZwZ5cWP9i9jON+s43byc31xTA9cczGsYvGWbs+YqYJi4wIblYzi4u46UK4BKn2Q6wXFko5EIg0xY0+vA+VeUyb95SvMBHYziG1aEaXqfShTo7giMZdntXhQykdudFmRuz0CJJxpQH54gSkoMDV0oDcTs0xzCEENYiZjkeBzHC2i8/XtDAytero8CKE9+23PWN4nZ9GQzGBwd+mgRwGrhwRDjD2eXwVz85v3uLvLspEWdJO6IIhcYIZ7pwrIBUiXQB4BsK871v1j75YP/RR5c3X12+s1LEAyhTZmu50p/90bj9ySBXKo/1YDphv//1/L/4353BzrwmFM8vnZW6+PCTSazTXE7cWMuevNC+dud6z7LPuldD2PcSblmmZ9pUQKHK1CVNS9N0v5AE89lxor980P+//S9eHe73Y9SSaQ+gqWPJOEqPrlLToEoN1BybBMyTATx3E15QnBQLDFtC4IGdZJaRyMZD2s/isT4cfgRctgHG1byfap2YLK3BcwiMTAfjsbnvb9bE/oXhmFFuJX9+GVIRwmbKz3qYvMMuYu35KNi8id1/4koIoFFu4UWdvm/N59d3l5+djZ91x54b7N7lSBF6/ky/mpq11Wr7ccu5CmSJ3Fsjr5rzi/uHs2FyfXNFYKBhS19M1Z/9yAnZ3nd/uD5a5s6aV9MXc6M9KTLURla/tGL1YnJtZ6k5m8bBfIFHM4Jq9+KKgpcTZhTXNpUIk9CzCRWwtTGNCjnTEoisgF/FjqhBFkVolMyvCaUg07RngnaZuY74hUTyIYZynUmq1AQ2RRPbmp+ql2a6uikzuyikxc6gM5bmODUtkFmxbZ9+YsW72Mo7u32jY3kJExtcRVzBi29Khft/NmBMasfIn/Z0/cpuvEZNDjROTDiyIGxnnZXsYhCAQWsIid9+8+vcrZvc4+OdEounQCHT7qWaBNTVMLh3IzsGMULCEo4WqWh25Ss4DnGMYUE+l2PhZYUGpV0zl4WZPH40sHIyNlk4gMGbbT2zDtEUPBkZ1XzO0tm+lry3WYk+bwcm83e+sfEv/nAfKyGb9xrJ2CFwrB955Nhdk7M2I7cR0ZmSy5Orz/yLmzXIxdvpdlBkeKQDjz+DCYfIrhbQG3Rrcta/f7a9beup6uzSC7c04XCdYf7kZavGO2f6tD213yRlrRmO3ueh6paMssvrwBOSGTV+/P5oeRkpZYmSxOOdudqcFcu4Q1EeSrLLoqEZFG03SvTIJV+V5MMrssLS+LbU7C3Gl6GFqRwKqhXsyZcjxwprr/Bu20ymoLhOzSdtFqVyGYYADERKK1vbky/PQPvUpKOVTUgzA1ygfMN9/3i0sUPCN5fLyPDNexvDbjiZmakdVnY4f25o/hjCsXUuOzH0ZBDYbqojggEFYkCdGd7dvWqM4cnUSxxnCfdEaGYtTxaLsOjVRE7KeAmOSd/7x7dmIWb4i6Ezn2ojSAs91SZBRBTBjBN/8UU3KidRm9m7kTMcyojcxPMG522lIovbOSzPUWKamighuDM1QJAIkIntaCgOIhyKYswPID8yAQTg8OKqqdlfeX19t1xu3XcOJmg7rdy+foND2FEnpjOkVBUjmu+/xBdTUw1gEiTmlRWVynq+EHA0iub5xI3bC9iM3Jm19/1dZE1+/FHHj/koJjUXOCBCCzmhmL1VLFLZQi5XwXmOFiX/2cV0ehZGAyVuUUtxyNMnn79snamnL4+sbGFBERyNhR11PQNizclAabm4dPeN65VinrtW8FaEcRzwFC3syYcd82J/HOOG3zqdf/jM747FIuaPfNZOgB9FASgUq3VZoldW8KZ78jcPL2ThSJLIeYePLk8Cfw7z6rGzxtdJhDZmlkFELhr7tm3bMOp57Ea28dtvV9/YCjA+vyJzVaXRYOqR+sWPL5+3MBbnGY10HbQ7ic/eb2KT2cRKfvGnj372p1eQmlTVgG77bkQDCq8v85UckWGg4y97rYvJtd/YXv3KLuFowenEHWof/fLoIkfR7+yqX5xfIgT55tIwhH7JVG5/77XHj/Xlv/e7YzVyaJFf26Aqxb/3WvbTf/uhm/cAQYf9NhPN83kWuWoaaphaOHQRw6l0MiICqbiKMel3Nv/4r16Q+IuX4WzlNo6XobqMpgYQWMDANP3/Iwgvgy1LEMQw8zDzPZfx3ceQTJVZXF0N0z09kgY0opEc0tparb2xEf6hcIR3wz82YiG8YNkeh8K71gqsAWmmh3qau7owq7KSMx/DfZf5MPN+H4hc3VtrskBTErLNUsqG+DzNT4NMYUAYsDgUKqKLorCoZJFx1rccAKXw4anz7AQmEB0Unx7o3ZnnEdhU0wU+BjTP9AFGEE4WE/lOhbX7eGqsFWj96ASurPogP5na+Y0av4Lo1tCm4uePD3gcWykXVlOPdOx3N+UPPywltPRn/278g6eJSUlnMzdNotU1OdxqfAkiUInZe3s3FpDDVyevjyYAEr//vZXt27VGFP/lv/7koqdvb7N7N0rCtTJE9L82nPhq/PTVRUTCWl3UAn9nXfAvRw0JIoiga1MRgWZ5Os6xUGhVmvSJG6soy1Dliy5D1O4X11YJem/9nRswiDQBbWVLfOd+6/6KpFGAM9WZ1wN2pKbT/a8+f7F0J7mqvfYdMawmLc/2znTKsY9szOYrc8VA62SOcJIX+nKZrq3Lzx51hhD1i88uZmfB1CfhvTzWrkJvrX37H63mv1U+yKXPTO3Vk6PzM0Uj+cXMBSNve6/BEIEOLG5+uBHqPYMGXl2eTTzNYSOG1tgaUV/DOscXQZTAqecgvOoxtDlTeo5b2K6JOQpLEm0xPRz82//l0MpCnAfwDPFikMvxIAiFMRjCQSFfwBS4hrIJnJy69OpObvRo8kLG3Xuk+exwKAh2veKYWRoyQjcaFbDXa/XZJMy3gRpZ21s+2f/4Cw8fn59cMM8/yWoj8gPspAkfl4LhiwvwxWkpZ622BzP20IK/2GdlE9AWhxfF5eGjZxc15fL7WDg7mJ+Y/gL3aAgdHZwf6O5ovOAL2PZ2bPSHXXX4w0dPIc6TZJCpYVBNwICQADVgDdUydKqYHpzGFCWKwbOz/gJPxDX49gM8X2bsLGKAQH35dOMKioQjd7yP4TZYSe2jDuICSIQcPP/KDVLiyt6wtdK7nO1JEY7VernmRb5IiPSVPSB8ffzkePlH/+7Fy31rMp9LsA8EvnY4NjnIv71CCUXdB8sUtNEkQjmtXIGbVaCBO9UKErjTZ2NTXSk22quKR88vraEKaEQ27jrmJWQssrkPPOkqx6eaOteBueV1pooyXbi2k2ahkOsNs5hFbQmdvHz1+Q++nhCeHgUQg4vtFkkJCYfZvhVFkeFDtguFMAnDgD0zIR4woAQD0Nj0XcWLDNdzHeTixB/OHpFsETKsOw25u1xwaOgQ/p07lfMBpoUeJECAzLdy61o4KHPxQs9iFkjGgP5iuApQVA15+bUvFXMww0eruErRUQIEJlnhOCdAWDD1fISMYDpMuz21elUMrCjwTIvwoTeROgQ/+5PJdkNcRPK7/7u3nn9+VieokMCdE62Ne9CtYD4OsoGhD6Dc+iZPFGITs/teoQGRKXseMywnoFe3ruLB+KS//3DBiglTryVVQrfCBu5edpeR72tndJJFyiulWMw3VjeCswFy4ZdqwOXQ6XjK5q+b17Yqh5rw5NjCJaa2XUx03zdiAtcmB5qLJaeXON/KimCKoFkahMb+TF+OHQcUoKZDoXqKCiUuUP18jXQ1p5hw3/3g+mzBCkI+rYo6CQIQWuLA5Qw2sth3sr0mcWGRz7+atfUMhuEkz0EiwpTy/g97w6+hlfbN51JSBIq5cfL+al55EX33u9e//NIyAQSpySfj6Hu/de/P/8NE88S9e8SQsTrI/KNeJ/vs7MFuDaMTNUQYgtTl1IwymsKUi2wWLBWYLIDz9yqEtDxi1orhbJoxKoPniILkx1jkYMNLGH+rTAAJQ3g8SZElhAxzxAIEIXo6U+SQ9D1XSl02AzMTRqFkZnHjhaomGQZGMg8aF/ob90ovB4Ciouvr8mhglkQqDk1LHai4Mu6BO++sh08yBk97BqvZXJyGTy+Olq8G/8X/ceWjv8jyDWI6xx6epzu35MAmDjqjerOE3OROLy9IEbB9Y6F4WJEMejHRGzlBwm042+1C8QqGU7IaB8fDgEbY994qHOj0Z4+V3iNtjXQre2KSohuYvnCx5g5J1rje/gUGgCWSshxGFgA7tnOFlEZw5czOMTTFkJbviNXUQzmPkZYpTsutDENZliAxcHWlkCv5BZQMXun2Kz0Lps0WxUsUK+MpEN1dQ1wL8TWDcBFW4CEcKW1imzQaDYbHz7007y2TCZcvNnhmOrMe3EI+83C4lXfO6OlHfc06a202nCUf9pNffq6lTCy8I1Pv7JR6sbK/qBBahpfUpa7E7uSYeK2N/vk/eev/LP6xrym312tB4sZMvDTi3VVxrCSqC65wSIuF9FnEQvLAgNx29eqNknN63OtOimJkZX6xDDw5MGEYAHIlA0zjYbTdzJ0ppj6JVwQhhnkyBm5LMNqZXCupfXiWHg1+bYM56arZwLx5pbnIMBNFBrOEfzF67wPxUMa1aRjU1ITNR7398HmI6PHDiZJGx3axlJBs3jMa5kIDE0CknnnYM4uxZJYCwl+dXW49zjY/pOXnc7zv25O+oTIs12BpAKUhsiRWSpmt+NMx0MqzNR5MOQAIQUKCji+B2TKTVwkMC5//cn/jZqu1sTtezBk4miuHN1aQ4/Mxzxenk8UvPta/8c3SuUHrpu2RVOrDhutROc6l0pkVDSC2WBMD053H1AoGyoC1V4d6F+pXv/IrBalZh5oi42aKTLFBHFUTM47iSuY9PzA3bsqMSOGO8+STfsAFJC3nGhKOkW5kyoUw1qctHAVheoCkhoPdbqOm6k1834rCtVvSwesLZei8e6Pq71MoTZBk7INpbhVPUlm3cEbASHcBqmGa4eY8BZgGV2OQFh4a0PJ8gHSLxVtV2gcBP14sTJ4maAJBnLBG0USBWmqxxKLd0IfTlMwQNMAIGtM1D8ZgAgURlATeoc3PXqn1fDs/t4yUjYvIRCUzrW+lXAhGlK11ndBkVqskvUnjn4UQXSRvrJBUEYXA87gf3Xhzq1Sr2UCghdxoOgMwgn8zX5ZFv+PFLmISGI/B2OUQoBKHVbvn2u0KnWYY2bcOA6QNeiuwqvmM/W+/3v2v7q2v/6114Ks/+HPHYZB9gbTmCwEI3d+824zR8eF0zCaVnTXOTB11KoHg1pv1KUjJNJ2/Jl18cvSyQF+9fq3AUtOu6tnp3NPDjktwkDtz8my0kF1JouuJbBxdwsukUeG9DUk90j4affrgzXsQwCPGUJ9mKQoQsZniTGWTnBtRXtXAwfRiZpRZSFNVEgOECmBeLF2QJJEErIh2yA+e9WeKUS/R5VW+DQTLCWRb8ZEG7JVxNvH1oYLGkQkEUzXyabC4wjJKhgVOvcEYITid6CDOwaU6ffnyjasFdisg9siLUYS7GX8x6JjDv/nN3Ohf//X33to8+OhRdtyt78E/n9q//Y8/jC5/Qje3K/8MRPj1Z18ErQoAwaDvWACUruBuTJh92+gOtIuO/mZtb60s7nbTYecyFZhTF0BFpoXTesShGS62c+eQax6db9QxCV1mrRxMwMopLXHjBAYxP3KWcNcDCwxjZlgSgClm+r5NIYk6DgsFZHHkH5MeWqgtPzsXv1NcYtDok+mb31/pHyxuvL2iqealGnQg8oP9g2UfypXFlODW63ijXv3cwyc48PmBw8LgdIGdpiA5idaqohhjJSZIJMFUlAi0sjjujpTYBngGHOj2+VCX87nBTMWFcmxaC8uJUuT4qbP7LWz3TWoa8/ilcTwz4QjjKHk2vLh9s5r3ZgHid7c4NY9DMEyRcMfhRaboDOalOjUNrTKDsHSJOT/yYrpTrjXu8wUFg4oNf2S57rxEgbOuejGYuAa20yC/v7JGyMnR1El0DyEjCOd1EC5KBG/DrQJv8lBm+Z+fqEC+sl70p5ZP+ilwqLN1Pgbp/SeDHT59Nb+EruWli1lAhH/6g09QU3vrzntyBZ762BYc5NjMq1ERQugNMRwAGw15IJi//tvXvvzXF5iT+/5/+c/+xf/pf2rezT06DHIsHqNRs0T81Y/HtFAkSw1L15cxmniZRwkCkZt8pR5igoGX2B0HKubfvwbsv5rPTiLjfFi4XfAdaHi+JBo0vjBiAuwWseHQeQehbHzrV6OXt3/jjeGP58VvXWXurXZ/8TghyCMMtSP218rxtOOilxO2WjeIBOOElx93lovg5C++gqo02mCQamGbSW1bRvX+a3sKgdU9jCWc4FvxMut1PkM+/e2DYUle731xWUzxeRic9CxHxrbqYgBk/YsBHjnEiIgAwJc3XnQHDZSVcvBwmEmkdu/9G9Np5He8+ht78ltyfzTJbKewWgjmxVTRz2fjK3eaDB43y4U4Z7xE3yzdVk/+6rmcA2hU+NFfPV55b7fGiy9/8mr13ro5UWcHipu5VnJugDW80FrJURKkMmy0/6In3XdgcONQPfMdhttb7744qWfe9SsiGIMXYpRfF+7e4c6WWfga0npGu5pdQswCROqgPzdhuAxOcs26G4mnVlTIk1gqAvTw0avmehU3jheLUaW9I5VZMAVUC55M3DLtVsiUgdMugJAwGk08ZiWX3l8n55d2z2zcbbs3as5MdS4XAQuCAtZgKboqcqmjZ1geYbKYCkjbjpZ4EQrVcFuS1CixIh/BcQJHUQRFkDp4OtOuv93Wz/3x8azCs5d20skQlSZcZSrVQDR0qzSeWodkkI2DQjsngg2WKxKv9o/fWJFUEUdSrB9mZ48HmrKUP6zWNmWXhrtjlSVBnKJQgHC01NISeSfpmGcxpp8aFQAqt75zX/H6d67Ew0+eZoWWs77yakbP1zMIWIeZA5QisIm2maOsLLuPLZcfjSHPzt+rwIBzEWYRVARYqLdkQB5OSrQznNdu5BeK7V+o/WMVvgmMKEMd2NqF5nlxjhE2b8uBDEd8yss4lG8bg6C0Rrh65s8BYBHr3YURShvt3RATQXuAL7wIQwbLtNzMrWzlrJFZwkHIVDnRjxFwofoRBiuLKXKq4lD0wTdbeBtwU/vjj/uVWolIgSIvyqtFnwL1aGQlIF+n5h4k10HEA3UntUA/gh2LwmZeXKiyURRlanLj1sbLX7k//2yMg7T/8ICrcxYNAsPg9U9Hf+s/ef+Pn47rxfDdVf6zZ+e/8Y92P1qedk6t4dcuWwFqnVx5m0EkwzB7SX+5VgJ7U4OsILu7uTCE0sB6d7XWFrNMYEZq3JmZiwTCGpzEAQmUFksCw9MxFY67MwUyf/BXenOFX4PoWoTsVbKHZ3FYzX4yd6ElisAw7mWFPDq4cDrdc5B0SYb1Hdw14UqZszQHs/1Kg+0Ok+1m+QCN9Ze4C1q9i4RZptr5+d/+Djw8dnsmv726bgyTi5duZQ0g/AlPhSRFInhenatIZLBJ5OmJ2Ym0jPyb39r88YsnrssWizjC5XQLXC1E12+Gvuv+6j+cTAFTuprVC/j6Gup5QMDSX30+xVFfyqPqQFlpJ8oZPLs0C9I6IwrDuTq2NVbkKAQGEnTpkgRLkASKcewwDWKKBAgyU+LLOdW4Wsj9bm6KhLnLrNhAxXsVzUtT1eFn4WKzJheqBS4bHCiDw2lIZBIJisugN+9npMRfqxBcIcZofeYupr60TqaGmfbCCpJFqC9h8WRs5DgJz8PRMAOeaIJIzA1czMf1XYa6V8f3pGRi8Orkq39nx89URA4GC/36zpIoCpibjl/24wmGQ+qP/g36a99cKxbXMScMRvrqm1JIxl8/m8Kwsb6eDyLdtDMQo0ASK4o0RNn5LF2PnWJF/8v/5vEzP3su7/3eu+L9G/SPfjhZDmeVIgXIoBllgQ0WVsDlpF8DLGyNq+54z1/hzVYBr0Pu1HywCf6YiiIZ5uyEjP0llYI7woRkkFnI5Hl97qkQu/UBPnLWOZZb+638lEL1vzgpxZfG5LImZK0cEE8gc1FJSWIk3zZGMcYVzv/k/Gl3cWtH/Oih9fIA+Oa33bjK5Sl2Ml1Yro9vlBY9kkBAmmFHMyOfF773YO/Z48lP+os794r1ajY87LWuF2gt/OLpYrNMGhayd7v+2c+mxYKgB8xiMtfn6Du1ZHYe+ba/+s4Vz/fI1C0S3nK6GHnWdxqVZwedxNe/+2HLXmgImpFT2IaTUca+Wdz68E1gDLxeaqciFDqe2nli3bq9bZ5d4macUS5i6NMTyCvkVzaK7G2oPzR6FiTKABzjsgTnV+rHjxbo5bzYJsV22NdGuh+U6DRQU8HPgEZJtULIVwscBoB8FpoylhgRgtBIfbWQGknvaO6hIb/TdGo4nVvp/PD1/NWg9U5tOrUZ00dACGVhCIGAIPHDhGUwLMRwHALx+NII/TiGQjTGETNxEYpkRAoMIALFEEbIXrmY++WxjEl4Gx2m3jw2VhEJNnUIryeWYbkJJlgijDppsJiCiXp54ztvhIRdkSF1OPn8i9HM6UvvbK39HXZWajYAdTYLwqFZXBAMFSlRpGuToA/XK3SAhdDrcTOXsK6DSJCoJb8jvu9L+yu77aMzYUTHa50he/Gv7IoscGuZotUSJk0dW8c0bZy4Cri9ll9ZVQ71ZlN+blvANOK3E5nLnCePzs+yjZtkpYhWYjTT4t5AK4mwx7LxDoq7Wi7RTl/EtbcafAHHAMpEUyDHTWx73tOP/ujl+7cbBwMDbeHIRvnOxve+8j8XDoRBz0HpWLW9wemM0BfXVgJn0nuiAxlLogRFcYqfkgrsBeZY79UgcLWSS6wrrKWaIMnIV+phGJMCjfBE0PXKZZGsYMfHM+NyXG8XuU3BBwDUdSEg4bm8xkX0rO/Ipdu/LYyH3ubO9a//35/iWqC/JzW+sffi/6Id18L499ZefTV657t3psrrP/xLI1r4kKlxc1X5pZJMJwgY7O5UA3NaRtLZgc5G1tOfHW2/lb//rqSwvnMxnJQ32dg4m7GMkMdRoEGiekBFJrV3B4ns+GB/9PLl8ezZJHQTl88tXo5/57pgXXujvGrMfjYOVI+BzNrGlnq07+tRmYPiIuK71txwmhV5fOFiGIRXscFwKa8WMdUHebFxrzR68rL461tfPB58/xvrnz4/ZUu0l+CqPs2KMuCZEp2tSVeISfbi5Yz73rZUgtX/+LK82wDuy73LuT51Can22fPu61P3b/16gyyhn/6iuzDxft+BI4/9YDO4ihSdWfFepZrBjOuFrlvkJKJSc7pn9sRptbmsBEwvDnyJv7JCOEYkSdhqu5TCQmSnWJaVq3nZB7KRljF4OFJoSQBA73TK1a5vDX5vu8EMkb5B45C9hOazTF2Tvln1wmKC+lD/xcXz8SjuI9u3C35ouPr8tWqANfEqg9v9ESohCrCQmrzoxidLpbMI+Y5Fb3L7dnRVJgM8ByQI5cHl7cJ1CehcDGdNUhGTqKPICnL5crg+HEWodO3vX3n+a0qIL3P/vYuObXG7ffnTr0oI0O/2hOvN4rj75zNz47++ovx3f1G5JnWnk0oL0jS9uN7C4czoGwhGau6yWSggQEZojgOTTpo9ORk/8gIAmB0thl/+6M49rrJ+t6CnkTEFCRuDSzzAoCgUSX6iKYhAVkedOJ8lS1MTapg+9b+2uexe+cvPhtDujgiTo46/0SQBkG+IFN+Qihwg1Iijwfm1d6r9SwfZHyGXhtMLQC6pS0YHpjRTi1wmR5JVgMaXHugF61dl4O/F5UPMUpa1XQBmgfa3RZrDESqOWUj3rAAQihwgJF2ihD530sOfPX/wu+36f7JmPdfGgxHbxmOPVc60xi7V8qGEDuMZCuk9IYspicnTMExyLC44I6VcD/YNA93Y+9FXJ+/JxJEl1YoBmzrngAvSwIM7UkZtPzn68nYtQNkUQNLTIP7zhz/+7s5bZfGKIGo/P/vVxPRmna9u3eD1Tebrqcmo4d1CDgOzUced6zO8AFNF3kosMkpWEnhxGXoCXG0yH7iJHgZaogcIWK4jiyFSbApCmcWt8EoRfzK1T6eRnPNRhsDhBI9oEkkkUibhGGAjQkYp3fYMAy+V6O3N2cefQnzo9SwUgCmB9HUzVk26UoIhOLUs2/AUjyCxUFjJOT6gzpIvnhyeL+Kdu7u7AommaWSCiN6iP7y5YdVc2Qf7ZhD5AWzDAJ6oGspDlmE5WEYppkYnbowD1VIQwoCpHh4c6BU406buyr1qrkwNSLwXmrAfT78cB5OkeXcz12LPLydIpoEZlqtQVI44O7RlXGjlshzDE+5UfZHbeb9wLlw7uG6iHbOBgyWqlni6qQJ07lwlUYOJlXEMqJ7KpnhZABDn4c87FQIoUCZvuHCdZFP1sz86TH174/7qPKHoPE3RlL2YkwsEO7STZVwsVhgBxD1/koVGCCCOhy+s2GLQyPMQd7cGXzCK5dJU8Aq4INV0ib9FOp/q9jINvLRZonzfqxRwEHPPj08E2C22ciFMzJa4EZlMHjEmSj5xRiPPwOk6gm7f5MsyfNpRLwaZsowqmqarejNXmpddEIfff3vjJ0tnfK5qiroiwqUy3JkHFdcq1rkXx5PuH+z/3W81fvTvX4p/A733zdzxcTD4ZLlJCu/cFjuPnv7d7+/98IeHrz+BykIOrlWKd3YX03FpY8MJw7mCRppyh3RyOaCaz2WgV5Nz12+VXx6pr79MSgaUgQ6O6W4aMpQNp0QdY4wenlCh2KbnlxoLQwhprkrOKRTs7Fx7/3eu24pXqQBj2woX9AJojGb7VaBHDXl1OHkymz9475oDQ2ZEAFA6ONcKZIiBgLJISQG9nDq1Mnnpqqv3an/+OfINkiIZ4fmJzhJonGDqYrJTVBuYR7H0T44I/6rY1aalKr4qWL3HF7Duv3dv/bi/GCTQyh1BU0afPBuUW5gHgH/8v55xHPW939z86U+fPv2DFzc4+Du3rvtTUjNSc5yNfEAWof3PX1z5MC/m4dKGNNpXHv7gTF9YH/yGNDjWiiucjRcuxxkv8Zhnb+fA0dyhc5hlp57uECU24pkAyq8+qD+4oXyhjPSvnwLT+XxjNceBEQP7oXlJTCF/iOgl2s+4ipyWKZ/1+p/1OTyWaIKCA3uqr93jlLlni8w0MHFl5HWGMcOiK3hg2CsgFcwtCLXjVFqAgGn4RgsLIjc0nMjwxBjflxO7GLgI5pzqaTKdRR6zBxa/ta2D+KZtHgjE1fcauOYleFotSgd6cOfNvZ+uvmDOB7JcsPoaSYs4Ri6GmJchPomwPBojnudSRJ5xQhjHoNZO+/92+8F//Jd/zjI5p0V/djpzwXh9WyQJLnVp3wJBnHmtRvUCPHwNIDSbaFbkhSxggiGaLzCjmChEQrsFuhGCERRakE+VgM/IXBMOk+RoZKch+fwn8+99K8wL0KvXob/vXv/t9b/6yXgH5cii7CSexGVg3wGc6EaVqGmjzqeBSkroqsCuwzeuue/IFHK9qKnSs0euqQWEiyhH1tY2Muj0ttb3PvzmygV18B/+5S+ufn/5/mrJzpIMzK/f5150LXuWoa4dQUSjLQ86KUTRJZIyjpcCMlptJYp/KcKCDCRlGGA6JxOEbhOOtb8oKnFVU8e9xSdT9+3fvRkO5wshTCIUK/DfY9bRdfezr4/cNnWbCN4vCsb3secf4z/6Fw93f6N1ZUMiIux0lsotZPU21BkYupshkMezEIwxSJHOCVhnYpB4ZkmA77owHglmhM2Rb9yoPIyIaYINRrk719D1POAuYA+jADBzYhvwE6lcCDNxNF/irFyswYNzg9F8RYzZe3kE3QVIogRyy6/PE8BHpSxAcMtJcRQs0mkMwVBgKj4EJAQDQjiWdJNoVSZx1wc4GLCxKDaR1kD7C4m927Y/fgFCRMypUeMaA17MgtsMvTCmqjsqCJaLEhqyyroH5rS8WaFCk0E8SRCI0dJIQQtDHJq8qk10g8K9QOIL69vFV2dxTRD53ULvcKDOk05nFE7gyhubztHJfGJWi/jaVfCXkydr5HTlYJhB0OeDkT6Kmn+r7HaC/iR2C3LLTs94Op/go95ZNQ1gMpgpriTLw6Vtlynz4WxozG0N3HrQWBZo7/m0NoJnb7f8ehFKx9MXc5zEa5uRBxVdyFxlJJuEaAKApgsZXDztpGDFnxrext/YkJoVBMbSw8vZ8eXIKIbx1IPoShsajnuiGYYhAkNIucaZihQ5IQm6bIECMNG6CBqRMzGJAztrZ0GGpF8fJW1M79hJojpRAOo0WMnBuaoMh/x8mGK4s3Uv752M48h2Hc+xKIop2EuLkqgP70m/+nIqgNKD21u/+sPz3/2njcC1rtCls3/b4Vu0asGf/GgwvrBx0i9+d+2Ln1hbNRPy46wp6opHQ9SpYqE/G2wXM/xqoZ7PP/qTp+21amWjAVoZBsylMmXjzEdGyHDRFo15CQpSEF0GwuWs+s72sguSBPfVHIQgKryK/vw1KPeHpCa/cwe/8L2vFKKIAEZf++wwxbj6x//zp+rjI+bB3fY1gZuApmf7LrHE1BnNrcJw5kqe4XfCYbqw925jweH8dzZKf/JHJ9Ia//EiY8VyPQDHgah+dRpfzc0wYnI6Eas1EsYWXe3Gt+vTkEJVT0BglEyWuirkwYKI7O93IhTByeCXTw7z36//1od5fRAc/PzlydG8upHbljHk5p2db5NMkUeNwY9fdMGKlLy2sjLPlqIQpIA6Lu5WtLP0SpEdCeQoS1jblwuoA6FUKUVylZstzNUCN1e7OPCPnp0hlnmXncDv7yqe9gqcb0tqoOdW08mzUiXv5N9osIcXyhz05pdncKIA1Qqu6hmDQKgO2voLKLqJBP7SugwJqZGPHJo3hq+RjEqiCmQNy9tchOzkKkpP8cNknNiLkUESURaS1/ONqCTKxazbC7Tz125k3WWzLNuexnhAgjydhaTbOY4gRi8Vqm+dKpWvCt/7xgf/8pd/cR5MvJZIIBAYYUYMxXwqEkjixWoE50DEcSA6jWkomHQmZ0evHvxv31qcT2VcWGuBf/rp8Hwa1vCkQaGIyLJQMrbS0Mq4tvTz58vGCgZymCAK+jKMyBhaummZHy+TYpkcusZug4ehmMTBcccqVGlGAFMnyHEyZYfy37wp3o8Of9md7TSqMyJYdIG5N58rWEmmC8SphagTk8MJLA0gb+bNHPbvfygzcDA0E5QGXpms4tfEIkw5l8eL5i49ZGDt5XTrFZS/t/Fhq718NOqkMb1e7CyxYLAUWps1EPYWCzAemcDOqFAQ5yiYz82VGZ5Stp1ylZpizq7fSAGo12iUCkhgBnlKGHM79ZOvBuK1EkjrX3/64+vvrD3rTBlCFi7SlL5YWbv69t3CqX6u7I99Sus8PGL+7tbf/1796f/yFD3VbTxpymK3a+uSKzdaiB/PLKzumiKHXSzscpGAkXg9tTEGHjqYFhENvlAywWRaCvIaVlr7XhOZPTlZuSOc9eCVHfHCRng0xM+WGIRAJRmEPaerLAnCJCh4kUD+nKhwKxS7HNqYn6UoB6MIeZWFCVTxEQiwXCPKCxwII7ilXMxsh/LKHNBaqTqLgElSEcwiOEwIEJlO/W1hePorFecQic6jmeWceAIRK7YTsD5SgtkkxniAb+eSOQhOVdiAfDIpSHhqGEvbphgO4EHYc+Kgjthh7tqWh3BfjdhAdcpAgVzAgpWq6lLOMSmGqh1D+VrdXqEoJOo+78lbttmZ17PFDHQbogZD1sm+0WTlFoxOz4wSA5gBkHHApoAgC3vw2hErZS6PP720CB8MdC+LcFRCAzuSqLB0V54eTX72b79k8JSGfXiBVnfLlTKPCvEnj1SwYwA5FtyhshXSmWd8JZxmcUIKjRKNxbhIe/wqOYZW195+dzo/Xb44waR0R4KNLISWZG9J43gxpmMIsSddz7IcOiEJkATxRuDAOShKGfNiCXhJ+lzPstQvlJDNHFVfyytKAuC4A0NQCk1OLRFwc1HmYpCHiPxKAxZbqjI7PgoqrUK0En9y4d37buszRdOAeE6HFdgb0eaTX9jiFXF+gW7cucXuVbZKDehbxtd//ZxxVV31UQzcuYLlMhzNguOuFWHjB9/YpNdKI2fiTCEDxG+3WJyF9KknuBGSBFqMzDPEgZE8DRAEaNlkzNNXiEL++80nxB8vHx90sO7wEC39p+0zsKCSIEONek5W3ij7RI9jqL13gbNfjnHkAM/W5IDEEFhRA2aFyJNoFgF4aq3VCWuaff7Dswe36w8fdTkWwapIrlJ/9WLWLAVzy0HsMZNFEAqYkzEYe3U5sxYqw5NbV+ufH88MM25V4PEyy0Jwu12g8kjvyM5v4FWR8zzk5M+GAEy0V3CQTH7nn99h99YP/nqgzbS/+H+c8qG/tsWXN+qaHa9voUQtnfUBIABjF1+8ClMVYVtpdjErwThTpRUH8mRSBYlqe2MWoebFC1+ZEkk0A+TqTlGH8dNPrJi3o73sx6NlNYuOSmCo2IOOItNwFuIMFoU1BKiStpGFUzEzjVBzJjk/yUOejq6u0eSmdHrk5ZLFs8NpimGuk6biTD8DU4kZ1+PKtbpeluQ+pGGTgmOCjrc87y3SIHYhioAbf/ceIaPzn7xGjtXiNwoe4jm6v//17GqrNuku6dXiOMf24jBfzH3vH7/34qcvRqaCQwwKIpEPsDyc6HYKAJSQpUAYU1jEohTF0igZvdS9owvUdffHlw8/SqqbdJ3NhYvl0IoLVAFmiJTPhv2gtYL5vkPjcbmWG3Qgns7jKALRQJqQthe3ETKHxs5kvtYiPShCWcF3w35/LkjS2x9uPv/5vjHoPljJbWbU/EubGQGawZookW/WjIgJwQjgWE8JGVMlJM8epVtVFn16OlTsVC4RKrRDY3ZRWFqe56YoEw3749UcE/kJDNnPP3FWbhaYNRoXcADJ1mo8kXhQMECIFVYWvK6lqp2bFDFniIEeLQDmaq3gK9F86FbK4otFc+/xKPXmMRqU6oTXjSNHBGKkfzDLteKeAd4Vy5wYwAmIIqRApp9enLy1urnBV3VpdKHDiIj/8P/TWb9WvPdrV6x5fNAdzRZBkU50Iwm8IQxylQozdDDTDVI00gKkUsEun6oNnq4XZGsGN3dL1kRd35HE5xmhgqUNZvGErbXFAuSf9nrba+24Si0GRmCZ1kkfgbQk8oa9EIJBXKTG8wAa9IvrZJAYEIJxZdbNIH9GYwIMU6nhpK5l8hQDQKAVwvbcHfp+mkNqNczzYx/xtPk0TZE4TpHZxGlg0i0cBSvM0bl/fb3yMgVyvrtwTJxIF+ddA/ZhJDnXkXaIrbSr5XVo9npMJ+i+S0j319F7ouybVMdrm2B/TCTFnCbz9MV0DFEzzHYeGaUdrnSlrH6+P0IEdWZxVdGqYf1kUQhMRmVB3fVx9onljgCgQmfq0qA90c5JuETalo2gMBjFcZ6aD4wsx5AMPHBMIkTD3oCOXWi7iDp6YkH+iA1xiN5cKQAAf3FyGEG5jWpSpIbWIg8z1yUEtqOlH0N//Uxv4sh6VSqVeq+Pd3gIE9ykny6OFJdLT3G7rOLYAm/kcAhFs8V5grEpixXKsGfD3lyHGWSrFs7MaDkNYAeb8kxnPA9ODik8BjS/tdXGrhRhI+GQSCZA06PYAhV3w0hygBREiMzwAj9MkDhSqKi5JlVILjv9oncOLWbQ2ofFv/7ZElwe0QxtT8dRQO6/fi1//+1Boq4KcG4VN1e21LH/+b/+83Zd2Pi1QtrBQQCxHLvzYiJAXrlcKUf0/GC4LEAx4YamUm6Xh/sXYyujM46AEthOk9TTk6xQIYaGm8/xJATZE50XpfPuIJ+vv/9fvt/5dN/8mHv/v9orrVdXCHQ0s/TJMZqmZS49SItOZ1nmK8fb4XC4tP7kspFfffN2Od8oFgR3gTCi5CcKc7GwpEL+12kqiaJCEdu/OGvd2vKm3esCgWIRdffa5KgLCGngjgmja1XzL0d61AMomuNDvS42kSDEQJeCQLjOqzbSPdCFSmnhKaa2JFK+RUXdqb0AEiovHkzD3rPP3qzwO9vthR+ff7IcjK0mGmAs8fzMSYeD977RMC+QEgUAKMy1SYMlSjwr+TARa0lRvnq7NMMAymLXdqnl9d/76rN/v02KNB4NTg6fH5y692/fKB7/oTrZvbL8JIVYROCmq9sFeuk0GzXwMkVgAwvjRNO1QPEnpldbYa+08bkDEFjqayn6cOktghC0tCie2VPKdy+T7BssWxaZYIPtK1OvGxUUSwkTsi7xgNc9O2+v8s2Stf+Ju/iX59TvtMg7QtLAdc0UBWHrt64evF7SnERGC6dvneNEsO/Qc4C4Ump5df9LSyzicz1mAcyzVFFEFxYoITgVZV5gpxA77Tm1tSL1a7UX+5eYyH37vVJaqL4aL/yzJc0gOot9NVtULITniXaRNjGyyAn4MHBrlQHEsgLqROZKimZlclLPxaNp6xppUlyYirEJwTAdJZDI8d74bHI5FjjS7SuPPj6+/c07DSq0STohhJU2IdZYf273j4+gVg5/Mz99FMfesNgUSytF8/WksoJNfMfd7z+FSi2qgBCS6yI4TqoWmllzAAQyKavmWHnStVnhcmpvaBFVBCqypB9qwCoE8FeJFQB70sVuV2p7K9PnX76zne9dagWKTlBc5PQ7v1npfx29+d31i788KTdXzjtdsibk4GhqJKKbpTzSffiMLWchiChGoVpp7Banmn2UoV5cK6ZxCHrJ3SZz8Nn+YByDTFmm2ZkznzN4OQPwDIfp1E39OwXg2EF5EpF7KlZvSOvCMFRWc+wd0GNiPNlYG57PPlgvLb5Usz0aa0FfzYc7qw/0wx522AUT1C1gs1ezqqzOA7XiJEau6oaBqmibm4VTT4OQTJ9OU6Itb1cnT7r0iV5oeP0GoBp9aonzQojZUgKGNIU3VySZtwb7Uw8CNCV2gyVJQF4KI1MzDC775C4znMxLG/LrztD04ggiwCzEEBgM4cZqOh8q6zC90lhRSKY/D0g09QOqVGcKPDY89+xBiGnBhG2335YOXObwE6VMM/ld0DHREHCXALUjY58sk3LTr0g8oeLdxdL1s2tN4qS/uFkjFdOb7DtnFx6wEeOtMp2Dozj2PNfiQTCM50OPcJISjJEUglMUK7FIFVJnofmye/msxzN0eZuKBEidw1TsbjTYwQTZYHkfwWkMyLF8f+HWS3RapDEr0mxifmQMe/GtuzU8S08vUjZIVlxYjwINkzCeJQSUwvnpfkRDEU7JOhLNly7oxCzOIMXw6GzaD5TGHi/LjNXL5suQUOLNq5gxVwo4pbtBxUKaN2pCHPuDuW0m47FeIKMiy8AkS9JUnBUHXQpDTV01n/xs/GsPOI8u5K8qZ6eGMCDaRbv/ehIUqFOda2/mP/8F/QbExdf4qoC6SRJqLkV5MyDuvDzRRwsKIV04kqB0MJlMLUufBbt0vr5CZvG8xOjjqZrnKakIDZYOB5BlipIrDAUuF2MFM4gyzxJapIZpY5vEy+EGtdG7jNSsKr1Z+D+83SoRBTOJP//B07iM4hWIMaPul9HmDXzObLruybUcgV2pMQAh4OnWGjs4c84O9eJW3D/Td1uyHbv+HMUEWQOj9qr944+HAE67ILja5o772s0WtTDZGMW5Et770X7zDazXyyolKIGQHz921q54u21G12ywCLoOEpK4qsCbEtF9Hg1wkCbA0uaVD79TmgwOLl9MEkavlEGfww9OZ+USsbHFnfQGv/ziEq/ye/UKTomeBta36ZmDISmgDQ0uzNwoTXmOKojX31/TnMhQiZkDbADlB8DV/NvRl8Aj5cdqajrsJiXsEsMOv9OI2eWy+YtUKah8K1pirK9WRzMsIhOckgIvFsrYzmrB9OenU+PxY5etMuUbYn+o1je5d9dpXa+E+cIUsDEQeH2xhBp77F4FWG0uVbWkJemMixFydUfMMmJSTg/h/NMn+odVCGLyx8+0JRxcuVNieOThR8fXpYpo+X5sMXeF467azsuo7704mug960oFVHOp42rTsZeDZQCIUgNoSkwWJGlqoGBChBnL+qPTJYWyd+/e2bq3Hj45+aM/OoUkGssIF4sLuYgqZKkFRYsoxhIeymgSrpTzloWtFNCMwDCPk1tFy7PpxFtbrx4rgezRjIyUBQGgADAIoRDUxmTi+pV3KwxTH/eMcxs4fTLbaeb2ygSTTykKCCRitNb6bOTX4agc82SOjIXSSRA6malOcSeK5W2ZI1DviSEWaXcOWyYilF3EteU8ufCdQqU+miV7tTIcRyRKhiCSIEiuiZ10g+qGQqE5le0uZsvNIuuYLEiUxci7GFrX3qh99uPB27/+AQA9y16ytfoqMDPnE/eN34I+/fn53asrGUN3j0NWwiAoadSxNBe9uhhxPLfb8nUPCHA0VyWWaCbmxAdvIeBC162Ap7IMAzw7cF0opi03gWke9Iy0KsOOBzJ5fHBp3K5X7CnkKE51A/7sxf4bH65+CZzyXkBIPH728s3bzO//6SvuPYirW52x5+7H5a1mxFlzKLBm+hShcwTUKsP91wtzGpKZaXZsLMETgg94EhB4TJ9MRiZDogRKeTg8SyI2tdIsjpUMJ1NKRiWBhiEIY/SJOiV9EMEYBC0xOGiK+vJXmTUIl3yo1d5hvn7lN0XMUnhOcqH5XFrSa3WeIukIIODufFEmSBATHSQamhJOlQrgGZXrTNRTT0jdCVLPAXzAn8/Gw3DznXasqV/8qFu/sdNKneFL9ayvkq6eY12EzBGSOwqCwA2v7YDr398YJVgyhCU7gQPvVAtGiSeIKNPk3ShPh5ItwjmCzkJ0ObVdXkZ3E3amSSk0G0E3NwpYFiqLyIERNldqrVefDoetBZjmUQCihhOtlMUZzAnvruaNSD49jUb9Ch8sliqyiC9PAm2lVHmvgmJQEE4bbG7OlsjEtTUWT+JyLrMRXVdGJIp9+M29Lz/Bv7oMrjQquehcFJSXdGZhoAbGMyMQNDu96OaTLM3xJ6eoQC2nfRdZW7lJcgFdU+3QirEUMaByfu9mg3s+OPvRCwPzmAax2pKTIJF2V3lJuEYCf/na4dsbyLrbPTq9+92btpe5dsXpBrKEt759l0+ULz49ryLpoAtAyaz+5v1mcdr9fDGBsEYTdNCAxelFREdn5lpNNkwLFvGB4bdvNkY9E4QpAwlj0zsm0BRh66k5t9h7K2lRpI487PWPXxJvksOB1rOCVMqHs9En0wgAxRVJUyGHjNh8taV3R7t0O9cgpgNzMFTjtJoTQZTN1XMl0U14mDtfBH829q7e8aUadf0bdftLXSxmk8toZY0EWQeuEUwnKcMwd3cLDBAAtdX+iIJjn8hG+9McV2vXqm3U+JoElmcRQyLAwt8ocAoAFgrAtDPwlrNOmiJMkSoQ5cijcTrTo/PHHa+rgO8VvvWfrmiHsT8+Y+7Ci0nGgAhdys8WKVbhJSgdPj+Jt7nibnX88LkynxxxzLaLjO8cfAEk3wfOgpGX3//SYOIoT5z9tF9Spms4SJ0Db4VA8TmwP7NY/xF755ZdY6gUJUylzNKa6p+fzk6yiMLc0kaphlfhL7tPqEyXIPXCYXYqNTQimXJkLIq5auH6fakYeim/ljmdqdUAjEaDqMXAYZhBteZ3qRsvbynYr3oLgb31d25+/a8enrzU2t9Yu7tX4J8veg4WJN2+jNueT8ySO2u0XmgOFVu5mJb2bv3B0cnV25z1YiwVmcTHfA0oyDjmxToijDS6LQHlFQFKQmBhPvvTZ2bf2i5x9Q12NFxSqf6zESKtcxt0aOMlH5Jyd5oAyQWDoFFElkS4glFLLFKGSft6Se8a8nzW3r19acYwhMCAy8CRMtITJym2qUImLo+PnzNQfY8iHeB6qzT/0khxDlV8FC10+hMBc+p/b91QZvTrs+1KDVldX6gGy0RwOOuOfMxYloQGLuXC0J0hPlMkl3M31mGGQTXdLOnuYg50nTN6U1ZpnByPzw2o+XYxB7nnp8/b7SuFcjMZj3z/ufBmUw0RgQwZAQYgimqKQDJs7RS6i+HK3fcH/9P//f4//YcZ+FpwrGmIxCw/+OhPxHsbi9t75sWIqlautEtR4j5+Nrm2pgeUHHOSfLv++M8/RmC/0EY/tsxdjBRgkGfZBbIwGKbshwxNz0O9nrgzSoQh17bB7uksa5dgL+nbNr/Knf3o0Tvfbv67Z4s3UeLVS5/IMZXS+qvfP1z7J3fMwYX3xRANlxTmeidGmMOgjYo3dpzhMsLh85FRF9jpwBOlkrhbWZAwV8dTJUHjrFKl0jQbcVSUUGQXB2gMY/QoWPaUqNGuJCOPzjIww7E4ASkMyfthMnMX61opCx/rc7oAP3o8iUIagyFaC3FjGfWcSqXIyLV+CPWVII/ylGKCQJCxdPGtljILj0cLPTXQBgawc/3Uun8bX8yWy8PuzbfbUgG56Kb6qXV/qz14OK82mNwGjQXE4Kvz/d4kSqHtmrx00BgTm7nNZh3Uaub+owQHorUHKzBLZ5618JVUj1IYYkE0ijE6A2XMVQ0QQHO3t0tJRORbdG9q1HM4jFmTE11Gc/XbRXubDs+9V1+M2b0cJ+I0D6fDcNgPQAehSVxdpLGXWT0ErWPVa/zOdxs9pDB+Pr5UfkG119SBRZWpGG7pgR5YquuDLIV3+8nJMnvwYOXs3z9l3BkPR51xluN4TI/ExLUNtlwTRucGU1NuM3hdNF68XAplaq2Muw6iWT7O07EX6TpEspkDx4Vyzhgpk3mUy7GtOr0caYvjZRIhyhQoJGXoNHzjzdVhV1sMZ+AS5ZGkRJK24Xd+0P/0pLN6W0YK3O0CWCeoAAs+/osZ7MTNNjC3sylI1Qrlytvt3vHcD/CHj2bX18G0SlxqDpLDVY2yHLBMk80NfulhSZrE8/nRwrAwqLpz/e6N1cUMSLN5o8UeHhyn6vQ66ukMYKMwRAaHLw7fvtfeuburXTrjV9OYjr3UqTXBSi7nzMzKqrQYY3sfFph5Zp0Nz/50lvsOeWd3ffimbYzt+cu+czFbcvw7H7bnfAhmdhBAeuZVV1CeksZPLACxkrkVpqjGcwyVFXCkvU3PhoA6sliMXCnBuQaBY+rx1xflVo0u1UQMiX2cAkAmFzgEOgGYcK57L7wH314/kdunyqS2Un811tbrULGOQWe2peooAueuIVNdmX70chU+u/9BCRKkp8/8C+DjQ2RMDadnZnzrev1MJfJN/tbfqS6eP+HWmH/zr+wrV4ESCb9ESt7+vHKfCz2cVAQajs3JUkxw0TfjBEUZvLtQaTbeKbE0BEM53Bnoo46tRmGCE43b5WJlXQXcweWAMk1eVIEgCFywr3pMGskHo/FGAC/nfBv/yWc9vy5vbRbjsRvCcBeKkTqRqwuXn/adkdu6zRtd6y9+PponJbmdg4qt968yLw47/FgzbddOEwyTmi16Mo19P8qgZXMF0Tq6q3oY5GIJ0b5GA3dW2RZx8FDrjNXdWu6mFDsj19QtqSW7LDe5UNAyXuIyb95nWXFmZY1m6fhQW6fzrpLM1+sXRggLAsVaoJ+A8HJtA5h1dZoOhzNPulq+ApDPXi0TL9q5KmJExHFu5zTcI4HkZDo15jVZusrClJyXx/Te3dzQzi4j8u0PbuZVJNg/Vp9aQMXJ5Ty1p6ZcvcKz/fNkeWZWKqB7rrdLudgDwMQXiQiU0gBzn56Ze016ews1RgsQoDJAPO5PawJ0OhgVomx7ZdNKUoGIjy9PyVJxmc+3AKArr9ahsv7Vz0v2sFS7cXCyQCk8FumcphZmFpH0F5mytrEmX216ZtT9vFOsQVVMd6/C+5/rEc3ef5CDXunDgb1STADTyGFBbFORrbkI0rFTmENOANjH4iM6juzpWoANpnoxH3jGEvwV3gIBMxxsFO0//0Xn5row4ANrrLoDp70mPO+OcDKtNHJsVVoqEez487ku1xg7Azs27BF4rikvPTNzYzY28AoMibSnRs44hBKcpwgJZFLcdwkUBVEQSiE4UXBACTFSLhEpBIEZMkQSAInt81w/iLbrwQL2C9FlVmSBxOrhTVRC9ww016TCUEX9kGBkIgRXMTjC8SmN9cLlrO9kxiRpFctb8YUZ45XAej0oZMroGtu4zf/gv3t2/UZ+/TfbL378lEWkJEtAPJqHTLGQO1pYDJFxRTKN9NFo/vEfGEWBkN4q0fdLXML1uybm+UTguYpbWKkSdgbRuKU6MwuhOBjxDIHnsJVykeSafDwDDV9I2yxvLCcBjA4+f81tcmwzR3mF+dllBsX5a7RTL1jzGTYB03QE+wBVqcjXiYyl43vlwfTk6PKsWOODJYPkkytO0Q8vAEbCsoW0AvcmDAG4+RSenDnsYoBjwIvTyT/77fZYc9o8JcsOa2kaRSWUBzLuT16Gk2nv23+vhtAVzAL8LDtiGZFIu12DhZ0yD7IpOTnSHqEuFOBcCcE4sHsa5GAQRSI90EnTwSJJ+2zKb+WuffsKEEmA2XcZ5rHtTA67b5SgtXck9mYegpmLrnIcasOPBkzmNa+VIN3NE3gSA6GTLe0Qo0fbv3WFu16zH45Wb27t7ysyYEECShlAGph+SOYca+DxD66XkHL+MZD/7Fnvrl1OhOkcAsGk//STF1SVs7oOpNul1YZIVirfwD8/HL1RpktrjFcDUMsbchBFV+PFsr66QaMAIWedfYVti+/+zpX6C+fyJHC+/oj+GzeCu82reT7xRovD5ZMvvhTzzeJuThEzVNXnixmuKkm7NI3TBppOe8pGI4+pnhgCNsyxvkNcEQbLoXLq1W2Mf6O5IlK9Y4v0Q9fx+DI4G3okoFsVfoclXcNyxv1nT6ittaul9Z0iYhBoXnDjyr3WFy+OZct17xNurEI/fsVmemkLzpSxvsXbDBj8/KwEnvBNOdgRhwEi1HHvbPT4F+nBOVwoV0bC+bibXskL0nfeZ90N9eWltUBaBWxoLCcSZcRCFDEPyNTLCKLgTKyQmYEylAkk9Hpq+nowq+XzeRFJ7PHXz2nAZieJP39WWYtGJG7J2KW+MFPxA/3is//fV6nvYG/nG+3x2cDYurIWj9A6iP6kM/SPjuzFFl4DczAcDRaNdqXKML6L+wPNV7Jnnc53yitfdj1cLghrfP9iuj9x7RlxfV220GA5WvgKkq/CGQmWULGABP5UPfxyEvvElQaGg0DcnZdFICrhoaVKKQPzjDH2LdgBGGHqKKhCw6IIp8jzJ/PihqAgUSW0xSRVBxbIZUrqhTjJ0IwdzwKhTKGBDZL3HpTYIvP60yMx9rRAFAq4Ne9rQQrHfsPC3E7v7MWyuyZEo7RgK7AdHD83FDSA9kq7zYYeOsF8XMCJ3TLXBwAy7z9czG6JKZMRDduPAVw9ngNnnrwqSiK6nEQeQBA8XUCoYUF2RlNCzMwsJFx3sRw2rt6c/fQjjIKst95YR7Ci8+jpyTK9lQPApYhiwNVNgCpvGT/Z/S82DOIWEpu0YQFuZqiaE31OX90kJfHWfWJ+eWBz8Pb1hnKhaq/HsJHnWAFvQBeugqQ2MkNcMTWJGAvCnJ/SOE2lSIQi6ut+QpE7G7Vhx3rcNT58sPLqB08LD7YO4bg8GpEKoKMxx2bhZQcKsngrIadECYQSmC4kaOfpECcgguRlhBJKkJUiGYHrgcEkbn0l52epF8SJY2NOKKBAAoXR0ozALCAwM8eU9GU/yxxNmyghK2OFmKSBNEhgZHzhtCCWY7M6T0phChrGWm7DNA2ctjZia/4ijrGWyUWjIbLAnTxkzRcs0apN53EiBtrEJAzV5yFLgM9feszwfHeDPh3P4SqWq+cefzqL00AvW7xs9Wfn9/72tekspOMotVCWlm6KNBqHL34+UuOoeI+/dVt2Lq3uF+MIX7yzWyok2USHYy9LggTy3N7IFmEcjWOZTDRLRwIc5+F04pJ1RDtfIqMOvlk6HXoGkG29UYCjuXYw7vrLKx+sniFc8Ex5/kSld9iNlZwcwM9f6BGEwSbKF2mXTIcDn1XEMs6JIglCpjW3nSkfhqKLJBYKG4oHYUESQKGHcrxksg6x5j8xNb8JzmgojRHbJXyDoKQgS9R2JSuk1sX+9KsfjsnKavvG1WurfDcGaQxCN0AiQymTAFGK9tI4hmdLzYaSkgClKDoc+zqSkHmsfI8ajxGKx+yCDCkE5Ga0lHNjGyDtO++Tt9aEg09GX/90yiPGwO4llJFfKxbYJocxaWQFc2g8VW69R2WE98Uzc/Sj87c2ybmJYQvxwV7z/KCD55Nba8z43EJcH4ShS8X76ER5K0+9jdRmDBifniv95bLkvv7oZAVxrbl77d7G5eHSXWRTdbR7m1YV9aVhFtdwCkmbqwKcudeKQt/3QM/QMajKMzTiDhbZlMn4jVu3bpf1rz49+tOXl/QU2igGDEdsS5Zu9F9EWae3XharOwQpNswjklZgDkxLOal3MR6YIVBlwSwmOOrwl5ceqqcxVF/H3R7Q+WgA06ntwastEsr5Xcumysn5kWXqWeDT995oUVVmugS6r8fwkAty5v23r372MqgZxBvrlP/UvLtOPXzUwTGo+A9LaBGVuqpyDgjrgMNk7qQWT9BUzPswLuMoJCczzrryZqv3+8rD/zWrN8CWznDYcrTwHdvYuSZbKDg1E9BB5ZQScAgNYhtMpkYQ8NlyaiuZhqUxA5a2Wm0JjyJIWp4u7PFTEY8S21/9Pu8Jrs1CjscFDM45dLxE2mV/87fqB7adb7KeR/ZkEIMYvEQA5cx/DYPPFWCDTWFOs1A4RhchIO8Jxa2N4lQ4M322nnuw8+IX/+3/tz+bFyvVFPaqVRgK5l5XS2CRy5elfGDpnkTqjqJ4S6DIuViN9g0vttBmQWbF4K8fdVbWxdpbBRMQlJm3iAgmj6IoDXtesFBLPA4x6fRMgyo8QtpLG+QwH8WYSgl3F5Zh+4yIlWlodjHjM1tAMPWJugrFXJMcw0GuRXd60fW/fX142ZL38stzRUYHFOvtD7rhq84mGzJBfrCv6CfmskTxUjidpUC2Wti+lZm6ugzIojpRBrtlYDIxr92U7EVMo6Z3bojb2XoBcIaq7bI4AGJxmqeij14vNloMjS2GkXtpDBc8W6kK15HcH3766qZc32yjLFHVAM1Iq617EgCsgUItCzg3OSgnFFDDgJRo3eKAkfLxrx7e+0aDhDeJVetgSKPHyfTCGus25aOtN7mcHVyqhgdjeTBEF2ZJhLszGCsWPAPw0oDhAp0FAST41XQ2VoxYJhAnft7x3/kAQ2P36EKp1Tkj8qly2J1MSBbpKTAeiDxXOTtQQs7HYYyUQZyj5iEQ+oDtxlQJBbgELREBaI6nE3OgsiKc4zA/BREwtPwlDAS+guNMLfTZNu+7YajaNk6zYpXPjNi3M6SYk8fPjo5TZq8EA1KwnLsEHfpBVnSB80dqYa/I3YRmOdUMSVBNYJrLqnkf4oVSagBpCYwiwk/v7KyuNMw//ETS4Ph80aqJTCPzVev8F6fq77595HZW/59HrdXGwxovL/TxI211pfwSdxXd5rRlusdUwNBe6N0zV95buXI1f3E6u9jXsRoVwwhKonKKoESatOkZEdGDEB2qGBDiIjoKzEKOm2uaPfftXuq5ne6p6bluXOGubhdu35TGj6aPf7JfvttmF958kJinMzhEB4c2tcqHmyXvcupIYJ2DCwOf5MKn1shz2wTBY/OgUctzSPxqMKAqtLJAYVS4nCdMVaTPwoFearIaKNCzWSQ386yaxGbCF0QUMG2YgfzQmkWowHWO5vNf9A9OnYtKiWPbpY0cuFkIwegAxyWUptGszoM0kOIFRNWWiRmNzpd4C2d8yIbkt260uyGc5mkEgnbq7AR0Or+ythKlZw3/5DNYnwfVt65FaP4azQ/GndVSs38BDEdQq8HxRaBazRZM3bWCNzeU7tCibjWgq/z+yfitjTWE1UhkClVrPGHHDEzj9M4FhiT+4VdToQGsllzl7+wWslGWUqGRLfZ//hUkKcwcF6Tt+/XBE2U2GN99bwtOEsOY90c+sXTHYx0VBmQVTFDUh5ClFmAssiGn3bEGj0MVwq9++5sbt1ePP37spMFl31MLcrGVu13yR6f69GQM60DWqnEbzZYeOt2Fi2Uk10AWNrt78/D85K0mcvODmn0x/MQA4Xz1TglejC1vOl1fr/CwHiBkDgWSkL5ztXGhwsPL2bI3HXPL6nvv+sVpS87R+7P9ic7I1anjZjV+8pP5jRnzoCK/3pQbnIIMxrTQTE1vNWH4Dyu0DYqRZzmGnprwcjmvkM7pQlSI9e+0dxZzoOXn3lh5m4ZOLsy51JRpcvjlgbwpVGURDMtBZpxJfuK63jjNcQKhJjgDezmO39rKw3nQ7Q/OvBj0Ewqh+WL2YdvZXTjz/bs5988WQB62kbMlRE1vfydfRuwzgUOE+QxwgFcf51q/DgBpiWWKVdy1CJaD3ByyXd8LPUKsla6mSyMutm6LsAV8dmnufWvvN/d+63/49f/Z5nA9SSQsUlyLommSYyCCTF4/xMX1nsvrXnwj9rMWFDBo4s7SECAR7HjoQkJJunFdS4Bf/vhhjpDy9zfOn5t8LiWyzMsyS41IGBAFgfCTQAesQg7hwVhx1amxQgLh1Myg4OL5ZRIYXoAbijV2wVxTyJqViCmoS5v1zMu/PlZ53NneEUu+x3rhb763ixCdo8e9zVIaOlmRUFOsfNHvdmHRiFa+ddtpVrAegOeollBMg0mAE1YKvupFBIoKORJwoM7RMQpu18Qcd7MNT2bUoyTbapaKMtLzMytYKNm3eYnOhVQYD7qnJczeWMdAO02nU0dfROW1ZWbZ//GPcSorc1q5uwBQAdhqpcfTpBuh9/nbteIUmIcjK5xYhA+IiaOulq2e2j0Jv4iXRKCKeQIifTUBdIX2jgZRpRg39PNOCuQYxifJRD9zmPFyeXsDuxiqF16u9BY3+8tf+pU8WxPTGFBUTynyJJZAZA4HaBvlyQbDeM7yvA+vl8ZOuCIGNhiiDADXSoUCYl5MUqOwtMjYgxCE831roitykcNLLANEk0lczAiUImObJlkqMZ0bchpYQKBFfghAIIRsFpATkL7/Njt/bR8P4UVS4uxG/9K0M4oEYlbDrKfRMq/NLLadkXUBNsLkcuyXqvjgUL19FTdhCJklxsg0piSVAj0DW79Z/erLc2w2lUVEPxsmQ8scE1f+ce3JiyVxZGFFemabjGfYph6gaKS4TzpWHov8vvliP9LIxYNvlSUJB0jMUzLFiZCWMJzbFJOoA60K4xM7ROokzIZopMUGYCJ4vskpCvzixAZVq9UkoYF+ObcHOeb2rgQ/nLsXYwYEm6vis6+dOIwxigbFDIbT2gaD8LwyRIgM7buBHrj4xMu55NAGiDLcuYAD20/4bO6TPIBLCAZ5VonSAjeu0g0zlx6d2TQNJ0AIYoQdRVI+70Mig8cFAjE9w1Cda2/SBK18/VDFMP2b9DVlpOTzlLTO51Mbgu3EDuM05WIEDwmaB+tvNTqBOZonkky8PoTlDTZ0EgCwPv7lUCE0hs0c0M1zdGmndD4FcmsVEWCijpdL5YNf+jMl+Yf/ZDcBnS+enIuVtI3Dep9FsL2KPNPnwjvv1f/q3381UlwLA63zEJX1KIXm5wECYhIAwmiydqed4DiUtfMA1wT57tlrfW6fIIVvf1AYdOK5sqRnJL8Jl8XS7NhU+kq1im80Oc9y280aBCcIGiwiHcfkSE4WlhkDebaOylk+/nr88Gd29d1C42/cmT4fEAFgDXTjyDFZqnVzBywB0VIdPNcYRq2uYmtrMghziLT40X9/fmu7typaz38xLm6QSZTdILJXT0+/CAhmjc43xIZIWP1lp+dhTXw20MQVolRjUkzkANSdpuovzv08ilMJVSG9BC0zUQd1xVWUub/2yNYozFzPycv9RRTg1i24uEbVjzy8p8MZlbVTzQHXm8zYggEz3sy4FQnBN2ToPy9PCbN67Y5xuhgZZ7U96Py1YvMUhtFAkpckHvFxPNMuT6x7exV7hubIKCRxslgvFTank/jkIkayFMCIlU0+8XlQXstIdpwu20vtmmpHYOLBwMnzmf6yE/0uuMzvnAGpAEDOwFxjlZhM06deZHJcqyrVWTuialJ1HPh5EJtNgrE9ePT68WZj7RaN/miQbdXf/N7/lTj7izPCGWOwhVK450EEDBhKQHCVoYU5mblRRx58uHq6mD0+cUzfa22JzjKcOiBUryEksP+TA+t8fPV61haty9ArCoXACfzZVMhLcZgCsXVyBm5eyzMMbRpmqCaICyxy0Hxh4HBkJUlllUuH0cgThSYP+O7F18Nv/V7xFx+fk6SdmVZFIo/+4OtRpmQWWvjp68cHM0SZh5u0HHrOIvrwNsPlZQXGnv5q2nt0jhk2548Hl4cICp2dEUvTatD581G4URC/Pg6aq4WUQFcKmW/OiZdB5iLBHt8B/EKVhCJwb3e1WW54S8Xr94QGc953KxU4scJXX00BQaTpNGKXrwc2VUbu3RcA1wR0NUuB4HJ+MdOqeQQ/m85RTyrLMS0h10tA6JeoSvdrhsLwHdaGkDQ2PA4XTtKEh7wCAj78Sm1cFYeY13+ypK7kJnopH5G5vDSeL+FCmgeJh7+cvfmN8qvHs9aNUmGj/eJXowKV0g4RQCCYsATFWJH10dcLjgQ1ABaBBBdZ1UlQCAwS0HedyAYVdREWC5WVIm5E/mgShHGaBZgY+y7smL7pOKkPCCNFXfgxi/luZpwHJAXLHIhAsAuCiG6azCbBJih4kwiJ8ta2bCnTsiQePexSSpjbcif3idnFkixIAU8fmX6WWS4jRoQLiUAvjGrX61xk2F2PkzGtnQfqhfFUo+1hcjUPUebdr3szxze2mNDx5B8exTU2e+fK+OfPmgBXJAiaBA5NLcehtAhDNLVaEIBephwvAtNlA5F3QR/LUXk4HKh5OwjUBKaztW+/xe1R2sNxJR7YY78DOGtlpLTCZhQI22C1zTtOIDLYHEt7l8vydnn/5aG8UkpgRN7OR8v53DeuizIEztsLEwKhvh7GtIxlS4BFAChj5ksbR7FmroRNF10uQyycp5uMGVn2GPLlLf7yTFEia+1K/dEXB9+6XxyAiDqIKiIWIvmYAjILxAIbA0K8GDOEw0N0B8CzROmPXr0cMNxscu+377BNOWIAj0dDalWZKPV6G6TsxDDFhGxVUScjnWWMmTpnecJayathJJQqC3WYJht3pEmIhJZBDga9gb/89KR0fWXj3XzJAWL9mR+FxRIgvFWxu/0cwgQi7kG12ZNzJtK3vlmCU6wCc/1pfrpE3rrN5gXXWNBIgeIJzJpor89jKD/71rX7GQD88BOt/+XREAqSJ9BOK1/dkIHO+cQWlMxbq5f33m31xj1lDhmXVqkVxm7m4TCIJNtNmMxodRlplg9Clk0iYpkOUGPyb3vmu7QOUhIXMyJG0MDr49E0JSi5scdye+XsfKJ9+bh/9W324StjjRK9pvTUZt77NkYC51JTeKZaYIre/WZucDgWAET3vN6FjdQ5L8ZZJN25Xdf7PYpnyRJVE9hpKdd2oFUQmKmxdKuNn0gFncD3RIBSpqk27k6iSL9NIAxuP6qjoqKV+tp6N+UqtQHNzyi/iWdnM58DbWwB6DB4pCa4HnE2JJUKw4dLf6oSv77+ojsNjr3Vu6KSMhIDpEjCjZQe6la28jYOTBJzvQATOdClkKg7CRQejW0Kcs5iDB0rpZWafK6utjBu5fruR/+v4w9uO8D1712hatLw9Mxk/nx2+g5QXknqx4B14Q9LOh0FzGkYUwxW95clciWFnINBBqLjmelhZlQChpnvfPryWn7lPQEPTlVllo7iJZMsFVMXeMhKKDTxoRQa6PEM9r67jtzcYJ9/Mv704Wx1RWzXaBhhFGtZXBUvenr/FAzooDsxQFCIm0ugyY+1gRWmc4i7G6MpGSYdB2pUrRi1Xh/GkL9BsgKEJVFIFbAY67TeTE6VpbxdqK6I4akf4WbpDfH5yWFUIGAEwmtU9VtbpdN0+hHctQEy0KVrMDVC3r2xCyThvzw+enzaTb84Zd+93frmqnvhwssTzZ9JuZAXqNr61fNfPUotsCgKOXApbOU9XmeF3DxZFuQWaXqhXw6ikPUj0AssUn75swEsgGej19feqQcx5Dq+QBcOThZEgdDjKMcmwDgWpl6Zryy+CO3OjGnhXBk/7k0BXkpv1LN4umKhTi9KORvwsPFBV1p1JHDJ/O2q54KTT051j2W31t8sMLPe+cSZbNwuYY6vz614Ydtzl+eoy+PX9xofXr2TNwfjm3da9tfKGlUKfuNqL8XWmLhWBVwEmir+W7u11y4xmTgikpxNRny7Km0Wxk6yKuTQcIYwmGcqS6pgorrHBRG4oE1BSrS5n/i+4YUD82SGKSWKxJIM0TmEiCwJytQXXSuyuxN4u4pCIBLESJIGyGLhbW7m0BSgKODNptz1aba0QRXFMgn2fvnyqNst1GKig1NlYmYkFV6ELZBONXcQrNFZAKIIQc27YR1HkDwbU2AL8n74x4db1Ubzm/cXl5fzoOuVePYa98Ujj0zYJJ9zO1GISEwr50csHmEb26uBDoagA1gwaPtbq9kstic+EMCgCeClEjY/VxfHGoj7olQT37tS4GtuGl65vuGqcFIjhPlYOQrYLWp9t31wOLIiv1h0Tg7jBEiRIiELdArjJ69UmsnulJqTuY1hSE8CJqrrAWLDJwdhiAN6f6BKaCRUQyURuJXcElyMJg4JgQghTid2+xqfkiYA+xmGRRQ8nBV/Yzf35POpouARh4WICYKRkIXawq2wqF9C0mlCSSzIs1OPVTF3pQCwWxDg6c8VL6dM8ht4sSCxRfbleVAu8jif4yhmobtEhLqD1HaU2iotUAzLIt3xIvPhBI45BIro+HAME5n/jVaeo7BrNSFq8icRG6bK9a0AVGeIo7VXci3y6pP6QvdCz08IjCrfqH9+Pl0LC7ffKgeXMdKibAh4+trmWTm/LvU6E5AF+ydmzk8vbH5fWt6py//5P73z3yZPMj0SsFBU7OVwDkLJgwcrI4P0Y+jR3M7n6u0G6eUnF50xR1JsSQZU6/WTy22c2WGDRT+esX7EaJBMi3wp1hJwgREoDqGhy6IEhq/KkurbnY+/XiZOnOH1vQpWr+uT8AYaKpZz5dea4nfgH716lQe5fsSVP2i8+LMueaHKhYwIIgKldSyblYkNhEa6czrUkiqK0uCJgfYhDK9jb7Y2v/7RNIPGw6mzmpcxQZolAYvQxfvb1cv4k58Yf/rHSvt72fEUWG9DTQpzGRwBlxhBj12PChMojECpyDg4VV1PCqkTaomh4Dq5Qoh2Xjv85SvQiTdW2mJRzAgSo2HHWOQKpAzhGQCZS0PMJVge01AqK3AoRD77y1dTfVpmEiafByjYUmblWnB0zC0B+qD6v28Cv/tNAOlkX02ar+NWJRbc6wljHSroc9tz82tezjQAmKUffOfKREI651Mv8rEEDzGkWJUKAta9tPe4sPwmFQXjqt8gbQdf550MG30ZoBCtmxGP85Yeqv3pVpt/e6+yxQc//KgTxVn7BrLVREyVcLTID0HtRC/BTIMPgBwrYxuuwXZOtJvfkS+/XmRueO8GwsTK5Yl1Zbekm9NIVynK5woIZaaQwTWuFS+y7PXhV4yZqq4eYWJZyohN5oJwVjf5p384kzfaCOGNVTOah2HHKFLo27/GLRpBBoWjS/7CTR5/0aPQsNmAfL8F0FoRw/g3cBaNuyOmJBazkEcw/h005w0vMxM4Pwvu5oLJxH7zFoqHoXcx4BkWwpfeYhm4poWyJqGBZFLY4wvXVnAkSOemNwuKcnk2UgDM3uYV0XTNBZ64mHHuLkCUzhXIJqAhWmuFohB4calNtCUDIVoCIhNt641iuSk8HmlL14845nho0gTL394o7V09/mJaLkLF1Wz4imOv1ebKyA+H67nGRnvji7kLFSorZeA//lxrfK8o3cRevbKbW/LwtS5spFgSEjkmgREPgANHm/ueYQYFHofg0I/AmszmCeJi4BMxghOMBOP6LMMQ3MJjy3djF27WmZ4mggQGNkQ7YTkkAzMLq5JghiIUAnulWFkUC5kTgaORS0sEjCBIaUVQsZQtilgANXr9gwGSQoZKbW6sFhr51ujxsBjRdrOAsy1P1472l0JFqLOJaWQBzOXh6OR0jGPROMnKDEEqcfPxR3Uvn7SabMiyx6OOnC7f2mIZu/p1R7gpLtw58uJ4/YMrBOpnMJbGNBV5+kwHElQK0sEM7EBJdL26UXRBAD7WklQG6YEDCDC4VbBx+tzrA1DCRTiY0K+JinToiqWNimicuVPO95tbm9rhCTD18wzo+yCkGkFir6xVyJdKBnuW74/G9sr9OupKDk95weC0m46QhW2kVDXFE/K75MlHsxbKtvisOa29YKZ2U6SmgUdxK2YeDE8f792vaqNhP4L0fvwb99e/+uj1/f/N7ZfzGICCzPHoGG1Q8r7iESnJpVGZFV5fXORCY0VGFxYHq8Og0z0so+L2HgyJWQDlQ0ZqC5qGLTsXihGlMZIkcFAuFXk0BB2A5GwvdBMrcJjSDUJmi28Q0Gc/6M5QbV6r32ANYD33oHzj/MkXz3+yv/u3NyOmWbfGS83W+ylfymGXvQpJSTlcmWdBV1uC+0OURTA6f6P1q798wr8aUXeTUpkWTDv83hsbWfkdEAAAYAFEPz1zpapIrZhG+Sp2PKEXWQYnJxNas+I6D21daRRN7+XIREpyncL84bA/HxQBtHSFf/7KLM4TDyCEHX6xjBbDGd6IiRiyx24SE1ZDFCiODPU5ghfyHowKbZx7RWJe5lNX2OMnkw/r9Ef/zWHzt2+16017fCKfLDtklaEDJJf3OiMqBS48t3R923FS5hKkmsXC25XTU2eDg4HlYnNjBZsrtQQSMeb7f+sf/UnnfwQ7qliMJjwkAyBlq2U5PjeiN39n/fyr2NZG6ytNnub8obo/D2Blke3PoH94F0gM/ThEQLzWKgAogC0Dbo/Wc0g4048fvV7SimiaVKOIFfJwyjZCAHbDUYIikJvDSFBLOYpdUKCCECJK1HN5AS+UPkT8h1MAdAEIZOuIEjouZUDjkBlL2//gH+QABAC+/OUnP9677L78NjSDqui5dENY/4Qct5pR5a0H5sgaurMhhY9P9QLBRDoAo2nXcBemVq7J8kaRzngkWnRo7KDTqRXEnIiqP1fDPJoO1Hwes6dBivm796Xt0Hj2k7OXGGXi1Bs7cgXxWdW5RPEZxYh5iMgijOBsRDQXNiahYJZQKX7+qKsvgI0iGvn4QjVASQhoaXk+ZFYLCQvMTrpzOH+znQ9JjMcq1RB1IWR3k0kuIu9imOfkNlt19Liwmy67h850mmfBhF2NDFBdWo8/ulSblft02KoXfR7NKkJrPrRRMb8HgCm2nIcBkLPTpacBCQbAjpcmwPWt/A+KCR6Mgc9Q3/GqsrS8JGQWzetilOJjgOiWqUzK1sg0jNN2Q/Yhev/lRbEh7hTQaAvuzR0lxQDV5taZg9d6bAEkS4CQ21hJ6SYFI3PLQAgsCSceHmdbVVEt0boBaP3l5HJZYBn8+sopUwmdLy4+O0sycevtYmbi7VoTUqKs7wUkXPud73T/xf/41s127sG2etTdeLM5VRzINd+8LhExV6rI4bOzVKhnHKgOUxSk9MuwvFVPQcg7HwIWYKEQlsMTMuNRQbzaHj+cGKrSuNksxk4vTjUlgJmYsBOhiJDNXHRpMmytsAMQDfl4HNtnIzpAaC0EEtu3ExABZEYAsaDSlJVZGPkpQkMIKTFzDMvfaj060fdBLxFBv2c5LxeehzRK4EFQCBccxEvOBNu6urrG60aQXPT9UoOFIM50wzTxo3w01RwlcMsk8asFKO2S/Qjpvl6MTrL1FY5gkySKqBoSUlgUMYzsAwA4VYNQDV0fmHw5xIKksibPlPTW1TVWJFRLEz3g/GzM5pB4EulmzPIUCgpBYq21OCZSESCwlAKRz4kw75qOd00wevv5yxiZjL2lrxMhxBALzeGxNNMiWwczWJxPgd2apAXjEiFJIOd+aQxSmqJ4HQtpKXyLxgtqbl8F/vQr7Pe2VppZeKERJ0iMYKLtdpYDvF5Z+3ix37YzDaBqVKo4UaHGL+XKU4vCt+raWAniFNuSdEY4PTonqUIQRlUAe3KmxY7DrBUMeymmfg1DruU5QXe6Az2o5FavM6JLmC87Xgrx9YIbYwgG5kp0ZsWxDP9ymLEtsUyThQx6/Pnwon/8h8+0jQpy85uN9h371ScHv3hqRfMv2vfFzdUVwxFOu6jNF90oL6/zmW0ErghR5GFnprkQLoF/9os+ITESz8AifvteWYycX/y4l2uVdlalvax4AgAYADQAgACgFjGd4+Bf/ckYbQNLQGpW0WoOiKIUisLxOKNLhkRQEgV5TgpnQG/uEkSwPw5sIURz4dzKrCS96MGlKoRgoL8cAi5AMIzlY8tXhipSBREOUtBUPGw+Hk+9qRLZdjCPU8/rT3XYk3L17119EpimOVsp1kVWcDCiei8bunqKO64WneyD25sS7Zm4rYM2LmFZxgkEjLGaF5EFi0lfZOaboIbVBYzwnrycFRuou8sc9Mx1ljDo4jfrFSEyRz0/1PFhzxotQeY/K2VYLvnU6P/gOSEQ1Wa1yBRBlmnl2Att2nuqpJGPx4BMUz4FR4V8xkgcUsoztGX6LhIhoPcswTgJ4FDeXZhGBGMhVssy7vS8F/vXdstV7LpnzHg81D23suNOE7fkMxHlfg7s7wCVYURhT819jDcP0k+OUu8kXX1/w96+ZuKGFSdwz0MZaHDujPrAyoqQLZVAsteFFMYNbax4UyPHiaf9YzWfKFV6IOjv4wSK4Q2men4Wx0jCpADFEOrE/1pPLAThGlK7xcB+djKwFQmLJCrwEWUBUyhtBthGuYTQ0bJvuI7ByohhpBAJkwU2imEzADca7OmLGUuRsUx0XvY4P2tsw0kO9gGqwOWQ2u1RZz+6iEdLT+QwMPWuN1FNne8fjzOujrVkP9JoBrAdJ0qWtRzwzQ189LlxMho1aXKnkT36eGZ8uiBRubnKre7KCQyePItXmoKqx7aSbtXJi46TAsZ4qooiMQpxZmaBPCmShbMhyjNCwrI8iR4eHqA8njLSs3OalMjAZmID+fL1xFUBVtFGatS8WT7ux4onv/NBZWETTdGgIcuYG9YkYBB4YmUBiOoxjL62ShrEYIBQIoeHFp2nnd7Cp5fqc3vPxQvr660C9vCr2dqO4AFi/f7OyR6Egd6in4t++81xRH788ek//r2No+PF84n1u/9g8+XUzDwoWYEnFwMWBgfjcBkkOC+4ECAmgeWE2zu1ytWCnpiDoZXZgnPuwaqxUafSGOwtAIIO283cbEmjcCnOJIiBkyJHx5YxMiEULiRARuSFNuLEIE1KTQHzFMON04ntJpCYIGYC+HAGInVwaa8LhnoaT+GZnri9uC6DXIPKl7Qe7t662jyZsuvNdncGQmqcy0iSCdRkNtAjaNAXeGC+mnMBU7pejTo98wIyt8QJapc2Bpcv0yLvW5lAGHQpXczj4HkQr1y7KusjDPEX/W5Iij6MNt9YyXJoXITrfS+O7Wd9da4FO4yXrmKsxEGZb+NSnEbkNK7amalr/utO7dcLsmJkn8xKK9LYjPOR0EDy+QA+CtOsisGKjWNRLudMrCi1cI3GOQEGRv4XXx5ulOCeZyEqsqv6dAMZ+PpedmD3p18Yw9PJ+uY1kcrvgvz4MTjLr9INhTrru/m7G+BoXm7we3vy/LD3we7qT+f95pX1VO/e3m4tF+C9HH2JJr86Wfz9bxZfHTgb24XR69Fmo2yDwKHP/72r+UIGPj6wWZ5KKdHp6SfuRU02go1ylhGWOuslPlSIcZPDKMygmFocNXn4HAsIieeHw8fHizqWhtb8O2/Xrq4TXCnnNOAnX/dea96Hv7MTpEDh6l7hPOZ0V1yCQC4/WL1ZATSk4+BrhcYKpT/pJmyldvsqVD9i5wDp6uqrA4iBkFZu66ZclHKoGb/UX2wHm+clbRLXBOT0Zj1/cBa2S3Ry9CKT6jFdcRPAx8TGeoEA9Gi6WBDgYu7IRLgw/GKBbZQ4bx0GXD0z7dHJ2EWxQRJdnsNv3ysqMYogRE+zIQIwZuYKjGFplINge6g9/cig9CPXY6oS0M7BY02xHpR3//k3zvK1W1/++w1oXjs9PEK/xa5WMxpkD8ylGfC3SkbMym1GQm0E97wkZllKVSa3Yd/KM0gCsfNFLb74V/gygtR1P9nYa2QZk39xdFaQueVy8NGptafOCmUbhUoGboEVfGUZcUgpsuobkTVK/MQfOVkBWOBSMQHQlbtXgs9ex/bykwQusLRUEqkIS2CSxZEIyWwE1tWli6Ecg+Z1BMlAslly4aE5CCPIHyeWOrTTyK9UsGWexSCH9kjRn11ulD8/X3AyjB3/QMANsuhU60gSu+x7u/qhvz7LVYRqiOC5x0eTYyOazLDtaj7SZwRyEY5zPMiP+0EI12v2nTb75GdPzaCFtgOugq1uCOEU00eXPQquIslKhV5kgLLIEj80c3xEU2uoACNxNJ7pqbRZlxPPWiwcOcuTFWgyNaVyc6nFcpZyZUrNXDUyhF0e8ejY9E2S3CzHKpjKoivlxbE5mNkmmfqQZejnR02KyG+0+1DxJrBMEeAF3ydu3Sruro5e9U4PoRAqVqrU1KRkGjZyRV5cxvACacinAdncqzNrVrgcXZxN0Szevl8BIygNINS1dA+FNI+6Vkso2F52Bxc6rCsP3izP0+Tx/jyTC6kHpLbN2WQAAgUhU7sLb5YTcAGiULJVIlBsHnABEvo2VljJwVskR7CSjyKxo4TAvTcLABIJVAC4Vnd8Yc5ImkK9BAwSm5cgIkpbCXj+2RLf4AjaKuxuRo2Ngz//GW7b8iJ0b+1F7bY20EuVagYmi1P7JM//Gknv/5s/+s8+3HyZR69G9pdX8rN2VR5iroRMN1q/+oOf3dyW/CzNxnF7TbgIopSMG3V+MdcWz8+Q7fVsl+leDILAnGdCAyeDkWoqUTw38y2yca/QOXa1UUzJpUKlArGUvj/UQ4bHkbyVyQqIgXiMI0yjqCyMdKW6DFLG9+mltnZFdiMCNU0IIwkMQVwgXYsVoJPAh+7NlZpGQpU27ibU4g41i2srryH0UFd+mtJ8jEKwC0Ywh9dpyoSh5o2CB4UWBBgWkMZQHOAwCNZvwItzl8t8dxkxbBKBzhtl5PG/M20j2Xk3qyWBakNLGunncjt3isEIfUMuPTtTn/zhUaMhpI6LGO67H7Z7CqtHob/0goVbZsgzJagi2EqLJ4hwdyerXHSJPphGMABU5bdzcy81NQulMfX0ssRJLuaO+jYBpVAQBwHm6RroChvNYj/I0jSrihweowkCJCFPeRHSalIAofz6GzeID2vaaMVnXbCELx3HQHN44mnzTc5/7do/eUZcu7H1J//DZE0EVlYZ3bJiAMQFjvc9zwIrBSwEQhgL0mTkKN5ipL3x7cpf/7wHhUnlbu2nx8uZBzckqFlgZ2okCSOMCVhkNF8kUAETHogFRlK/8msoDjFMMl8AFJBNkjIeIRF6tZkPUb9yrZAAFp0i9Z3s0avxrU1p84M7waGpKib7q/DZYfqNb9zzCKMQEG8C1sWzwTqRCDXu2S/PTYzKb67FQIJmaPEOay34yFejE92YzVd2KkICE0wCOWjCzVaAKoYALtBgADqsrl/77UY4YozH6mw03LdDqSRwGUMhcQbCIS/xVSwv4vGFvtNghydjOI0v5smNRk4GY4AIdmPs9Hz8yY90TMxpOW6sqdff4IWcbVzqIzjF65zAlq9+sA2lqyU+Gzk+zobr13eBN94BAPuH4dcwZ/Pr1MtEKJTth9ZyYjLvvtcY/f7zNSqo1hBI03zKW2JmkIKJy1IQsF9zn/QubtS3AtD9+YKG20HOpeOZEdy2ODo4cg1w4WeXofU6eOEZ0lXWHif9IF1dZ0Eq6Xxx6XBpAMmKrVOrOYn2XpyMhJlrFRq12w08guEAKssA2JTCoU4qsFBHLSdwh/ZQdWtFlKRSGgIELLURdpT5xquIXsYLdIlXMbHAu4BJegAFRpd9//r93Gd/MMop070gonESc3my+yXty/V30L96SGkLHAEgAgyWg7EfOJmXCkV0Us8tdYO7dFaiZHyp8sUyA8A9LxkMrNGZFrAinmPUTdhhMG4ReX94pvj2G7wE05APQcuFhspk6HjAJKbRSAOWJJQWJYTmzUFMwjSkBAiOJpauBk7SLEJu5CVBxmIkXxRqSDSNbE+FUUKoFTCWAk1AE3kUQeIKDsFSaF5YIlossymohBSA32g1z0Zjqhpzo6yAAduA9Mh+vrYl1zXzoz96yeQp+V6pikczieraQBEXKhXc3ncSDt+5Uuj1wBQR9CVOE+A6l1WFDII8h4M9BzJJVLhFwANdXReeUNTFq1yggaUtMO4HUTTGiMSrBmeJyq82A42AXZxBkIMnqjYlytekUhMhyMwIaDwB9dMhwSQ+EDY3RQCCItR3jNANdCLzAhCmEt5JDIwgZrMQoVBhZ+9WCfS58OvLQ1JRQWJQ2qonimHRuJCnHXU2ngeFMjh61cdTMscLoleaHBLN/7px9vpSnOPfu1nROmC1VWRO3MXHs6sc2Sr6E5yld0qZnVBLsEqk5mEPCAO8Jt94Q+z2HS7K8hQTh4VyRRxa2rVrTXWCXu57WZBuCKzlONpMP+h6BgIhTtioYBiABA6QhnBsON3xIuwMcQZJ3KhUEjwgthJABPEoTmmJsQ3P9z2ENRH0mXebFbc26TMLxfNoMQodOV2euuAcePHVUuuzB/3z6l4xRBBaIIulMpPgQ8s1FRsg8T02lhkH6l7qWYyiQpGj2x74njk7vbU6mc4xvmDEwSSFpdXSVm3NeTWtIeQ0TvxiETlW9VPos9kyRLM3PiwqEV4O/ZOsnMCObQUuE79TJi5otoGRN8rWfDQ1gDqIuo+nxhe//6zRNdbv3PnNv8c9fsEMUmFSRywikXkuHPlemUjQULItlSGGJ7ZfBhAkrNVovFVOR/Hq9dWBC+faDSEycDH1//8E4QeMrQtiGOb9vffT+5ne587t5fV9u/u2L9uSouhQlKzmmBYcR7EROEEQIBAcITIix5Ll2IokqnjJbSTFt+31enuZmTt95szp/fy913yfA0GrdOF8ZC+PYJG5lo6Q1TLeTbAsRXCs1VHhyF6+wvz0p927t++uf/3mn/6y9Uf/1evv/uwBgCfZNNvrOykakx0gvVTZP7fqFPPL2bRUh+Wjtohht25WXujUkVGcXwTlUT8UMt5IPjjrtufguc75VxD97IldulFBF8hSBqe3C34PcHNkO4EYMgZsVA6JHAecq9Ogr54f9SoLVUjR19NxCFtffHyyfNRKiyS7hN3742+6nz7RpuLa64JtDUtpdlJZYMKOabGlt69PbDA6aZdDtN/y3WIRdISdq/rQCyc8glqI+1KNV+N6xj8Yu6swaYLG0GSNdl85HotbWa1klDEojwpWHDUNZzMnBjmJgAgaoSLEdl1jb292OdQKEill8eHQGMa467vYVNdTwtwc0zgfOzRSFaBHo44AScVUlmVhCPOIZWrp9VekXu/yg4ubZUhd8WWr9/EXu1+/crr8cJKtqyoYB4bLHJjXPeF0sSq57p3VOWk0JG5l3n/Wu5Oncu7oYy/OSjzSAphcbMB0dNxU81VP1ZR2uOa5Aw5vN9qCH0Xt8Qo+P21NVIrQZ3rV1Fo+P8yV3ONxhgxquHFwFzBILKKM8/vnxcXp8q2qnGCZoDs7CnXMGQskC4bjF/1zVd2hcAM1x2NXSqfKaalKiEMl5IBZs0BJqBceDV2l6ffsCeFxdVFMKN61pr6LptB7csucajeviN7J8Y6Q/jCFZgguySAOAO1NifXN4ouzs4wknSDo15CGWaSnstafYTSF1ll6JniUBioJ4Hfb1fV0rkpkHf/zQzW/KHEYuspTwbCd1fuTuGE4oyuv373/QlneWNQ9fLrbJpNYCBEkJPC0IJFJbCmMD04QFACYLoJsLmSJnkJVUkmEQjE5lqP8XJrkYGVi4AieXcjJQ9JzIZOGCzDg2AqXqYlSNNi7dA21OxolCUd43ePd4+XFFLoh/erB/kYtU3ne+8WDs0gehVkE8xj9WjG3VKwwsdaZvnzeSJNe2cPFVP5UfQYq3mWMDHgeyopClsuicpxIQ6E0RMYuaHCYj+G+fakiZphanaNwaeU2/n5iX57tLgOIi1H7QzuF8+NLFRfBiUIoLkBBELXGk4Jbpp2LlluUknMqoaBwLsvlqwSPAKiEnj+W02Xc08zAgMeEEBN8VwkSiODAIAq4ya5/tnu29NYaMVdA+yORVaDAI4jkrNOdghwzjnOofQFCdM7WplEQexUi33n3CCwWz8f2akEAD3sLV29++fEY2syiFBi3jDe/f+2XX5zwedoJCXdvnxNSMSXECbR+u+6xwOC8l7YoLsbGGLOwyWrHDTXp0T6Q3mCE6oIwkMGZCWqmbYLpnKWiQuLasI4Gita1LStDxtrUtxqNOC6xJHKq54yqY8UAnoRJ7KgD142smWbqOmIgVCbFHGl462KS3abMmf7ssyGeoWKUwtx4yvDcKi5UBBeI2CKTylOWrbc6qgcaiG1yqYhBIW8S2QTKwQSrx+pfaimDHc04JzLViX/treD06UGuDM4IR0f0HuSXcRr2UOGLfkCEeT5be3PhZOa418TLBxO3CKJ5+uyFxaeYIo/GqpFzvNHI7ug6Q4ctagDEUPfR6N1L75vl/Patko/T45FGVphVxuwe6kHiuRYMDAlk6IRW4OLI5QC/voE5ctQbOjriCjEuX2pwDnEtvWtHX3bdooQ54/StpNb5tWFjIaxpoqqNHMhVfIhwIZdoeEBCAIsb5u7z/jdusZ/8R+PLS7V0M915PEZhq1CRAjy5aFt4idJsP4mAaYSVVysd31NxEKwQ+hSax0C/baZAF+IdBIVxN3JPj72LaQ+sGnkepWsX5xEzUnbmEd10ZR+lChgiBwU4jurUB8cmJSHZCbTzlYoEFr/Y340tpLiDSzS09V+8AwASkOh/8sFDpje9/r16AkgvD49uXlv7+dnIk6mV117lAXT86GXi6LWbImp7ruE2DgaftqytN9KuB8N8MgYNpjMhSOA2LupdR4JIgze7e8YP/4fGN/4hu/j1FWTcByahC8BtPVZmiqHaEiZADDGwTTAyXRZYrVTjJFK6xsiLSot0px9qiBGDWqzro8FID4BiztA1rbLO8os1NiNIDHB/nnGAeIfHo0DXHerTd9XSjeyNKnoyTRF054BEGQCDGDsYgo5SyHVSo2B+6RbefN+rBSa/GIxgn5yZ8xLg+J4cUj9txRRDt2Uzxc+CSbiUA3FTOzh0KjsZDwFJlGLHwx991jZ0sP7XFp7GWOd89urVIEhiVQdXvz5HehMGQba+s3G5yw2Pyck0mL8GyPths98Qa4wf60IvLh6ZpOgBIBjIVm6+RCFY1PZDjCnM8/FmXY9lbupAoUXROLVD42mdzMN0pKG6e2bo9Zx4KU/0Z9pvff/G/6yewyyank+zsh71fQymEAOPcdx4CZa/QVa3AYKAhidtHgMjIm58roySpLgABc0Z7xid4xgxp8oLgmKYytocwWV4SOR9n9CTzv1+NeW0Eb5y/drpyVPANXmanmOzOqx3htFKLd9ruYCICVLeRemRBUQTfP1OymiErTNn4zVWjiCMgOAMaEOgo+FivhwFOgwgEA5xCcEQHKz25+fT2gxCcKRQErqD1tCPGJKxWZIPJs6UJRmWFFxFdZ1e2BwMCt/IakDCsXjRRN7crigNryUPeUFYyjIpFsbGDpkiUJoLKDxuDcghzSGWAwaBbh0dKAilwoZ/sTce9q3VawS4lu56AmmhXifeulvt7MvTS0vMcs/O4qtLMJ+SOiqueUF+hfchwmzLdGA2FENc2aJoaIUCUQhNkYQ/C04HWiElZlneHLtYgEsoaFveUA3EUt60DIyAeQ/kUMM31VFjkFsSZY8y+tHaSvTo/Sms+fm79NDHUQoODe9yd2j1e2FZgEVld3x+9276cNpazQN7oBxkfKyWTAK9vlzUx/ZojMSAKMQ+NBl2VaRaT+XyWSD2EtW3xtOga0plyqcJuIANjNbLl/t3VlnwUp8GMp+l1EwAJBGAE7ACxAVYDDGRrIkR5xrRfA5HU9HJUKZLbJZj/MgPcGNqa7GagBLgDiDbsTxP9f0IhAAkjtAxRbE017lw7IEqm5aXwQIsIrKJ44YUHICeh6OgMnJPu7pEFW0QZvM4TmGmgYyUmRbq45zaZ63Grvlbeh6pZJh5/4nmrLbUYgEddtuW+fjTTCqCY6KPjP2RRK1AiMqDjhCTXApjK+nQGkwOJlKEEkcjDl6C08mBAYW2anTlbAnSVzHAwDoNB2DiLTgcGGdr24V73+VYVj/fl9uvXElgcP4LuVLip+32WIGSHKmXYrxt8wy7sRgNphrLsHOl1AdGFFeScBik+ocXHrSVCgoem3JdQyar88yJhtLLFbVutg4e5V7/fQMgRe0k4wF4TD9sDaqS1Lo0PoJjqeD1nzXXCmAEC3svgloGe34+nPqwxEQX8pjOkrWlUmuiXyI0Jrppc7gqZj1QaaHRZUjhQYyEACalsclQp9mnGDO3yrC3irAZdWNQllDUZkuIiWIhxMRyJ1GgpAgR6yI+MQaYFQ0HPhGj22v5uXkewjzAetp/AOKIzzzlXv3Pbup0QbnYxa2MJns3PNhcoykeUV+c5iE/3C54adS4vICnKsvQqSUmI0aBMvqLP9ldqrOZXNaRxy9ndhKAQjLY2C69/ncrl1YY7X7alfmZ6/MI5QJ+KpUyvFikdBxw8ZghEgVlo97UIkBS6UOlLJFbrYGBBeougjogDH35aMSBLuu7C/W5pXmgPHdFpVZh2BydnzLq8+orqXZkNdvRCqV/5e4cv4h/1jZ1QqEBRzTP9mxazJjMGEyjy5875lpVgJ52JYi2RhoDhL7aFZxuLYO+PDLa9SpBB6sdY944V3rFDJVz+6B/dn6ev+Z5nXmWGaVBtTMtviEAUm35dvFXfzFGHf/4Z82N13mfDA8+HhXACITgLtrNXp3jBLC5N+xrrUqCAnNbpjKSQLhfJI19CC3zQ5bNsERmSrFzaXwx8ofW4dPprYm79dYalRXb3og1HA6PF/Kr+83D7GJVaVy+viJdnPXnS5njtKN1TwEZRKsSMoP8z84UFykXKUflHEMrryxgaqL07X1PzuLpoNORfvBmQ3tx8fgULkPPzg9dBnNxAQudpUWOxqGpgCFO4HrquJTmbaTVs89LgQyCradPkT/a4H46KtMlvAIolqn0Huq+ipNERCU+BOkITAG2G0NJTILAgFkshBCdF5KJYqUwPJOOuh0f98kgdCHKKK4Y1hhJs9TEZVS86AHJdoYeaYRrxHEIm63eyGLTQtofKPACk6JLT3uN9bV0+e9ViZ4qptjhZxbfTIgaLrGEp4DZb+Wf/+IEWsLzgSUHUTFDISzFtlRDn4DnBE1TfIoA87h+MoxIVFoob71aBirSWd+nZB+J5OnB7sLVa25SYuFZXYBMHnr0cDhXSie0jxGDh4861VzNIsIVRgM3r9OU5LRGGQEfzwKwCBVrlYVsFhja7cBE6SiEcWIYpYEaRDvTDIVWAQEVmF0dxUNiAe4HVuLOVu5J+x+d5wtv4osvd/cHmzV098NeBraKWFZApiRvRBWu0T5Vo1HsESgMOLKGoMrZg5dQAGp9w3czIEZDk0HqqgDaZ/EYyNYgk47OxxN7FMUFBzBVSkBZnNNCG+8PGtMvmbcqk1Dt/+QEWZBuvlaJHFOBaAdxPNsUXEJkiGoKI2F63FdJTsCdfmSZKVEU+ULU1p0MJhOkQCVp0D/fPcc5orhehqTI0gLE5/GZDXCuFWZAIE1RBOQYJuADJBFgtKsMtEE/IquQkMNn7Yl26dc2a50JyntQ7EQiYAWWCwSuBMObHJrsm6rhjSNKYOPpNMQq7MVLObsM56Ya60JurBcYKLbU7kyWAGLuamXG5/afGrgXp/MsSRujLuu3nbNTGaCRjdfoLMYbtjlq68HY8m0snXjeXj+bKudfv+Uloyey81odJ3pnosjFoGOMY0VzMgXxYhDjnheGAOfSr31vs7cTfTZF1Abqt8+PzXS2QEcb4rih6T5xPYfPPA4zrIcPZILES8TAaszGbrIK2DEwnZ1e2iqB5kTc86ejaanCO5yUK5XwyA1iKMNxZ6fjwANrZakA4hgTLGxVW7NBgPgUANzLcu3+xRtrWP+4e3LexippmhBwB8FpamqAUp5gaMEZm4P3jCfu0bV7krjOMIkz7duZeb6raZCD0ikBNuVlODJeTvI4r6guAAm369lCPuWZvpEk8oXCMUxjXP7mX1sm2MrwhRboRPlGyVKw0qqHj0euLBOwRSyxMhh5DoOCFMsqfqDNbxYfPTr1ZxfT8XFF4kBlMkngWJ1mBUQe6Sf7LjCXf+OP14afR/6TYZoNeRIZuNFBY5gIYBGHC0JiebIMOVGMgpSvdqdQjPRHSGRPJn0rk8VRVFpdLpYlz+jpa69uXn2DfnZyaZ/Zrn+WJFqKmb13OhvXDwsjY9Las6T0nF6LH0NQIhMZq+/YcbkMuYJjNhlFTa+Yqf6YGJCDpoZiSAnBh2M3BnBtKae46lKJ2P+ydeV1luHBcR9zZNOgcdTA99tk7SoTkCYH6z4DDucw9m4RRDZ6TbS2land3jn81bOL457IGZYaFpdoB6ZmL3oDI0yIavbKoorjj06GzvnRoo0G6FAPYHxTAMCIpZhSRNldp9+dYtmY5ylpPeyOfOLhKb2YLkohoUiaamZMbPYcGJX8/cBCpyQY+tAKM18Wm8+02whhPjPym9iDQQTYQFeK4Tx9aUZXFjO9rvYqFg8CSCgHn1/Elaa1lCliORWFwMLOK2IBEr1C11CGiZulIq8LUik2m0txQDJrBIgp+PDq68vCy5cQmhBGMl8FeCYLs3YcX1cbLTU7R87UIIgDuhQ5oeFgUMD4Dc+J0MSgYicOpzOrvkDGOqCMjK2l/NghbWBQr/oNLQghWMpwCUoAabQv0QrCiFysyRMBEgeyUosAGsPn53NuEClFkLwFQAAMTG2zbaqXSWWnNsOjwZFcKqdSEj+4ZbvZYHQQeLCQyxSaz9vTfpRfzOburjSaOFNh02VAnsoMB165k5F16tyCu6G/uYbUEh+4yJke/3f//lf/H3/0I4mvfO8Hbz9++vTw3cYAA+9enVuoMRhF5goEn8q4IdrdUzIwxa8RmbokSIHaChRZkSSIEo10mgO6cjQTTZApZJIoz/qWNdDxNY5zsHCqWhpjcLbrhLHfBwDL/tpCKhrgwItBbhLcvsn12upJ08iVOUIO3fY4w6MOFJMs+uU5kiqmxFxgutMoUSDOxbDMdNyFptrumcJHvDifwrKFxZo4etJ3Ac8AgHSahcLAxcPuxGgjAMthbtugfUycL9IM02xbIg1rHT9wMSvgnaHr+jMyDFAShkQgAYlStmInCObGxVTJhkMLSUAUDH0kVSphAsIIPByBFBkh4UwHE0htaOx6mtrKzwJn9NlZCUmFHkCj1vxOrrXia/sneQzkqvy0a6K6AhXLpqzjeoJJYIMhsZmBqB65WDjz8IWVahVG21N9/9f9TOcFMCZ6cndpfv7wrBOuUQLPTD9unRHi6s2i8FpeB3Loz3eRbgKEtA+Z0mJErec8SE6ZmHo0/bJtCGGD+OpXpakpQWIGJbrT84VV7rI5PAt7tWW4FdOSRwPebFIwI27mOAgEIkpHrwJRfQXDrtXPLQB+oeiH7gRM/803t08mSu9oJKt9vFS+VJryzHcSsrYojPqjpTzKwM13f33yv/+/L42A8Nx/mQnRiHYTO1pMkScdLV8mf/bFmMwzWSR12u63W3F1qZ5ZojFXztF0TzaeDVzGseZwrAGCbsPCYPHpJ+OTZ9HWW1dQIUicxImBcSgAhNNO/KhzgI3p+kLBPm+09s7XfvNm8hoMMqyD9yiSCVTdUBkRdijMtgpXQrUjXMkgvM9OJ5ctI4BDUOQSYT5Y4NHNRRyvBd2z7RIY7lTBhHf9QdfE+QJqnfvS2lwjxnDXFVVYPXdSJdiAiO7hfq83Ip2BtMr2pjMiirJVDNpYPpvYWcTOo+5wrHkn5tuvlfcqjD+lsKlF41T6lgEb+qXXezlUhYiwfPn8UGVTmC3LaY5tWuRWunDzjcX5K6IfKTghiWVrqJrst251jfO4bY+nqr+4tINbtpb8/b937RTPJyen6XLVW61yK+KCHnyi8ig+1sOohswQ0+BzwOoqgpx0qvW5NOzws3Aot+5bhkbBVGsCBkpUE4eHSoHHMjkobLlHemw2dY0Pr9/d/Or1uuPzHo+U2IieTKZv10A4Yj7q0ceYWijGX38l5XjO7rR/pm5996YJ2lnAS76++lw24pajH51tf6PKX6uGTX36zJb9UIMikQx0UZwbgw5Gll7ZBORByxjbmM2xKXQOmQFc+4NT/9hZrkrp36198BfdLZzxxgz/6jvW3rP6lcIXL2T9nKhM8UKAyEczcDHsiGA2ArI+JF1J7Y9niGcMDrsoA4uZnHo+yeYwSR/xBK9l8j1TWasLDgv3Tj2cCvlINwI3rJW4CG5qfornWFKEUWEzJ6FpyXNpLuKICqnw9dBgcrFaX6b9wxmoq1Z3TMDI9KWbyUqTBJpNooKIz1RzMYV2ZzpkwcPFxcnhYB40hqWbo56hPdPXV+rZxGogLn3rNfcn+/V7O3BBNN0uB86D7kBvHlRWXcXJ4fBK+cBJtzAuhotx0G23izb/EICFagp3YXeonThypHtPf9IuxE5wbqvTHgFLzRMNNpLt27XOfH6QKSyaGNw7bbQdUaToSql15hy+NCrLLMaCwKTbeHEazVK+nYjALe43Jud/+sXy4oj/zsqb37/Se9KNDtWQKwpYYMUlJvFnQ7vMg0Amr8IQ0B3sPZ5qlyG2mLtCgmaYcJyLpTm4QCWHUr/xLHWt4KRiNoIGnwwjJI7SMJBC2SrKj92NZRHTICwO5qTg5GjEMlRjmDi7raRAGwuF/mmHH0HcEpeggPP8okxzZZQYNCcQ4KMzK46nLTdIKUbeF3Lr3xRMMRBG8frWOqCpb/CF/UMvjkKGDdpoSMRmPne7Ys1SVJIn8AtLSidmrNGYHrto3vcUSTJMl4VRDtGtge0xYMQaiTa0UTKwI8mEEgiYmCaegXkOlVWDYKkQBhutJhpEUIgjzdCIcIDOQrEmNz81+oaX9h2SxHEN6Z0q4C0wvUKQQso60TM5Wg7V3S9Hc9+UynOEog9mUzdTlMzGgjoIJgBVTkuVghBoIBcG1XIWleal7y9ijKH8/KS/77Ne4rSi9jGT+v05+J3VT3yz+97xb2NJvkhDRdwGsE8fTiuOyVL5EE3wtfzWm9TTvxL8Efz29sbRnza8r1e4v/OO8cNR8OJg7msghqGjRA8YZG5VdGxcHnbJMXysuw7OpF9ZSzZROEO6R0r3c3ktiFwK/eR9u3CNvraBno+l4x4YpvMsjtrtSe985GozTSoGQ70uZgD7ikYSV7HXM3ft8dA0BiYR0FCMClypUkSJMJjtdkaKz1Qyq2+t4Vmxe34gB5a4UKxgGtwXZmeTmMTNOGJJASbBb/2BMMxQ8tjy3QkOI0ySwKiH4U6codwEedEde7uTO3ffWcyuZrgrxBUZTDhjoCyXMhe+t1FOd1vRdjkdZslREj/enR1rYJGFTBRhIhZjaZIUREu4vHAWtuq+dzZqD8UcMu1rRpGXIKk76i8sV0tua3QZYmnXkWH8FlFklMZ7zSppDrrTgmB0x5YOeHbCoICqkpniBtfjdCCg9YvJAR49D+x35hYBz+51BrOGRkZGqhALJFVIcyen6Ou350FWFLJJ4ODXUpxIgcV56LOjPs66/sSgjaSnw8pMJ2XP0y15JwPcS/EAef4f9lq7faaq8Qi79hsrDxKiI5JWqxueHkjZsIpwTJNcZklIG4pDU4vllfQg6vmRqcyXMEgkGUzrXyRMNT+/vtD6DJaQ+Mn7UGCD7pheraSA7xRGTLlMCN2fT2aw6y9KcGBRZ0pw2m//0GQXbkq5XOenL3vPB7eu5/sS0Rl42sw/msrMfCpFca/spM8+P3z+v9yPKgtrq9l772SPO1S2Z87TrIayUBiYJvD00976JnR7MdXrm/7J4GVKZDPSymY5GDQSa5S3fIgcg6h23Hq+sbT5wV+9zD9MhVX2tKE5GTDIssBWDQ6wrevs0Yk7P8dTmmmeaQqji1Ls5TCU82AFKvHQk88fXluazwKEPdAAG0wXONtzPEsHEwAXKWeCQjjEwYB/oglUKsBTnf1EOVHr11NsyBIoTfiAv0I6Z8FCZeV1DmwcHuBKPNENZxSRxUggyN7QXJ8TT56OVMsvVNjBScDDvg4C9BYcg5OFHHyapcaKEOUypmcvW0GIwtWJiUL+d3LFXySsrp9ly2Hkj3efv+Sv8/0QEiu5a/rcVO5ZQ/Y5gAxGM2qF7Fl+lWIrXmT1pjRITCE4QekRBMWXoxFCz3EFlqMpmejtD/pPpi7nBDC+dLXE5BasBBIAg3FG9AcPETKYHR2H+WswSz4Exrd+Y+6TD44jXn38oxm/UNniC8w6NwXgCIzyiDY6lokib1KTUf9SneJO116r0LU7wqEqdyYAmYWaMxZJgAKc5VYIPZobnVzoWLxQRfw6MEkwXKS9QHOCRDGAsSHOUfB+Gz9ray4GpUlPmzgD3X3revbwox45bq7WS2SCKIMokZEyh+ojmRdx0gAPTuFUKr99e2OwL2MxIqK3HKnuk5YKAD8G9BngpQlAsmHA8kkvyTKIDYhsfzOSTS9lQtfSauDHvhcQrO9QIB7jboyiPskwSRQwfBBQgDpr2zPNhUielhgOn+lJAnqWHegqANiBgxg4yxA0pNu2NVMQWPOcAs5mKbuhcrY6n6NTm/nLcxNTYgzD1Y5zgiXrBDwtwkYOgjWaI7Tx0xcyBjodv5KGdCAgbmXxRw6uISwK+btolhccj8nis9rv7ZyJa2xP0wTpRjVdz2HJa5vcHVev9x+ZnXXrwQ/mqq8A4w+oNwjWyCZQncxQ+x1ciM4VUny3AdHi3NUV+WV7jqXPCd55Bq//bpp+zdn3yjNrOurZr7xZBQNfH8/6F6o60VJQKrdTRIr1AGaTDtidkfMpHr3GewEH66OnzcnT/fHtbAaqktc6SacbPsXMK8sSdTxkrwoWhXzyp94CuK5L33gNWBwDL/7sZ9oqq/uaJnsxXpT2Hj9PY6S2EHYP3LCUgwQeVnQoV7j5vTfw3dbe0LqxlveFlHhr/HQAobba7zlZQu1PzeHIywR6wDoYShpaxJO6atsoGsq6hnTJ195c/Mb/4VvElGk1rK4hfX3TZCrZZKbnSb0bhjIRDs4U35LdiVsSffZqgYkdribQEMmBkD2Ow1knTawBIHXeAIrZlAcAPj6oi8uJO4Dz+SCxRKw4aTwSr9xbeSceXr7MpONdy10l6V0LHsbYzgZ0eajiOrCaNuICcnnabH3UXmNSWFL+xZ89p761tj/yOr9o++psVsuxVndxs1ZhUT+GK9uFua0cODMw1PE1IoajVtud7A2AfALOYZTfnYQAG5rgnp9IAJyji9PmFwPwRwft6mF/7cpmWrTxDP5nPzoIx9rs716TnQuohLI1MNQIjQgzgN8O03smWH/jRtHl9j//HE+TINR+5gFCCqqK3AKEDL6YZVm6ITvF2iK4VY6aiphju+Eol4tPP3+JX2rpOxU3TXmNYXSGWZOh8wrT2yaqICY9OnPY4FGARxg3/fXnV1YWuetz3hjMae5PX56981/cYk5aB5/pT5+2Hz063vnKPPm9K/DM9VsSbQ+FVSFvUR4xiJWwXk0vu/izieHbSLHX+rfP9t+oc8cn0wCyIm00G9jBPLj+jYL11Ny6mSeoETGcbP6t713uvTA6XQRIz3vaSiidnrUrG3xj3Gbm8ipkgRi8a4yqNAPdLndYzn8+BklBCdiskbSKjCgx5KmcIWlWwysiPs2QmmM9Vw3uphC13ToQefsvE1/yCDEEyGq4eE4WQUpaLGQCX+w97+2eHmynMUVBad8AZcRMcUylcDwmahiMoBptwKSdWEwEdYPENNdvLh8/kW9eEalqzn05yn51dffSu+sB0vZi7mDh5U9+Zcey5EwW3yxMT3tIlKYL8Fy61D6bSZki6JtukIr00ADh4dhBB47GiSRNLa/kEwmQkozWGqka1D0f+Z2oVqRTAjcqJJaQIGGSE2lDAWDP91V2MDiXcExTWvJbGR+C+f70IH9ExBRpK4+6DMxR7vOLRxRVljJpCTjSTAiM8ygNIp4xnAIRZ5s2QZG9w4lizla2S6mUMLgEIOUy4eKk6mSENXKRbOxDGAU8eiTzIU1AwKAfF8gQUHSOAMeoPZ6oPIwdftitLzPWpdpWejO41A8Cvz8u5nNH1/kshkKPxkjoa6RqOPAtgXjycsovkfN3KTadDWIPOHLAYVLByCHQH360//Y/gF8CV5TFL7w+p46jAslhrlXHaGes84g++Nl9Ja1nVlHQQyU3nEAoWUysvmugIJzCQdMLoqBUzmM+rQQB6CI8zeoqgvjIOEa9xM6AREIghuUS6pSqUHCMUgSNZLKpIAlNOtZAl85khCzjY5RUx8HYJtU4MSHkDFZIgKAAz1YxGg8t4+hTY+1OgeBBYWcOWMx7jT7oz7wR5maouSvpfhOYTCkMoy7HXCxSbyRFEjRuriwUN5EJFUs16Lk33gAVxIaZXPDYQS+afdIJnj+2/qv/y1f+3a/GC5vi6tJ850fy5U8vf+O3dn503zsZe9f+cO3wWP/4X58Cob+S476a3nkKiKoygq3LiWMZHWzjxj0vyVQFKDARqJfAeRZA0BcNuak6XgorFZj6NmbpcutUpQdYiUt/JS2+e3wexdEUQOQZCL5U8Rm+fHVxEVg8egGYHW+eDrpDjUIV2Q9KWeHkdCzAyf7Incsx9Q1xYGcyNjb4pPvsZ/T8QrJSzj99fo4EwRFmlzNSGHBXlzB1aKMAmCdwWE8AbzZTYorBBl6Mk8jY9H3Vevt7N7/2977PMcjDj07mVyp2BfjpBVP2u0yaDxLDQyI9ilnBpjxv/goBhpCuGmenE++pHETEZlnIZvgCnQErvmJExzKzvp5+oClemFkA7Sd9LZXhDydemfUxlHJ6F8ts8tEUKuFYPkUNmk5kkZzIYpmEYHUh0UVJNcby3qPHsab154uVNUI5CAqQqUBI7bsouHS7l87R4NJMM1wXqLB8KUraSg+EIsvwM0Eo+l4qxSJV+hR1u6pKaR2/O9ZC+vW386ZsN6e9EuatPjxzx971Vxdm88JfXnTmtHDzWpRo3IjCJjEAxsp418cIXMJSraHdbOgcXs+H9MGHk9bYyRfiycgWA0JqufrneNdgUVFiNvnMSJ1CYtQRcRmBgOjiJ52rNwKlbVVK6JnpWmcKMZaKDtbpehvfKgTrbu/8+PZbGTEsRotU98gdUYMwtSKkK+//4ujubxbdOO4+agBSVL+Ffpsuf/rR7oN/c7/wioJm575yp/S8k7t4MqmVZvLMGaGwHCAOk+K3yuaLs4/ffX5jx5Mk8sNfDStbhFdAwmP4yS+i7/9B9c//5OOFG6mv3kj/+b8a+YAdmE6vOalxTFFiYj2aXlrpa5lmGGSz+P3HmkXK1ZqkKgCXKQCu2ZvKd27vHL2wW1YAVng2BNJizgIARhC7bjLqqakStb6xnhjKsdnKVmkrwiq8AMeY3XFPPn2mSOOTV9aLCFViPZ8QpfkMIYaTEH7ZCxar4WV7XM/5Ez2MLNSlg+ZEFUrx+bny1QVgqCFLMd9DFFnTQSVo92drbxU91R4YU9qGdl6bv2itkFGjpTYzl62aMGdb8WA2KSwVK9tXOlN5Ik9C0pybr1i6HmqqB2A7V8oqIgUaDMVIlpPSJaEzHJM5dKFG27hDpThAcs9HE9OAXV9gWB72HByzyCruX2q/vt+8bDCvfbtq87ycxJVJX1xE3UgvSXy2yIIJfrLbC/RYZEDPje0o1nZ1GtRSGVieuLhLrS8RQQaejS77TTZbrWS28b3JuDkab6YZhicsRyMz6SyXoSEYtMKFhGAcKjE8OHAWMjZPtaHWdBmlKmDcVXwhgL76zsbjfbVzHL/+/cq7TTl4OUZ98N41aey4RSzsvRycNKYYVg0GkIcZIqFyziXxMjMlZ93JbBaf/fgFQe14rosW0IQRgXRVEtBKjMJGwoAJm2X90/bRTIRIgTAMD+ybRgSGRgDRhDIL5baVgfHAxyEn4Kh0Jk8TAG97CQYRWUQyQQgF4yAOcDJOQFwS8obhGY6M0A4cS8JcHpcjvLMvqzNzZbVA5mBFRyGGZocTc+Lxa+wo5YzOZPLFGOLZudclfL3qTMOoaxcrkBpR4lKinRqqoe+rrjPV5L5y48ZGxGDTQw9qv3AcT6glSs9wvGGAc+WSVhP12BxEzy65tBhxS1/bAA67FHB5CoBueHTCr9RKf33p8ebc4xARgvDlFy+k7+zAsrJjgQdnRscCB9r9pXt3wiIyOkdy1aqeXc1uCSdKutt6AT8cmbnal22nMmdnC1wFUVpTe2SgLAjktyUlgfQ4fnIhX+ekwlYpPBuuLJUnw0tzgEoA2lpfAAAgajV1TgCxYGchH6ak86czeRbVd1Z5Ljp79EyXo//fz8brafeV//rbV8Rc67P9Xx6rheiCqcIsAJx+iYK9cd/gUrCM4I4ZAJGq2baZy9NZGN63khlISp3W0NA31jKZO/xPd4cLviq0o3/8r9//x/+n36ovAwogAhcT3Y5BJh5PSRaDKRY+s9np8FQfAXUcSjgSjLzG2UxrqL27fhpLDx+eX084JQooD6DNIQxsRj2rWKLsiaPvdfhqSu7JpRJ5tV7bvTyDINgPnHyEUKrRaQeUixhpsg14XcUoVYuFnRU6pmU+9a2/k05q6CJMzAAYbo8Pj/uVFYYKLBEKTxQf3jsZYdnlK7iTBSjPOQ2y8VAJZdMlEmSJm/VCVPVLm+VjCCwBZjuyC1+5cidbO/z0RZjJdMfpQkoiQatz0lxAjQBDccwttKOAJxd3Cp8cKKW7q0B47N1avf/corQQW6jbaxg5U+ZmRqsXo2uk+8pWiuYIjyxOJmkzfGz7a97A6UTkUOsxO8wrCJpGVkKAQnFVip197+52GghJ8gjqdv1z3pk0tBU+E9k2ZKoyhOV6StEaHtvr9dvZRz9+v/APr/0vf3nx13uT6m9+Lc7tC0j6i73jl0Pzt79+J7eTOY66VoyuJALLMJe6EjddVm2PSd2bahMrRAASvAhz6ermG8iTXx9aQG7+76/Kn/QzP7grfl1xfvjpamFOz81DXfzJRqF5pgNNDCzRLgt3XgxvFAtyc3TnxubprsbA6EnfkDIpH4okjpMvG1UP6DM0twm3RnYtHyKBm6ChedyFL4zCGwuVN3nclT2hrgehacPb13Jl8GrztK1aCqfunzcnFdEqsuLzPS1l9Ci6BmZws6vpCA5DEcZQc7V42uyW7807Lfi8acAw3ekPcq+vomcPUuASeLM4PlRIAMBzSWMix54hgpKhG6o9s/bla+8Y1rDTilB9nb7CS3HkmvuT0nKRu1qOL8YAA7gZ4xgZIYGZkSpzPOmPVDuxI4qkCiUv8GdsKsBDKgawDD5G3Yp9igGKFQ2HfpBxJ/LUFqUslpJkAl/LpZcZqH00ylNIZxqeNS+PGUyslWIUi8OZcwkiIo4CIcMBOUnsd+LKQhnH2SQ9G3RHDqT5Jtq+MK/OVTbvzh1+Ojl67s69XZk5U7ABjJy4XqREgo36UbeZ1N7GoTAZgFMDVXy5vXWrFCbjSo5auSL4ufQVMC6U85OjcL6d4EnkLeT6CWV2o5nSbYzk2jatX8PI0vGMDfeaDfDY/B70CYzfmQmRtsgX3PYgUVc0nRuGZAgZ8VgWxT5DrlGKYTipPA2kygk8HrmzNASDbAHSQ1NRQwJnXTfujkcM5wpSmcJYFkIgSFdVGCcYEg8F0fQi0HZozTIRgKlkcBCL0MQFMcTzYcGKsX64mcMZHzt9OLg8cgpe2s4h3clswfCj9gwUMwzIcwkoexqFUexcnlzJQrfSy83g9HAUp1EL9nDajo8tcnGIi4CVivE8FCsB2b94Zro9VlvMlUDPm1/L9EMlDhEwFlPUSuxPLvuUQCctHVteIh73oa/87trhz3bP/tEnOjir35kLW/0bd8GnSJSCZwkfWlSy/r16Y9c9fd6GTpoCzxkODGEFtDgf0XTBD02hguY19io9hUiHdRFERZt2jQz5RIh9ZPSxunKNNBJrgntHrQlcSfNMtt+NT3YpJqAyCDNXIAAAYGrIXJV/9nRgD7UXH+rr24Kl+y9/3ugbOl0NpkP6O+8srdTy02PzxLOrOeob/wlz/6MGiSsJyLz1ndTue13Cniaic9Yf0QIjcBEAJ4+eDwkSY3IpVzPArpqpIFYm18UJCJbLhSR9umf/Uv0R5fzW315AccrCYCRBhqdQNU/OlTi5NUgsOxH4pSLGztAYjg9b8L1XBUwNETieXTZYzi6JcOvl4WY2+ujLIXBzJpL+UX8wVxUHJ0qVJGjBkmfAOHDuP+ySxmWdBqpF53HPSM8XZB0mYHAYMMacUF3lQsDyXN4VeQgDn581L/whn0D0hb7jWA5eMCgrWGOgsQLG1Fu3l07CblMZKCyCxwBvGLEbL8Soe6oNh0AY4HAJPhhMwL7ng5wzjPd/9rx1ei6+A2XX/P5nsg4DjV35iRwWxJ58oFYY34Vwa8bUCzQ1Zfpn3uJGiKBM7dt1lMHOTVmYWWGrYVp+baHEqpEPobgGdQcgLNLN04tSxZsTssxbN6jtSkFTXn4wEEu5a/OMwQjuJgzoLpvDSc3LGM2ZYoybhvORS2XoG69eLd+qdh6f6oFiXRw9eawf/ur0xmuFH+DIeWfa/PGzFIpVt4nl/3T98c8vd3/50W//3nUtZuK9qHGq374j3biH9U5bv/rhB3A2ArDsxA0BiZiagfHUZAgpl5UezCZf++b8e88V37MpJN9Re5O1mE6RYNvO2YrbH9NcEmbjBYlodu3NYubo6ShPuGPKnyhKfQHRjl3P12pZSZvRJpKc9dsQn0EIqNfQq3VsfjlFjKnJr7pPh/YMx3IM4DHjlUoqJcEHx12uzgTLKJsAHmizsVwGtDqRurOW+fkPJ1rQ8mdcOUXQHCar4OMj59pSCkqGw0OzIjBjQyclxVGYso9YlgjqIMRRYewxnFcoJC1kyODoK9/5+v/3v/uIo4FA1fvt3vpqzivwjVljOb+WqRR7Unb4snfu+R4UQ8qUkbRxy8RQBiIimyAhgAYwlKpKSJFXhz4hRaYfIQSsOYmDux45QWDtSG2HJIgkDFPfBjk0vToXzLFBOhcRuuf2SnnA02McdhrH3dlElTK1YpkGZpZlqiITyX05h7EiTRASWK9DZ4+NST+4891FKApePJMPH+iBBmdYoTFxhI4qCQmlhFtpIuiOsWy0zAtHcFbtItAiRQJTR1OIKoHR+f2xlH6jHsyD7SCGTrH6jbLgcJN3Pz0em2VZM7Dg4txlVoszPJ/dJDCJ//zL7tyNUX0bmx5DnxyeoPtKqpLJcwUrFiVD1B2CgqamCiD+bGDKYwaLCb9E+u2JnmBE3IchDsKZLI3lQt5nK0TIQL1TDY/gNEXxcCQCwHiooTTk+MFsFrtcDBIWEZg4hgi5HAUFMZBM1EkSEhzDI2EC4goaydre7gwWffF2zfuoO7KsjfScZZnJgsCtpT/79y9WF4vMVnm2Tie2F80c69wu5RSJpikIkQDwPkDgLBJH2MSHTdv2u3Y6TXdINLA0hwWDmEeTPkJjdjBsJlYxbkgzI3Y5P4rrjSDjzqaxmdyrK09P/HO89MoSOQs6vzo2H8QmB17UsmnX9B+/TMXgrx2jnCslV4iFG3Rt7fbLl40qFBoNszg9YWZzV3D6Rcicz88ZaCCUqDQPYO0gm0J0KzzpebHs8zkidADdlO3AIqaI8cRkv1bt6TouIuFydXbcXBtprdnpkzFwncQCz6HzaInATo7P78/sjTXsv723/Mz3Pv/LNpStnAyJ1+YSsUxdymawZwh5sd0yrMOOkyvnN5lqzgS4mhTgsz3/xd5o80q8c5O9ODfdziBwdEACuDwmzpOIP8vp5i8vQZEyvvfDrzt/dfjocbJ9bb0ITY+9dHEZnxfZ3tC7mEVwKsdFcehFXVnRCGF1WwyTtJz06gAqAIAPAa1pMITgAs9XrrOP3vus/pWV8/f6q6NAeTEQcstHveEGQ0x0B748xnIKzUrzPKFPji/HwN2lHDSxZiNCujsvmv2waVFeeJ6MQmiImmaxRHHlxcrUPZEkpMp3EjjnhsIEwvc7I7pWXAaJQjbnzoCxrUzOn1I1IBsTHAcDRH5hDcCp1dLmBnB8cW6NT7uMh6YndopepAYnrTAqFMDiq9zK93f+HKFNa/JzHzSJeD6Y/Wdv3Pjon3z6Rswg/akXFuV9lRfQBWOcjZBnNFm5VmdqbOgQwaE2sCcRn456h3cEeiUVdberDO9b+0p/78mnIyuboyunUGJFc6WsF0XwRI/7clwMJ1ZsnOnJ+CXpEUhOOplNwwiAKsjw7dUKrfPdxv7Px6Vv1KS3wU8edPnl4j/5fz76w9+5uvz21R9/8bT/Z4+Wsxubee7+Mv4fPjh96zTRXilM37558v5/XCR8K0cu+naYT6WLzGQ0rO3kUpEUn2ezBg7+sLVGgMdk6Hb8MU/hw3ZJidWRw75a7zsYM7LJ9vTPya7wRuHHVldayFye6lAaNVmf3yoMPlK8anqpxo6OIF+2s1upx1OfNe3UKJI212Isow8uKAtZEiPHQ/0TFROwOjKeWkmE+b0sK4DBegpLMM9GNWqududvvXr58OLZy0NThrmkQGVSlC8EsQnlyOm4WazlScAoiKhrmNMeURMDTEHjqQViqOJBikjoCQC8f/rXtvLKV6qP9y5Pu87+7kmagzfh0qRX7cUdkq2urS4q057+rNlmwRSg4i48n+COKI51b+phBB9wlJROg9NZFxeYnOZnUY1iMDAeYb5POlYD5FFzLG2tpIUU12RJGVDZMlb0IFM2Xr5gEA8IxnkKXoWcKQNNUUjTp/GRXyYZxUjqqUihCc/DC2visDvlm42/+jjpHsmS6x64xZ3rovC1DdzlzvfsnMA7naa1QQ3+Y//rCAdnoZiUramzXI5kcIyIOUwd2GNT8YuTS7m+uRHPr45/8SfzEcUT6cm+rORo+ne3R//raako5r6/05mcxnkgNKJ5xXYLPrIZ/Exv476bOxejmPPc2PS8LEkJUKBfhrCuO4kVxX6HYYDYLDAYD/i0DhYpuoliaEr3x1NrMq4u1dF09jQgPH0E0zS/VRABHHGAPhrOwCQA0VKdJcnAh0BUU/XBFE1JEUfzOfH86NILHIlHgQRBZo7FsFDfCxKGwKtsIcto9QByg71PRkUGA/9oDhdzV1yw9ZdtIRPm796aCwYX77+UTQiWfZVV+t0ppzPZcsZ0AAJRAlWnQRh0SMwCobEKAKafxJZjHuHg6hJxvy8bnOdnUaAHl1ZYnJFIh0JHCJ5hHTgurac6B5PR+bRanJO++05owHFad70IDNDzp4PFmjRXzy0nxPjh+GxmqovW3XnI3Ncm/bY7w+pRlsSQK2mcLKP3u8qnD+z5nHEvBLSOOx0OXDw3lKnNm7lkkaoUludmiLlvR0iy/8QdjydLGUp/3k9BcUo0E8R5bYc2tclEtQ3Asr1RdoH4ez+4gowv/9f/68cjgW2omT/+P0pPn00vR/jx4/2sIBCQQmPw3RqJB9k/e/dEywX1TW46iHAxna+GG5D74PFBNu/kUmSWDJuyaXggQ5E0IjgKHnvON95IP3hckHrg+ABGoOTINcASyi8iRSx3OR4OlUBcTvnjoAKBM202v5CaIYk9CkPGTbjcRKU8mDIuZ3O1mML5D/b0a+Xqs188zueZ10vR7OV0dSkqpeXZzGEK7qQzhGAV1i+NOEJvLCD+Yntio7qWMcwbc1cCy939UC0K/gwjYDzBU1yqAikSe37q7Dfc/EpayGKcxlYQYvzxy1HPF7pOmArHk1E1h/D24LhpL73C0eqU6Ex7TYXNk1RCuc2947POTcdS67n0a/VpFHtLvCFubN0tUk7/03/8dAAc9zGHacUBJy68WrID+sF7LTJ0rv9g+8P+cckyOcQEQ0hXeyFfrF7bLKXxgZ74AOkCxsvmWG9flEvS1e+u+3UiwlD42enkT3b1UP7uOsXXTD+gFgDKOFc1Q9NMm0uFHkVenRPO+jCOwBwbIoF0RRL6ejcZG4bb0Bdyy7dXJYC8/8XlW2/OoYXMN/7Wre154b3/2yP4iP7B7xQ63RBLKVMMu35VxJHR8PHJZ//sSJyjl5lSAecYBingXmyY4pLElyAzsO0LYHKCuGKFw/kGEroBzFrTOQf/4GWA3KXjFTpBCFpVDh61sw50Zb60Uk/+7F+9fG0bWMgwhu7kcU6SiYsZmMHl01PwajXz8tOG3LOWljMC4z1575jaN1dvbC/c2z4689raYD3lmWlC8X0Qo0BUNWU3lShHg1gG6QJLS4XowLoU8iR1S3zt9saTX54NR1N3xmJxoocAwQVkNgzDPgiGnSaMwClNnXgBk5ZMISvlcLAzMuT91G+/U3n64PjFqZO5tcQmyc6mae0/HJyMlvFCscbCHmAjdq6aXdmuhJ7CDMfdvhNLeZjgaIQu8tA4VEIF8WCzG3uplEbhyngSBaTrQ0mnrRsmtDfzadYKeIJ1oW6c4GQgZlP5DDGcyvKzvR42XLzJPg8wsN/DTCisF3CUn545RmCJVJgTEPXUSOUKmiH6lzDCCHakfaV28b1/+5MfPwn//rd+L5d923vhpUUfYEMfG3eG8o1s9biMfTGdpRLzjTnXhxknRCYCnaKypxaZoxx2XlQRCsWCk58e2bt0dimRiSiT0VVnQIUATZd7fAFswB5Mo469U6S7clg2mMz2nW2gMPnCJi3WLFPpWlSqoRDlBKEpeL7a0vypmsvly6QfRbHEkwiAw2joIFhoWKnFVS8tD4/lzx+0slWTTMG6BWVFEcel2dhPZzCSRyoU1hoas4sEBYEYNE1jNJ1OkMSnGApxSAzDURaNItDxPSTFhQihyi6Ea1rcArUnA+mVG/OvLHT/w5Gxa3rvm83s7AaV9IriYF+7KTQnC3kNZT3PTmwGocFKjTeNhFKSEMMiBhl7CYKHVAIrCXuOg4g2EoAAytIobF70uvACEava2a6FlfncIXCyTmp5qXQ2kUT/KAlSc2IKYGldVY9OVHeKzpUQgr/44ODmBm7npPnO4OORl+47g08PaSI5+y+b7evEhSHM+9PKP/rPf9Ulrv/LF/ZlT1m0TRbQri4PSaz32QRn6NXX1zuKPR3Lx2pEyRlmZIMBi27tLOcg+XCgwefPdk/BaYmZF8IXgVgA5H7XJYj8PO+NiMCNym3vg48OzoYn3nSUvUHW4sLzj54us/Xg21cbrfLigy9P3fjCTdyG+s6i+Ye/t/z5fvPxkxHQcFTYy9a5rZW0tH7jo8fPT5vBNTjSYY5nQWljlbgzv2qIRJ4DwmHQuvzwg18jGDw1gx+Ua/wCCdhYjEx6bJ7CLE0Pw0AuorG/mT8eholh50TKmEAc4RsxtHyF0S20NVDpKskkJJmQNYE6e9C9+saVi/4YFIkXf3G4uhnox5fx6exGfH7SdFUT+6LRu7oD61lm/9fnyN8qtt+me+/P5ueFjigYXrAKp0botL3XlhaJHYkfuc0sjMsfHdJhTKOj1rmluwBXosMStjLz6NNWu9lPVTAcmiS23n4wkdsmW0Y5BM/aoWt6S1cE8mb9xz8ZaoOGgd85+Pgp3/kUm4VjAuY46C6OU5X6UScgp3Q3wLEH3dGt0p92Dec8IAq+Q+B45HFLZTFTS2FJ0lZqVtQKlKOOXGYj93au+uYVB4KtfTWFEYVo1pDUjfkU+7XXdBCjO5DtdvYv+naAzV2tVVaF6czCknyWojOrxfVXst1ngySgZkJdWMBXTYn1vd4S9eihp1HApw+OKUx493/4ReX7d3/3n7/z7p896X85HNjZMHt04XTetEpLlYIyLxV+9sHkacsKpnZizbQslqXYGnPy0qWJrgQRPTz70ThY2KJcb8YgWHdhKTq9uKb62/Nz2GoGGqnD+xfj2BUCMP2teZsQ1X/9uFjKxFczMkj23u9eY+qRSlOVIoZouuYNOZzdyIbGFD3v268szf/m3eMfvzxpHoRzwJtfW7zQvb/oByk+CnSPLmLUJNjIps/75+bUv3lnOe+MTa+/rlw+SxcpMDROo3sr/nEKYFXny9FMLdfnxypKoB054EgwKFFczAhjcIHzRgR0PphKuUyhtGidjVsfGLn5+caLi3qRpkKVqNCzM0b1wxMlrFcoY8h6aWJsYmjtRvP0OStAm0vFAQZ0ezIOQd4UqheoAIEncCAnXVEPRtpsQtZ5YKTsu11fLRdSCZSKO0DCUpyXKSvqw+5EHB5GCwghgKzIYkLv884lZU5nE9XD8xu3qplWgsxYFEYHalDO4AHijHwIwNEqDdI4cGoChIj8v//533jv3zWW1yoiaGEJaLUbbjUHeHE+1t2H/fW0pKXZ5HCvM9GQQhXRUDJyoshkhxjU7y+41HmYHpqTjKKDOyJ7Z04+sE5QnUbd83fP17azMpjvN8dJYsO0KaY9b2YN9l3j0qn8xrXFVzllzGgpHhpe5I2JG+upfJa1YIsxuz4WOQShIyzgRbqDgEhIA7riDgw4PpiAmCsuij4N6YbPyESWyYARHsZJoZQCE0/uuEarxVRLQJRU5lmlPZ3pcPErt+sZEeg6k5HreUiWwUDQs2MY8d1wNLO/+ts7X94Xo0StB8GZ3v/sp9N7ieoh+tkjjMkQOk+vzrEKg0YQsshhYZXZO9HJNGnIcvfzi+rNXMf2cMIlswkTK5CKMwwR6tOVnNhlUDzEKyMvCyFMCMdq0JqBKEZbF+G5PIs06Pq14qSDNLpjJoWqu4MAxbkqHWvg0RcuH5lX65k2QB4zmIWAHzxw87RT+52b/DvLx//zJfzB4/L/uDoL8Ml/3VsIlDczMZhudjtTcCZUIFptgfV8ihKR0k3eSSKgOWLEvm0xpk5qg5CdualAPhom3/kqM8dzh2SBCFbT2YKJCymWXSgmj8811UZcT+O4ZKhBgWbfuLP41n95+36AMjtvh6cn5x8cNhx7aTXDZFJUCK6UmE8fOv/Nn52vMsrNb3AsAs1t5IcT5byn/+LTIcSBq/Vc1rPBlp54ILpeAWuF/mT2XRpXPDxS6Lu3ts03I6bON5qJjdOUz+MBTgVC0dJzXjAzZ1EWtHBa69kZA8cCi0HjVF7TZ6DsmqMxKHHJsO8Eg1YcogAnSqjXGLpt2VMgGzCtjUqYscfPf7mH4DBIxEfDYI6LbN2fv17603efqJXglW9RTbj9vD0CMWxmorphm30dzQMIUEIAMR6ZIxtLEZDHiEToc3cL9xjGsvyIpYeNqTMzpWohQmkMYZIQJUMAjfT1u+ns0gpSTzEQCtzLn3kG9ImPf+zN89X5Dm71UXqge5RUulcMluDHX46AA6gEw3mClOUApqMFb3J/NyDzrI4GmqqlWVRMi4EzdTo+ZuOxoYAgyOM44RN5EeFGFhCxocbBWSzaerOaWqVY1OPy3qkPj6edIMksU5BPF+tlUzWTmQuC8fKtshyiZwaAo+DhTz4mUyxRygaO2frX7frdpfMleDWmB18e/+BbxOhw8N4/er9/Y+neO4vDs8E6jff6Ic57CtVsjfDievnNd+6dU8cxUEU0gOYgveerPdC1yZZVnd/JvfZ3lib/4indHSxdrQeaff+9domMejZ9+63swdjSemZhM4P5IQCQx7Ibnr+Q2+OtGyUgNmf9EarrFsPmaA3uObjhFMloMjH4tWr/FOu8HGufjzdey16/tZTCuD//5OWv/83R4jWU51lvarIw5lyqjjVTnThNUTUcy8OAK8QNx0ZWIHk0XMnTF59NZ4lTrmZH05EwHYAjTgbTHAXBpjLt2pwRpQpoAoVDnej6fjmN20pUmg9xSlSnepVgdUddKmRe8FCcBNmVwtG+mlx6tKDNbS9dghyGAPnCkg8C3aP7g0sDZEDP91w6zpEYMLKsKNaSwMTcAwCZEMR0GKxghQ0eTphyPhVTeK4bAB3H8yf5gUvRyEsXRLVmW8aJUp094dOqYazck3zVz9RYEo9kHWALAjQhMCCmARIQ/CDCB2MVQm3Mcc0IFaW7V/zw2t+4FYIYyAJMGR4N4sjXRCg2OLKtBIkzSnNoLge5BiaVgKDM+Y4YW/F6qtj7/D6IWLdi/KPzvrCE42my0TBu1rCHj8fAJoZcJ+FAzKH4zA5ns6ECIrDLZ4sZusC5hmE9GiizKWfkayjoAF6sm06vg+IZPJdNl0R23D+7UPo2QiKsvT8UaZyCfR1lQhr3NVyi4+7IQSKAShg8A0YBipDsZBxbscNiEAECbRXZXIC1xGidDkg4JgWasmlIo2xTxyOCFAswCwBBIAQhUqRBn0lax70sSQ6RdFftUqez2xW+CzoaFAtkRBkzVcTojXxniLj9MdRAkJxIvmxro2F4bQmYOolvMxX2fBpcpYKziHWHozu1ZQiM0nii6gpGs1lbg0EDR7DTI5O9WkEncDCb1T2wp9sZKwSyWXO/ZTeDIIW0ZIOj/XKpWl9kbEz89NMh2Z/QSaVy95Y8SsgzT3v35NH3t+6+LbSwJRdclowJbnQOfvFF+VpxGMf9iVGrZAvZImlw8YnnWJh9ESsVbP7qkiTHTzw+m89CLDtnkXSWG/KT3snloWEQr6xnw+VCvUyjbKgGo7YcJhhHuDCNIrKxPybyX6tIZPhPf3jaeHrwx38TBb599+p/ft39eFd5ctr0/YHiVo96f+97S73feufT/8/uya8O4TI7ORgGOQLAABETObTlHHM2QdhIH7gw0nfLKUcZ7LVx4Jnxm293KhnKYKScf1HgZCzM6yCMoXgCA7CLWwFNAIbJKPGkTwahHJK0xKwVUPtiBFMxDaCdYDwd0DhezOEXLdC09WchwPg+7I2D80eY1T5wzHJ6JWlHrcvmxo3KZmb605N+b0Td2Vj7f/2z0S+myN2/vzlNb2I///CrYDnijQ6hcBxKz5XQOUp+0LA0Tz01sjVyP3Zy1wkOwU8o74Rw1+uk+WXLMEx+k217s0uJEjFPk62YiVJ3BDjPn5Ig4ruFjjyd7So7iwFjSLXYZWbPnZH0B9dS95PnBjUc2ajjikniEYa8vmwAHGypEw/GNBShoLRQ8p6cTbIEc2ulGzCxPgE5PJKSmivRulO/QjUcPI0D6eFwr8KN4/DVICE8uOZBqKYafRu2YUwipFLO90CTEPYvdbpnVjPJyGUjx0+nMC+KycAIux2Cq698f8Pr9tVPxs0v27Xtijl2RQrdG6eLV3M3c+Pe3kU3sxASCGkyZYFqu8jRZyfMppfqjaJ6xX8FJJt9MBsrFpnKj15QtR8srYALxImvBR/ubZLlvV9+6MtAeo3YYCA2I842l/VtQvjJw8AD25iyO55dRwtgM04NzNRGtj2/Qv7ZlzmWklYrjxjx3ApqOGhgtAIEoW7HXqxni/nlIIhD5UVDnhAdTN7+w+v7P3o2PLQZPmIoyIV9LParddofauzrV7La+a8/+mxnaWbU0DuZOHNp8p2L905AUgLrf7xlhIdzPb0gHz8kFm0iVSvDsCSe7OkmNJJEiC6B+YSZz/vPG7b9Zwc37tWduaIKxbrHXlyo37hR+fHnl8XqktfbbQ3cLSq0IXOJ5S91GeCzpfJOTyfg0a9StoEXmQ8aY0xKwABD1zKrrWiETKeGJ8PTZ26BmScKwHwdPe7EuiDi/Hx5mTYPtZDUlcDSAIrntuZsfxroahUeC46BHffQQPp2Cp0iYLSd40axoZl+gdUTC3HA/DyPjpPAkNMCwpgWHsRWHNUKKzZCqBj82bNZLc0JAMiQiYaENTywjCQdBoFlJIaey0xfhDRNF/KhHc0oFoQBLZSKbPenjf2/U1+YM3pdlRs5sAFE7aScyU6mdhqD+nt9wA75QgpYk5A0qhP5eR/DcXaQgL4d+lo/y7sBhvgzcHI6zHNFlGGSkCvygIW4qo9Aq2wO1gKbpJggkWOCiWQPokBolmDmVNN8AyXC9RS2U0u3eyO77yMYUlxfaVizWjmRX7ppIRULWdJOQs9BUHymxgsSKXGC5aiO6yIAx85lceVl0NufkMs5UeLzSyvZ1fJus3VwenH9eub8QptZPtBxF9dJN5/YzVlqLnX31tzh/X61Osmv8Y8+nEgwK2HodOptXaGfnNNTOaBdn59ZJOQT2QgDLC10HAvNL2c6T12r67363S3QtgqS2U3cCAPobVE1PVCGGZVFYRAcA+AQqqwmt5fhLz++PPuJWsHreQpd+ip1SEOHR0fAZ71VyT789CQvMK2dm7VXypZIp2+DVZ4DMlUqnaJYNF/AxpeXTt+FE4swoHWKjo2ghgHv/ezyIuLf+m2Y4Dw5QVff2GAtpv35RBvZEcvPz2MgFkeg1R2ahKGBLrR13Z2QxNmpkqElYWUJ7asH//2nYUmovs74S/mEor5VK/7sn/7wn//xl9W3rrxxXXj3vZiGwoE8kghpGUL9MOpcgCChxhBVr5M9wzIw553fv6ruaX/1f/5SPe5VvnsTL1djz4WnHtcVX47lVT4N8vEkgiZOoDqoYkdgGrOnanmhhMECjALDEa24al5CqwVEV93AnsEYWlygnz8B26bNhGDI4TlM09HJtTSxzMHPD/jPjgx8WQOBQKRxk2wjjDTaM76/LN36rvCr0cvsgXrtrcX7ur84l6GdZHI8i1UrOo3pFXrIYLAP0lby8GcXZSqBSLBKFgA54HmvvsI2O56t6fWdxJa9YozHNJ5Nlw3HizRgldGGX5x01cHy4mqgGubIVNXYz8iQoyBNFyJ8EGeKOG4+GV1cqstXA6I31puKkIZ6upzNUpcHI2AYb726guhMd5TwAJMtIFPbhgSYEkDNA7hK2hbZUahWS4hjjA1vOn6EArKns7yHYjjjxrCn9ww/Atl0rPdtMYoQzaPZYZgBc0QyO59GHeOrX0s91+BDLVjk8a9+jf/xr1ppkSxnrAsYMDBNJsshRlC+7zUni6/kp3uWDMSby5nhFLM7Y1vQXCtk5gXPzDuqagQAsr1zI7usIxzGwDhGeMAb966iQrbYeLTbOE9EHOG4tO3jJ78cUp04t1ZM15lE2d2MdBliFLGSbGTf+7Cfawpv/8Fq5Mf5pp7lSQhmuqqD1gWwg8gHzluvFj5tAhhKFraFeZf45Y8PW//h9M4mF+qAbPuDfswVwnKZxGk/uU02E3t00KNg04ViYYo+HKOSwx5bJF6B2oCzLMyDtWFux7HPNDYeBD542g42btbSLH8+mJKjKewnuTzU9sWtr88zI/vnHw+hafjOV5d+72+99i/++fP/5CuplTl0qhNEac6JrS9a6ow0buQRhkgrukxrCGezsZB6cbgvWvEOB0GQO3at6YGaslOZm2VJRflwcQ6/ctnqRI56gFtZvkdYRFlK+QmSgYKlWra/27scIVcWo2AWEGDQVNwMipjnuUU+Z2IlpR9yqEmB0diNJ85M4BLPCo+faRIYUxyiD4i5xaLHUJ4aXBiThSxXSKNwWwYVClQggMPyadiJAobAwSjGWSAAEtWcXZljrb7NC1KMxX6Kb6l+9Rq2/hXir355f++Q2rx761T3uCuV2EPokQNMjChqIwNz8fq8yiVd15/2dW9m9Vv+1qJbFCU9rWlTFYoVnsHBORp23f0nu6XaHIaHiA2Lge87mjmwppQP4IlnIUQYQTgSEnEC+AwKxykAip0UaJ6/OLh6czlf4Ad9x1XMjU3+82EUeIQ0twCQWY6lBAwmvTgGbdOaaCM1n+G9BIHABLEaXpNJv/KbmeAm6DzoDo4HwvYdOpNn94ysMjDri69+d2Pw/Bfyo4g/0GZRhOuTZweTfG0BW1noPb3Mr/DpOTQeTDLb6Sd7WvUU2VgpZCIyOy8Z/QCEMEdxMCrunliMg6Cx3H85C4z44sLiaP6NBRCIoEvUIWE48hCWR43ABwJL60/EnfpnHWsbA4SvXy8rM2Bv5Gizj0fGzh+u/8OdpRfvttsOXluj1MDDcg6cysLamA6ia0vMBQoSZsudTrohGho6Jgi87o0ves1BtwsTXBVksKarZb788UXIhNlb86+wROtocH5pZ2G3+P13ujsV4mUoOy6XBfWB4xD+sK2cxXLkxOzphP3WtcJrWyyQ42bqe8fN1mTCHetv3Fm9+Tub6eX8yb+4fxEwEUdMDIPbkp787HjxNSGXSpVXcxCV/PLRgTIdpxWBJmkASOFbte/+ifl5k7h5f+9HGf7aWkCCWe6xvJ4p1PJVpXHWjoE4MmPLZWDEUbTFFMqyXDLyJj7UxRMvIsIRleEjEAFdE2pMZ5iUz/KTtuXujaavVUkOAipcLFaD9HTwtVev/9P/KfOv/oP6D746kQTi+L76czaoX5Muf2NtxQwK/9Pu97/7ymcpJm7plD45V81YC1IouFzPuxRUWShTVOzs7cJZNoF05aJfrDPQZBYjrKfArY/3TNS9t71UzWPmWez1+wNFF1lgRWAIywEQOX87z6BgTGqGNHnn1ax6vTD0IXOvZSpuIDlykU6lAOYcAWGiDSPQFIApdGbEWYy0n7a56zf7dHoTB5dxFEV53vQWOOLQVdJZXDJiHvfGCAGlCb+F0g+Gj5dTpu+XEcAV4Vu0uefA2MztDAZRgqU1nbEzVL1KhVPNRl2K+rIz6b7fKqEWTVkdBPFGQ3M6Ov3oDKGEhQIyxVAKTACYMylg7tqS4iTDQbvVbCTzS1MtgmX99e+u/PivZuFT2VMYtARulDLYa8tPPzsDu1NHU30bmL+3nJ2fnwzGH7073vrf3fO+tfjJnz9zn6tpwwIun870KL+RikTOSos4RABWdBhB3Qp3bzzZ+XSQ/s7VS5jinzSWc/liMd/E/PPOy2uYWBXR4z1FsQx0O8OP/ZOfnXG//ZXlPyqd/OXD6cSJ/IB4dTWZWGCdY9C48+yzvIROji6h1qMEJ3efTMpSFoqJynLuTgF87FMiuzR36l1+PGjo2OTCC68hmUyYb6utc3q5Uk4jeSdHq03Z7hmNjq22k1uvrqz8t2/DZ63dHz94/Y+WF//w2o8/P//urW3t/fOsWEWDxvFlPz0nHjSLC1eZ2E1gxtmaK31qpV02vbv7NJOzHZqqGkZhI+0Qtq1oHgStZMnieKIHQ04FiWQmKiNOaU3mWLacXaZieKTFIXH3Ss6A/AIXiq6me2oOnO2xMA2QzRnfuEzulXLlCldM0PtPp7DfMzwNLMzroZZNC6d7dtHwCZIqZCXLArAg2f/wHOZIdRqsFuk4ArIUfOYo0Sxtu+78TiZKQfqhTyEqNV8GmLLvaDKNA0bg7Y7vlSENLbSxcn/PtlKGl6TSWiJGpFcoN44O8BrXGQ5jTU+vsTiUhGSBTtmtp6Otaql8d1kPzfaXw1KV0yGXWqoCPu4AEw0HQtBF3GAKe+ZE9W11wEf1Cp+FiOl0FtAkRaOwhbXMULKnwlpaXwyed7ulDXH9+qp2YrIJuMjaduDU18pYN2hPgG5ksAJsDBURjFNpFoQiCLBgOETabCLD/i/3B6SAF3E3VcRmFyd/+fD4lfVskoce/OxhqsJmcdJbhObKvP3FxbkXJmNQJc3Sek5JzVkkVd3IjaIL04iu39vKVnAqRlRTORqd01oAgZAocaMJxrJcDiM++vxAb1m/+zd3yAoE17AGHIKqhzLowlI5SdyBZfg46IfQ2JKTCTjH8XbPFsWlrcX1iZzwEgMm5z857C1L1BR1evcvV8I4yWWwKxkGCJ591Kp7WCrHQGPFDBqefo7ysOlgnp8SMitLb0h6w2UxIkLg8lImksnx1ORiL2h03zuxNtfE5arT63mErGH9mc+yJE4eHWocRyaAK4roPY44ODEX7i0ftpwv7NE7b1KGMsnh2tt/Y039qPen/80X3if8lW9LuVclNY4W0tnPvmzd3GIWr2amPavVHS3vwFE0uboAjy4KwmL6lTfqIQC12+6PaleBVGnvej0f1TMwEbWGR8/Vm29p4TB25Ci/hu9eqjgVCAEUO4HjcslYQx2EZQFM0xfSnGICLAQ7BOuOHQ7nGW5K2v7YUq/V6QrF8pKkT73M1xcBpBL9OfL13/jdd3/x/J9/Md0bzq7fW89eu61u+Kmv12wFqdUGBxz74z/f/xbDeEzEVQThFlEgEVCDz8/H7Wc6hlpu4/Rbv7/ZD9k9SYsY0MxBIm5//N8/Pv3Z2Z2/dsX3yYYJ+EEMJojXmgYVvlpgMtvzkbThQ3boYYkD5slYv68ZUK9cwcBFJIfwU8PQ9yY0nyvMM7Cs7tSEswysWtNcEBpnMoVyv/PN+U9MufkkXJIQACCas6GQwpgahvIEjdhGO0oSBWSAoCcDEXI3m271zHTKsVxHU81CnpzGyNACuUrVIexCirBBAyqRUk7szGwohsvb4uzUGlueVIMrTDg5DRKKvvft1aY+OzvWNksSRMVHu8rXvsM3F6WTfoeJiQql4VNHnQLWTeLuLXTwJCziOADAs4/bV2rA9WXaLTqFFBPwAOlDH7x4XA/KaRYYfzi1OfV7mXLqa5ndFx2MtvkUz9/bENLE04sRAMSDeJqflwop1TsxqiukuAD+xYPBliRm7xWbI2hs6yXGdTodMVsjOPPpi3CWRWpFlKmEH/780cbr89/6PuM0vJNhZGfSv3Ft+6P7X04vRsrQGJMFbHvBGRjeUW+qM/VCGa/wF0OArMyDRBqFskbbMGQy9Dxhk+dXxO6zTtEKIxRrPXMAks3N5eICiUNAiqZBy95973whX7m9vtZ2sr/4RHvnO5v/4uP+y1ZUXC9FwbQ746mFYBZ5kDsruIw7liEPH6jR65v3htMxprQd/fLVRYai0omZ/G8/OlzaFKvfWAGHQsoZpVimcXiumfITWc0sWreq/ZiA0VYA2THukVAfpGWnChjO4GIrk5y2PHji0UKq41yKTB5x4uM9PUsImTzb6ZDKdLK86uzfn9aZIp6SWkbgT7XlDDU/j6IIweew03OL47HLczuXlu43R/klPMOiBAoefHrCLOFXuQwJ4frhwF6S2Dz5q337erVentvoPYmWVgwy4F8edVBGssEQ9my51TVRniHSUOgagI8BBDNziYVsyKDludXC6+TwUL08Gs0xOAujF4+bMRciSESWsmCaUJ0gxcKo61SKrBfyUOTJR7HeTcAsLAL8ZL+fEPj2dqV+o/TZn/YneW316nKgub2+EnBg5A8VNf3anblHJz35zEuZRJGwRoNeMI15iuBzWAxpsmnO5Ikf+giievUe5j3ZY8p1HITfJTFenq2Mnfb1GvuNFfHz+x/8k39fgL0r37+zD1B7OsG6DuA4x0dTti7j21UqDvVuWJSE3tTgEJCywuaoifkqzQBliG2iIm4E1UxwEht6e7CQw+GvV9JvbXCzVqpPvefKjE9nQ4UAYeFGadLW68bAxbGSFP7q3UaOp+feyhNk0Dnr2yrEzIm5b25VB+3E0yfEVMsLyejCvejN1YqDT+8jX5y0Fyv4VslZALEMEhtkBR4+NJec0fAymR9AMyEEpXmWNyzhraqH5G87wLPBkOfA3VZzUsv0Jw406adDcC4KDjyQ1tUcGcIkFkw9zMXuf9kQMpWd15mCQ3Xs5PGFVqrTaL/ywf94cusP6r//b1/Zf/pUv78P4/7Bk+G1b91EYur5Z53CBp8uMEuS8VAdi4CldkBEt8q/+2p3baX+8SfL7a5UPQY3ymkZwhxkzv7l4Sf61o1vNt4mCHnXVPphXM6gMcOTk5ZGViQw9iw7VCy7ZEUSD/mWTDMpWRubMVeYw42BvPd0mCqzGze3CsbUmdAgjwkQDezJwLU3HPXke5XuR4zWc5AEIZYW6+vfWB+bu/6/bPQGAX2tVhvDV7I5hDaPKHE2nmaORy9s+6qASrfqTReBIJPfrD+LNGj/Mn9tqemiOY8ECnT+m6Faz4XLYss3CdUDQFZHoJVXC2Gd6QxAwQu/aE41fwBP48otZudvX2t1QPSYnT04ONMA8StVhwe1QD1vT7cWl3gz6nXxjh3xNaln9ccn2pVvrvU90/vxc3v92lBkWv/x4fpNse1AVpShT0aZALAvG0FMRXxs6gDzleWUj5XK6AXKkbZVFIjDloWE2JUbCzTK+gBPG1aA4YjuJ5GHawmmQYkJkTu1J6sbB//+l5RNMktLwe0FWSRKY0IeIy7iLAq03tp/bw/Lg3RKEggelU/jEs2QbPzZxWwhvRhV/Nhygxog0t7Jw8uUQPkAUBLzhwC/f9r2zp0oZUgoDB3cH0LiJAeSpYK0CAjLub/6cGB+2HhjJ+3/+lDcLqOLAu9hwEu7NcMpEel9eFR2oIXt7d2HHbUd4hK4sVLWJ+bIgNc2lweKCgLQ4SDJ39nZnimwNrnUwsV4sQgMUYAIPaX9kQv9+tHSP1jIvVI95tMiWbUdsOLy/flsbmtr4cINLtVxDcARoKSHv/HmyrDONtqTL/78iIPJUZrt2oaYJMX5rIAabCXTvtBIDN5eybojR/7y8H6pL86v1iuoOtXuXC0fPhu7FSpFiKtr+rPnk+lgxnonRp2MEGHS7y9IBMdn3vzWmx8A/Zfvn/Ubxlc2cv/+LxtJ4p/2G8BAnENsP8I1OkOuFtyJDlKphpvkj6YIHjNkdrUIUwA8p02R2SgcX/67wZisyOcrsXQDeML5TQd8q68LNU3XmP7ZfmZ5OSmlpl8Mnj7sjzvmY+fBm29cGyRImsH8SVwEoN3LJkzOMCsiMNLW9UQIyrkY1SaJn+Ui9O6NLR3pGVOULPAoYEMoCoPEMGFfPrpcyN5qy+z+s6EbXaR+/17utjB9cgrohowyTUtYXMqAPFVfi8NOt6WOiQhRL6bQsBncrXE7da4BDjS7cL2aJDoDO8pRNxj4ue2MSkcBwzLNuHugy7KaW8/fWBVQH3NlJUpxqfnEd+zDy16aWaK2lxvHDSZlL5eyDgifOUGdRy6OLmNOoTga1bzY9DjUyS+wHIOrQ4/k40FLA2bjAPJdAkJsGJhM5YiR1kCUSZFXSQg2Vc6Tx83ReLWe3t7C77/44vPOo8Yn3/5P38hlMoET53KZnD6Lj4azs0luoyhmgcGeAg680SXIbgoLS9JJAVMQPRo4DA6FuofFeAZjUlliDlMLd5epAvz5fnxviSgMYQKO3T7w5WEP7DhXthY81Bv1ldUrC7/xB8n9f/fZ5+8Ob928urSWamjjxz9/qqJYZo25crNE/+3XWsdYqTMY3B+payWHAjPmijEO9h60oyqVsLbuUkdCusMS0yNwGXQCKIxxYmQ5louqz/TebLpUYYwwCUt8+XZ5PIQrr1d0gF+4OX8ZYDqMzyCPzLjGTHaAMEr727el9WXoh//s/vz28hu/c10fBL2J9Wa19uHz0ef/9pDLAkyepUoSZE18xyGS0evlqN0Nnr//UlrMQWCSwpKrG3MPP57Wl2r1v469dB578mzlldzeJyH+Uc9ZX1gpRM9FaeNrUv4GNgs/c0aktJ4ReM+4DK1hnCtKGsbYAGOYVr4IjyYmwwBqCGckIrYZw/Q4EtxeL3hjRZ+CFc8zu3ZuvnAOsqX1reMXR6thh3mTPHQyhSutvYfZ3/ru1na+nu1YsgHmMr5QjeRg3Dr1dpbQZ4cKjoPzLCXluSS0T0+Hs5/15wol5Bbh5gnnS3mym1BBkruaIWeBo5tmBF39nSuhFnb7MRe6uKIVC2lEzKqxOtV99mhQ9Ny0h0rzKX2qNj9wEZj0ccLns4I35j3V1WCyB0QdNMA9qypgmA/0Z8bAPWma12sElvKPT1qMhK7eACbuMFd1zp+eRqkqlOMtg67UcWINSllyxoXgHDINcFmgAVcGfZXwzcnLEezyCxu50Iy9IOE42CMgX7Ik0qDuNaQAAQAASURBVEQNhconge6/+OKMWI3Lqyuuq55/8WCF8IjapnYS0AKBBcHZRTdHQ5k00OnIBhwQIDicopsigzm+B1uG5lj0UeZONHjhQ34qn0GakwFFZE1VMXLAzdRyFYf33DYM+hrnR0vxa2XoF584AGIB2mRwNN3OZyCKSeByds4rlYSWMYlmzrAxzpSBKIFGM/P6nQVDswaN2c5yHSSZ3qhHleBee+zDKZDlt5lkoFuwQNXyuSJLgEocnxkP90y+41340rd/e/NsFYJvkk0hapxelDVWWtxamKNjLO1NYM0z8WB89BefLu6sagLXcBmuUKYLhVcLxfBg0jub5gWgkE8Tkdd6odSvIOk81eoNZQPKpHJ51LOcYP/L4+trJYslv/r6Kxj6JQBStmPBQaZaGY91bzY6bz5Kbt3ZTlC9tdfIUfcWV9+a/+3f+d/GRy8/3gsYDgv8O19bdN5A5XzJHPmkEXemaoatRuj60tXbUaeB9zQiBbM00erroRvkOZwcNKZu8o17Ky+808aFOTrqMAR8/UoNA6I9XynfqO22+w//4sHqxvxaToBKhPjO3GcfPX7/s4Pl21kqgnBMHdkZhDEUWV1clrAE6Sbe5dTIue68BBcDN2AJ1IWSMqcGOBlHVi7fjsGbAP392/Vf/EXzF4f+7VfvjkUWIduRwEzva60P2mkmLi5V2bAAxpHRbredcDWF5FHWl6hyOe09a73/rx7mlts7xWKna0F5Roc8l+c83sFi6GwKoIjjTKNCQmViNC8Ska6BKMbRuAqSLcMeaj7LooGhTfcamTI5wwlFBVyeIDWLCxNRhDlq6Op9IM6uXFnsnZrNbidwAJKUQNSTL2IWjU3AwXAEIjGEFzCBZS0pnRd5CnDisVsMs2PP8waOlPXOfvkQHmFrr75hz5EQim4J2N6UtY4H9Gtb5eXi+OXsSE2iGHT0gHKC2ms09vZmv5ggjnp1epbWmk+jGETIaeiJRojWUcIsePen9s+mTB3vTbx2S75b43oCmE0TGmxTGJu6Wp19drLfVNiN/NXfWpJeaHuDqXVz5dW358kM5WoUMRn1fq6ly1DNxGiqzL8doGhm3xvTaR5dyQCHZ+5ZhzzsiHM8tkBCPbnUG6wylelZ59hNBwA0B+ULnJtO807fjDqedgCjYJhKp7RYC4eQ3TIZWo/ShRCFnJ5l6m5ayDf6ZutyQNVTi+9sU/Tc0THcfTIeW31n3al8ZzE8NCE+3f/opMQF5zShUeTo6TSNQWExdS8LnZquO1ZMMvz14+aGmHE27GBmbRkqvrhwfuLyry/kBOHiTP2sPYwhpqANDv7lw9dvSOUr32tGlNy3RTYXMxjqwnjoUBgIK0A0MXwblA1dlNJaYwTxUZoirPOmDEhitY4OA2ua4jNcGs7agUvmlxY8/8un45t/vV5/tfDaLHVlgwjzxXCEe6cjPunKLhYsJDzKhkniwHRKhNZQUJGk1uH54l+7srlFHj91kkG/86cy+3urhfUFa4gIOjbaHeksvkEHdzimiyIpOCin4UEToBaETF6U+kgaJfVqAo16vGd3QVLpzUazLjClWQa8suh3U6iIF+3BSefTcXaltnFNOP1iUFotU0ziWu7UncBDqnn3auXVncMHzhtffxPhOc+VQWvUfHGZ3wxeo0CCzMAJfe5E8rQ5UMFsnoM9S7KS8+HUgDWg+/Ivw3TtSq4UBMe9oJJn+p3xjTp31B5Ciy4fHBwlBHXHmdX4wuMPbx0a1d9Z+/RTBhDENT1+OfEvRowf8MVqamiRfLFQ8ZPmboOVnOJiFqukg2aSPYzdGIbP+ucWKNi02UxwLXAXyAu1DZSID748+YPbhTxf7xNqowuc32Y93T39q3YUoPHVgp2rSy/ZTJkP0Pj5k0uoUEO1SGxPPjdwfikfY2rrZXO4smHB3uTBsX01s08Eq5g1q2QhKvbm/AyXcff6iZQS3bEFwHhO4IBULxo+Hk6WbwoB5OGIZcnO6lLu0FbZR8PXSbQ+PPnUKx93gYVlQjTMXx4pOXtIp/nIiU4CbHEdeGpocBLf3qz4/Q6XEi5cr3PYCuH0hpQ0+lrhahbF0RkWiXjSa/piWsHddNA3T7oD8yvEJLt8edn/1itXPv+LZ0L1arR75uqyOVK63UH11iosz/ovZj2ve/XK8tprP1iiyrE/uP1bwieRuZijuUyajbv62AA1MV9Lc+mUa8QEPVYJ+cXlYBHNLS9XUVdtt/sCjHQjv/eFYRLJa+vzKRHwmyTnYD4CmSfdfGptaSfPwUgmCF+0XTwJT/cbta8t5RIlGsJPvxxulLGGo+zk3U+VAIXBXOCuSKzGwl4PpTk06QEgzCNTN3VXINfY0w+0PKYjfOHDixe5JPyb31zs6HZ6oQLwOxiB6ucTutO/9dXq0dh8OnRq8cXzgb9KzLIMAUB5VMikwKALkultes5LZwZa7zlri8je2ZnNRvObLITPQQMwY4OOZjMApQDOIDAX6UhD0P1ZIw+urNYKvuYn1jQIgQwby6o1OB6VyzkKBgGMYVh0+PyS20ybNAQrBuElD5730iBDZhgf8z598LTI0JEO5bMCgcQBCHkRgPDlos1xOY8NXPDYR6x5geTgIuUmSbYKoC0vg4hhebPobKcuHuo7V3PF0sKDv/q4c9iZFrC1NDU08DIXXoQ+WqSClVpUdKenzdWpK/emCMeLNH+mQjESQ1Wkb+h0L8nDVH0xnYS+byYsh5/q3dE4uvpmDUhwn2DJkf/q1erjFuQYIV7IbWA8M3XOv5x2thb4fAFNASleBBK59ZkxavU0IgJzWsl1Ets9ManSHREjeIic2kqZaHGVtSXA63skDTgAQhbq6YIoMmmPssD+ea+bkpCACau1au/EcEeD8c/Pamur/CtJak48UqHEBeIIyAhMpAdJiIjV4tlLPY0RSzek4SDa/E5uLl/4yXvHzUdu+3L61rfTfIr95IeXyFU4vZaTBb/ZD0BHlS3XJ+MAZnkSTHNJNZ2oaWtx1T/5pBtOKH+aOlUbB4F5fS1t4dLKzMIG4k/2Rpyqbl4JYjnMcYXmOFjJZZxQZ2LXmA1BGAN5FCLR82dyhk8QKolxPJXBDk/xjAPrQxgBKX8MEEV4ZngWBU5iNbMJxYTzZw+evl6OavmcoY8e7A7f+daCpkUnD4DFTeh5z3SW5hYXyMOn7bKAti47198gL4ba3r95GATI6hog4oY9S1I9CqmR+TvFNQt6/nnTdMkpgtAjqHTay+MxhyUwbccMGrZCR8/JHXso6yUBsBObIuGZz9GMVOdYIp8BiuWMb47HMxvlCjmITSFCicCLeJgEK7Ua+zfv9V84uTkpf6O49yQuaQm6g1yeBGs5IqhdXfvjKrTCU/Ni+74TDLoBFyO5PEyNn335cuXm/Lmc4gsSpEUhXP3qzlyulO+1DZr3bHNczkFj28Ay3sDXL30gvFXfJSRiYXlUyz/fQ8n83NyrGKiRVozREgF7OOSmhCKRRHJv7FYkrLwFGx6o6rPpKBBpFs0CqRTZjgG+Git5YOhFnAyW1dH48HktTs2G9OnzXm2jGpQloj+b6466p1MaB3kWHx82UmkutLSDz7oEDsIcJOZS2gBY3SwJPPyrT87DtnalVN2gnOHjaYmBUCklgR7uAsskMZolKQwPvDawFjvQkA18eX8YyHGb7x0chkGBx+ckIJh0Og38ctIDMS9Dhtk6glstKlfGhbFGOjOdTBNXNouRy32jwrsi1jepo/MzUlVi0H32aD8fBiiasS/N4d50/as8U6F7bSt03M0S8suHrT4m3b21cfgsoBGgUJdOVaPTa+xslR78enwk0CDDqAAspARLo/t9XYd7DMZslIWxnzm2tX5QdsV75Kbw8sUXAWXgpNo4V/Jw21MDS4tvX8lYiovZaKTZSQTzdYkHQD8hHcWanJ2ZgS57fkM2HR+e39p2CClBi2nOm838hcXM+ZNhdNLTbG3QH+W2FopUkU/IJ+9f7P7lcLMY1TO59VqRhrHhUJ2xsSihrc81DMfKGE1KaHoJ7ishXgNdLHZJvHUUrs/T61f4KJAHF2cry7nxaAhQQmFemjhQOYt1+yBDaRMiKEjZuWrZ7kLzNyVBTYjd887L/QTRkVQUy14wCy550y2WFl9NH/+FhuCAZQpkOcBZNAFDM4c5DqE5LAjAEWtTnto4SfAaIa5K6kg/18KsSC9L4XBkxvYMBygzwgIvGZtmjPXzc0IHT+LQRmrs7kcXO/PM+AhCiRK9BCMYAJUQwgtxw9U7SWYjgyGI4/7/CcIPmE0TxDDMe3uv3/v1/vc+fWZ3Z3e2XtnrxzuSR4mSLZu0ZCVKDFmCAdsIkMB2ECQKHFsWBMsyZIkieWI5Hnltr2zfnZmd+s/f+//1/vbe8zw+0sCJ96p8OYa49/X04nT+BzdkPlcQ9FK9UZDSL/9g83LicH24d9SLx+FFSTjqu0SC4UYIGvooDDkiD/umORhIr92EtjJrp/rKBf7kb/YPBOPu9RxAYltaMqpA4MBHvIRGmH3Q7YdDJiSwJ1r5dsYHoxYfKHEu51KrBPVsdn544eYrbCix9z+8jNQoVxcXaHs4OQ3RCm4Tg56avculAgm9UtGPlWKjYEJE6eKYxRB7HOKWewTj5QJANBckruwhk5SOZW4WwHRlojhTcKLqFmVPISB07NDHzf0+ElmzHDEqu6hohxLqhmDAYQloEbEJZVLD1EM0RIAABZC5spjLZ3x9akzcD1+MeMu5c0P6KxX94N/+5re+tnnxWoIELoACXI6RgItzHwUQQYDHnq4ilQbDu2e/atf+ycv3P1bxY3jW71o0niU9hoyUB61DT/FuiCvz7G8t4zF+ravDwz1v602xvQnaruxcWEEmhTBaSRJIdhwAzK2w8tkgV8+THAzbpg/GljmFyLyImgImGuoIyws6RoCGiPrIS3fWmJ0jVGkvNet7D7XbrzTXrhJ/8q8PBijUcKwKCbFeNDkZh20f+3JpW9fVEz1bzc2gcP7uwounZwsJ3bwq+l1dGxl6KckIAPEKF8rwC9PaKElYX5kQ4Q6CoEVk2A6hs24Y8THpZuosVUHZidM26cAAE2mVYyO0UuciW81mM1M3DUv5a+OjkLDPJrevv2Kftq3MamOl2vWS1//+G93xsfnvjw4PjmTboc6ioqhX/tHXJhmUiYfxWImhUBRAn4QHhy393EwCKOLIKppFQEM24OWNq0nC9v9kFLGc6rtEbOdXcqOugpUuPmNaR40WThDfBg4R4Evrtac/r7Dg8cncQ59vzg1YSfZ02BLE+awTgZjay+JMNHUvaxk+z8MvBldV7RkeexTBR1HTxTGXrR0MPJGY7Bqjx70tLB3/tphbKR4/sVbsoXZyRnIZLAkOHTJTylSXhRdPWkkSzs7k+lsLzFWeMDU0ggBKiCJQlz1W0/Rcdjo9GnaRYgPGl1bAFkoy/FkJjrv+da7ICtQZpprDccVAOIt43J3SsQdgIr5/oVxlnk8GPK6JxlknITxOL/uFcgD2+8FZVKqXKkvI5cnNYoaNUBlRRyn/xMjOhyRJWJND0KdEffSFUIeuLs1P68RL6RefPXYybJzH/Pud4ZwHjyHicIq9zo9CNLB69YbYvpwMQ61AkI7Gf//NonypLROZ2cQj5xYHvtY+OaNco72vFfKIUfLxkgfv7pSavMKyRYl7/tOPilcLUnHyk2eTOUJip+QIP394PN1YxMhSJuWDTkwi8AQKTZWHI6CLIdFxP8KvNddcvDJfOX58cGZ5tToPgLHZHVev5K5JrNVXVBvYPpwynsy9cu2r/5erZ5+e66PuF/udar5iYCBIJjvTuDHPz2cjyMa3z4xKnmeTGGaYjutlKcvHsHa54A6Vu9lrUOXGrQrQBxxU1Kf2lM4HBEsvm0zQ/+tLpzcuC/LO5GoJq+LZ/v6FwpXzqJ8SpH5yosbg0hxVpfNGalla+uk2k0AsE7rSnOgPThDPLecaYwCnDXmYqJZlMKRv0mCUA9yhi048pLqcWcyMZBPUYS+CU4QhMuU1GnMBMgv4jqu3D/RGATJkGSITmOVGOlZIYPtynDpIZg4Gl6WcDruuW6znqM2C6gTx3hiZjGwKCBCb1YbD2KOKgdN9cvD0waMrr9H3X4SLeYHPkgtptsxM7p8N0tNeAXBdLED92DC1RBKBVFN98Prv3Q03SB3Q05hbynqTV1669eXsk84hWCAIVI8HLSoUK1TihrIPxOeEkUHIq3NcMgwMO52vFwUNTQbWi96uKQ3shDIirBhlVwjRL2qKFaMCL1vK0lKyQWcexGrLtCN1KPLsMhmXCBrH6YcqgnMUO7SEVbyaZM39WZEWLN0ZtxRrNvKZ2IpnIruQijbPIz3D7ZtQSjE5BJK4NLQTTBs6OECXRLVUSYgsoPj+FKFREkfcKDFJ3D/qO+/eXGuItHzSkcdyloPpIrL9yP3sffn6Fh0dJcrwhM6q+0fBSij6mlqkkOnYAbEIwtKl23kDspGJZVDocnX5/VZvZaGKzUvIgF7OUTgdrmak1wmspQIXII1eDtxyniOWsmtTPTXDF+eARHq+GRogI0FIYudofxC6lflCxwkgGjDVwFAcngWdyG6uWcMTGSTkDA/HIgQHGoJYEUa4hldbqBjjqqN54b2bxTduTqN9GN77O3+r+Zuf7bmGzdcSgcmV3yycoIFd4DNQRMNM50R+6TvZYjPcOZ26ipMR0DxNQFPnaNZNaLAk5swUnBAqOs+ZPNw61BgVNwPOp8s8kMfzRacADWhf4GLgDJvLMHFKWbbO6GpH0RE7CUx/q547GTPXOEiHdD12VTZAXBV+Yaov8OyXOb/OLK9pnf7Mae0wInJORjSy39+e1OQJRwc4wLZ0PzLR18pkh6C9IqOOMW2oARmL5AMroDAIFDOV0PVnA2j+Jm3uuFxkASWeLC+89ip/DJD/FsAowPtHwN1N6MH2Tx+4w6q4JLRHVmmxGGMAlon1Dgxz2QzlUDigDF2OAiMExXP5Spbfo+ynh+MvrWcwJ9AcslJNjmg2c3erGJknTwI2gxWu8SnjGMHF6PJ86/Wl73x58X/7t8eBZkBmjyyLuTcWlrcye/vdsJsYQFoH7ZgxejMbBdJU0ymRKJIUIdCKRlakTEwiqWMBBh0ygBByS0zSGipG/3L3IqayEQYyCC041XrOnWiUO4PHXjQFTIzLi44Lpnhu9Qq/VmHOW9bu9i4rzU7MMSnnIiTwR/HMTyAmDaF6tSCaYm1pdT2H8SVeQmaTeY6+PG47msQLTDbD8aSwvA5LLAMed71Br/hWTQ0TL0HrVEY+1xY4sVhNON/a7ShgSHAonRUgZXwBImkE8zgdZfNmnpUABqhEGey7v19ZtN/78S/6J1Z+Q4SKPJ2D98fQ8st1U/Sf9iY0A0URhs+IcibmoNnhSX/r9Wufz6JrlXy47128L7s2+sb1gmyFthHFN/PD/jQ+6dfz2PpWc9AHHV3/8efPNq8WGk1qfm3p6LmnjVGSsQnXAPD05BSsMzhDo60+YHvEF12bXS4FIHQ2mjQy7AaBY1jh/rMRGBZfbQAVgErX7k5nj04fP1tcQDO5nK0gooNCUKogaatloqijGQ6W6bVVPWUAaiHLIoheZmATff5xnJUnwyhqri2wLIxxCCHiwKBvqQjMWVQmwbqzmEASiww1OFco5Bq81Y5CgO4dz5zQzOYZOHC9OE1Gemau5viG4XmCkJmNtCggJAK0xkAZIZWeQVAUhgER7Aw9Bgxipl7CWDaFkmkCGVochQAC6HoR4K42MWOx5kyUDIQQAujnxc7jx8+KhWeDC6lrn83fvfHWfEFzEwLjUd0C4axYWS74z8fgAuW4RjqhXelxL6QUe/76i0JutQFfZrMFUw8OO9Y5aTHcekk6OxhiQVKiI8EPn3ggtJJfoGujp24d56KuCS/wDmi4HZ+yIIhJfIJUZqAGRxydSIjbZaK9wall6UURdeJIo5Kzfr8Sgh2LWS1K5Ffn9JPh+TOZfLY3dyNfd5jxVLa2D3dRUCoDIhIzpQaclvJk2G/bnATlgXShXCaGXmxbFzwJEfHV1+dXM42ERaZAVAncOI2JfKXTPdcdIiXJcoULAfTpM1koVmiBc2cyRJD1uyvBVI8gBBOg3V7CVMTsaa+oDM871mFgSMv58U4nd5unaoD58/1jCFn55nW1H8/7XhDE85Q2LoBqFBrHw8u4F6lGikiqCmqgn21h2WLVZuru8elWaJm2dVokYCJXgL2Zq56ddyQUV5Ekc3UlbSsAmWCZhLDgi46e9UIeJyOLCgFPneIsgXI83R1YoJ+IZb40TykXGlun/DRxPvmEfgU/LZVjRiZDdfxyGW8uIb860S9GK2uCUJO0R0Mndn/2v3529U5j7SWpezHROnoIxZnUjGmaELim4wIemox9jAdZFZCCXK/ftTpw7fpKHkXHpFBaLjA7R7MoH2dII/ZPNbtg9QC/QAIUDAgLVSCB8KiC7hvoXCaduFS9KHkmHPNSaJ79T/+3/9fc798bg9Trm8h93QjXNqK3Rd+x8E87DGnsrUpTCq+THlQQdx/OKKkIWHjO1zDUu/B1kbemU2aRZCAyc2xC6nzqFdzegcZiwFoBkt6sl9Jnk+DFf4bfuQ5ck4B/8qMPd35rDTCvS0cXL4C5zbgRgREcqD08IspiYs4MW8XRGXI06Tqe6jW5G9++hvZP0SgjyLGu+RLOcRia8ZN2P3n86JD1w9+9c/EfvhL+Blp95Q+/9PxvHohPR4ckLBPS4eXhvZt1S/d3u7a5fcShfHU9i1M5Gsf3Agx69xbbHY1+/gUMgBYCmn2twZeFIub2+/va4C59hTDNJARhPbiKyu8nhkFAfABZQUSl+Oby+iWhcnkTxTkoBH2cBl1dZuMRSkzbKj/pnjxUvdmktGfN10PkipgMnWRFwlfJXFnUT4+jrjseTBOozUG5Y8uJAvnybDbr9MPNPIET0a/PpwtXeSrvHYze3prr3WN6OMMftlbRJpygz1qBWJxdml7t2jz5pa3er3cz9RLDq5efdANLlzm0mJgDgvrwA+TtEoA1EABTVzb+cH+RMv7iY3SBPe15Obr6siD4HDaiYgTj0sP9tFIrLBL8oBPO4oSmDNnzBOSnL87mqfwcC+Rvr/t0Avz6eJiRSlX+9L0DYaPaY6SDB3t8dh6/XnlpPVaOTN9UzyMgVytBjnN8NiDxeLFYnGme6Vo1kiSW+dOeIYhQZFoCxCZsQVgQjed+9Q0Bz+Vm45PDp6X6TZZMyVWhAeaVKqXQ7LpwldPfwz/94vnKAoNU5kk0pSkgNAOQF4gS5gOuwUUwSy9TbOErwvGDVrfdG45hMPXyCA0E6fgyQPxheJ3nmlnMCKYh6OswoEyzmVp5ZdVohBwBXnyuWzHcv5QRNEg5FsuUUIGxxonrOtksYqWIPbMaNc5NwzEcWVk8AXxy4kE4roazugagQWKLpTjlMStiPd+lcaSNeiGJnM7cSuiPXHUie3Rqk+Dg7Ezj6BRIbV0eNL9WGWc8uWQgmaJyEcUAtMqhGYlCIdt2Q7IOqIY7V63vPO9+7u7N322YAbL/4eAuC047mgPxLoH/1HDQMrcaMaMPL4gMtV6LlHPfpnWIjEIHDfFwos50fxpqVnOu0PL9FdgkUIynORwHxrK5VsRN15HVKZXycyw/8/KMxAOMnxcyvhdxbsRvleANrjxGX/zJo/p8lWVMi4w4EgGDfISnkSHgQq4FmtA1ST2f8PGE0CIzlTA+xzGawkUXYnGOWlhKCStOEjD0MfTF/ixNEKpQaV3qUeyFMVZfRKQc3m3pygwkyySDBUqoD4+d62sLB4NZaGG5rKN5wLWbwvPHJgMm/KpEi+DuL4cEyG29UjNZ5rg9ZbE4YCP4ZMKO9uB5ASFyEROJJdEJWYHzj1UftKbO5ZQgwTT1W6rL0mSjhJva7F//D/u3X8FILCdP1TTUbwpaCATHLTsKzPU6LSwWx+caTEEYD9suK5bo6cwOwQTLCzjsd88ssFaoLtV7odm//97O02H5mzSZoDxUbDZEL/HAo/3z7kWTR6ZfaMo401yprG6yzt7s5GDoqSJZgKhldLQ7PX42Xfn+embjqtWZiJhtBSY3C65tzHEL7F//LJJ7IxiTS69VSzcz53RybaUet08qZRC249Q1+KLk6Uio2deveeM44vFk2THWqth05ovYlICCmdWrbpXL8/D+nz6kl3GYZ2YD9PU17vKkzS26tMDxr+V5cLkGRsrBpH1yAQqTBAQrBUR1cD9Mxt1jOgtFrmWMfatRwkFzNU90I+XooEWxBFMqJV6k//j0Jq7+n3hg9657Dnz8Hy7ZheLNTkelB0B2vgIt5U4Vx22fCAmYJkSH1hWltxEXEN+9PBs4esAgcvmgq4zplUY24D1LP5/woTWeciiygOD/9oN+GSF7v1OGfvUABwdzr7xFn/ZwDB1CyNvfvXNxv19k2cddmG/S1UKlmC13Zm7qBxURKFE8YJgXLy6XMmmjyiqgyeYFGlZ1X07plPC1TWlw+PNz5uaaN4cOIZu6RS1lePnM1ROTDSY2xUEopdmQ2jYYrVYEgMttSxIVSD8/l73NO425Zd3mquJcEamDQ49kAcz3COgQUrsuMPPRiXJ5/zhOeuTqIoEylU3um799p72fOYbBJocBieZNe1STvwzgAzvho2T26fGsH8N1OHWaoacZqVtYtDVDm89ibokEBdEazS52ndV3ogkUd9oqVxIXysikPc1HpDtT7TtIid0g/o7wyUjbbAbWY5MS0Fl7GHPczdrWLAUTxBVoPgamk6mH1/K6hSzGRF3AyQyftNTuYcsK1DwJL76CXjw79loz8t4Vw7dYAshV4EFA5AuUhOcJCO8cdqYDr04CMYBlChSCY54NJRh+qIEVGiPFnG65EU87Smb57WZAS1hO/2DbEOgZRhFNMe6f89WFMoE016+E/VHrxbiv2rgL2KJkqyeea7ogRS9eWwAYnKrlQhjyuhdU4pGyN7JQPEJimr392jIOcKPRcXtg+BScvzIvgHT3Qns2DQgYhElw1g3qVVFa4KbD4VhxmgWpuSwVMHFwfjI4GHMwgUFWIFKlAt02zZFsUHzq+cFoPOGpWCKxDJeyeRBV40SETM9vGymg2GRXxmtLIYFRJFsVckgKxzEU2tDMy7FFAB6v5y6nRIZ2lrKJ/PJmGdCf6gAIA9PtdhU11bam9BxeZMxLPblM0EI6i3XBCBtJZI00LIHavzrS9lqrN8UbpdzBnvH8MlpagNHlin3kzotA+9GlXscuapl3izqB5H6oxlnWTU9NKEsDkA4OUquHhwUCRlB5EgwHISD68CbsRIntGQ4nAvNE23Sy4xhDwvGJgqeakkwbeS5UMAa2iuvUxquNw/dPM6UytVIr4lQREtjLNlNiEDVnlKonkb3AYnkZwJJ0OAutDFn0PZHPRMEMkIpotsY7NHgej6xIG/UWKXjGpkYA5ljbG1poadFJI/vDF6cMVb9ZewkIQ3f2C1cFSytd0cK7ewUFWSuMf9GOPxmx5XU2tEyDTMctJbV0bJ7vIfi8Fd3L8y/2T+E/XAmoVfxffEohmZEWMiG8fR6i+lQs+i4bXmsISEnNAGhEo2K+6F9oaU+bPj0xupMvlPR3f3eNYDCvFJ7jaAGhFirmZcc7P3azPJy7mXMdJfWiGEUGY13gKTL1USdKWCZbxydERLqqM5aBAFm6lqODYNQdjGTFHjvpxMf6XfcmD//WLemxO/j1sz5hf9HIRN2hiABLEtX53Ji7y7A/WO9yLVoDtKMRVMN9ljAuVEjTvzg993sywrHXvl8LMc50R8ExEBhRG4vRBJ72lSCMJZbPL2Q7WnEL1uWpfSghKMkW5aQtAwFXKRFQqHURmB0PQ36+YlCFX33Uevvvr/3VXucbahHXtGFVmfFZUWPeWKwMQgVGfCCJ9i+8GNZ1b1rl51ycKq43Z2nYPjyjfOoUA9lFLHs6btb8fYeJtChKE5T233ZHmx3gs68BqdfpTP84GxHKyczKFY87Guxk8s/cDOMBoFBNzHM6dAU0gAhndHTZCYAqeevt2vgnz48Xc8V3vhScdryhZUU+bsV6igTN/PDB6at/a0Fry//tH03fLS401hqnn07KLBpiC6Xmgns8cVPzhcoANr55hz0z/PGhIi1S9SyG9P24FfiGzegNsQ6MahowTCq+X4ahI9XLmdyCjZ/6/k/3/VCCMr93N3YeP3RnGSKgR4asALhSKEeGHWCJ4JaoYDYYt0bQyNDQ64s8gaM82a0njg7n/a6777NDJYpxGMtDAxYWk+FZpMN7JVEq3m1wPpspo8oNqe/bVMDd+NaW//HB5dlMrFc2PXUkR8yK+Ku97a3l7OxsVquKd15j0PFpfNpT5tZTGJxT1NAnWqF9q7GADbY46eIXnx393t+VDDvWHk3gEIOmYDxIiDfnvMtAI6Ig0jEZWcxY3antuJYcJCKNFQ2bXcm3d05ROYk1SDexvpU0CVWC5whtRBGwBttrNxY6XcQNXLqQ67//9N3fusLTCLLn5glW7UXSIiz20kSGhKtV/iVm9L9+RKBYSAMXZxOCLbBkKpXy+qVhBt6rt4XzA4SlSfHGvMSIKkrMujPPde1UNIadXZ5e30h2Hmq379AISDYFgjuYvvTK2o93n7/58tXPno55kmSWcmKjcCkbhJO6rlmYOZNhi63n4SYGBhq0EI+H7SIv2CtYI6R0GIM8V4qjeDMzm4b2zmSEARJbCGOwa/m0mBFdBA2wEQEOtSnsa8Mw4XqOtZwctUYlmsgzdE93sCB13TgCEtxDimQFZWAkp+Eo1IVRmqE4GBOC0ISC/Rcv6s35+oIYRQCCoaTrxnA51mXVGVv4kVEt4vDWPNhYKLy6PvngyZ2Xi5GY+FrYPnZJWzaVkORybEZCIsA23ciyIhShIUB2kcbNTK6EDXfbR/3Ri14/nCX1LI9FcvqFVRkkgTWJgnD1pRWwFBl8tXMANCMIj9TFObgV6sNZDKdUSeC5JEYo1+9ZDcwwXS+ZUHwUlirl0YjwPD5G7bSR0aOAAyrlAhLok8RLMmKQJuns8d4ff65KCZu7dnOyfrfKWeqjHY+icZgo5MtRKGwt1FAAjPGE49KJFVS4gMBoj+dRMFOBq4bX6PicnwC+N/VSNL+QqIqT6AkYWdPuZNPop2GkRU5R4KtIvPdCubKG5EWmucz87GFvtbBUXq09+8mzhFTmmjyHehefD1eu5Y/VIDfHkDWazguCa8nj3WdPRiqU/cHvUA8/l6trXrcLsTy3WBW4eQ5fpsa7LRADCYmAcuJsGiYo1YMwWHOEhcX/7q2bz84v//hfnn/5HfjOlQJpTEWA+Pij6eIdPkLdbkdLPT3FUqFIwzGNY7A29RAeBVA4nRjJxKJZyaOoMi36Zdc5wSePei09fOleoZsQbAYNW1iOQ4GWrfQMmELGnbSyEfsr7Gx/yOTJRiXs/nJc8nLz15fRE5sYGuORhy1QFZRZWCEenPdIEVu5WzeQEtef+pNxonnpMH2sQa7jV+o8v5jlPAaN07Izm68kqAfs95Pzy2m+FiM41VJsHAAEwBSxHM2Fxnb7H75B/oufD0htGYzSL56cN5f4GoaBkOv7sh0RdcqvzWFcY70ho9rUOz/e1+0WIogeygKGtbZGCFB6pBmRr8qSTEAiidVoYsyGCuzjnW6BlM+gPwfK3zJt/4LKrjyaxDQPQ9Kc3EloCD8PwsIa5OW4yfBUTTiuIc6KEFCL33016z4zW3iGnsuTQBRDsG8BngPnm6XciW6egZBUemeurmasF++NqTevwS/VhmIQTMfeWXsunz3oOQnpegIKD/3tv9iO/fhGvXRyqneZiKKlEE4TvVSpLWSKuDf79eoSrs5sO2vZjId14AxMtzUIr2ePwRRvmbjuUG4PCe0KGCeKGqYwKJUVozVJZD71hLKYQhizlGPubpRCmJkqnwQnaMHTnrhFpTedQGRBAEt5nLWya7V6grz/yeVlJ2jcmVtZ22ylnWkOzllUBvE/+tjafxbmeDCJZhYPPzmyv/5KTj4z12qbeSwzPTG6R8cnVpq7m938kvThj/ocXX33natHf/ZphsJCnHzrG1d+/uvpOHa0KYLGoGaNSZeAq9mJMzLMTJ4VZ5mK4PQPjvxckpx2wtoKH5hxT+nbMJJyDOD4mgnkS7xyTgXsBI/1nh++VaE6w9Hz7WMsE08nKXXes50wYWDVM9kqOB1CEYplUarAi0PdCN1AnMPEb248e9gu55nh/ciaAdkczaEIVeIDCP/pF7Obq8tyArN26GCz8ZTQx30YEqeX05de4vb7VkWJb93O61Ok2+ldvTJ3aT6/BRZLtY3dwydLV1YrWVKj8X3HIIg4TU0ANrgFdKL6aRyGQ8c4mQk4mOf9QsaYxnHPJbAQS13qnA5iMERgt1xkcRQQKEIZ27NzE5QoMkYi1LPGw1n7CAe8fEVMIN5nQgxIR/1ZsSBV5qX28VAz9HqRQ2PYcDwIQhAT9z1HI2ekEjWo7CR0dMiTswoYo5kxAYEsgnomH4U0DYZ2ALJufDlFjpWjjp1fkPL3iSU5imKyj1JxXbhCQj2KAc8cjiDOdRtIiZzgjbwU92EYA4U7tbQGkkWWlwpJZ4K5wyEKv/dFlyd9KaVLDOeG/NYPruReXrEPjmDdygZ8djL53IkmcoSKuKmClg29tgDP4iDBQhkKR6cjbiNXyTHhQE/6MeMAqh805sC1OqnCZX3iJv2+9XItGKapb8yaK7e5KfOLvn0883m2zq1kp391+HS4PkfO8pzRi0la5GX7/HKag4fmHJhun/JCiWNqyuGMkqjCR7P1e6wO0pEPT6C8s4C9yKqg1U+CxGvpIiseHE/nV/P87QKKF/Pj2fhK/ocD5dtf+7bxl8+vz4D01YXLperU/sm92zedYNQf2KCPAnJAgxTDR3QCufvTnZPjXZ5n6z47u3S2kcLV2sCBy/+fV6gMXJ+Cl/ttvFz5p7x2thdqzHJqOGNCIiynQubVSbteXiOi2VU4bS0w1zdqhShWEsymxVoZUX1QR9CFdSagCD/yDR9zVL88T+Ggm0jUI4+pcn4VylRgQMbpAZ+6XkhC2uVZv+NGaCY+CSCmFzDTKWnjxZdxB3RWrkjWZUD7+TGP5bNwuz0C7111hKJ2fz/3zavPKe5VkbHPW+HhTJZdfIMobWLP770MAq/wQPr0lz9uzpXpDhROY5qFhGhochQbowyBQQiSEaxfPz+pr6+9tcCdtcZP2ulm2SaL+Yso4N0MqLqlIDrOgKN+uLiR0T69v16MF/7OSoFv+F0tCcFpAf3waPzqG8tnJ4OPOyGeYqu4N/+9ewfDjo+XCSDdHdhpcFhkqQ0K1VxgG8G3aPw7BfaX+0Ff0J8/ffTBr2YFDPjtbwISvzmdkUuaCwrU8LydW4UUPLehirVicZ7Wdo72wWuZ7PPJ9XzWZJAXQpQMjfjB0cu/+1auXgUeDXplIYAN0k34ACWlpdNPP3vrjepZpyeI+O/+lzeuM+Annz7g3rxypnogAxaXxckTJ/LE2RC7kUPW7qw9duDYQRsp0c9SRF+2GvX80o3VMVuBh62yWJorLq/M//Av/uR2MRgmNsvgzqmqybFx0K0k+O1V+brknj0Z7xeFylp53s/0PGeI+pDdtyWUDCF/4oYlxr7fxjsTg6CEhdTzwp5jwgvr2HINXikqQm2xMwHQiAnCu18pbLPkRAeFoTEPG96Fldj+EDPuuhPq9TofagefaAYlVSQ22bPiqNA5dUoEeYDC926vqr/cX1/ODf9cXzlPsDcyT5+1aQqKRXS2IonEUv6s/8tP73/jzm35Rd9zYy/MS+8QznzVH/Ytmz0a6u0ALK5n9Z+rJEX7PG8bHkjCqYtQMWAOp60jswSB81n3+Vn/t9+6caKlCqj7K+Lhw5Plm5vtQVTaMarLeSVxHMNCzgPk6ioF4ReftbDbdP67i71HcueT7sJcPboVYEnIT9GpbWIRoZ7Ia1ubOQpst+CNpv/Jk5JJmlgzd/jgWJkp9Rq08/CZtDQnx9xHf3Z+4wfrXBEGSHsyTRIqd9Gybt67/eSff1b3w8mZbYo46EczDFkEjF4UMLgrrJRaR+f70zZDYNdxeqFCHYuQIPv5oqdOgCyRAwlS0VIrxNkmhkaB2jngFhu4Z5FuqtKE66XeRPfCNL9UBIGs33O7BwPSsUAuIxQB2ne7R2O4yvF0GtvmGR6wCbcQUvV8xXZNBXOmscaI0FSPF1EWgnhAVhQsRDw3hn0UJ7n+VPE5IEuBw1YUjPomm85GB3kSS/UoKSB2y98zB8EGXSQgDoaCeXDoxaHsVFNTIAEfwE5mMgMCmdCy5LjpEQ5RzGZQvxLiOd9wkSHGvrR1d221cXRgZhwJi8Ie7CgcXlkpBxed1tORT0iNa6iRqplMvNOPIscAXbdYYo79Qu/C/fbtjJ/hFlfy8s7w/k9O+Rw/nXqVYtTvOquswHGg3p7pm+jixgK0tDEhpcnkaP+Xk5IYd4SMC6AUQtKN1BHwWX+kFFM9Q5jL2eIVTuwgi6BlqjiWJVCSd6eqBvIWS4WEP+ib8Bnc/fmhgIeb9+pRETEJgYMCRkDOByYbIMmpnv+t+DPDqC4lpTL96LDFM8IMVPM5OmjD1bk8IiFVKZioth+lOAwz5RrbKC+shvZety6KX/oHb/zR9iSq3Do7uvjsNy82/mHpUAMzas1zgyv4/FiXKQrGtVYzE8OTkJyaNBWSZvHv3pKyRHys6JVNSlWDqYWLeQbzJmbgykYEpAQpwLWKNOzrAhw7aJSAsGYhMQHEKMyVCBhOD04GJMIW37iaSXRn4Hx3TTo6U1UIKpRxmkejKaMESZpiGZNqYjlGzLp74xNJpd+tX+611x2PUJUdG03mYVjB1D6wfwnmfr/uATUXQBEAzX/rbdNK6UHy5qqVLyO7XbPN0F6GHlm4OZ0OqDNxq+OeGMp9vLFBKrY5MZI50LYd0AsBtgqPJmYoIcOt6pXV+NkHu8vvLmS+dXML43/yz58UBK9uaA4Guo7VOjzP89V6Jf7gx/tbrrH/bFCTJhMcup0l+rPocmcHqJY3rxeue0k0TKJ5+u4rFbsE/PSz89tSiuTjzNXFEVK+/8WlF4QNI3ne8nL1OXz/bBB5Wbbp60GMhRgW6K6vKAm/KJb7l9NDCxBYd504G2j3lpiK6Ox2ujaqq6iwsSkMLmAkG+h7abPMAVjy4yddtkDHtqVM2LW3lk62Z6huvnq30QPo3WONCAKikQCMRBJVGInAmF5rFGgL6owu4rhdzGrvzOcAoPHf/fadz04ezSphDAhUFV+bo8c71vWX1xQD2P5wu55jpQBqRiAegkk8pjgf8F1/e2egUVlwLnaZiT9mCTMYtK8vlHcGQKWyViCvsxjkOgzR7Vy8dyhscebiPFFaz3ouFzuJF8dj7LZuHLYdoIT6HBo5DpHJa0E/bnE53D95mH75m4W9Z1PN8WdGrJ5fjB5vrxIeZKy6QpES4c++6JZy8cMnFy/Nc7qb3v7W5vjPe4CRIiFuo6h0HQaX0veeDebZdBENopQTw/jwF318AGdv8wddF0rMZhV/+/bCg48608lEyOVqGyVOdDodGQ0wcWJd7IOLC1FgIoHX4eu09jxozld/2mnVqoKQI6AQpLJINI9onmp/2jbOA92G+q4Op2CdLY0591ieZCv5duheDh0xwydY+vjIlYrB/QNjAVNPLl5Ez0aZ797UCPG4FTDqtMKWy4gvT70cPvO6xp28u30B0DxnQsz4xTgUUD9JIMmBg0i3gnyatDt9TCJa+54fQle/t4AG6c+7FylJI11DtvQ618gwpaEe2gGCrQk+YGjjgLqSRxP/wV8frFyX+mmmXhO5spC/mnEIgpqQZE5lZBlyktq86AHh/kGvUGRL85wHmT13AuexyI9sKJ1ahB/gMxwInYACkImHpIhYLpQcJ01ND5E4FsrzCQ7gGcBUYKRI8PP16suVNI36o6gf4TVbSvoTwjB2Pt9uEhscAemODzXR0KaoFNCHrg/D3ut1Ngrqne7JRx13Y5Ug2JnIn42xt18vtfpd0Lgo3LsmlatLTp6VZ1MG6iEOLPmkGMheiMQWsFbIEBTfIKzLKWaDyslUG8Ov3sy+/Ae3XgFyf6pfnKrA7HBnpVulNspWRKieHZAIXU5cE5m2NLeRm4fA0eOhTjdYI5XSSWu/t5BzjKm+MKP0sBaGwei4l4W6K3inq7eAuflbC4Y/9CqGEpFFdejE8/EIH6WAG2YNBELYQJYvzILXTtd9KAKbmyu+MuocKbVFXJyNMNWVbP/aIvLFf3gfCmAQkPjztvhkWnlZ+OR56+03V5+5CEiDpqxnC6I/6yNYtvr23Ho9aamxIBKpCHl68ifn2vPJYbSTvgGfq18PMNji0OWD/a6IfeW4Ty01WDoYoSaIZuX1K/jkL9rJXCEpwf46fYICN5uvuMap05kQvgPrcDaJWTa2QUyeGKYSx0VOKhflRPcTYhNMcYEkKRCNiUCHDHkwswKKTje3NsnENeFRLi/Cc8Fne8UMCWL2IFeFNIZrwAZEWND1yqOPzYaRS8/kesMNl6X2sXnnavnR6Wmjnw0whq/7jH5264Wh3Ya2UuXw+ZzVI5+5S/emOjHp1AivtjH3U4cb9R2DZuYRAQ3BySABu6rnUrPRNIHNPsZvAGxqhrHA83KQogCUrb/+1brx4an9BjC3wvyyZzv28N1/+NKLnz8++eQiVxdYY4zH9nA4hUq11R/MvRhzWAWdXE4Wbtdmtr2xWrMNnedZV5WbUGQK0sfDIbbdfZEBm//3b92VZxR+eTZojz7rrqQ52feeshH/GnbAppN5ttr2aSE4m4yX6jAgdz+PjBcp/i2sfgOa5PD2L6/k3HAAjaMIjh6P4WJ44tbJvtkzT0K8nh8a+sJvX+2ej9iz034R/8YfLLV+PMsLQCGIj6cKInm7xweN20v565A+C9qPuzPfLGWTOxsVqyo6LxyRfRzQCDk9jo/PgMGjH27uE3EEKm4ZpmKUqsdN2ZUvU47YGUMcofIrhQWshxpTXspDSAFBRl7MAngUMxcj0sqwcy/zkQwtMPFj39uFtXKRoflCbxexpwoqTbxWv/fw0oay9rX5bK8X7thw4HUVVBt36ogf+fpCU2gPvdlALb57TaitwscvsDe/NnmgeBemGbi529R/dG/u6K+Obv7tq/s4sxL6vDyNFKdAY99YzvxozyJYemLK1xfy1jtf+sl/8+9vvbNm4malAZ7s7TF6cb68yI890zRnE12iQ3IZklNboPOw5ZB63PnrXR6l4iya6BZJ8H1tRDHB53v7xPevbP//XiwJWLMqPv1CLdyZ++Tkw+zv30W1ETm2OZyMDJldkZBGAT2EgF29ML85v7I0+vRiScj5fU+4xms9V2+1cuW5UAsXssSLKP7ZxezLmRyLTlpajlmaO5iEP/uv/+zbb9x4H9n6xm0gZgjcdeZy0Oiyw2fn02m7kiMTPt+8Wz79QsaKWYlHB58ccAwHo5jpgjlCuBgGG6vzQzUgZf8CCb3jpKK4Sag9yzC8OOtFoIKK8wzpa0ArhAIyL9Wh/ntPLXN8CYlrcxk+ZjwfKkf0VAcyGFXfzKhyRAge01z0p9PRdFSqi2e+x8ceZAGMSGAAokE+7Pp4QKZpokMu7JBOBFWrVI7EAXXmxCAiz8wsCYwGAZSmJZDnXERpGflyeXopU4ZGZPyZ7rAsvLm6VFjJeJlM6+yymeM9NRB0P0UCAoxh06KH/Uw2/PRHT5yk/OrKWiFB1zPmFQAFfU/WEJgpXKFFT7bPQgUIdBek2glfKwDY6CTVrHpDLETIJ49THgD3hkl6YTYhrr5YWPn6zceP4m5ve67GiJKk0Hr3Nx3YsucXIIighmHyyWOnuM6AEDgejZdovsGSyjQynbBYLAjo6QAbjcBBjsubGDaTQMgZpunIPHoefzVTYiUVCA4g3VnNwjlKk9XM5XFSL5GNyg0gejIYRSRESbhaW0NeJ2CZwq/cKuqy3P5UPnbpnCeK+PPPTr/8u+u9n1/WwSRHEJc6eWyOyorQPR0clWmW8y6GQWMZV6dKMvNq13mSy1/qssvg/V66KhbPwpzPiGtff0mPgqxEbmjD97e7vJdQ+exyrfpqE/ZTQP8NvN5kYqsLL5LpcnyYAiiAJZpJE1gCRA9/paTdViZPmZaOwICaEqUmXqpVEn98tK9FeiIIOEhGIBxO9JDIkhiFgFFaKBPzPV0dRpnrqXwWJgl0eaigfLoOIAmQXIwAjyNu1IDRtnx47q9AQjFnD0LE6oOtH45e/WruR/+mRdzU+SVw3kKFAj8JfW6UNVggCx6kz7n8eXjZ2yTmcVqkQdA6ezHAZpf5xQWhgZz2BkZsyQsUKK3l86H1N8/3PhmnMROukNpNYmwjRAHVTZ/kAAbElwHhIQekRqyfWDdhVRka1a21wrc3PyJR41L92S9G1kTh6XD3J4q0lL+yRVNbzCGfMm+Vgr7c6TglTmAlRplY7b6RrCT16zWhJ439OJ5BP/zFBBf12Rx1m8XJh5FQF6nVQj7rnex7Cz1b6zEZ5PnWUjY6hyEAvnuF7eh99JJ4vi2nhC9XYsQbbVJRF0oNCiclErYwL4M5Efpyvr5/v18s4gnJYqvl1+4sHm73Ol9crNyrpLNIQPzq1fzJ2cXhB8d+nG5tVG7dasIJ5szGn310gM8VF6+wJ7FKAva+56Qg/dMi1bMAz0U2IRKRMwggrAHssDeAt53j3o8rrxXXvl4fT71BFGVx1JHVUlZcojBeWvcKTbxGmTNCoyFA5PrOELSHsBowcS3VmsvLJRPg+LxqlQGIBQ2O4UwQsKV8BXEMvpbq4q3FgKaDdoht1r3j/mLR5iG8Gs1GZjje3WeotOcQXIH0odgZoJ89Sf77HyyGU2d0cpiG7ubGlwax2AoK61dRokpM9BeYU/7672w25ho//+JFVzlcv4b+8ocOQc+SqHF5NglplUJMzJykqBu5fK6ZP295Ud8QKwKd5Q/a+rUqLtzkDs0Kvqu6O3L4oocWOScO8LwAnMWkklTWSLl3+bZY6P2ql2x66RptmH5wYVU4MVjIJEXIoeOkmUqUvDMcL2Zy33xj5dP3hqt0Gjje/ove0iIWIagzPmd8FATSt+4s3K6snj8/5WczIdMjkWWBjwxkmEOd6bhd2+AANVQ0LwjXGPxKEvZgK0QwtL66ohuJx7EkyjJbAjGyo4wXDS76ftqYExrl8qd//cAJvFq2YLpO7PcjUrlQmCpQnpMyMyCanmiGAr38zTu+xIkgjaIsUYa7ho7AcJUTIE2DXVVoFqoFbiC7VzfqfWOUmrDhuXYYkSil42kCOtkwgiIUUfBGnsUSBIQCyqGRHGkAJAj4CAggvVG8nCX6IRbpdnt72H0+BnSwJkGBHaJZ7LTMFWRtf9zjC6UszziVTG9mkRlCSl1lOqXF+NxT6CeaUid3GfBrby7jMkcDyY8mVhEIgiMdquXbI58802BIz/HpJ8Xaa5ELm4VTyGmcx9KNucMn592uX6BKu5fDLOuybB4NMXwhfzBRaFMbjAcqCgn36dfv1rHS2i/ud8MvTm0UuP3WnRdAFA6dlICokBrqhpcnwCSk8vzHo2PGUpKBV371qqORVmz7DsYD6hCMH4fwVyT/Yeqvgf3bUrqkAe8/Ce3DYXj7Rr7IwOByDzjkwgBygUIcd3MkhooF0tz+ye7iotD4xjVne8fxEgCwXYJ2YB7J1z/+yw+/9p9+/8lJz/+UdECqvJB/eB6vXlt6fv9X3tsbe/f1Elvim6xJ2RN/gCPUUE9vLM1JDv/kF+c3vxPvyKG4vxe+kf+BTuyX4HApQnSvCAB/ZqbNdzkEriKjz2SZRG6tRc/S2/Dx7qcjhkd+/sOHOIILV0sUCCyXk9MJSM2RZxftWiHSS83Xvr/59M8NaDDgr0XHhIXkhajvZHXTbqydxEACLmxUhmfDXrs1Zn0gdbzdX1yWKojw6lwioOjJcFabK72+7n/xyOv0TVheQ7n4Fvfw3237VmP5KyXnwan0d1Yvz7j1kOfC/oy05y7G19d9YPeHf2Fdjde2inqnAIXnspKvQgmtD5PWVBN9Ok9O7PAzb5EEdRI5HHiK593+/laCwIYu8xQW4oLNkQQH8wz48c6zCEiQDOO8f2rpKRlO/48//dW9r111zbiRZYCbZPszP8XhNYQNxxFrQDsHg8sXx5Y69hoZbWdaqaWrFuhXa/yV3LQ9ND5sl8BcuZy7//99cBREN77eiAv+e/uDr6IhArPPLqg3Ah3fs2FXh/KkkuhFJyFwyLCA+QAPzzsacOS/dcvVfFEzYHm7I3Bh6IdUYzZwKvFqFcjKFy6QnbJRrD0+1xh4oKtXHc3szlauF8Sba+OHTujC0My5+63XIIA++eg0NMKx7vRR98arxY1rvqdzhOE16r6rawAItsCE3vfhnvbdG1HbGYIYNGKZy/ZFvg5+ZXGlsAf6FXQmoVDsLDLFZGrMLDLLGzAYxRYwO9dNnErzebmriRILHFx25G5YAMcGkZkn1rMFOJsMIK6SM1EryTVygMSKvnpoe3atVJ0TYkI7B5DcTexTQUyQcPLR4SYAoHRIL+VmLIYn6Gkfai6SF3sO/nS/LJJP/uZgcODEi1Hjt6+bz/Zq9kr32XTKMsSX5r56+was7hx+3N94kzbR2rMPz7rtQSEXYlzkcH6PoJRLlR2YIZtLF2OAyaidiwhCmfXqjS/zbfwu96pzfNjJpdl59vr7az3xcBqq9tLNeeWLp7vGLLl5w/zg2dK9xll/OlerIATnicXcW1u1956duvR9K5CKTAXWd148KhcrJ31luUzsPurlce/mG8Wdp3Jpgz1XgRtxxpwZdp3O5tnHf7YPyIe3V7P/xf/z7zlRD95j6MtJ6V4TwKzRk1nCNR2NRoUktL08mro8Pc2xBAbBNEyslAIn8McQ2g7l+z2pkZHcQCtViCfdzotTkw51Py6XarQhAKaHsbMQswEcpfKWydLsiSUpSP7tl1AMBcd9Xe0qQIHL0o6jFAA8AoGd87HDgfkc5yiG5pggS/B1cdqdxpMRv1KkSD6r9bt43EsUhiFTh/RjaGbYuIJUYD/Ie6ajYgmBZDJlxbPGAxOlHDJD4tVMZDNChYMKWOvBBNIcy5EnMw3mKeXJhJQgFEonlwbfcO3EDj0fTAkvjBxZjrjGV/7weyWkBEzofoQjkCuUkJk8mIwU0tFZUrZ0jCinYUs2h5mGM7GX2BEuuooz1OMbL2UrIPnTTxwYgMLILN2ewzPAecu5dy83TwxiHfdHo1/9q4v5Owt/616+wFc/ee/Zsz95WHslK3sOiITZnNTtWdHI8XRjnmFlc8zAPA1zuSg3Nn2GtDHXvlKIDlvRzfLL3YORvjhsYX73qN1Pqz5GhQVs7eVbFSxzHA8vH414D0RJJJHALCYqI+OoC6+ugAEe+iIFzRetlt9qBcQc996LqSAC2bnMYHsCnHcL2eDp5xe1THI+cGur+SkYNz0risPK5qJN0e2h7BfBgoQQSdJN05iGYFvRmMXlrcKwBeJQCJOc5GW5RhWTRAAAlmfGNY5/2hnerF9Ru90iFM9VFRgEqyhElaX5kt1oVsYupu3rYRYbGsocxiUEDtjO9k9PZrXyd35/tT/KPzo7MZwEh1Se5zwXFVDAnLp+Vx5CMxZNaNiDi3RGzLy5Dp9fqEkYV+fR55dK1M5xNwt7evbeK03rhBicW80tbvk/f/nFwFuTGKpat3cBixw5ub6RWjjhWfszYBRHGb336c9FjJ52ctZqacyNk3qc0qDs6sO2lyWNILVXxWI68tZKTfJbpa53TN8pDPpjH0F5htWDUK8mMRd0JzILwVWM8KaTDgwANLnwyrWFu0VNth7/+dOb31tZ2FpOUMBOHfJ5ovshwvjX7lCsXQZjCKOE8ncLcu9y94P95buc2bMFwxVEWkvMBQRuhzEnMq/G+bQtP/g8GGq2SOG1hZLmFwovi8WK8+jXz1I1vt9ti2tUCpLGlKlWl8MySgKOfpmM1aSylGOp+FDxC3QjJEg8YhRPIjLeDA6AVHdTJJdyABEzIOXRpA8lyXRSqJbO98z+ibtjtIuLxcaGmI78GAjYAFYOZ0SG3lqv9jpKa6YVfF8IEQkoe4pXIIyJ2pXSWYjxJCRpWa89gNChcuebDV1zoNT2Qyc2fM0OEgh/OjVhMgimkBSDxmDMswDuRda5IhCwYIFwQ2DytSHpn7ln/QMVgsCZ2ndPp16LQlNzDnDmV+lZX5EVS0eBqMjSEFM8nMG9kbOvX9uA5VrueEexZHO+yEG2ZrWD0Ah9Hvnmd0rts5guY1sv1VQz7RzY33wLJK6VgdL81A5OISCTKdBbam8EvHwlK9iN+58/J2BbAlIOalFqQGLgXseGiDBWMcw3t08vF1/bYF6qnFjmJTb6MpN/Ot7VfvgBWJrLn3ozBybOE8mG3ZZQyEFXoOBJLypMa1SUPvzsbI3lkcCm9kfbT3oxyYG0GLGsvMZAfHw74/74zx+6NxvQnfL7f9X99iuk4+hKr4Vn63tmxC9Rz14Mv7Eh3fj+/F/90fH2r4evvJvmC+i9BoEQlXJevOxM+7OgeS07teyyiFo9tTBoLZSxaZUB0hD246e/PvYEVgCx/nhmcoTo07qZIHRIpxxJ2TOGzt1ZXalVwLOpBfR6umKGMcenj9SOuq2ugHxNyp36CcOFDuJlFigmGF4cqvQMPh6G6nwxEBgWQ0Ao6fUvEjROUDgrgLNDmc5wU80IgwiPVUYULchTjTGdYEQsgASRWS73ZU9+0HNCt1wgEEOfoYkP+xCF28qwb5JcvcYjC0VLnxkelu8aQP/i6NAQX1lbyAqYphW4SCsLM1ln3MgMzYlvkqU0kyKRIodPji4Sr1nDI9vc0qHDQVuT6VFrsJbBnuzbM0PxJ0m0DA4XUvsRyWgpAafD0+FafrY9kD/aA4Ui39xAhioRYZ431aQY7z73LcNWcWBpk5V37PZlq7W9Hebp27/3kvZXOzyROr7T7kGHg+7yEjcHJHaGBeGkwQCCkqLZSmHmgjDwNJ7FIVSQNdWwRnTyVvAUb8UVVvYKQsHNemh1/s1cADFjINTOemIWgjkJKXCxDOiylhgUyTuXun9lFew/HjKkjBK0PZL5fI5iwp32GVv1xp+/aFr6E4yfkiTmmGQxn5pefUEwT61rc/WFBcYI1KtLSFrm0SgMLg7/ZD5ubIkv3U/lTjhDSeonJw0SNIfTyho+ODq789WXgRBoMjxgmjVUAlqzfLBF702ZbBaYj/INDwDTdRpMR44Yh6DAUZG1tpX/5dls6Xp5si9/+wfz739y/uGP73vzxamEStO0HMZlPk3wRLmcjgfqs9Z4hZ/V5rhN3DGz+JAk+AWJx4V1stJ//kxkmci2kGE7quG79w+Xvro+CPv4p918LSSvrymtaRNy0kX8F89aJ/q07pnZm2KG8f/mx31mCNx/BuRG7xdvfckxAX6LvXTVOZKOiVhknf7lyAIgXQ4kCNZ4DgqjuUzctgb1NC3jZCMrEjCidHUfpi5/9VipEmtreSy1l96kx8eRbptqi6bOWi+9UVmu52cPzO6/e7z61kpmse5SvQOIxBXUx4N3/uD1z+/vfvpc5VeW+CvAV99Y/7N/ucNfKxFXavJHJ147qF/NPdgbn3xyanrTglTWaI+3jWbkZ1drY09sLHJeKg4/+5nM9ZDlMrfUZEZ2VwmhKPA/Pb3CkbMrualxIX82zb98lbL4op7SODgDeo6lWgmdyYP6FKRTNze/VkBT1QPN00sNsJFyoNJpk4rgqB+O7AOTwDRrgmIcRG8yYREIsBC4+cobXzxw3Z3n+YwQjtDosGdp+q2VI08AgO7s+eZ5k5kDmw11GH/e1bPFnHIZwwg4PJqEBVKBVY62eYhg8cj04riSpp7WZOKWED1MZkwMXOU8SkSzGEXQqK11ex5kl7HJXg74cLBMU9CbRaYkYhObWlgQR21UAduffJrlGh7q4Q52dgyzhRwEXKxmiYEWsTwaqe5akS4sVtqj4cUH8so/fiPh0mf/7w+W/uO7J1tp8KefEEG/UIXpJRqA0nK1BgGSPjndPfZbA79YEa+vXgXcKG/Y/Pdft9/yjPefJxBY6trk916fu9UQaYb/qw/2vE8mC/hyftH5899w/+XK/FdvXJppC1eufnfz5cXys7v5nU/25680xQq7QMNne9rJFWo6Or56zF99daXjkzWuGEZ+VZ8JERd/cdm5nJHr+VtXs+4DE6LCpVtFYDx8/ngg5oZz18tBTvyz//bjf/T3r3/rH98Lzrr63pAC2LP8QnNhbecvn3kpQNYqZdidhmgwsRs8cOJ3N+hsMVs6PTOn2lEW0sae+fBxd0tar9U45eI8DpQkTZSQzS9VCxQtS9yRhBIHHk9n0toWmHpdLUl7HRZP9BzWKNq8iaKu5uKJnSsqnfP4wrBkEmL5OFfK52kkiJEkhEk4i6HD1kW7bRCSSOQZ8FKmIzizVPIU+BZDPQFhWfWLZByNNRSUHDTuH/XzeZquwoiqTggori9IPS0aoH7qTWcnyiKeh0Gg2CxeW92ctobuVmpTlG5HTDH1IqQmInnUti6jtmYLZWnampoDnSwCRCZcvhp73jGKUAXaHjio3+BvFMTOvz+7RDpf/vZWgQ3005MXKExlcNb0gKGDXEku+6gx9a8ullJYPb8EmDkW4rlgAtZy4NT1i8Vsd+juafLcIoJRZB6lHvysM/v0pFqM5aEbWODGRu5x+xzuWzMnSQj6FAQwF83lWABH9roeiyUMrLNb0PGuFkFBjcubh+x3N8AOKFOwAzw1RiMbYFfyL232A9qk3IC0MB6f9HS4pfU7MEuCcOhITdk45KihPD+fg9fFAnDzxz86K10BHJoeJo4ySq5lpWcPrEwOwTP5O5vFSdtkYLFvWLdfycNZUj5ts0Nbu8QLdSbiM8uJ/xY4GY776aGbgcBPd5gbv1ugXhUzeH/yS2/0xSWYv0I28KMxAwYZebxazIMgL9q7F/QwApbs1IzBNDaemeEcFgbMb/asV19b4C+jhTJ5gRZ1E7r+pfroYVt5evja99amCd87k3WDLJXzhOAVU6xQA7gqbVXg8+MJcNqaJuQ0SxfyC+x1zIRhmoUhgnz/g8l/8p+tPPyjU6B3WXkZah7jg52RA03mb9ZtZ3x8OXVexW0bnLSik1239vqGVqhw58k36uHjXwyR1qFu5dLsAkYH4ykYQiAcx6VCCAUQHIwAJaUjPPJxAwwBCEIyFIjhz4IApwMZB2th5JFwv93/wI0iHiJDXxsq2SgjaMTlMHz5rQ0jSqGJzC8JJ8/OEXMmRmmTFOMk+vR4++cf8BtFaQkkFBXo5ZdMIPvWu9dmdeJnx+r1zYaTeq1pDIe0W6uzwhapkSBkXgyc2hxXYTPnn5wB8nz1pTkEvvX8Ly7SbliqCYssr+ltVmNrHB25vveBLKtcTizl2UVnGBk2aAQzhdNZyRk8ujTholSsLa4vW13nk7955lH6xqtL+wAE4FCOSHPNjNtSaBCWSFS1wTQOTFbsgRCihepnwytC6Xdenv/1Ja+hArs6B/rA5MXjiQn09oG7rwA3kNH709Fugrz+u696PTr1wmjPUDRbwpCkBCt+6oPQQLUYQiFnNkSGIMHphp2l0aknI9VikGX5wIv2ZdkaSyQSswXThDhOnL9TWSkK4rv8XtjHpirdAwYHKmINXQIGS25rYEg5qjyPCVzueL501tGq64g3SKZaSJczl+NWOBjk6gTt9T/72BdZ6Wvr+C/+7AvWjTa+vajKycFn7cVmHit4AgLwIlwqcYd7ARBQMbcyMQ2bUfdenNSv5LK/P+8h1RUgZaazwU6bVj2ZlLLj44OFwt3ay9s6/+bV2yuA8OIdI5nCUeWq5R1cYcERk3qvFUerjdA02JdeEl/JZzKUBs1bFogpSrlcag8uuqaTmMM//WBnpcnkVosft1NsvvrgbIpmpXK2LMAKaZkPd7r/+d9bexo7/8f/tgdtdTea5MvvXov4HFWQrLEC+MJyBYJBwO8H1VoweCrjm2QM2DYwBWh6+W4WOwRHJ7pzahYSukLARQLcfXEMSJFE5VCEYYmCOrJbu3tQGSsmcGiiRsTPMULaHkRmNn+NMHLm5xO7GmURny7GPjLSR4dps7mUrjEgx+SbTXeggpCfsJ7dHzJMBi/jIMxEMBkg5L3Xtg4fySPTi/2YtJIcxIMsCAg2Eia7Z0c+hHJ8DsVi21CQGpc1i7CqK6emzzH4UuQyN8uBr8dx4HuQN+UziBBSoJy6EA/jka+3QXMQo2yRoYBUc7mYm3tzQUbt3tmETEFrJD81kY0Ckk7jqEjDB5224JlCAGZKl3JozNIlUty2cQSddgVWIhR1rOqTSEJQZTI+BKB3X6+RpRqishnSPJj2ZB+fx5hbiQXTXks1jYdx9ht3rrxe/vBffviNf3ZTQSHlE8X6XK00EAgB8FqxY+lcMaNE0NB2K+b5cQ1j7bg5dTCL6VF8q/Pgey+vfHqa5vbsyXPgxkJw/DwxXxK71SYY59yxihZKUdAJTh0osM04LtVZwaE7dgwUixCUiADZM7u7fz365lawslGST3YRWs/E2ImZ7F52qnmJxNHMddqqEufboxgHNZ4eAjiuEsrMnblhtZyFaAGKk+IjBdV7IyJBYYu+t/TSvczgl0fiDDu5cNbmG4MikqtDLz6eljaz5mx8dYP+4RPzSwTMrq0B46PZp6wk0mlRgK6mPV3b2Kg+7x9CtLm8xl4+fbL6ze8++OuP5+9lqt+6OfmTg8l9ja+T9Gt1EMKT1ujUJxh7IkIJLqNKLdbm5igRnici7nKQ4NpRFIu59AsIWXh3eWG87Ueq+HrF+fjp+j9680j0Xv7djcNf74Z5dZYgoyfj20P4eYEvB6AXJv4DiqjUaNpE6NH33gLASFSSEqSBhYW6GkX65ch2cGmjxJQQ2AB27usJGU6ciAfwcl5MNMxx6TqE+P3ZfMr2zy5DAMjW2SIFDCqw4chO0GZo0D1QwTDZmeZoOymH6dv/0csfnyr6xZmtaOHsArGYwhqphrP9S1+c5uSZMvcK/fjDvSGDV1Nvk4HZQtn2U2omvrYldaYBncLQTCb+4yvJ425hYvoSVLm9NA84Eh6kmWat+fYaYUUs6XVDWs+KAitzKVRg12ey+bPp6HeWBwKSP9cpodjpaUxqZ9dySCZir85pAPf44Yk88Mx5MijlR9OAy5cpP0HG3XEOFTYb9vkgqmUd2dhITYjVIoF3qEA9sD76F5+nPygS17Otf/M4eAtR+KlnzDYWgDwJXGSA+xcAuliag0rSo14YZPqH43QWEByLXaePfDcfoAEcVpeTnobN0cAMpNWOOpjKRj9SrKNIyFxcm7uCeCIkcCJuw/YmN/6oGy2WC//g1eXeGTrY1k+hyTVvuP9kUFrO4puVpWuYbOv2+4RoYEBJ8LqDfE063D6e+iUotVlOylSyp5/0STqLv1TTTox5FZ6+fmV7NJSfqnf/4OrJkZ5oaOnL65jlH/7k7OW7sJ/iJEDFaBaG9Ah0XTTVA48879hn2ys1/hTcOUGqQxuV9nba31m9vtoUPo/BAO3G2rKlqbsvvK0MY0UvL1Ljs/va/Q7nrNyGpdBWKZnZYeMlJLw4H0KhaB6arShMxSxwcqHCEFfzlxvNtDmNwDFrjjFP+O2r0g+NvNwPHMIKYcCKDUgm//if/PrNv/cGcmf94FFPGZrTOrNzam+SZmzqxfXFDpyW3CmImYRMp0hKKGa9waAwxUdGCV7pnT4a9x0winI0jP5WNSbIUnK1f6pnpHyTnLOQlGW1Ym8aPnd4IWeJwloze/jjL5QhsPaNVb488FwoGXsRa8gh2mDQqKVWQaE5X7Gq4k5nULgY5QhzTLu7rsxyQJJ4c8vSJQMUrFQeR20/QoscYHnzlQKnA54OpD25E2v8aikvghmDssxwqA0i1UAiEOEF3ADIq0U+jSDY1iYODGq2G4YEEs2GYTzW5ChGJTIIYrfvrq+sKVEWi8lskeMv4ukTB/b8uW/M1WLBvJxCDpwBKT4GTDfyB45EQKYtNF67+vY6+sN/1RFX3ccdJbdWuNjRXGeY+MHYHWEiQ82LkyH05t9uJFkpNrDxgWwQMy3UCYaYzqyo563UgTgmFyvc9Ej//b+9VYFdJJdx2tbcZvlSbrNifDk2GyzkDSxghhDAsHKV7qrq5CKgijk/xx22y69+486z37if/ltlDOWHy5RXjy4c8YwXFwn27vpcB2ZxJgZjz1IwOPGUc0fiZtbQgmIeo2FtHDpTUzRUokaTefv9M2rjRsHE4iqiBwrw0gZgqwRaoEuZOGVgdRRZUQKT0EJF9HU4l4v1XBES7LyQ3fnYzc3XBgg7a7A33lw++GwwVAfE/cve8eCVbzNgKMyGqKhG+QYCN/RsBj/aH7vZ4tZN6uIkkU2rWq0zC9TjI23yQv/yt6WLU43mXUO3ntnc1lfm/+aPnHmSyqwKn/7NWV4j33xr+bOPWp0dJR8CxQIfUEGxiLTOyWGEXbm1lpBDi54EaSiJHF0ieqfx+GjCKKrX75ksymLWg2N9YX5OptBcyP9NS04xEK1T9VwalekmW7ZmEaFp5y6N8HhkN+ITHqlhRI0Zn53N5XieykXN0oxgwsTCywwc2W4dZmrcbNYnlvBoCpaFJMtIYCTpOGaEoEFBvtPnKaRWC+ENcr8LBDObepagSzj2lZsTNVmlQL9ndFvmN7666L2wnvzqonFljnxnAzGDKAEvv9DnrjUkAOSSNJeJJ54d8XklVBRgxswgfxw2v5uvlHFEBRbW8vKn7esb5PHEZIe9IgtaFxYBzGrX6e77M2Gc5AD0RoM9+LyLD5TcwjyVb+JCpsAEJp72R6mmYfv/+9HyHJFYKV+GGAWdq7Kd9wbeuYnHeqGCH05UpkqKK3O5kGz91d7Cqy5YZSIJOX9+kqISxqQFM6a3ynRncvKgzdzIca8LTTvRRpO/3j0iCvKslmMyWdFIH4y23+sCtAc4n6AnU3j9D4o47x0/se9cbWhR6KNamfG7BxMCtimUXccy4CgEw+RcT60JoAyhSMwljcxado3yA8WISjkiSxCWYbGUZfWnfInyIvbx5YQjULASoBcmlsu/8lvFTK3wYgxMdo2enNRsdGOhxAWumtrzFTRYZof21A9Ce+BcfBJHSkIUS4oC7Oy6v7Oc7e6ePmkZm+80+mrWHEYZPsZD1zKcQpPY/aKfq0BL1exXX8ke9np03crANoEkKBepe93DZ2owGqf3bnCVrH5NEFZK7uUM7PQQjc1McUN3/GeHSvMKkUC/em+f9oHVe1e3HTOdBKicLr8VWvu9oQFwbGoeO+I0gN+us2Vu9HDmWJ6ZFfyuHyMLta/eOlenlIvtGbmEtkSU8RCkiMFZEYsYTrbgp3+yA0vZL325Ek3jwcU5AQjcSqnBYY5vqrKXZANWipyhRttIauUJoZgMZnWEQR34nWs3DF6b0dhUj4aZ+e6wT0nislgrZJAiUDoy06RMLWSVfIU2J/Dg2ez0Lw8unikbNxezWeii5aImhjrwKHCCII0xMohcbpmFb0mKYkeGEobgyFYurJkO+bcLFXWoyUbgREzeRnJ2EnF47mbp7EHizdJiiaU5hM4A6qxjTboUUgACAvLdDAshIou4IUyC2axAByPdj+JBBnOSeGFBKqNgjEGzmQKGJMMjrhEVMhheRIPIMBKiGVuOGjA8JOYLyvRi+jN/+SsrNEakTiSr1sFwGlCMHINMz6u9vkzB5s9+8zgFWX69OjL1X7/3LLATb+q9ucFf+9435ksCUXY+7kWsZIGtmeXzmAACfDw6U69n4yifJUB8RwHotbdWWL//yLw8HKk8e/5Hz6r5VT/H2oMLqmNK8/xHp+PbOHZjK6s4swP1stBoVj5Or9DlPuVnCvBZam7+g2XhwQmxUD/afla/fu3yWZ8qxNWV9ZyKPQl1usRLlweYqvXMQIVDHCfiHKiFruPFW2Hi8VymgEkVrAl4k56mmzaBALIB5PJ0LQdcmIKOwJBqj85lTwd8g1nZgkU63P/4wbXfW/dFe6YZrZO+trUK8sr8m5zpuvCLAf5wujZf1itMprZOzZeB7JHyoRPDkuwOtwfBveYMzoPPv3hSX9siKFDBse6vP65eLfJS5Bhpe3cCKtP9k1ljAbh48GxtPkfhkfViu/bWsgHis0Plcuxz65STZSHNKTr2MeArsxSOff+802lKE1VrJhbkJ4On3Z00eYkI6qxMkiCRL06eXSy/XX/qjTD5cOsrtD5ufbtogLEdSMzuexe5GB6sCJsZgO6ZiQuiRTGOBAVB0b4nMqJXEqMsjuqKyhZ0GuN6AQ86z7yQVHXZOSkhPbNeyNWE+iWQQDYcQqALOzoeGlFWjHOg8xtYUUGMETzRVqdPonptfu5rX935y591BkGUJmXceTHUikEI92d+Vjo03JsIjUDhkSIvwX4jx9AFcnTYHvjy7bu3jV+NtwScFBcGwyN8jDx/f5+JgUkFyxHA55/sNlcXCVleKjU/y0G7f7rzn/4Pb/yIDf5amZVs8zjylr53BQtcsrKY8SPSSbtdFex29/etRcHy89ml0E4DlJiY3ACLpaTJ8hPWm96rX0xMYBZR5Xh2MeVFmLtCzy5HUnOJu1mv9JjxBDPHs/Q3+9DdMtIs5GpZ88igjsBII+wYAid+X2n3Pj67wwnX5pvJN684CU0Ouj3I5f/xLewq4+3LFIxMlMkMAsRQ32klfmDQt4TLMeCPfD10fSeFcIxZqmzeWgwQ0NZJ8UZ+EQ31i4l5rgwnrjPTc/Mu4I+zCpdS6L6dZCEitNKcEVNYqNJQIUFeQeMcGf90XwfGltTIanv95wNZEIpnZjjzHIBA60SSLJDuIo32dAsYifPi836bg4rNNRGIouOHZwvNCoZBo9+0s/MMnsnJWX+7NbrzbsHT5wqmdvmsA1m+i3mBx7uvzC99eS0B4S8u5NnHp/HxvisB9qnmDAyvMzvKEmubTDSkBjMfWq2Ak1Gna4HTDiWUCRqBmXx41uVwFOUd7/prweVhpioN3NFAhucXTHhX5ubme+d96+ER91vfmPlA8KkVZPnTvvsyeGpzzhZ4dgAQMvTSra0MVmC7XXj3i07gyJVX7y1cLaaKcXE+dW1kuSFyRIzaEKyCul3IrOZ1z3I6ZmFVSn6j0de+fN0wPh8/fful0ohHLh66uA3eWsYf91oEBSRgVRgEVj6bIZNp7Ky8Lu62xvyXuHSZePjoPAfY0grpGlBMQvhIt+xpvCSoiLp72YY1o47DKGT2uQQMQtxxQ6PvezQ+Mucr6fDQsGBKzJDoWUKkqmb7s5nJF/KhC1ouCk/M/FytUGcvx0no2BgSIvpQt40UpAKStHwCgWOyUmU8hSNQz0gSH6EdDMMd15Zt34lSGvSdKYhoJEZBGboeclCtQM78Rw+Go18/8AZ2QgOABBFUiJuZpUKz/E4jtsOLzzr5RHjr796Ru+29zw+bS1z91tLC9U1+a5V0YOPF6PjB3izWtPk0lycoxldGHjPR8mDoCdzTo/jeDUG9378Od7c/VfwvEgmBAQHXgmgli13KVn1OPDqkNFVfWifZPnGw08OKaEcT0qCUz0p8KauYF1gMHj9uvbNSeWRELzWaeioN/EBfXyAj2s83Z3buim/QqHH+YhcHolKcltFUObZ0U2FYhpKkjES3up5Hp88emyDsICBmeR7DcGDgnV26kOPSOVJVLQwnUgLxDGbzzQLKjJPxCyDXuXSwtqZnOdbkxRu3pIuHl85UN7dPd1sH47R5r4hhQeVP/8cH4WcX+71eAkvYYf8r//WdZtU+vpCvrInDobb9pNtc5m6tYkOV6WsjPqEKkNXICd0xXJgTb3zp6v/yP3742WNdjbyd7UPI6t9cru556NEUEzQLMuXFuQqRsPAILkvB2cWYSX0S6EuAbNNQQFJGc7MSxEwcZPMQ2OQCBY1ns5hPV6sCY4BDPHqyI280EFbTVM6vvkTFu9P0yJBJJnZdwDTKz1RVtOxczkNIfyzoWBMVYoaLodS8madBw+kcTukYSLHkMvA7lqmem7lCadgFD0ohHwuhAy/kMyIXnB11rAyaAsAKS+l73vEZvHRv3VvPKl7QKC4Ng97FyWC1hpqa+v7nHQYOr0fhgksMD9TcnPelV/PDs0Fbs+KgW4CCXFEZbH+enSCjy3DtZqgl0jjkF1++/ujDi8Nft37nTeHwf7/U8/SUg9L24Du/VfnzZ8bP/9lPF3+/CpQo4bk+vQgIk+Rjq/v59kAHSmVSjyxewq9sMV3hSmOLK6Dk7ALIThkvNIf9kR86rWFS7qrekQx6npkSIAGcsMp8LlZ+7nlHM5hFQM+UViXyXr3Wt4+ftg0Uvfp2E17j9STNFZymblACmcnMkZIoLJb53PL3Cs0//le/CfmlmDhPTPjsT8/kkf9WsR6Zg5VsaaoloTMLULP7ySiQs8A8KBTJek0q5NZCmDidjNmYiScaTiezHITRWaRg6/qEzYlxGrGoX6sGhj21KRONKmof8VkpBMmK4vimPgZjp8gXFismYyN51pLVtUU+nymIUen+E5dFCwxhB7JltvoOiNlglMuQVg6CpPjFzOl+7l5fLx12ziWeonFRcbCWMdu4RU5ave1HZgSDCJOHIJBn9NbR3oUfAOs8nowZIXcV4/m768ckpPWDZKWWaRZDFg/npcmLPuTrfgx541Q/jyrNYlgK4uQCBgPLIaY4mmalExipALEIkTktrZWIyfO+6k3DvuzMDqNEPhko7r95DnHZ22VOeqXOThjUsgD1NPWQTQOKq+PZkzAIEg/NLr/VFIU1gkV//meP5ilnqUjOVSpiESPcVNFjICJDitZQJoEpKO6Alg3hgu9xG9/4T1o/HP/NvztpvB0nDmKk0bNkPEt7TgZud+0lH2Y84/ELq9V2Fm6XqS8XkHRMFNM1LGudsvIkxKCURhMG9ScFw0UA/FS3wUE+n2k2rihDC7DcLE7GBBqpJqSmtuo+//Gxn0Nrb22iNJyEccomga7rWiRJRYcFxEVmMJic93pUPoNiQKgbM1VFOEmANX9mqrkrcArYx1+cERhCBYVknkxJDCcdnWdBVTN05+mLkcAAXE4t4NJziLMigJIdzG8uvpJZ5QrK2STEMRZHGILPwHGOF0cRpAz13njCE37lpnQ56bKh+uW3V4GXir6Oz0bb/9P23kLPXiGKzaX0a4USIHR1FDTjUMAoeTaDGY+Ognl+Ld7TG2qUO520HaCqoequ6lVyQG7ebGlxkS59fUFzdfjibGRpabnQ99Ng6N5cXbFlVM1nj9rtF2SwmYe/pgLzVE59BYP2eswCTxoGW6+5fX0uoUPHI2Dc7MgWgfsIPBkY+ixE0ti2qUaBIjfYp7OJMxEIFQ4wQk+jFR6gHChGUJIqoWzfF3gUmmEA5niRbyEI4caxFZ1MDDS98uZaP7IXX66oilyOgNl7B8TASSj09e9vPZhbyXze+dfvndzkTcMCy6Z1/Wbt/EwLRKqVQDhAT2benn3e2Fo+Q8LJyaFyHs+9Xuk+G/BMpmVAWixdv0PLx2etndZrdzbl4/N7X3l99xcfOTh9qnmdM/Ot16QRm0uGfctGc5WF7ugyC+gklDR+d3l+XXLpVANpZgLGaugVSdxJNcLxLpwkYgrZHOX3AAd05K7PVRo0IQo4AcHTy2l/qm+tkP4wjI3po3mx4iN4GhgEgVBkHJOXj4YM7ddu5dTezARDC1cH4zHqDKs35kepzRZoOi2/PvGzXrizABOMLRMUl6eQkdoELveQeGjyVUkqAQALB+hr+U5JFNTj1i92K+uF+HZGX8mMdgc1T+GRDrFUc6ZdosieyeP/+Y8++7vfeQ1eLFy+wsET58P/cPHyUhnok1eq2M8u5S4JBRny7M8/ufVPv3Lrnfpf/fLRbuWatK6e/M+7/L/+/vFfvnDNqfD/+PYn/8vPv/vL1v1y4R0a7fNw8mzvUYZB7Lg0VzuVQF4BQ5aUNWAdq1EvAhqOdUkUS5Q0v5bvtFqfPHVip2BAjWK+P5i5KTKHYWafhdmkdj038JJIovhugrw3xN7J+QuZqxmx+1fbJzvjuRV+htJjx5UvrVvLDd+y19Y2uFK1/WmrPfZLzTqOG/nA/uWPWhmavLZ8O/H8BGUsj+9OzvC1lfli7SaBuGeqzESqPrF6qQDHbD28KXkXnTgLQkyE07anxPT9Z6N1zg9SG6LhWOAHCcrqeuKPx4FLoYUyC/qocNye0cQsalyh0SRNGRV2XEHwUTuBaNtyUJ5DEsjXJrtdH+mPi+UcXiFs2zr6rCWJ0narO7fKiFIhLlIHGtgUmVczgriZ6XbiKIjKm43+cBdxnE63T6B5DU1LG7lk6Boju0AMYRI+/uJCRdnlV9fuLPD6/slOHyMzSmf/RBoFIQuCE8SHWZoD4WRiHag2JVAk0DNijGXZCxn6YkfymFKNujjWCqi/FPmzval1Y6FUKqgl6+u/pw7+dulo5J/98YMjUgagTFl+enM554wz+VnUialwvYigWDxNB4rx8aN2OQjrHLz57hXKsxATlU2YABMaA+V4SuR4HlEhZaq3TgHZsuRvev1hrtn4+vd/l4x+9DOoP8uFV5wi0p7wJd4cKStJSoD23v1zaiG3PF/npl5vRaKGrnLQQjLLR+2uWYfvlmg/j4wb9LHWv07yfDWB2gHm+Ucvehsl5rynIRNTY0MHshq1xUwdfTYGEhSOsWzo+5ZpuvLQjqAMEozPzZJEEzQuieQ8iGV5FKPJsQlrpovUV0uz00m9jn9+oZYKfK7GizkgOoZkJRypcpIJYiTgA2p+dW7jtdXzk8nscu/0aRuoi5MJXlvI1ZfZZxQuXhevfHnlcACtTGEJZixz5kWuOTBnk7YSQ4X1miCCnj+bDM0MwfRN8PRCadwuXtkgN2O6hDBQltNHzvFDGaMjJIu6SmxhubEtgxZ9NYe2H4y/9Ua2V2HmHfplAHFh7WQ0qS6RjIzDqDv9sW6fzIAA31zLBT5GkCwlMRs4ewo6Hm2zGeaVOq/P0phMz1uJhCYTJWFqKWGi6IEJz/BIgJsr9MycHhz3DR0ICQSTGIZ12qfx1t06COOD/WE41K7OkcWCiOHYcW/WfWLEUbqwnuc46NlFaEIakAIAAgFogCWWOkrcKfRf/c7q5x8avQcGf52OVWB/R5NgPAmYzXfmslXmk9Hovb8c3hkZGKjzf3j1O1++5p/MWMsFYyvlKVBPB4f65g3ug597iwtKQaCebnsMAePnxEU7abzKqJHSezHYuEkem+TlT2Y/+Kr4+XuTEttdaC4cTHSA55bngCdfzN74ejapNgdds4+ra7Xkxc5lOBgGftqyRw4t+xLtKUlVA20QcSjQggwyERDbcC5BJZ1RNAw5dL4OKh1zZ6812p0Ur/NRBvnjbaOGciJKIt2QAi0XC6mx5QBOSOAwBpbnIMxQAs8MMenhcEzhcRKnDcfNjgNJA02w4E2DXXca5VwGDI2+9soCZ/TtXz6xejl67kuFYpR7sTutlVboq8D+03HUUWDFev6RlhzXmm9lB7oCwb64JHaSOFeh1ARiV0q/Xb8n7yJGq0P5i3/4avXfa5eFgIQWsr/+aJdKB70AXL7XHL2wtn+8zUKe5Hunh7urW8bJz7TFKH15UXr83/8SgION73LKr+PXBH42SwFvkvtGOUYIZiehGWDUtiQR32/3DC1c5ZZAH+nm6dFU860AQ8O31hrzLmyR0oGbvSFFyuQkVyq5Z76QBX0VnszcFAGACiLdJoeXYbBz+dnO4UuvLjW/Xd4eRl+cD+tr2TqbocoSQyWQEp9+fLk1DyqdWKoUXn9l5dNPW7ZVXL2zRVKgY8fjyN8qS1g2J+LXaFxUeng2j7FUOJg6QqnM0oEv9vE0UncfKTq+eecOmZucaz4F5HHPNzUZ0casw0DNXNRXNJhJilEhNTKkpAwNGZ1oUtaGgJeuZo/3upOdjmXrJYr0bUMepKUsUODQHM0nsSO+EVkplTrBs2dGCAYWLFRYfn2Jo4kwhdPx0dRRETKFP2v5X19dCHAixyWp1p5QDM+Hz49McjfgC4ZnoEUGzROZ56iF8BOgQTAWeXI86B8PianS7ZqZOkKkKVNk7UTWvZbANNE4GvVm8rle2uJkAYM1w1YdlmCLV+7IDlLiRKbsj2I0vzkXFYpkIz8aerrqnkxj+3i3FqTKs0n1O0uzC++Lj7xbwzFczh8fOMkyYTmxyIY8qxmmd3UhL1WF+UYp9KMHv1C+9b2KCZOfP51VaRQvgrggh67NTSeO3AMknykf2wg+eKgWrghv/+13Bw/+/Ic/fV+4e8XuDSLFVgeh74MeL7CEpIZBII8qFIG2kVtJ8ULXUUOBNCsHMpYBBmfDwt1isZexoGQ2kpfhEliRAg/rsZXFNxcRO1L2T0LKAht1fRwW7ub4GjsjKX08onTVCQM7sbWEJWFvIAeWNWExksNRL7JdDwChFIQRBEUgFcHh0I5y2VNXI6aW7JBIwCyUKLgEn3rnimJRedinGSi2TDMolGq+DUV4ir9ZevvrG9719enHu/5H+26tvCQUwlE0DQyNjH0m1VdzW9ermwLje6BxchBH5oltrNNZJCK+s8yyPI/HDkviv9kxCkvR0rxwdngGFoswHaMJ00cg+ll/8doiTU1ot3vEsnrLnW33iCdoJb9cyqcf/uqS1kl7DX3puyvhtXce3/8c6caUFyBXyeur+c79keqnqzQIDRVWI0MdfYhS12FXLQnpFVLs2TkIA0h3lGWLm2vK8GD7WRePDWgdc3pu/FRJKASf43SQaZ2oTQopidzpqfn+B+O1+cxrN9Daq4t7z0+O+l2hxBXyIKsCEYwHvj1xkwABq1KEk3b3dDS6cC1d80OnEEziJiyQQrGWG5wOnj/y6nXv7gRoBm4Iwta/uv+U3WTHe7VM3mdE/+Dj4Pbt5rvLynSWv1nvbV985Xc2jvxy58UQGpuB7v7mwM8XqIvfDBdfu7NxnTV/tNupVIr/xbcOP3lw5R++A7//WRl18l+/0v3RM6XT42s4f4M1+zOPS0AkJdLxDMPbFzPEi1jgbFxkmoWFQhAaPjYKuPUscaknUhY4xSwRKLCwMHvauT/tESXp1s0axoJjiBx3z4EYSgUQ6BhqEnlsShXSGQktZLJnsgzDjG90Kw30l2GCqNoin41yxphnNZjcAGzfgT+vI7MPxt/z0pqAZinE/OKj//A8It+sWHeLTLifPsXE23fm6/negxc3eqOLNCUxXHyLA1WfeXI+xfhsjYcwLJz2SNg2u9PZpZ69Od+8QlwczjIn+gN91u2MhddFMQeGpOFSotaPtG7vzmJC/37tl3/RRo3EGOJAfZEm2u1/8yvkv/kB5HDRH73X/ad3e5z11UGKhmiDY8z9E2uUg7yZEmTAyL44hfwcsDDPjz0Lp7OE5dd8CaM0zR7tfmDgSu723XefYepu+1NiiyndvYq2Z/ut9vqyxDIsqXiHz1zyHldeyfocwFfY44cvhGZ5eWWxe3qpPj+ZZytEQHSnRtwUl69U8C4MsqDNkUfnw+HAK9YFutyQCK8TJw6AkFeQnaNuVsqJkAiaWpdMLMcHSYRI4AFVnIAwqCurTEmscTZrnU4AvWVonQOeiwYDM0sBxUzeCmgtk7iOgxB+31A0CvETYqoOcSBPFlagnl2xehyrSV9b6e9O4MDJs2IIIvc/3WGLpcH5xOjqQAgqBufaYXmxvrwEp48uu1gpWViYxxxwkpDjgUX1zFyj/+jk1Te/ApDA3Gb57C8ewfT4tW9tbf/ycS5HPB5oCybgJ/zMtO1DOw8hkNwPGAAuFpt1uvHdjd98+Jk/ceBsycc4/HzQ8Y7sFJPtGVVCiatLhjWJOZYyx/Jkeuu7bx1/dOZgfPkG5Ds2jfrnX3RgZllUz+Px4CbR+ucr1DwwR/x93m06xcXlO0vvoing2zbYGxFNaXuGhhGagQFUZEplRD2RPzlI465+42omQ4bbv9jNlfPXb1DyxIgwkKIyxhCUiPn4+YMYaScvV3GoCl9MATT+CpKR2eJnJwM2pRTDz7ySq87QkUddXcKPNf8SSnHPhD91IBYFNtYEJMqgLOMETRE+fX4cn/oo4cW6jBF4JzVLQnx7fUvxEGcWrsxJehSPLo4mz8+MKV6vVHIG6CdxmlhW4rGBiWNh7JJwYlN4NgdCUYqpCejKuhUDARDTDIe0+h2pwhwdQXAAZaQCKZQYMi2UadWY+GFK01xYIK0KdNQNq7Vcdh3pbLtL6xvEGw3rldIkVicz7duLCzjgPjr1p8+PykuF0IUBgowwwg7Csxhb4vDxwGLyQh4FvlSbL8/NUwEGdJWqxH+6s7dHdEXWOz7m0ErxOS4UXaU2Qmsiec1PJjN0Ehk+jWb+zxVPQOsJzoJlxcLxwJU1rZClvvMHV398fhwNp0LEYH2LL3FEFn/6SMaGfkiE5gYzzSSYJvpuXL8jHjwa9dXoxkbj6bbV12Gikc9nfczBSGD67GTI5SAKJ9vnY0wBKkuSMpaxS2N85E1t0JuTSDaqXKu++wZ+cTT8l395fusuvnCVe7E/7XenV/KUcjHG4QQFQzJyE8yVA1NkPJPLX31n/dPnR1wdZF7Lb+FgYZKZDigKgLKU7+WFG0VqHOqcJ2xkFhkvd/FcjTFy4pk5Ju6qbTbDdJ0Wg/k93H++PVueQ80WnK3G9VrxRA82bq9+vq/OmGxnlJIkrnY7967gf/xpKgUwk6/1x4Y00F+/Wxydm8bIQxE2V4gIRM8Ao0fnszyfL9TJHMMpBBIPZ6fdiejFVh3XnfhJB5NwTo4SyonNS3PsYJmV5t3XcqKbKEcKOMNPj5H56tyNJjHQItQrL760HEX+xJctIIkZmENtrQAlKDY8niIcXOJp3k7OdVe0DRCPTgYoM7S/vsKf8/ypBvemCZQQ7X6muVUufh397KPdgFHI7NpGHe/vyNCQqK6vHe0NmDq13W0JurlAcAVMWsxVz6cWjlHheFhCCje+8YpxMTl9Ms5vSkGW/+sHu67aqZ1Apg44ccyp5K0MHSvxL/aU3I9O5qTw4tJJpj0DmhPvFJGB3v7lx/deW3n+vng3Zc8hH8pGCE65HdtP0OUl/NlnITA1svNwmeUBIlCMmCd8Mg0cEM8LtGxqFYaa9ZDZ0H8TRLfi5MkLm4YDbjyVlngHzHYHLsdHlEjlWGBqpE4AHjwyv/d/XfCPywdPtK1CQrK466RBA4Kp2DQjQ8VwPQaNFMNReTTdGbl8GXcpK3RCWGNjA1pahJ9/djrq+8VGxgMCC05olpw6ZI2TaB6JLmx1vyMWEEeq1mgAmlpcksBseG4+EVEGzKcXNB76UVlc6fWCXDokjoM6ykB4fzrA4Gwmys1V12pMWzWnkA8yyRQEIzKKQ92ZSSxmx34eQQiWIbh4cuxmRLDRrHtI5Jp4ENHV5XLC4lQMr6+S577jpHZ+Fe304zuRZToUS0Fffvudf/5f/bPyNQcX6aEW4iLpQ4l70SmiIhfFPun0JzI5l2GLQKc1veJUeRMKKbw3GII4tQCIhXkAXq98/LFfRkTXMGOrz7KlcIoQJBsmAV1AOsMjYG8SdO3qWjoeJhUNhOaWuWXlovn/Jwg/YC1dEMMw7++9nt7b7XXu9PbmlX37trzd5e6Sy05RFKNIgowYEQIkCGLFCOAERgwDsmFJViTLaiRFsW3lltfbvOlz79zeTu/lP3/vJd/3chHoEUBXmTPDP9OBtRpSoK0PL8Eu4EBsPox7krtQIfBLpzsxpW4Y9/0qHmZey2bWUq/ObTBkHtxK908u1cl86ZtF6yLqK6XFG3TwZIaySDYcg7EEQFGAyKau3/gBH5k/+Wi7Ap3MyObfdp+0oTe/VRyfWZAmxzBo5PuVONvDkFiJHHo2G+PyHkRbofowCTvsG/94/RcfHHp7apaNSY3ITyu4x5mW8+SThhNJ4dTu9y48IclCxGCABCynU2hTUZZYmnUVUrVhVsYhznchk5Rkn2FdGEFYP0IAKEBUw1LjFFmKykGB4CwmKUIgiHU1gmVdKoglUGLuOR5fyBaHPTgnxR68W4m2cfPwwP+ymQGlzGqaEfk6V9x43T9VlGZ/Mtepqg9UaYTPgf5gNJsr/b5y+06NGAONC6eumkm0qwESipfT39uULveg03HBkQg0ncuSQn++uImcnDT9TApgIHLsI99Kjgfz2f5IiyXRPv2Vv3tzoyjW/8W/+0CmnnUtdIbVd2f5t1kyg9VPpldu5L72dnm3Pk4UisJA4UMlu8GfnU2lvn59PdH8+fFLDPEKaTgO5JfEx5/s3by30+k1XIvgvQidjtc8V3LI83YLIMCRonsUt1XJl19fAxLxnDI/9OzU6/HU/eTFXzUCkybTJfnoQuFCqlxsnAySuZgimzQCwzDOr5dDIiqlROoMJl0128ViDozuPbLz19VSWI4DgTm3RzYqkEije9r1uSvs0j+46x2q/AwXLmWWqR0e9H1Zhxl/rZA925XTA6+0krnQ2Pxg6MOGzJi/8xZtGk+nO1/PeWA2CqK6+doqjz0+DEs0ShMf/9XZvW9v8yXaGU+I6WykuzZLzRqTa7jLL+GfHTTDoXLphivVCozZI8yRdWOFhByKwiQwOhnsAcQazWDE0smnH+N13bXGP+qeVzaXvv5//bXpq8PmxISWF0vbgqxF0em8DXpoDrw4Hy7VyLEqJq+h4ctzajkOJCz1UOnHoqAx36a1n+0JhcY5eMjEv0IVComHr07Fc4l5O61nSmD98YOWjHDI0Wo2/UWDH4JGmjy0psnrcXBk2Z/PoSgEckJpO9GFfMsR8j4pWwyZzCkWRaWR9ZvQLMXPMuz6jILNZXJzBUf86SiCO8GM8LkalHlQmssaFWMQX9HmEL2VVVzWHQHJU7tLteUYmG05kId//LJtkqSLibAIsmO5+j0xPg9GxsTtWSEYEhSVFtCwFXgQ2cZ1IUMRIBpHonEc2DPH3ccf06GHku5YU6A2kwaRjEjMbVcNVDQJ677G3Ugmp+P9P98TXitevOzCkul7Bjjxxxngsj5CebKY5nsDI1LQ2cTkBTqfgyXHVy09oIy2Oi+J1cmgrQ+h66/vQC1FdQBkLQ+Gcw5AdZKe2wgDQfeyq1DSVRTPE9ShIgWA24NCrFARPXDWYeBNa7Z0440rK61/+8RzJnIlE5y3u0yBcMJ4bLGHY5xju/0jGEScjezTiXQ9nnJYvr9bzwbk/d/cmn6kVvLVaRJ2pdNBj9IpHos70ER317bZ3HJkSxpsvTpj7I2CRltrviml0x8ed26VQJ1MMCJ68+7Gh88+KL6Wfvqit3MjqbSiB7crMJAY+HqDojfzqlhNDvHgYq+T3Osl7151Wif46cg2ZBdAO8nlStcT8Fj+yvrpr54iem/o2DPdX3qtdLTXhywmsKeDSwvp7Z6GsL+6FptKZAh/1Jp8rRv9dlxBXwGf9qLIHPeBJmm7xIUe59HnGBrXngE94UWYTTgRorvlyiIieyQEzlD8+NWwatCJYhGYk8pzaO21ClC3QRsTb22i8isgvwH6oXpuI0OLuL0KASiAVMPlO5VK5k//9T/PsXb8bh6+nx7SwxifIntREHlqZ4iHRJxFzfqcaE1kHJ5xrCsKtdU8wAklNS6eyFi5svXWG6Of7bccaWqFmwwnZDhXA+IcDxKoDUcOAjG4OcNIEkJ5CKJkgPTxcK5LIChyGRGhpVCHKZaPi74e2KoPwyECJ+GjQTuQgtUUHaq+b2gwTuE4tp7P1SeEcqbyEy+/Uswnak8aHptk1cYIk+bzy/3l+8IZRM6Ou3LNGWp2LDK4RHD0pJPGhWIu3jqSFir5I1OjFtE8wjk9V+9ZuSwDF6Zz0ObX2afzoXbuJNOMZ5IQqEJNaS1Y2G24PQSI42hKghU/Fb4c+7GxT1rTnkkTvhXY//2zz9ifeVcMu6upx/vuvUWOK1XMJL0FLp5Me8+fzReMRAjDXAwUXXrvs9ZlXyoWBauLwAKEVgWdMwtr/k//1/NvsaUERrBA2rLRHBJkaO/RfgDFIxfTE5wXVVxcJ8RVBg4IqTUdfTRRUM1Pu8eqnb+aLK+z+lSFQqq0JB6dG4UlyiFRhFARQA+sINScjBeMe91MOrV1Q3z5i97LCykeo8MIPzvujUw1jhpfXeegIfz4i2h0acJcLzFS1I8RvRHF8qUIKXGBA6FAuprrtqUgcm+/kX30/mEanAheR52pOA3t/pv5729jf/k/ffxb/zexbs/dSwy9kSSDBIloUIDhkDavACbs61NTCINUiZ1MzSzmdSWlMdRrw1HKjsqrVPHY7Dw3AixgeQ91I4fAgQQgj60IK6xdq7GOsHZjK4Ns/rTzLPAb3zczu/+meYmdMpbsyEEsOXTbs8be9PoSk0G48RDSm95JS9n5Ftr6K7f1GN/MUXrTQTsQhyXTr69rKJ4psBn9Pgk5H7z/6KuE71lxsZZ1sh5HBtYnFGKkwnUQ3Bt2DWrjQdm5lvni9OwWToi+fZsg9QsgtpwQN4vPLwYX0+E6yw10MLkmqDOpjc+u3QtbXVvvWZQp3v/e21Mo/M9/9qOFDMrC8AKd+OXDSYaEPCxoDJXiZko70D/581btKhuWSloKwlEqeZ3+296o+naO1MRUJ4R0jNcC6Ug6+2w0VmEyZ5IaBxUBF7BH59xyCh3x0elwRGPCldW8rzYf/efDB9zGlauU2rPHisf4UyhIq7IL6xoshBLuWYrEh7hkSVc26PPOLJzh6SUxtsqf/PJi+vm83+4ZIy1iYLUeaJoOTzC2lKZisDWyo6mWLcYx0MVdyxeRw/aglMqCnUH/8Xj5XgpNkBePrOvV/NSBZCIoJGMciJ53VNUYARauErGsEC0GCIfE846VjRXnpczqN7eAcFZeWIXqzmAigUQV9ZKSIRjniMWLF4ZvtkcpPyD9qNH0clcYgae3Xl+6kNXD7giZu9tb6fNnknLJxtBELFs5lyc6SixWE3DoCgTKEvC4hQYIcb1QevTTT3/79y3DmR+3AQ7Ribl/57Xsv/4pkIDI0DeRlpRE4A92VZFxFq6Ww1NpdDoPpr4tiJ5OP/u0gzenoSoFaGijpCGA5FTu7U5iIUIxxrzp4FEWsqxKDm4/HuLJMF9LlHdWyTjjHA4YzzBdzxy28mvrK/PYix/Hq9fpqCwuKnjya29dxhfgDWfOtAIz7flQw5qtLruZpYTjkIMLHaB5jIK9iWHKYDLN5tNcgFAXpxhY3QTIYjjvKGiVKQGmUwsZneHiTmKo2ti0M9uAPbBYS4Ls7/zGHyOq9Of/3f9wrSwmeVl/NRGK+bOD8cTVVrfTKSEz1pHBfJrhoYRmJhfy+a2slSTkYfDJfzhVfj5jv1EBfSKfSUqORfVnU3/qCqANO3mhvCxutNWpA5hUFqVRx5mjBF8AfDCAnLPwkIFHgcw7gG5TJuUak4Ex6Coa6aUyKaQc46a6LFN2oGucAAdUANheAJKuYkMQQIjCtNcXZN3laC5DUqgfaZOCZ6W1ybFYgmKM+4szWpqEQzMqon0fyVBRkfCYmDtJsGRr0B2P4yW/ACGE65FVkUy4z42zS7ivniDf22Dh7lK+37k8mWULzgXqKHHHVbMz/8u8zZ+oc6GU0x43Fr7NIojOb7vHJrhR2f5DfOJ3OuUsMV/FLV1e+drqx7/SoU/PNUHML1MlH3dDN8LDk0cX37tTLdy7NtZGY4MAFzHZMhMUHAx7Zh6i7os/etH8rcoWjhcUAuyYluN4pRxnANZ4BDFs2Kgb48ZMm4WuMPKG2QyeD/OuqYIFWm08HgtC2h4jfKgQMazARXZPjYmC3jtgkqTUHiV02Bz6uQz+6rCPUTRP0wNz+POvVG7eErEvZpkr5vTF6PGoW/Eq/O0H4m9G1iTkr+eQxiS+xo7bGsM4tizZId31CBBQjzUv5gf5RUokAdgE+1Zw9d3sj//6BCgWKm/e3P/Ly5XXCi9AxT53YrGcJGnYWUusBUw1OWidBhq0mkNmUx3AMGU8fvZs78av5cCUVZ60LvZVIi/uFJiZCjxuW7llMIApqT4cRoVbG+mbXFoDk/WPwfJX4uX896zTn77YDWvY5OyX+zf+zrYVSeZZl8UX0pF3GLpy96SNwtd/b4P/+SW7mQo+a9x5Z2dybyV7qWObRZLhayYQkBAUWNVr5Ax8Y+MBK3/49O47hclFYJ5Zid4odoN8ggPIdG4/9oX7C88ryPDkZF2blCG1Xx+LGeuCjzuUtXRyVlLg9tDiEpBTiA2m8wDwsIFz4WssROYCDVrEYXxGD6Rb30zVZ17/4cjIQ7lE9OnPXmGpfLqfDBH76tbq9HRK1zj5YKiqbAK2ayL7t2ik/vAZvSQqKAN8fKIQHuypVCogWN2l2Wyxoub5hWySQyFr7tqmvhiqbHvCgM4k7ORuB8hoD8ASlxd6cz7oBmAq00BI0kAGfdWUMyjMgUJf88Z4xx9wCHT6wwEiq2fz0dAlr3zrShziLCJoynKso48ieOe1tFmktfPhZKYyt69HGJaZYQROfKod70+tZRzcvbxYTSUCEn/5wwPAxkcIFyBzMvCZakJMLcVdj0Qow9dkH6hUUnHD1Nou76P8vWsL+ezKePrPnrxcTAuBmGmcKq8lgYc96FaKMQrL1VqmP5y5+aQjKTwG6qQ9OjlQ4/G0GKMdxyJFPRuSKHxFDJ++WetOoDwaBiB4bbnIEIb/1ANei1Fbca/ZWrfR6gwbpzC50WcxYBqxmZ6EsbwcwALnvjp6xm9nD947jqczr9iw3DhFRkomlzuwOheP/Xe++1awkH06es7qDEsT2SJX+c0Hw/PpxV+cGpJigZDe80qvX1WOThl8sbzJsT0zEBGPBvujPhaBC1sL/I31cSP6xf8uRWUDJIu3fvt7COS2wGgcTu4K4dw19adoeOaMdn8u/uO35hNno+9oUzvUpnAEk7AGDdCDQOXZeM03HdUIfSuZWdIwfuzYiW3Bfv5FbHQXr3CAEwNAN1nMANiYBjF3VKf7NJzLAyDw1d///Z98cPLlJ/9hXauSOez45X5MpISNMs7Q/NIiPnewWCLSzejDM/90tHtwAqXp9UwaWcVGyFaNjjF1z1KgoRPhOKC6uHbaN+IAxs9lgsKAkMUwqz9AkNA1KHMIJErpiS+ZRowIDFAMfDLAYYKX+yPZCliCx2kcZ5Cp5miqbQYhkjAxnCEg0gENH7KGKkyzPIanmMWErAYvurJnT3xjtLHJd4Zq344ZHTxlODeKUARSOR4a6RoemXmENwBy99hATHjUVYpxPoKV6fHQ0rmtu4vH41nfmtBvgooM/epSX7G1CUPmS9zzeQ9Iqk+OAySKe36uKmCG3s86846kESdzclHfKQUrOfPymerJy8uvF9KY7I9ePf20szc8SS8m12rCYc/5/FddKuZBPB9/vaz7Rs/yZooOg3wmsvWOTdKg1fNHLTOcd79+Y4HY4lZxivCnQ42qrYbRpWoDM8v1XMfQbdeWtRSQysLRl7/slFeEIAVcThCCCFIkm5fHTh8UcAa1TX0YQlgEBzJHQlIIYJoCK1aMFgHNwoUYEwRMNuZcW5E8mKnmTt5rzvaHpjWM9b3i6yvlqyvTS+niM0aT53GeRDAcAGFNmbEqg65bEcStlqiJT5Ex/dWLieoqRCnlGXq3OxXbNMJHn5xMf/2Plz74d8cu5WWLoDGVSqQFAZpuqDwkxsKxYnnZOOFoGAFT1Tj59HmrWqSnCfODEy81I5ZXauPh2Gkcs4u5dKHgTdqzYylfqW1f32BQ8vMv5nyJTJrDf/6vfb7MuTk+8eaiHyNi758CIYfgkOzPcNCLVehSIZ1bSPzkyZn32SuWx5sNY+gIa9e3GxLnncl4llRcYao71deTDnTx7574/VLvv83+4NOtJBwNFaj9a1fu77dfPvmPjyVLEXx3IZZFv8LPrLNUaNAsYNgwFjJzEUnGaEhCTzpQb2QSiqYNOAhyMjuMFARwRLIsCs2hg8+6VgHwjH4FZ1PrGQ4h8O+ujtUmoxh5MV3a2Xz9VunFe6cbQq2TZYDzaL3Kghh+eaROpXmFx4M6e/lvj2Uu9rUymsjiHY3x3TSXJVNotLFRgmD08FTq1zWQRXHKAU2dTme4kLJf37z3PeDlqwMvpJOrPHCO6Ke+5ipYxZsKrFefNnfRWoWRUSGVxZ48bOe3yqltKpwbEBgx8dQbb6xKndnjsyEdApxI3ivzmVrq6WUnoP3i2+kBGOqUi1CZ7l7z8kX/7i1R8tpCylIivHVh71/2f/u3rvfruhTIyxvppocTiIbThjOd0xG8naz4PfDViXwvUdnYyveoaJiz+i+HqYefrV7NqQuELWfjIvAtItV66cTz7qvDeYpmr1Zis9J4ZPdWE6nphWIq9uFpk05gq/cqkuPJkTvRnVC1qnRMak+uF7hh14IhlMwEUneGwsbKWmh2L89Gg2rerbejcpxevpZx7fHMdjkBvvnu1i+enlyNofl3qgFVcOM412u0Pm+QZrR0Z8lxKZjgMyX0nrgNCXBooMP9fu/9JoODAhudaQ4IAbpFvXvrZpfg/YkMJJg8iX35t4dOGGZLcYhPjAtV1OXmirt8s6SvoIcXtjFPZmLk+GlXniT2nis6r1buLkC68f5nY/Hj54POGJmzy19jD08VAg/sAAY0nMWJrYIzPrdjGBFLQdGC1e7a13dIzWl/Nny51cGuoteBpQrQk6SzEZlgLc5k2AoQI/UZwMYBmsj+8T/7r5/+Cpcah+m1OMHL4EqCun+lBMPDZ7OvrGT/8vMTAfAnjj/eHdnqnFrP/exZkH07u/HH74jdYHIyacvyjBybWFAplUgmBsNAX7MI2jenBgP6OBp5pgQHuq8AjQMjtbn01pWU1r7o2z1StLg4NbiQ554XKxRxkAMDDOExtLaQPWwqw6GJYLYbGdxa1hEBdO4Yfd0ZaGvrOZcihipVKwPNTw7VsX026CVywFKpgj46zouMIcRir4vs82f7z0/KqEd8//bwopHSO5dGkHmw4Z4/R4tcS+OLVoTUZ9U859T7ZIJc2swunLnzYdPygpoq6zeFnRro/VyiBIUaKhgrXkg2QWMfKd7f31wbfPbFDvhKWnyHtJaa9dF+57ljnxE21BFpmog+fzGmtqvX/xgat/vtlq22DH6Kh0mZL8XN/YZHkGGc7QogVK6srezow667PwMU/2Guy5V2sgw9+LKRGto4Fk2jgEpi9mhu+AAaM48OtXQlVtnMjWcTlGIc2X5yPsymiHgQovZUiwFdC4wLiNrGQx9FIXrUtXGECaOIJIKxNCNoeyWPeY5A/LTjkV5i0O7mERRHryA421Me7v0CGIfNdm0jsJRHKBPHn71wkglYLERsRuzBREvS5MsxwrP37uYaTzXDgqYGkEoRkgSuZlKN3fnjUKm3IyyaQ0kRirrnYxAqpAEHH80jp+8BPpVmkyAcDHDcGsuj+aB0Gz+vzxNfXTEOAdAmgoWYDyin3VGKFi0TkzOVto+kJrTqNAWc0pVJhicebCZoH1U989ULww0FJBFv73WBqRVuJqLIV0JZeWEEV1Lrt+NPfnKOpFfyhpW9u0F4ofjFMJtPs6tl4GIOUeLnF2ObQX6wQf70R1Pigf41bm3YUYl7186f1OdtU9VBKk3Wfv9qikhOXh7mEQMTER+B5grlcERsQczM4hUW//kvLo7N+dqVRdfCCMulTTigAgtAZ3QilwIxyci2pgOVHZrhq5NxLg9f3cpNGfLDv2jGd7YLvNBpOOZm5XgqUYuJl9P+DdXRSWNEeNpEyhxbbmHtq3/8lZPnamvZ+sKF70N6qrRQzdPnneHnjy/AydS20RhsGvElCpkBTMCxMXFZ9GmEchymVp7bBn18KfqGHrIPwyHe04VEhl6oLjtAgUJVaTxluOrtnUIszlAM/0YSZdHhq9bLj/fr/TmxzCRd1lvjZ3gafFG35mrxqjXUJPCL7i7O9LO2fd6/v8TReWbvFxdoKe7aiuHAD+7E+60GoNFXbvFAW8eKC3UFKaWTKZ84Hc9BkggGc2ErabHCU08mi7AJAk8//EvM84f7T5q16uoa9+GRduVKXDp9lbq5grssOfHGY8cX/BeWS1r4Op8By3C73Pz4oR59MlpfWuIrWbNrcI1pORGeuapj27Ab8zHPMymBC9yugZbEjmR3x+FylckIVGeGLgzc1EZ22NfnjcmkLW2LwvSZ4RFqaA9NHLryGr98685eB99tdTy1xQqgGDgfP2sBiRimokUMGkpOio8ULCrcWMwHsMrS8sNntjeXTy/0HtCdAwUPBjO0TcgD20410bWTgGpDlogz8Wyoz+29sdSiuCCFw8juCM6UAF0xXFP5+k1s+JuVSWMV7IZWEZw3yO01oTtGEg5wu5jBZljb1GOxJM3i5sU4DoqIIzpjZvAY188u+0vcN39rBSpzMZEHRFIfDi1M8jEcQNSjfcThVMCfXdnefIXP86tJdyODuibx7Hwxm5q0pc5QsxqjTIxDv7UdM/r1UScV4Gk7nqZ5aW82Pz6/mNg4gWmh73ckxOZTi2mcoOv754jNQkg4Hs8TuajfHN5f3S7ucHLPOt9vhBtFvhDvt2bBUAV8fX9vjMNA+b6IEVn1QEW4XF5IwjA8MdtzKEAggmFSOcnSYNx+9eKRI0NYBreZuGFMQNkK52f1Jt9xvRYMv631fRboaICbgs93J0WufON3iru7J9zYmf9CyiyjdAIWDeXi01G8AL/5vczlcx2eCPdfW4XExFH70pFMxfM0mpFVJLTJ/oe6z6CTrrm1nB5IXqLANWbI+CjeOZ8jee4qmJad0rLI9gxtMj3nBOdKLl1bTX56NE5PdHaDGoXNX174k7aE+1gKQ7g03Q+8wJgCNJJd5NAY/MtnQ9cDTl/Mr6ybJ5+/SAnU9167wyX4sz6Y5DOYO0U9N2Y7umdjEHqzTGTYkpP1wkQZw60wHMcL6MExXL0RGx2qDgAGAW6pYIDDg/Y0A0a67pM+Bki+yEEMbWKITxUxyZSfXOAxppLLX9HPzeuVmyVGcAoOOOu3XZ9IUMRvJO+Cq7F9q6/EKrXVeA2YXE5DPYJ9tcpg9aMu6MGAASh9YKbHa6sZCpXBgRxajDwMQY+iRYFi5woOlNPh3q4t1lhbxiwVzODYaIxW0nENiY9czyFgz7K0IMBsfudK8dyrJBYKQAtbTABCZYF+2ht9rF2OwHf/6/TcgEBgIhshgMyzvEthZCR5JwMzm8ZRF2YBoHUyQyM7R7rTR9PzKMRTMueyLx61YC2iETa9slUWeTlFIQG5sjgKy/ypUXdGapHtJKv+5+cTFFq6/wB/vg9usINems6w2afhbHE7dy0Ze36yf7k7mjPu7VRSGuL1w1lkh9mrtamdHKS4OAsefjrROStNWeOnuws7a/krV/EaDJIBxWKy4iuSkdhY+Mbf//qXT+b+ucF31GF79h//2a/YjcL6ejF3LSlkkoCjJoby8xeX95Zeo3rW472m7xp4CHNCHGBJfdSzWfCtG+6pFVJseD1Wto7D4bORBI5NLnDjQqHKGKDiwyadLS4k8jFEbPaP+lQYmq5LSYBqiwUaN/RcQgsIliY53RWMYVzAYVMKSQjEYJZdWEpRFBvA+w3jcnxypRxf3q4RxYtWx7aH4+lJxwkPT7/s3X+3CHUd+Yx748177Qhkc8wokj0qnJoRR8SXs/lQC01f5YOoqwdEjNLGsNPyr6Qt3TGgMWMjIEbzIG1Ph261zNY7A7Kid46Ox48uldPW4tryAYQYEwwtwlQaOHpyFOKBQRmyNiDUyPXD0alcKJIgGo5GM3SKQIDznXtZLKDHfbWjhmyRHV42LA+lskjfgyk+lCYeDM38DkERMAw4Nxd467DhSChTK5TvVRtnFpfDF1aZs72hH4ih20/nMRKjbN1Pms77f9ZPlQicJrE5pIz0BjHmri3cfFNU+vMXn/RBCrHSEyiGeg7AwcHctiJb7x4fAyLcupiWryTTZX67kO+026ah4r5ZIsqYGzOGA2l4QP8JjFnqQpFHYAjQpUE/WLtR4muBxtZnXblhA4mhWx2ChBI9fdHRUhmNwdwTS3bBoW/IXSPOp3ic6F5YFBfL31poH5qsQZQy2dnM11H1C9m6iSMEik1eqgYPJzPVA6WZ5gEmfw6B/OWj4+wmSXBCv60jKctsRaYKPiX7yU34cASX30krBk5UEx4ORBYT0zlKoUwEQhVNNiFBgAMOAmcRicQtJZA9mCKgZCZlzQwoi7Tq42kE2QJ42OgiTlgrb8ZN1ei3p46FMxaKEpJpBgIWqQA6p4RKfO7LCAtGQyMc1odpE8ts1Hw8lMZGjgVUnKYoHIXlc0zyAz+N5wf9cQQi7mSy9t0NAvAvP3i2Hhd+IqR/byk++E/7UYL9RInyolCh8xl09/EGwQKC9ze7S+V8C5MXsPBvjz+qpRbkabIIKddXWf9I3o3Ilg6bDWPVAcJaAhIqa81WzvYhKqkcvdo/gHiCp5sM9T8dlN6Eu3PWxS4MOSA9uAAQ6AVONppog/VT2ZfrcIDPPc8uJ1zE4OxQj7wgFrcMFm4gmlM3/FeS1464JNHRPX1myTiQu7aytXnP72j1LlRayQ7VZnM8n06dNEDOFEQfKeCKUr6ySdiY5ECJWkGazCOfxnW7/ehxdbN2kWTFqf1alj6lEhii6GMjtFx9PgM5sBgnQNUZnsp7onUtk5j3JEvtg33JTGW6JtA+GpV5OleLwYuZvf1j5MXnxTHtLq9CG3BSSEBhYvRZYy5RWBwFO8pyhSUT4uHeJcFZrfGsdWyIPlUlEdzqJVPlw/dfsNlcxAXa2HQAaHWJ3z/o5BhCeD0bLaeSotDBQhEyYvSofjaEzClM86U4a3VUHMOnPog/8fu+ir6z/eAKuX5mdL+Q6VLY6/lIEYVQdIpZ522zkvVCnjiS1QBzBsdT/mbl+ldXiaD58S+/xJxwZLs3N2J2Nv+L/+FvS99/wGDcfGSDnrB/MmGzGG9eKLZ7fQ394XvRZgqMSslZT8k53oTLBXFzMn+V0jqvXaUfa5ZmduVRW/BhuzH6AnDCsIKxgUubiq+t7CxJh3PBinDK6dxOTc51dS6PojAboZCF708DfVdFm/3Oz3ev/s9vvwToF4fdBwS8awblB6XSVaqJ4YQjOPrcM8VMPj4812/cqEKvBnZvsHorB/pABDMIHkvH8KEPNk5PHp+6GSgLSJ3PoFDMZFK01Bj7BZSNxw2rbbuzCb+cXbm6YOHik48a2lC6fbNoLgWN0xl5KiEKIGA57rRx5Tt39ER1gHON8w5EK+gYwSMhlcvuyRYewOPpaDA/g3VP1c0nLsav+DGYQkRob9yR+9NoYzvM0+1PHhZvZi/Sofb0of4MJ9OxSowfdKc6xvmyjYBDuFwCZLVAxxIJ8KCj17boiTYiy7ROMlpvbkZ6xgd9KmbrUHyoxaOZNurrzV7xehkEAQLK0mhp/nj2+ta1x+NzjDGU7pBFC2k6nLlsKo8OmyoIKkghlQXcQZM1uoQemCyqxwFJpHMCizqSxzIkaEUi4gAUzXCRayhyHu95dEzVXr9VMyfzsD13qWaxksDiUHs8mTSAdx7g/+svZWJqvN/tpdaWKyhWy8QHF8MoZ8z6cDKD6LPg4qBjkMZAiMEZmGHwBORGqsfxcRCHhp4K6QiVsPFE+raQnjMwTrLKuQtlMwyRYZtT0sW+ODt1CiL2YKd1+XThrVptbVkfW6M9PRl3TXd+Vp+srhDTiYMrJ+2fQbEDJbm0encN6wO6O3X8tkOwIs0hvm4g1NxHGFSaQderfTIQHEl8KxevJrv/4iMhS0bSkZS5KpgQTgQuGMi25UeZ08tDgExb5qx5pALzBhRE3tzoH0xiLEO9dvW9v/7Zb93J0VQsSqRJXWGeyqOH79d+63YsT3hzE9MZJhGLavb0eMpGUTkZpxbpyTyIzaZ1PcgX+Pl5qyCWszQ5aThZugjJeuVqzufIeX3qEZEu68EU2Kwlu5q7srWmjsZPnta/XtxYqJYRGYhcVzUnEizk8MjHXJuE1dnpJFUpvH7/+n675bCsd+lE6kX7aFRKx2v3a6+GZjaO+rgNJbwF1jz9YBgEzM6Vpejw4myG0gIEvX4dL1qbM+6kN1lfSypOsv3z/g3Euv1rxPHe8ckvp5lrRdpnzb5F5zma4wSb2BgDKdc9bEejx0DgEJnS7WvL7jRO3blTngyfZagZCLP+tEMxFMnB25X7eqP58Gl9by4nKWT6BLj5O2uXZcOVhqDugYoz1qNBIFOIPpPV0TiDpquVsuDAwM132QRjW9Wrb7/9OkCtnemHDOycPJ629p+VrlI8LrpzjA1ykRkdvux2JDxdWs7XYHMOnh67m1dK9cNJOsbRrkF3nUycdQncaMgRrJOaMmyqNQ5ZyoaVCtvxOFjq5e2w9/7JyUtPQcLFW5kjI4Sy9vIAHf9S9T+uezFcYMJMCFqBGR+eLk6Kg8as2bD8EXnZURa30uRihkiArcF8agfLJdqbSXBkr97YhEj7g79qf+07kNYMNjaA5sxs9v3y3eWRR1mR/Y2vVPcnETKAFCKGkpom6HPFVScSLZS2vnt1f9igIMVsw5KrCGgpmckcPyP1jfjGKrr/+KNRb07geDCfEzYsj4gwFnd8+VpMPHVlNO1Arhpofue9p/XO7ObrJbYo7j7qhXC2d6BnC/l3ri3u7dm5ZS5kA/wGzjFos+1bgf7sgqAxDjyObrF+CjCEaQtDRkcNk8wDYz1IZr33Dp9dkcavvbnVGYT9Rn8IW7liNhdLsfh0GnOb3bNcBlTroNuLMN60mvqdjTLPc1IQOgqLTCf6pXX+0VnYGn338MRYJgZ4s6dlME8e76nJOBG3hlNJp5NwXW6bcjYAyHiMjvBJmmPFIhtvIDAC+b3B+SMb4YRYBnBZwrbsZDV37c7S0avT9lknu5jAyzHaxJdZ0KREqrZG8tnWYYPNzkguAGGntTe+GueHo4vRhRuILC88wMvfq9y55fXPfbBAZKGbOe7zn+7WJwbBB0kh3DtvpgS8kMg3TmUqCx6ed/0RmVutlr+1kJ5tBnKy06wPkfidEvnis4fFOPCV3y0P257VGY4nWnJLPJ8o1ZJrDxuUScTFuNSls0nFFF1Q0/QelMyzYMznCAg3nPVlRiHctZts68UxO6cWxUIQpV0smd0pDtK1PDV+9meD5xP77m9uNdN+38BCPgKHdjImcLXQmOLdnuoIXqZCRFNnoYB2B4oS2YwT3yky9QvDGSEoiQ4kM8Oj46bJs6Hbsmo51HKDTCj4gijkGJD1XE1hZg7vOUBy+h//wzMx7i5djZm5lDoxvZ7BV0rF++nCMh/fUwx7Xkje1NVB+/MTle1mBEwHSLCST66nwak9CBybIK+gAkaJhkEAkDiXpXQ8ll0ie4BDwLhYzpw9PJjO6+LWtcT1hcK1yJ7pk8/aNEfroyGKY8Q6QWPcRcs18ER/s7adr+BvBt0hjg0jTNKRGMNVaHcQKboPc6CuGXjam+NARrSbs6EQR2Wb3eIVR4Cw/rSY5ClrpnuUCxiZYiCPmdVEvHOEA6JDBGhmO/j80352nZnw8KgpQSHIDS4WEXDeicaoRqlwIZeMJzBEWjdeaPufq2kItPBIRYPIB0HMgrkETnFIggdB3e1p5ml/ZtlCmtFG0wKOwQAUx8SWOpu9PHrwlQTBUKfN1s56DfNwz/Bmc8bDEiKbDR2jc3Yh4hDidEYmHS3ditEwrTXPXwa6jzjcyI6L+dCGeJHJ0HAn5akIQ6RpJ3AiHOWC5KIcLtRimD7OBw7cH9hXturHPe7C/Mq3NgIIFyIlOiQqcVr+vTuGKjPJ0sUv9oRypvGsO0S43LcL+eyq9+M9Ib3E4WAoNyMaJ1XkxY8nDr2Sy/g737sTYc3G55eLfew3f3s7N03g0NAZwNYfVsA5sdB2kJlrG0DTsicQ/EBoG+9+Dyl46UFbU/35cjaVRtn+5EyHtkqCmhL49Y3rS3F7HCrDEZtmRhJPqZ6EAlEIgIso1ADJkbSdTJOAs9sOMiwVhlNXQDjAB6dSy6xrI5GPwYWVigNZ7dZFMeaBNrrGUgwededKniF1H2IVI11kFxdYQZxhMHj0aILiwJs3EvsDX/vdwsZymEiJnSfv7U799QxDlgkAQ7EIZnmQ4Sidc6OZEw4unz8PZ9PwztKSUAuH3QsFjPCxN5+bUIxf5HVpCCy/tdpRo+6utnV7VUyub9yKnT5tLF1L9Y7PkwTSaWsb1wVMm+MXUUynSCiwRq3mUhhAMwdyHnwn5w/O6JfDMITKo7meFscQnEKgMorgZ8NXvcnq3cLuCD7yQxjU3wkCkgueIFRkesXZZClFXMpg4MGzvXNCdRJipZxM7p/Kk66Rueb+8nR2be1qY4ojNohy4LFqUpenY4OUROdWkfzV593Nb2R2T6brIEa47guecCO1xKSK4OQVoEaA8YO71ZP3ArUfTiQvjHG1dG55YQFOQ/Lxc//AcgUcxBRAIfA89XhqMDeLaIDbANbXrbg2Ftogm685t4DF9apEx9K91C0Mo9yhkGS74177ExMkg9W1OJZHFNg3Ut4OR2mDZp83xQQ6n/kZa/5cxZ0IpRfp/SayM0onUD6J+z3Vb310eeRPYI4SA7tnXSZzWf1AhgpFsJqZ9SWnLWGN3Z8gxjVm6mdxA69WYsWu74LV9bvf/prWCcwvvuxdOljvObGVa/dRaJ1LnKrqQL2cHw/jaAoJ4MgFS7SeifZPwQ2hIDfbnOlOGqFhMJY0vfb9N/O6NoAErEY93XNDeQQm0XyxQl5q3QA1Ed5GQiYh0knjWOkux+IDjstnEnRjCp7NfVdCMn4nwmOaaUX248tZu6E4IJlOF8sr+FlfA2wlCOZACohvV9Hn4+F41jxFF1KGUzcDTaBHhgTOEjcXV0eWGegXuqUwAdT3QB2IV3NTAxsd9ZBYmQQCmiKnGBaoWC7DChUcnysxg525OGHxbQInUTSGKKSbCmYoAU8gHagU4L7t/M1fHq7ff31na0n7Gvrpy9m1DGieK7ESo9AFUeSrpdX8YvzLjw6krBfEE9RO7vS0TXfNcy/CYmncI2IYv1FMn6Nz31/OHLZkXJ3bbAmFm+rICJ34TgFt91v/4yS27Y/7LmI5RDwd7fWJdIVw0ogTZNSWW0rOCGw6sB9sJp+ROEGbywsJwSaaL60eIOAeDgqsPDVsg+cTwPRchTJFMIbkScJg6PgffN0/6qozp+ei2WwyJOOu2zQ5gsV0gAuDGaTS/tHFuAeb+y0FQgIUhBHNevzTvTd+/x7JC8jL7qxtYmZbgcKF15f0eDr98PSj3sjhw3LIe/MpsZiCMVhuzlKKnEzE4FjEm+jgqGWssZrpo2OdizMhYXoCLgr0pQXrDpRMxBASr1+MBIjIr1U1GEmQKJpmNVdWdRexUCAEgz4IobDFx+wkFh592bcCGGEhH/ZO+uaZ48TTkBdYYBmZjsyBZJI0aFJcGYW/uITBTJC5Qdja5GI89gaD1EkcJ/iGXSZSyOO4JRRmFwMxmy5Ik63sCnLeOechqnVqWvXeWpYpVGjpctY7Go1y2d/8B+vlHa7ZL9EpYHgTMzBESKd/Y2kDcoCzz/VoJg1oZ9i6NC8xBEfQgM5vkeuFN/wJ+kq/hONI/3N5KYAIkbLlyehxf3zcge9Uk1/fEJ8BFpy5OANgf4bM5ajfQ4HiSixWJXkAnGVp49V88OD14t7T0oluIHmCEODBMzgTh3GLii2yDpmcyrCL0Y7mnL5qY6STrmRkndDJhIM4uO3Isk7EUStdTd4o+Hmxm3dHXU1hP43XqCPMSv8f1rDq2jkw+dUz6cqvfyPerB/+aMajyes/uD2bMJhvj5pNze8JaXrfMRZuc1ezuaXiFrlIXz5VSyLcfSILBcBn2eNRCEcRqcKA5DA+vrOTOXjYjkbTYVNZuZHOZrFalUkKhhuZ7fNgaaVILt00XDAEqvWD89i15KQ0eYaq6PCYL4GRH/TntIvTQGzamhqpoCDYRsCACuheLYv07nDU0OcigVgQZwyWN3gfo0GIBlwQ9sFEIQM7ajy0Dz7ooRn4ToniEaPGIcxKwYPY5WuMP9DBs8sFAfVKFNrU5n3r7ZiwSloJ1G+MvBnloDo01ZURDGXB8Aon9erEldXa7ID6/E9GpU167Y2aYSMqInh9KVAzeWL86MfP+HLSj6MmjgZutlQrWBFLzoHkpBeqfE4Q+iaWWy3fvLpdj3mPD3VGEe2iCGu91VwBoPSmbPt0GsES5axnkl5jZvNXsBgdcm0MEJgOEhEMJXVn0lgvEFka5EOU8ymfI/TD3TmRgV5bLQx7MqTDrIHHNjZxRiTDwMnD+RR+elSI814RYv3jqX7qVu69/p37q15h8bOHrf4X/6WtAioBcJG0ynVeoPSrvr0dxWnWf/y0m72y6BFg3Q+hdKJ1Miy4AdHvtp8cYp5f+YfbleXC+D3CV6KfPJ/RNISnEq7J5VJQKLizoS2pRo1VgOOoupRwbFG2Z0k6gFVr0QEKBQHaLJkaC8wJBfOSjDkBTBc2u2swury0MBd/9vHe7qheq1x1PRF62vrTh71sqlC6VqATVEaQUlwUxNJukgi48MWP9+ONIWIaEIoagsAQXghRhQVWG85iHDynGR61Td+QehqWy0qaDaFs/8LLJhE4VgwMXwKipOAhJkACUGotdeaDgg4q6gnqiasrxZ1V6/QoeHTwcuHdUlkkHYbPLKVHkxk3IyQLaV32ZC2EdsTkAndnUfzibw5XYHD7raWXhNxs4nAfOT/QR6oOxjrVdFoeTXSMD3BkYOogZRSuZPES57bs8eNLQyVpEur4aGDQhXtXRbKY5xMF122cWsNj9fY/vvKp0XoeUCZKbYqM2gVwGQfleQ5H7aHBQSAFoKZq5uKIoniVYmg7xIlkZVPsWo7wqJrCUIRPSdI8m6INkPOAoD2WQ4Z7unseIOP6YEqTc1vVSQhRaYrmIt6wmxcTKbIyKMLECYh2Qdn84kXLS0Vp0Lv9jdXRyPLPBqFrD847lSvZWA0xLhzgRJcdZXW72OpOe/uTUpacBwoQWBwYL/OUE7DKqTKoGx4kO5dwYjHJl7MRyvFEBLsaCNmGPSN5Holsc6ZboisZhfxcarc9UUjHEMXZH+ocI8IiCkznCK7aIeGBMBa67niWKoEznc2UExgoxUJL+uCYS/H5N74avba+9zdP7y0UT6C4MHBtE1LTSMx3eAnZ9MlDngbjV9kff57MxMaN/YWbqQbgptbYG2R5d2/yL//8RwyeyHUXJqsU7ukO5Q+pCtf58KNf6Wkyq4ShmNGfHVhfjcImUcHQ0GxOnRU6tyDYLf/02TlcP/sygQIClLMc3wpkga348Isfy42H/VpyZv3grc3q0uiRetxEyNNO7dcXltmEA1xGA9UIkZcnUu9ZK7EURGxaPu1UkvBchkYBM7g0m0E/czMbTk+QsbeZRLoUY/qAKUWlUjDzQ9U2QgyAzCi9mYS2BLsCaWA8aQILX/9+iRn9PDgtxjHgcScfnXeilaswiknaxO9nNnMIAEMoyfvhIBQKS8Ukrx0GbinyISLrYynj8SBrR2SMpimFisUoBJ4ez5IZsXU+7PR1olI6bMynfamcgjdurLi0v50V/RdGCkYmhJ3c3IKzDNDYP6yH5U1ZvU5ogCfwVny+m0vN7GNcU0hD4GTSV053JxmcRiwkACfZnKu5PAYiqhnj8YZporq7VYlv11JjHjlpRzhNlxFY5dN8dg2liPO/OKF8H8CZDw/aqTtXdAuIx7HLIwkmQB4G+pD22Yfu4iJ4Sc/XirnsqXSlJGpnF7lhk+bLlVKmN4tsCrsV4Piuol4+zlfvf3k/nRntOlvxVhZMx9NYx6i58NkhnKMXGVrgONIMAHyhGM2oYx0r4hZhAU11Gjqth6pbvZY5+bBzmsKXEmsPNleCAtY6/8CE/D4XMR3Lpmo2FmnSJcY5TBGmFSAZMrWJ2wGAXjefgTRgjs9QdzODRG40YgjZmIeukdoudfqNvYtZLFvdYTxp7fr2ym9/cv4ns/cbGa8FrRPChFk18JqlHV7qs/2nZ469cls4fHbSfLxn6521EilFTCHL20t8IQNcTqNsHCiW46MBHWKYi2vGXKUS2UDqKmcnurW8/OYWu1rj364eftBdIJ14FBW/UxoHTmw81WwQF4iWB7sEfuV2vNW5FFO0EAvV5qlmmP6qECfJcOC6rSlepYXV5FIUO7680PSpOlOeue4SjoQT5JPPnjneoBf0VRlz1zgGjS3cFSYSlC/RBEWshoB2MjQkSBkYX/mNZfRb70w/eG6tJrXI5ukMJFi44v2nv/oC8qHF1bvbyXwsnHcQ16DIHCVBtUKMJj0Vpj0nTQExkdAHhkCjlMA1j1TcUbGFrAKNDkcjY9isu9Ti924uVAvnf/u5tau5qtGLhS5rvTqaMYUoi1qBNUc3cggZU1r+8Zf7FZcrbG6cPTp290boaozY3rpdBL0TazIYyJcDJI9DnhCpgOWr4WQ010dsL5UpXVl69wEWZ+ePHrZdiXu9hNo+ZMMs6+N9JcVoAcOfvuzVP2isEmqIlg+QkX8+23rzncGdjPB5H7t583I6TxVLdUUhLZHEncbAuHlr9dGXA4YmKJKq93ulq8mzureW50wF4gXCRzEznFIEvr4tvPerA9sxG3U5DptjDIwK/F5f8vsdera4ubXdfXigeDjAK9tr6EI23e5I6senmg4ZM8WZqrTVj6UKCy5qKo6ZImkiUf953SqIr6+vnQ6VTIk4MqOIhlHFn0huZ+zFl/JAgknwSH4hwy6mICfhHUzFSnZkh5SkI2Dohw7iQz4Eer2eicsTX8Z4IowMK0XgkAfhTIxULNfWdDVCALXC8D4HSqhjDmR6LdUNAy6XEAT34NUs4QCcZWEkKMs+m3XDeufzz4Zf+Y3ax39znN7Ji1nFmKWgRvzOlfgHvaO31lepQLy48Aoxdzye6whz6+9etU/ls599ET6vA0R2vSacFQEwgv+3I63dGH7jjXuqpYwQwMcigECd3lk+E+MXlFMYHbW6CExgCRahEEGMJN0iMaj4g2scFQlTJ2Fz4ToG9lrQ6FUHyMNo4DBBYbv61W/dmFrOfF8uE946PIYhDr5BB4HLyiotABgAdOagSuVIE7h9raIhLUDUVdUx7CQJ5eN8TMQMHNWGquX4bpwBeQCghLmiTT56BFOJfNqhRYQbtgESVPcuBownqNWlFIG+2icW+A3kTQ5AYx2PEDlQ66JZIbNeFo73T1ktdKwANBvIUnz7xkLPcNtdA6JFAieVttGTnfg19GjkJgsgl/cnfY2t+nrgxVFGnfpCG1B6ClpNuomkEbhnH+/WUPrWZsxIQrnIf+YNQQBtoyoB9f3VRGRk23XVBaxYVoORYQDPwsqK3lY5RDQjkMrRUncaBhCd56gF5kxTVQ3iYG/Wt3mBxwyXXymz7MLb/xf6l//2IRcibjHDpFOeLpIB5EWai0NTzJJ0dPEKy9kqOyZkZfKYdKNOmyORIBO3pSmliUU45Cd+vkS7q+hRhLi1wus72S17QdgU67NPLr884Z7XiVbUcnCxWpgzIB/iiWIWcgPOw+Qh1LIstTcWMu5P/uRLZy29sFMVNxPrHLqI8JQzHx+TsJcx43oQxzfp+Mu9IIBNIB+jr4DIrEuGAeADeIKLm+g3imIWhj5j4FiMHal6gYqJqiTZFADz3XZnIRNnyEwUBlMbTFWTECCXQncqtXvjnqOCb33v60PZ8W1ljcVa5aEukv/+0ZNGffB3/9FK+zwcdyY5e5RZzvVr8NllCzXnA12G51ytlDoP4KAPTk+Qa4tOig3N1a/c+e4f8drYV2ezzxR9v09srBbX752PeibWC0RpNj3xXiSFlQwnAGa9b07UrRtCt6moJp9ep46H8pCZ3Lm9KHfD1vjVlBkkWNxojB/+zWOODS4NAKBhaAiNHFftm/5iXly4Ey2XfZ5Pw0S0N9Bj0un4lB34myvpMOK5JHjZnTu9rg/y7/7B280Xl49/1Rj0+gYGpln2/s11zCqs8rHHH2oDA1q9TWuysZwKVFMHHVJ3fRc14QxNV/Fn48lrcTqfwoaNWamM1GfHZ42XN26W3FNXejzLfbO28OAGDQJ4dz6aO1LH2iglNt5aMFzRGQ/3WpOIMxEQn7np9RuF81m9c/biB7mCqtahX5nLd3NT2kK2sIkFyo4PWGo1A4/GTiikoUICCGZHpwcYm/36N17v5cu9C2fUmSgHXZIX7Gw+8KQZahVuLvReXG7k8xmOePXDRubdeFPl6u0mU8RH9qiKjqdqYCohz1IdI7qyntcc4txydc+baaTm64ELxEEbioYaAIoJCvRhwJ2nWKjb7LOklkxCQUCBToxwQzPSz89GmwuCHQc1wOZZoWHyBYo2Jr2m6C+WQXNm6T5bEUSRZ5w0bQKJIIrabdOfWjMGTpaYnddT9liuZQvDnh2c6XmNUqbhhTxy6Ti7sGKLdjqVTy3hAaeevrzMkkEui/Z6LQXRZbWfFJlYPI34poVGgPqysVReF3OcEZLt7rRBQ7mUDUhOTYCJ7EonPPKkcZ+HrYkMqZ6URCERaT1phcNx/nqy/LWriK7Gxv1nc5e/nx7EbVaxq7eE8qYI/VDKXUvCnJ2lGtWrK0Z7lrx67dCji4tF/pcfWx+OMxXjkjVHKnd1JZtPffVJrCfwgOVGGYcu8Yjl6+k7WZAwgCAKRuprktowwlnAeYYuUfjyijFTZh4bxu0YG1lTg1bNKLD5LJUReAZURigqFP7O4qunDBxa6wkPOJUmA3L99xbnFjB8eIBDfgfAZIWXT0cRadI5lknG4UNzPyRi71SzhmI+aTLYzfHnJ2vXubOFhDoTRNboq0Zgs8R8Aqfg8dkQisUSNd6caoDtrYmY0R2fW44orm7GgMwsU+DgZz7tyuG6DlXR2QHtJxYSCT+dpM0LSeYZUFCYg89OLw/7kQhO4FBrj6m1q6OlfONgYOAGQwOoi8BGkF9dwBA47uDxOInJEWBHmQI1tg0EtAf1sQAkatlKkKGbJEp3pHgsAacEWWMiTMrDANTqPnenuVAr4uikPXusTF2KAUPgOnF8aHq6h+xWa/xCmDqbGyDMBA5VTK1yYSyfRqJQmQYu48jKmT729w6j2k4115KezOqp2UDd0+0CvLDFhipwpYo/2h+bfmD3bSy0bA2l5fPHPWuRZcQq5WIMiKpuNjrsM3dXVkSL0AIbwJjdgfpsvynQEdw7j9cgUNY//tEZ4D6N6W0iYqFlsSKUhJkxlEKcDHwPoVgMUcCanUosLcubCW3ealwx3LPTy3/3kPj+gys3qt6RtM8jujtDOCySnOjT42gGscwGX6TInXjMUyZdfRt1dxmIteD5EvL80RmejHNQHKehl6hHabw/GI/zOfO8S8Tj8loCb8udlpZ5Z6Vqjj/5l/8vt/dUD1IbZXhyfzGzsjF71mf5yHrzB6lgX9ytkwMMup3uG2ZtfSnPko8+b3r7QBWajj48VLA0jhnS3P2jby7Wp9QkSz3hHPm4F0kWtYJGUH3vV+9FpxCYzW69dnOxJuXQ+ePzhgbN5bWFG++g4Fn9pNOt1ih6hz15Yrw8ORggLA+A2SFxl6HgefjD54P7WWGMKQmM1RvzH//pXz/6eCQI0cba2lCFxFx28wcb+6+apbTIwVkhyz8JIrTZbJ+dw6618dUFF+jo5JI0vCAmbaXC82mMqU9/9v/421icjVdYcbmKWdS95QQ2tMHM1nxsIiQZL9b6I7lbd74ndP+mHQHL14jJuH18UqYWIQYU85XTM6mQ58g85CozczrgSoiFkgCnNx09c24B4+AXF53V14rJrDM9sUGc7h/Np9M+nMIebKCdifVZS9layAysxvhsYNvCwTmYD+jZ9OT/ffn49lrQ/MHvEDYaH5ishIGR7JXyxQwh+2MnEwcemjBZG7eQ4bE7Oe1OuxdLPOzWRBWFZ5f7L5/tva1L5NwiWLrbml1fLoUuUq5qk6m5lhw3k6B6qdzdqj471Ja3+VZzcj7mEoXVl3918ta3r7SP9FTRpZfEj/b0N7azu/VmpRYfqTMGxjHOTlQKr47O8JQYn+PF9aX3z00kxuxAtMD5ns2hLN1qNItbqVSAjDpAPefbHNDoGasJcM4LT3/1Pi8pq7/+TmGRnP31R8nvX3d9b/rRebxkHze6UaD4Pn543q+IPJ3LQineYmCPTarmgOLgyVmn5Y7WawlCkSzb6bq46HmY43EQNppOEdkLKIELxeLE9k/PIgoWysu3QBHUIxAjbRSjZN2grQwUsIGh+QYkEvgChU1enC7wcb4o7vVHjtxNidgnVsjnk1UG3v2g+9YDviW5rdb0rT+6yySzw3H/0bTxrvFwcOreXq0GW5nBSDcKVxcW40fNR3SkTI9OllaAWBjPbxdTU511cPXAvH4F/vAywDh9MDeme9PclbhJWX2XjQK4NzKhJPPBB6eKrhWqhUl76LPu1J6ZAGRNtNbjkzhLxRPJIWAAOnnlRlbwdWI+M5r0O1fuZbpJs3MRjwIHnaEhCWFe6gr8+OmwxKJyH/jN3733k3+2l23YV7byX/omGXiFlYX22eC1txffm/pJkSMzOjBSe7v40YsxBRBCGXzVGsW5EEAUSIqHsB8HIWt0OPIC11I9MwImrWKSCVUfjqJSZCfgCkqgjefnOBpAc7wzsSGEKO/EQsKWG7MIi+eTi7wF8DRvSZaIRe5cgnFMgO3+Y6caEwHYiOMokuVnisynka5saXAQCMTEg9pt3cfwRj9k7ws9gw0cwFHSvfkU4NOCMaCjSk+3LnCTf2sbVTX+tHfxJ8azM2Dnt9FkeZ4hIo+GYlnaUSeogjQsAsYgLpEp3fDg2emn750jcyizUAmFeEtGWC/szLDVdBRlRXZlARGg5v7BqT4nKT7tRYYapqYyK5u/tpkNOBZy3Is2uL6T9WALF8LPGlqBEfkYl79DH/R7R+fPEFlZXd0SJlM8gp3mybh7/OZ6nLnH0/ky8aViKkEBFsCJK9iOh7EoH49fLxEcB9kYxyd//R8uWGcd4ETuzQHF08kEjqKGNz3td+YkTSPR9BRlSjdNoII2250g6954Q1TmkxTmnDrgOavAWxFRFOhPQ1Yi/rCwPESm9F1WU/euJiAuWdIj6txzMNhClGD00fwmOb9ga7kr1RfjOqQFcOPZ8V/858Fy4s7OtbGHJWtC0Q6X1+iHwwhaXnoxTYDXCwmGmOy9sq2VzVvXtr9bbNY7R+fz9knzlAEXX1932/gqyj+7VNwXu2iRefvX3vIT4E8+bhz8aCC2+9d+987trVv7B53BwTACcKYYO5taiwn39Y1Epw+ysDZ/GciwZ7Fx7o3ypA8M44gx7FHPRtvrqZNFoUrRbTckF2O0AGazopMNULS4gcetw4n5fLS5lG+pvVvX/F7fZ9rQ4XvSoPtjPuYAgYzfzmL5pBJOw/2ptpgV762h4Brg0P25/eSzvdp1PJ5OCw/g1u4FYGH3bi4YVl/aO9/eSPQ9lyAChNG1gEF82yGoAYtGel/ra3/5vC2BMZkXiTwiRMCTT1782ht54mXbPIhetvsQgl/5Zmw80bjk7HziNU6s+Ea6WGMCyLwYWwiRza5wehg9+/zJyYuTHw2B+H8LTGeNdATXbaDkQpTfIeHoYTMlx21CJpa2qhEQprIRx1fCJXhmShlgfnncVFTDn4V31+9IJ5HnKvOxZOCJq99/nSniN9919k6GpoJxJXP/fC4u5DI80hnrKOPhgVzFxo+6l0lm44veOUiChc38+aU50Ck8ZCQ7cAAvAgNrZiUFqogjGAz9lxeTckHfWrBRExqPwemlAuN0zIefPdu9c2XxR59dovJ5PpvlaqpbIeZJdtg6CLtTXDXsR1+MHzGjV3URRbffuKat1tRes1ROt2aDJJJbuVKZ9lyGTnDFpNJvxGmLFnEkcEiMz4tllOJMQzYiJ8FHwURmEkkXweDARaTeMOCxlW+tpSxw3nftuShkCqLIEcDsKE10oEk4gQUBVFpYZAMQTk1tP22gVZaXVZTmIGBqczwipNDWoTN7NN1cJaJnkvb9q/Bt4dP/z3/e/P/+fa0xDz48W3rj6lkrBI87M0LcTsHyJ5fZG/fSi6IzvBjUUADF4f35SkzgAs5iE34gQZLXmoelN66rXxzLL6ZEks9tlDvaaN1mlR42rs8hRTIgQ0zwoqUF2VT5ajo7DieHw6aI7NxKgYOxNGmnhURfivZ/anFSP0NkvpncrGYr8nCkmjN0mWuObK+hxXneoyOCFwat2Y2tguQRq7eLk4ePVu+/OcxEBfOS2in8RaOJWfWlq4vj085MtnQOz20Xy68M25o8eq/ORS73Gg1zYENpBR2IJj2RTEpm1BI5MnRElBtNOxMjKFRzS7lSQsgEEfX4V4eZPC+mChMaS1cFZiEB+1NshQuOMY7kQ9UiWMt3rUpe3N+zvAxnBJrmqkCe7p4MpZDkOVzWAJbDGu3x2gKGRGTzpQEhATnxlspkqMxmdaVaWqubeOjD/eewaHOcF1qkEJSlDmyjF1+wn14uvA5cY4CYZ0Cc6aH2iZ9A1KiEw6W4N0txQQIw9AE49FKgsM3Ffzw3FDxccSBGCFSOIRBqNlL9wBqCcBHy7emgEks6oxC0DMizfQoVE5nzV8cUtyJNTixqw1hCaAcSBSBioAJJwSA5rWvw1hay9EW9AbKoripG9Uqxtnxj4STbfLrP0TA3dl+jtdYa/HA3hFteGm1f7E1LX2EePxt/hXHEaDbIEnJzmlsueVs3b3LqpG8GijUemz5lw66nT2QaS8HVRSiemjdPBU5fvLzYO7He3b9olJY3jmfjxRg8BiaHTWRecYNNEbAqOBX6uKNDRtXFerqsuNqUAFEqnYr7x70pDL/9f7p7KJSvnoPVGfHDf/Q//twA3hW1S+XD6FHvaHuxWgweN/RbO6mOPB0Mu710vFrJPPqXD/k4tldAHj6//EpLv+h1rN8uzpqjBI70Ts+mv/sAeX1K/PxTNf/g3//VRVy9yC4m/sH/8+/VgXTX2a//cIzBtmT7QuDfWcsf9V8+PdLzqsSlKRcI7YSq5TNytwGMxleurKOf94sbS0//8pHi+oONDUwYvfn1G6+Kgvn5RbkHJt3lCOlcfPB41gV3281v/pPftNIEKQE5coJh2tVfp9J7IP6tq5fGjPWO1RgD/3YyKHi2KvvdnuJE7siFhRpfuYLDUfUqd/QJhBhakhbskPjL5z6WWnj2J4d9xEzcLpIrjP9qJu068c04QkY2CIeerXS7dBJWnw0ILqSy+MEz81MUPD88TFTZt3/vzUH3FBoaE9XsAb3KlUXpnF0EKCiIzCikSSFrR+0H1y4+H8TOYpU0dh92MZhB2mo1WRxSmfqXe1kG3Cip42Khli7zR1PMv/xsGsVSTaXthLghqVMuLlb41DKVaLIR51mPuCATW7n2e/lbN96dO8Twh4+/BL0Rgtxj3RcSDPC+LM+Ya1dOD0fcxMysY4PxMLGDfvHoy9KdSv/ZKzifWy1il6eD9Z2U3hxjImbDoWkBnDRfWM9MwGmJJ06Pny0BFMpik45Kx9j8crE7n2cRgPLUy+7JMjSxX+hPf7EXu/IGnqV4mF9+M3bAcq6r75DMIl1oWa4zHdkMpEUy/Qcbl6/2K3Uht3gjW8QktwsKqg/aQ30GcyAqRaSpswoOc3wIIXFTN7WAwAlyMW7MzQhwEAKneJixERCOYct8LgySIU0P5n6KY8xeZ+7rIEYoQARGOGqxC0nixWnns5fynXspEgiltsa5AaAFFy+1++9WPvxSCuIUl0g8/svWymooSSF90mYkc+/Sqb2RfTqalypJJJSePBx0n/TfXFk+fqjW+IWjRr+WBMSKd7o7HbTOETY/do0ySdl1j0ZVwHS3ri+kb1Q/dzpjhAItK46omuhIEBEXCgmYYhE6VsoGsYDAnYQJkzixuMR2i2zR9s2OFM3tm5XEZWec5cnqVm58tmfgJMAOZ4N5TYQ7MqA6kTJSahloT4H6muF3B7/+Ffyf/jx8+sJaWCq8/+/33i2KNzaR85G3mnIDlCjQzsWwH8ZQJvT97ux+hV/YWtWK2qGs8DU7loYv91ujL8+dbNwoZDKVlI55MJXk9BE+Umd+dvTK4Djn6lu3pN7F2Kqv3CjtvJMfAEnv1GeN0GCQEA9cPLB6GstTPRWXXZQBKE92xARmewBECJmiOB6azY6xkRf5uOf5QWvSuTxVbryZVwDTV3TMBBk6j7OQ3iBal5IrxSm7OwTB1XslomjCgLPNrev+9NTwF95c06nAche1gR3zUKM5cQ0XIcEFjzA7PSxFGSBG8gKzubZGz2nanfRbdROC02wiJCQ8Wk+jNQyEuhZLRp5lTBzPNU0o1BKB/urDIapF/2avvQr0vvV6RBwlnXzSnepskhZTk1Hg1ZPkOrCaj2+Er17OI7B8r6yzDAkxidUFACoOnZl6of4U0EFHN0/7/rkDZnainQ0/zaqj/ZaRTslTE8ogSVidahYOdSYBPJtNAQIR3cksCBUQMKKAiUEmpDVaXxij9IIQpoQawUx3EPUYfNUUBDcaY1wVjsayQcWsmTxIVxkhKgkqhrCWLE6PXsCrIn18cV4/1LnQAOBoeNo48S6qqfT6Ojavrf3Tr25ZZUUedkoLwvmlHVzzH//NS7FfLC5lg9vxs7k30tzVt7fb9fbKbFq/mA2mzhk1/kHq6umT8dFuR3o5C1dlsOgeHeo377LO17IbTPbJo87xh/v76gdJ3WDYmJVKvfbd2rMfHTx+eHJB66VN1OhiIGRjUx+fhUFJKd0qnPRczBq9bLZvppl5iIYK9q1bmX/xPx8I3u7CD+5SOvXyx19U/FjyKrqPC7d/t8R2Nmau72jmiy/qpUxQBtC7N9ee/dkHPnHRLlZoFYTOJOMuPoDmt4rE/U3m4rL/+Ud1ptxZvn4boSFXh4WU5qzGdBUhhFKLneRotyv6sULt1l3a7QxPjzs71RUF0ybTaL2Cq8/GKT+4u75hWK4Oyc/P1M3V1K07NzXCkWtZtJRoXsj6F73yA7jXBY1TCbR5P2lEsDrzEIxGoaUiZVI3UwvzTTOZ3/7dG9qnytgj+DMgNujRmytrFD6Rxkbny090jk7Q0VB1ghBsqCGh2omlguggigCSCVHfyvpNfJ5NbBYyRNuVc9j79Ytf/vWjX88yuAJD5zrAIVkcU13EskBfDhfL8MmhLQz9umbGOMayLV4xsIjGHQALbB+YEmhwNOzdKaYHJ8PVbHB6MRPyyWJFvP1aqfMT+/hwTKf5iKau7VQMBy1hfraWnGHMg995I1MzvMD3fzljpejwx6N8IdBS3mKaMZJIK7A9DC1ViphAfvKfn4Sno5pYqaQZyfGJ2dSy4Nb5JWUwaQrstGcCjxsOCLg0QxEA6x28uMSzLIZDZgwjadTre47hIdVU2kkncHWIRIbDELhtWOrEVaDhRZ9OIewSKQ9813JRkqUxcNJTHYQIgtmFFVWS8dnhuBgTiZww+OWrsyNtgaKPztTtd5ZPfGnSql/79eXA8ueO2Xv3xrxnFIQqnKNggLp8uB/eWmh604NnGnm7BkyQ8/aM+I0ydYftdJ+B0pjKsyfddpnLwbxHXavm8unjp1O3dyotF1P29Plhj1i/St1YZqVZ9GLCp4FJKM/bMtAdKi8aoE76V5a81xavFME6GJiQcdo/Wqlx+Bvxi/ZxqxVHdyrpJDtvOheX6tJKcXd3unOFrV9qJEo7kOJnEk/2g9/4zmrjbBb/o/vm9+wftscP0rUP9ztTdoqHnAy6AOjkBH4eZzwJXFhJg3EIcc0FVTFctW3o6VUW3p/uwv4bLL5ZBeqXUVFzZq7XH58Z3YhF2VkXvfvNMpPQPvqTL0NFiUBk6JQr84Ym6XBiIbGSSJDa+f6I317q1d2IJ2MBYE+npUW8156mUmT7pOtNhiEpyFLg+uILRRU7c5aLBrDrozoleUYuhlQygQJ1ZuZLK/Iv+lfkHvnOm979DPnqh8Q64Ly1hYD1dzx/sLGAqjP/ohkaaNHH3dDQUTFFQRkAcTXIBoEZCLmRYzJEIkHllN5AscV+FM0FO/Lhjmtl433TDnQLp8jRFMoXqaY65XKBbdOXD8ff/Y3Ef7VZ4J704UgdmD4QByGMvZm0kamKsekVy3l1cHQ9QW1ciZ9fKlxMe/Gyk1STBkWgtVwaCDv7oYGlre74f//T/t/7bk369R1ilrW7l8tJlFOUOhI3NShPCCnMiM3lPqwcJOPRcMi3pbrnZJwooNmYCCkYkGKjb/Pgjy72sOkkZU4efyyfpyrVxCY8HXMf7VliDCCFIXShJ1fQMc/58tW0WL+oj3EXzoOwEAX1vuHhxQe15jCAD0aDAPrqZuXlOfAH//29l0ISMXeDX0yXNvIEmiR/+TQxM3T5s+at130F+OaYbJ2NmN97W38fdH++n8/GegtExEMXTx/dO/yid+O1/HfufvJpG+vSq3fv25+NiDOQu8uHNxLmuL9qkUBKYJZeNz/6tM+OF97I70zpzzv6+dOjLb7zHocvJfPMFIeaiiEk3QyzusEV5cDYk753Y/Wj4RiEhe3vXZO/PMT9Y+zBJvhrv9HqnhSZDXqZffb5MBt3AQF//TuVcYI0jw9+9OiLLl7wLQVutsA/uhE8HHNfTPJXt7//G6XBv/rsWWfQl01+jZgRYQlVECBof6KAAGmDfBUBpIZ2vZTQxmPIj5SrYjKGdB73fCdlRl63PtB+a53PWL/8bx4vlkQhSdtJbPv6fXfUavxCqa+IqdlO/9WL8O5G/q24PJFurrAEaGUpLCD5Rr+fXOVaQ4XG0VsoIV82CReyFv13rqvnjtFs64tOONiT5rMiklcVxqCdMZPUqqFtcEI+m6QZJGbM+g+yElpM0cooZIUQqAawXmXmDeMeyzDj+ocHLe7F7tZXH6Syovt+azlNP/LcuI9RPK1MozdvIH2FtgP8oq1ll9Lzlpy/WnvvxfS3rmQuNJZSlHiCnI/aLBXUm4GlOHoSyyaJ09Ph8loRZVP/539850VT+vxQixbzvZTot6a5hYwemt2OlcSE6MPBEDN2/uhrFSsA/vxnM9ZzEGzwHx69/dXcIMEMHp2Ld6PU5s0sy6ztRCoKjHYVumcYQFzGKJDQVM9jeDaOgvkwZsVp1CIsmMZAbHlnMbJ8G1RMyYAhOFItz4wQj6YEDvd9GlJ92bAIqz8dG6YGiDEMZbgIE4VkCCHFfk9jVRd0lEUK7F5OwvZZ9epd7TB8+HhQ2wJZMlF/qd9bYf/6VwcpMjRkAxkheIZ5edZFInPjTez8ST1ZCTTb63koExc1EjuvOyncnWkz2bchCj0+twTEJQWCyALxKvslq1qEg5zIGcV4ONRtNyyXCrE5MjwLigxF7ERwNZAfj4cHF9Np2h6lCktIY6JPlHkyDuPLDO4A2r59/mR8867o+k4QMHMs9Uk4K30rQWVzj36pvbWU1HoXgqFxHNA40o7q4dd+PXF4Vt+eL00srN7ke6DPtqZZcNjo+KVbK+wpqs3muRQ+dZ3uORmzoqFs8BxvDH1PbtuYYoZ2ANkFz2AwkV26ItxIFHLJS6VFwkFmQ/SlYc/xz/cPbr25U3pt4ZT1FC96+3fvfPZB/eXPTyKrdfXXrjCVxNzKQ2T24FwGfZRziLO6tVKlUFchQzlNEFNSpUiczoYOTK8k44NRUF1kOk+jfjRZrHIm4iE2ZuhBHkbUoWxGrgGHoTtygWllVWSLwfnBOJJihI9XgdcevWkjuw2erRx87kOR5MO60pshEA6I9DmImRRDYRTv07EobL1q2aARq9JodSujMSgfHXU6w+ce0FOzXkQyAC8iasuNaQbtB4HnkWJ8emH4IfNBB3jzrWUcwiLQnDg22gsyS44OUNLEZDEYr65hgPLsQ28nAbh1rPGFzGE4zynt1kTpyIUFmBWpWDqN5uEfxGIkVG0po0UGAfPGqxd9EKBchC9wcVgmPJo9H46hHCr1W6XIJlG97Fu+6gjQHFZJLyW3CYyJwgUAyGST2qUHvwYvYGnCVM6fTW+txiFVgCEbkcaFCh9V0c9ca2LJ9DYZeip+Ojh4YtRqHJ8j202v/mRS21pcJajGi9Ss1Z8Vyb847n1zsSKupfZ8ZrHg/emfdLOr9AWi5WWTHIH00UmAsIbeK8UCPCHUO4o9wnY24/DcK377Wk/VT8+Pbcf46upy/zI8/2xUtNw/2Z2P73Dv/ld/aAOlBmBOnQ65FD369CPYFmQgBnwnNi8l//ThEWW5UehfDPGVCmUa+jxU3v+L3t2NhcuRzrTm0tPWRSr29h9s7ictXOSQApIPgqaMjC2nzCMPH+96V7BkceXJxajx45dbUvS7X1v1XTLzDzOhZmg94eg/TUkMcvZ7HSRsH5m8qF7dTrP3Vx5/vjerX9YW1jmeN11xGIBjD3IAk7Q5aW4uLeBpNPfwp+eXz8GVr3JShHAlNvIo/2wGhv7qopjkop883/0F3FqPlT3Yt2S1ulVFN0tNL5uAxtLs/eEuTLiWbcRcLpElWHzi5Vh0bFlTQQJE5IAiuHuZL2z/xSddrpiTLtv1jpwR0SaA125sAzdo3pX9vQ41cFiUmEGKl8P1KCqgHgGgZOipbvjl0TlIRsIU3//i07vfyCUw+GBO5pPzwcDjROc8BEgyci+G2XipNQ1ak6hQFde21PPxxB1HZdxJK+NpqPgkB0p10o9IVbEkk7QolAdwOHr5bLBCALOD6USnBirUbGXwPF1+fW5wNLuvYRCC2f55S+IENhoOE5pQyqcH/7G5p2qoHYpXoStJ/KfjuayliqUyv+rDs6HycLpaW3W6dsIX7vF8tYB1Rm6j38gzHIxBkAYW1hcDktbnCi4ZIQPMWtqdW7m+MdptDCMOBT3cMb3ABBE2tNudbhzQkqZH2sF53wOTSTIGVpaT4+rVsqrrl7NOAMXMGYRQCJuWTR0WpVG/91dPf8l/Y03IAUeW9dq7mdEHU47zrr272rk85tayKuBBhjKzQZx01h/NQTI2HnSyxeXLvbkYzK+t3AOkuRoEF2ediQVkq46y35B8kt5Y9xjjtGmv53PLOPbD958OT7zKdmlazR4fd1awLOTBAUIj56H6xSut3hNLXIgCs3kvUNMxGB9xWaGcAupK69VYhrpHHRljC8t8Boi40t64NyQ8lKPhIQEbvZ4zUdUfngHf/Ec3f/VP/lyo3TjGymZBNIZ2XRAfrzErl66N8SBe8LtH7b0pKNLSZf2UF7DAFQkLh1gaAA1d+XCiZPIwhzhdDGMFLiZNxiEUFvw2Mp588BxTlBuvXVEJ8NW0w5HKle+s8DfXAmtsP5v1IS8VgplY6ISwkGHGst59PN24zxNA5Jjz7HLFGavlBFQugM0nsieILYdEBbGuQYEC1uJRgiKHozOpXK5scNrzMbNYDAnfGKk0DhMTA6oCvQwmlIbZVb2zwRGxfCxE5s2xq0Dr/NptQBvWh/DHWhqN1mYKvQgenRlZLiJjSy6IE/FlQrT6ba1PK+C0f3racufzheQ6UtkwGITlyO8XSi+g4Sv7M0keZU6j5FqmIVPZlCXJk3KeGbVGJhS+eTc9mJv9k/3l3JQjyXEX8moMsYCPfvKkxl47T5Dpo/P7Ca8+V46AqHjj6skRDJao4/rZd7dKozgyyfC0YbCMF8T53/z+yvtPmOjpycnhMdZ3viHUZMeUYM4Pnao5P49QgOQEcxDTLCwN+V3ZyxETV6Iot4vGdJqhLiVwNg0XnQ2Vmi8y9oxIto3nP5uYDhwX8n/1F1Scm135tXJEMt3DcRLPaR6ZQtRXlhrjoTEIl69eSUktuO1vxFdEIEfj/vm//9PU9zaHj+oLPm+dmu30xjXaAfZGdxeATyvh+r2d8cuIOnQMO/uVt/JSTn14coltMGCaVI9Cpznca/eS6QILytCffZD4Ow9059WfucT6P3m7mr2pvfzFGWz//PTx5L+cbv7ODZtiU/n8zs3vvPh8KB1Z7P751R1n62vpj/9LZxkcnIWlQxSPJdLVSedvj4bD3dPSt79+8VF/Z7PqLIjNmY6bcg/QYScVNA1Db5ZTheZ8UtyKpVeML5sDlxXe/sM76j//FN650f3ow8nDduVGMXmuZMKaGw/LYvry+OzqVmr82hK4ge/SYI4N+4a1sVAQPmHDzlx0ECJmzjMINZFzEJlOuHt//Qoa0G/e3oAR9Fgy71+jewedxYz5LA0e7M/jbOKaPRsce2l9EHcX2+9PqLXV177/g+SEyOw9+4CBYWlQeCN2PDdylM3ZLqe4VQi6iFNwP+B5WH81C06Mp+NnJ6r69g6htdx7G1voMqFkyq5lUz+dCBx0M781pSRQBwY21O309xB0x+kFuTQy15BFWo+M2By0RvXdQYieStDVxez1HUpz3f3LS56YDZUSizgFui3PUmnSHrQpiooJ0BJf6r9qazsZdzrhRXgwunQaYFAiZ0EAa5aIqNapB4pjV+8YkLhQpsxOPZOcv793rnfmyWzS0SdzM8xlkt4MUmGCAwUNBggcSmVTJc6UVVyZSBnxasI6jS2ykC8o5xYXUajrCRZg5tkLUFX7RmVxTeu1ZcnCcKySidEs4w3nWqRoJkxMbUgB0HKRssd6d8I4UBGlUJwAfFJzNM0FEd2yGcQYzTXTCVIsuFblnUJ8BAAGynSPL9kpshw6+UqqHWBzFZZMIoWANO/3JW/SBWuVTuvlWftg+HF+1Zjrf3JG/eH//e0/bVq3F2sd1lWb4NtfX5kG/Q/+/cVX/mjjk3/7LFGJMkHwbNdefjeZTmV5MTpRp6kkFq9mfSajaao8dLEwXIxj+RGMOo4xj9MZvOvBjuyUUgsexO92DoGmdp1afPHxnouYazUeF+itMtQfq9OJF+cZW43a06nZH12C9tVbfAi5oIeHEaf1mZXC0tV76cO9hhDzzzUQqG0PjbHizl4B3hKrC665Xiqfnkwu5lMx6SmwCUwDIXSFitAVSRLygVMIBxQs9FBQtWdeLu18eaFInuYqBGQCBkwblJ4U6bjj99tdfH+Myl46TdrT8dwP+TU+EyYKPA1wbT8OrC4mzb2e0bcSqzzLJoZtGUw6IpNDKvleqDgY5fqwDdow4fU6MAKFNJ9sh3Ea93xNS5BmWYwOz/qxJMJRIaLBOlNg1lag0LYmFyHpGKAtt1ssrIPgaCmaZMQWAJCTGQbpXroSQ0H5yUXb+5FSvrFmuIBLQ8xUy4tkspgiIFFz0QxNDCQrsxjvOZ3J3pixbaISq+VSNuxgAmbuK6OZPjxp+I5OQG5/ApKMQ9vkyDdlANtgXbmvL3JBqghtV/jOzIXw6LTr5fNsnHd7u0MExdmFEBUupheqP1ZiIuzMsGU+eTxoZdJ+phIj8ciTJmLRaqjDjufgq6l/N738+q2V67jglMyXH8mXl30/w4kbsOdMVNj2nACYg35KTyKmNARjMdaot40p2qs6dCAHE1xgeHI5Xs1GT/vDmA8N99Tjlp9IlvgT4Od/axxM4t9mlgJ9hrZCTkgMO9HKsgSc2jsoZKhOkmfxvQmAWpMOQqwC4zR6aXW+BIc3h9CETWTJlD9BwO5EqYHdromXY4lb2MmUzsUz6nQPiXwZCs6O241TZfu1AvldVPq82/hJhEKJh4+sXJWk3723eE846aoJkVvxen/yT8cLZenOu1niUs0fHiMvZo0okunIPPXfev0quzE/em/4wb+qn37PW7sHdb5AhKEfLYvPR/6371du+ePP/3/zpS1ja42vFYlHkSopwORSVrRJuiSsrMabj1XvYByNdFDHz1/Yd74i2Eum9L8ce5IyvzhunndyPUddJvkYVb0b69uzYSyGfTXD4nGqyJ5cPLtArEoFhPuy/OzMPcvrnh0guCpaM8t2sDCR4no6zCbi5RRM0RCGCDurBUfW8gn3oqmKHL6yyNmU226Si6srEYtDBrpSxONVEdNGjT89opapaz94c/70ZcyXYdU5PmmsbWB6KGuIkEog7XovFyO/fWPt0a41Qza3vwJHep/34ZvL/Anvl3OurhC0xtmX9d3pIFX0KQ2vgNhFgk94Ud3UxEglCbq/q0BIRNj2Kwupfn8Vy+GubuTE2Hl3plcQG4PycZ4zom7PAvXAE+kxhF9quZniVZcAIwpMyQFYdK6PyjyNZnzLpuIidt6Vt+8kmyNrLllBGM6CWbWWV91YLwSgVKbiIvplAHABnMd128VgnMVJHY+mkZvKM8euZ8J0cUu4PAviCfj9H4NC5mrt6s5kCjpBv8LAmtKNB8zRYJBYWbaGXVmWiQoeI8QJ5qOMxOqeZs5iZTFK5Fq7PhBQARpNXDWZoeiA0QM8tDALIIPARRRdy5AgKNK0AIeWOHVIRLVSoCzMW2M3trSQ6fnUSJ0OFRUxYNYAzqzAYLGTzweYbH5OJLbSrHjpAD5We22nZ8MRvbz4/avjs921Le4LAFlK8eqzNn+3COcrXHX46pNu9SqUuMX2/cnhB0fLHJJergQErBrm9auLl8/q+hkGK1P3TJEFeAj7tUq8vB5rBV67O8BJ2YRCKhmPLWUxDLt2Z8u4klhIIPajkTsMohCx/KiSExumn95ILC/gWYBJ0spB3+24M94IrespGrN7n3QVX6IwO22Br3bbN28J4xed0tUlVfayKhLSACVN38jilxGTKiNPW3pmKxa8HLbHQ1zEQc8BHQ4KTRfGedI5D7EeweUgFVTd9GIhkxfbj17JurpaEF1BH9puIsUn16rEdBZxXKWYvJHAvjRDf6CaTDINUcsa6mvWB4aZFmCqOVATEMWpRqcc40fV9XLzcCzrIZfl7YYUcJQJ0jHNQQPKm8/AckwS0N75eepG3Ib5EBrmFtjSBPIYzN+uRs32fGqJvNWHrZzoJiQ0ZCnPhceY5+ABbdd7f3siXoya2TjJkA4IS4kkNnXBCGhSfDIEkBjqhRbgQnxDN0EifbXcW41jqnrmRst4JEMogY13jztnR8ebD4pQihs+6+IBVxY4aiJlN+KfH18sr2FhT4/8aUAFPcMhiHh6EU7HMediap3N6Qobnp4cjsdM5qqQKj98eoD2kECSp13AM0+ZVejUxvSk6D8/vvAGlNAijxf/m2Tv2SfjN43hezi3+a3l3EH4eIC1jscZAhzx6gDAEiGGO1YakiKONQALTrrMIrXfHd7gq+mtDLbfgV91nn6grc5RrohsyYM/Q8m2r7kToxXYMEUUvquX7jO7h80pEUSZTDDW5yZ0bSX389aryDVBhoIXyZMnczU132b54OSiWjRfmY0dNAh79rBpcbZ1oVVlpoT/xYt07bXoyDLweWoLnYnMzSR4+DfyzoHdjvatUe5+Kn/pvgT+3tUW5Mw+3V+He8/+bLfMmG1MmIXFInKDvLAIAMFeZ16Y1yYcd3snuZDC/sP/9t6j/+5vQDK28Z1cpqZ2Wx/fIMJsKAsbqz5B5UkBmcGQIa7x7aE2cUusetJKbdYmbiiIID3kkHbQpWaF5Dyw20tv5iwcGBycaJ9ewlTxVDnZZPs0cvP+3736KnXsf78a1FjpuUKjy/LlRSnGoeOTo7+ap7Tn/FbG7XUTdYxy1heFlYDBLmSwMUFqSwVHmyFj2wbZXFbnPdMrxiUdRECo7wAJRFMHkwwfL+ahvb1PvcuTLqav/sGvxXPfQ4CH/f+4j+vg7Qdmz5hsZZfELH7WVTbubO0+6vmCx+Ziyrljy2aqwJyP9KX+ZWn9Fnozw0p9sx3V3ll+QVQGxZqp9uKStZBAnxDc1FLhEzeTBBkMihRkBY1hrq6MjZExYLlSCM/3J2p+u5qtxa+L9ivVgicDQtIUzcnGkEg2phNbZ0kw0qORmQTdSzq0fC59qoi3cjMpdHpOohy3+wPT43tNhQMYFoL2fnZ8vrM4lKRaf69VuwWRYpaa8x7vkQksdPEyPk/6xsSwImDT85DQTaHkj03Xj0UxDDx53Hdk4nxoLhRyyWIGh8KztqtZYCwjPnp8mVu9hlRptItLTkDgPnst53sBb7vWSNY9dDqUCJoGFWRumrwAcjx8OVL6rpr3C6AVUDSWyWfHCKpTNoLRSIQjjm4DA5CGw4jlJNuETUcCxfUSMJ+OuyPPAgCGiCiW932XiFEMepU1veGT5rNP63/3H96jkUUov/XarexJfRb1qd9dp//5v1Le3F54rz5rFfqA6f3GRqK5f/ndd7ODuuESRtQZeN1XLDj3iESMASIL6c6csxctEOa//oMbp1886v3yM3GhGKWpby/krKEZh8A0A3ZOZFEIl2/kiveXnUed3b+pJ5O0HAlMOkYWxEqRPP+yjvhINQeufiNz/IIK9yTfCUJJBznKIizDlytgQo2wdDw1UiYCAzy4zVkI8+Fz7817uf3P9maDbikutsyQNgxVHVQ5AhpLts5xadp2QcQ0ESQqo3rvVBdtT9MNhKFvfm01bbHNx81Jo89Cfi4mZpICtyxOQ2rDDMK2laRYI8EMTdWbouOeAmRp+GI2Ojhli4lkAlC3aWxg5WCrPbg8/+BAewn+7v9xh7wmSF3VMpwYHRu2TYKgzMhSADsXp4G5EQs1peMrJLd8XQQJJ0WqI8WHkBhKuniONcYaiBHZSkbtDPHOgAXjEgB6UIxOUyGlKfD48xfNqD/LJJNXv57SPd/rK/26BlezIYERPIvhWqc1qmNd36bIIQ/ESfJ20mEhRtGDMXZmOGzg4rnswreFxa9XFjcWz/fPI/uFZzn+tYwxit30g192g+l1bPvu0mQ/3kPUzDtZngu1vfbxc3sOEbduZy97WjdDbrCwYzfPfqo8vWiwgJB4kNlcgs/nMAqT6iLF5xApDAkJd+5mhrQYEJsFToriCProAq266s56NcWlgkxsPAuDCYV3LAhOWPgcsmh3dnnpMHDQadlfvVNazi8/742+eHmSqqDZVY7Uk3UTGuDcYk0876BEtp1dDQxVPoS6n/wIgtBUPknRYlDfb8Uwdu8wsjUJRYjU9kzWxghtrdwvUbjtz/TF6ysCaSc1ZLEGv9drYYqeq6XnZ7PhPrn0BZyVZb1xbr8rqlvo6cRMZvNJIe4cPA9FaN6ZqvpsWUQ3WP5jCUQQsmqI81X45kZO5xaXS4uDL/GXPzrr4u7Vd+/tRBjRnU2s5Df+4Do8Lb98b/ziy6ZDThb4lCGBuw44lbytYmwNYTNcRayS+zeoOcPyEBNf49BYvP98EDloZen/TxB+AN2WGIZh3um93d7b3/t7/+ttd982ALsACJAgQFLNoipFFdsjTTyJx55kEkeTcTJxrMTqskRJJEUCBIm+vb9e/15v7+fec0/vJd9HT8vsAMLe+I3Xjx70mI9m996TdH2Kjf31v19Y/L9dyTw1TC0PTE7KPILaZy8/FMXPzy9982ve0BLrqpKPetNBer46bxjP2/Z2sQYtLCFBNjDE1kkruVnpq6iuMmt5tsSaoOgplG1h8ETAEStik9DoJFypls7qwRfv34uAlrAEYGXo4yfH5W/3nF+dVPxTmqYvbOV/+r/Wd8fyetx69LNhBqvm/bD3XotbyQkc2Hwh8VdjSAH8/OOnF0H/lTtr+7h/PjH1vje/pVVcx2/bu492idX8WaO/PI9KjvHEJNIuipJ8ODZUFnIgYsqRB8fjctEvL2Q4Asc97EV/AiV9oZq5EUvsfGlHYRQkKjbhCM6o81yjcxED+OO6kikTmqRBcEiBEY1T4kv59HRy6U088OGzIYExVTiDufuzaWPS3NP/4WtcoTzHGNPmrFddTbdedp24jVJRsZg2sHAwdbkkdvBcXquQtICIk/qdt4qnbW8pzToKOjGcjOlNfAWO41Y9NMUkuw6Ph5HBcef14eoC3zxrGH0fRViIp5l8zE/p3kyrJPiJ2WjXtYQdUCCAASwXYSFLd0cz3YhgFGV5HoHBcKSJxhR+JUGOdSQYOwBiwZlY2bT7iuwO9VIs72KA4UK4wG2knaDvD3v11JXSxnbe/lnnkz86KXz7HW4uNxmLphoNjp9hYfHKX9s4ccNYoHWbITePff7+STKf+DM6TiaS2b1pwaW7X8mrb8x1FbsouuVECqqln3x0Ui3aQOfAMaeFVwrVN29EZVQdKgef9gHF5BaZ0mphKgdFRfMeHJ3rlkiDW1p9FktNkulEELWPDvcOmhk4XolYqp9lC8vnP/sgQI0AJ/CEQ6HECo5XmTJMUihq5+DQo82ZRCmtsByP6TMkvLRiqgGI2GgMZBbAl/f1rTkMdNhnj5+tX7zU+ri7tIgQabxnY+YCtXfPmIsl8yhDio3UpXllO+98thfooJpjVq+mQHN8iobJeSpRgprjaTDVjMhAAZrynMyQ8CD/XNObfVtcX0AheAWOBebApkmUJPKvJi5/f42gkf6ToeqD1EYsSyHgZAyhbAKnUHk0kZgU4uJZAEh6hobjatIYIzhVCmBSmnrF4oRNq2NL7EgYSlqoYUtPR50yHw/Mxy/81dVgIc3FVssHm0kUTH31xXgxWYCQKEmhss9UpcgkAAiSbMLRdaf7uZGQCfT7N4uBp4l2ZGhlrBT2us0pe225qC4LaWgliGCQEVO3y3hJUFk98CeNY/S/euv2w+EXaWlyqE75dEx9tjuOnO7YvZndYK9WX44PQS6akiXrQZ/LBp5veVN3868tEEt5++OWAzgby8lQty3biBoPmEJOHknFMnbyouvtwftcg7bAxKn7KV/mXL/VbLVwO+/KmAK/7PkBL4MCJC8AwRw47YpXrl/ZXknXn51x4ezmamzRwXwjrYCF1Vfj8mFbBr1KBYuqPneVxZ77D74cpitz6Y0FMkO69UHXVKASLUxM29SW38kFJGzt9OKZdP3a4oY0XE8la9lqz7IhOnzEABLxhHm1svJWFjKxvTKQMby9h1PYBW85iPxR/fSohf7l/0NedWhcTi3EDuGppZnaH3yMJfOU59hcIrmQ9U7OKWcakO79F/WL69rv/N7Fzz/oah81n0/OjNztC/pz5ztRN05SAlKgw54WVAxIJ0gRh2trOS5BN9pjwPBUPyIqCWZqUy2PXuE6Rx3WNoOF0lxEgAOfXnObHRE5FSe5KHynfH3uCtGaqUGxuwNCn3zx3TfekafoZ81w2f0qZaLN4aThzeirZLg3XlqJy5dSEkIwHfd6BGDXbrhhzm64zakbK1IkxbHNho9SUIo72ZVCiq1VS6MJmKyiloeUMayDTFqWc/m75Sd/+p62zhscNSsXEqcnjfd/WYZCHcUnCnx+/zQe9cSYPquyUMxv7Twev7JGMuCRL72yGS9ALHDa075T2PinV17804Nn/7FTfrWy8ebSyOFGp85dzmG3Fn9hzNrDs0uscPS4kUtCU3tyHCA+Fd0G8YHpDCHbjU3n8sxgeq700Nw8yzOq0JEbRQRkSC7Bp+d4JTSHokIFxN7pwMcnbsBnQjajR8iY9MlAdh3A9eyQBjnstd+6CSBhNuNND9pfv0oJlXnvf/h4e4t5n6rMPuyvZRW4thSDkLMXp4qL1/gcP+pF3X6wVTrtqHcWa0pomQwz0OXpwCbWvBPznE8SBzgpVGLzDizLEnchrx5LqQww6TXh2pXBRGOkqXJstNGI5yOCjcV4siuZAox7YXh+2NSEkN1K2G60IMTDia/N9IlnRyQBBgFMUgCKIiYKwDiQImFR8kzSC3BGh8LRQJ/aHu64GZ7DhBCLSBaMps1mmAcX4jjp0V1NDFP8nUsFqFngEUGniLoExgt6d6Tv7D4H5tnABZbnkM75lE9mqIuCPXGcx0dLhSpJ4V6MM3RXHDnt0ylsJaEtMl4pbW5BkNJpP366vMDaXEqvy3GF6O43k+ysLysCBhNFUAv13r66WU1VWBKo8gcj0WvY7GVUxu3BgwOr06G3Ck6Qf3quzd+tfOM3Lr78i4+LOFSqZBCICHEqvsBCaGjqwEjHQQh3MNBHwVKWcofSlSp5PHQSRYqbRrZix3Co3sZX8+j7u33WtWsxzA5tgsIBSg70AHKi5UpcapqThqYts+lXi975DHf8IAb1MaRzbhkh5lpaqsh55y157GJFlMcpyBMmkgWlibWvL3uAYqtj68R0ZHKkh6W5tdSrBXD1zshJ7f3pE5IFcq+nA8QwGlIaRXCGADp6ytHmMM1QZcckm304i5NEDOq5mB6BSRiFjWm/D+FVH2YJjoTALApPFa0bwD2VJsEV1A0f9jHbYFiidrlgQ0QAW5RlThU5NH2GcYrljByRo97UgmeQ63W/OkHXFl6voJOmwc2mngOd6yeTSR8L8eOG4gpg7PKGosrm8HjhMtfYOz/ruFfejX38sXQ14s9m6GKZHhLSrcu0OjYWrlbT2vwyzvzqcbPd61387quT86h9qK2xyYV1vD7QSluVYKREhLO+zOUI/umLFl8ijwZcOi+4WuLwvaB6Mz5NEEfHzYN/E7377cCu0kcjNe+ZPjE2jbGznFt64zLjD9vPT+1jj1NU3MW4heRBX2pBerRETSU17qKuYfRy4vELZT4iKTauJ4eHj9vUAXx2T7n9WuX67a2m0//oj79IjeXSWk7rW7EEmsz5JG9mUhixUhqyAubaJw96C1TYh5IdJwwjqxSjVap29fZS/0to/LDlxMAuDuR+89KkN3hx3JnsdDJlYsvT7a4CDMDOTJ7/y3M/uHjz03/20cuX09rlRWYhFV+JWQqvTRu3Xq08b6m/+NHxZR0mf31JXxCiPnNpY53ee/TBz37VQcOLXBZYtMEepUsMYpKzaR8MPDmtYIQtHnkgKwSpuAaatiEyQ5N03MAEUkkP74+GjS6pZUopYn6F//mJyKVQMZjwlOr/cp9vBT/7o112mlq6kJpNe/v5OL9aPAdiAhzgHGYWqfhCWD7Vz582pr3UKrPCHebmi7yNmYo8C0w5UyPH6gyOdDyWLGwU2z1kYkAabqcggKdcIeUNT9T7B73SWyvZf3h3qJ6J+th3I5YsOJJBLvGhhKFT2UWl6pyvtk+c1fkrf7MWHrgcWc3Ng9ODxmOxl0zyxCTo/8HZ0reYhb9Z8O6Lz/94kqyGy28W4mxqt6U4ErB4I//Hf/B8ZYEYDHEOMvGxXGBRcTzzuDQVhEUeMaZwPok5dszsu2PWCBGlsElnS3R/JouuaBPQUJTCnowYlL5Xz9bo0pyim0PDyWUxz/N8tO9HEI5lMMgEcIphYmCkBgEhAjQAMNdfuyvWlf73Xptzj0EYIwGAhHE6t5zvyX1NQm9vr/yHZ7/K6EkTUX/68b2tbAoZSLAjba3ND3sT3vfcKY7FwRgpvDxpEhk6xs+HofXoxWFqnr0cAgkH+/mfPE1eYVd/5zUIQUGXiDBs1u6qmLuyku/XG4iHVQrJo5N+vxeV0tnRSFIdq5Qt4jqG0gSMIshCn332enl9bjboNAyOLJ32X0+nd4H4MkUZFD4AqEgU82h4EIdNBvliOEOdsFSg7xLeDPf7DsHpnq3iVRDu+kio8u+8XT53o/aow3YmkYBGEKY960BrcyphbHIxYCQNE4JMU1ySM997nAD9lVtFHxZHOmL5rTwpGduZSQg4rpEnk90PjyaGFbuBovOL+wcqKwNKZb4/NxgdKYubYTWhn/vgybPWHEtfvlkVrsxbBCI6UXYh1hpM7H/xPkOb1HIxzvOR7o4sjP9aLkow+vEYtsICGvPU5rOWzLFkAoVFDwubJBr5ddkH0rx8BiwUMp8/EW+Vi0J11qt3ybnU/UenNy/Nay4MBXqCQoeNqYjFRhi89NFZ4/gYgAiDwAVEGu4p+SwJaAFuS2f7jVQ6nZrL29OmkybLy4VgrBBxTHQ85Vm39cu6MtVe+87NxGYmmFpBpEjPnio2dvP6Us8ND16Kl96qoUUnrVtdiA4KiKaCQa+J5fgM441aHSjCXANla3mxHTgshARk1OuOSSJOEvqeBbk6iUZUBj37fNoNVCFjjXcaS+vJdDLXE0pR2+dGVmQAKkJe3qQKi5ueBrb2ZSoAEDRZZAPpFUpA2aHI3UC8B8sLVcytf9LwT0YUwwO4LvbRYd/sWnCvtb/3Ibqe84RQW7x2uVNNUAenZpW9N/HnAiLewcu5klL3UAz+8PjxaBysl5cvMonPz/e//Ve3HsvU6cFLfCV79Ly5YPjoKjT1krtfiLTuw8vc7e279SS0WqJ3CfgiZXQvs69eCf/9B19FmaT9ezfi+0+KIyrqGirijwGgMrVTVy6CTnn45flXX3x4/Tslah182cYslY/1UB8j1Fiq54fYAl8FwLmRPPDo6acGMkhffyO/sU6WicnRi/Znh92LPLT41roBs5aN2brE49H54NDUiaMDEXzlMrs7ulyv9y6v9jmto6C3Vkn9ybjko15pDgTq8YuKlYz7EO6a2ixAA8tTpHH223c5Uz38/AhiBM4aP/i/HPtfX1+4s+1KXzahsfp89B01WQnwGRVrdez2CvL63cXGv3jxdgHubL5upMAXvePNa9S7yQVGA3YPwMGjmcbRBBmoQ6+IJzIXYhbKpIKZjU71kp9IsQhjo2NIH88Si5wlD3pqoycq9nyVWkvShfLnf3Joe/bSaoaFweYnwzgX8bH03/pvb6gb8VYCpfBVkQPUQjk+65zNhr7jl95v7n/l7ba6eczJX19IDAmHEOYEyxnP7IjtJ6AUmwCiAYhiuheQ1fKsO4k0dy4LgaNJpVTxTc2GrUVGe/TkRRgcyWMxm4LqZbCWgYIiah2cZ1dyWtvtW25xqZphnXwbNDSo4ky/fPl03Pe8Rmu568B/5S1xKo9BcPJZGzqzk1sLb/7+9eaDE+9IOcaMEyKMpq21ZozGBXtsrC7MWZ/symkOaB7Rm8x5cwzmEik0rJa48/6QWAfCKrvw+qbtE6QfSWGcanX3jxuQ2g1YqlLIPP7DHpWdp5AAA0tPRgFPwuOptJzF1nKZxnv9tW8ucksLzqDFelXOVUE9hj6RgWpIfuft1Z//m8bl+dAQjw/7yxAWo1GSZ57MIjMUoVdLag7o9xsLl/N/9mF/e7VkNuGAjKZxQquf4w4VT/FT0J60G+JBa4FYkXa7+y/O6gqA2YDcGGaKWeL6avZSDiWSyGFHhuH8nHC1FB+edWHLZVHKOJj55tCPAsm20rYXwxixPTieyhfXL3mgO5nOEMKgrtbdxuN6+VXoELKiDRRoQXmY4Jl45EaYzTih8bwzrPu6zweFBI0hRBuFOD5myDrrSqoYXKhAZsOMjAjNAp+9pwCQS7FElRYOedqYs+sHKvzBCVMORhYAAeBsOgN9YpEuPg3p62/k40UGUyTRNk/tZjDTeYX3LUIxJDHmkUkml0Nf3DtncgiAsdOOBanuN98sD5zB4w+fDlvq6tfXb3/33aGvf/5ULFUTBJNH9LPJk3MAx42JeNATUb7yxms8pdIcjm+gtHnSH43GK4Xc4d4UBvxCFihmXXU01r08u1RCNVyZmr7pyV0/easQX0k0j7WbGax53luuZJ7cYxyiwKP24GyIc5hT9MrCwBrISRpLUnh1Ze38TJ005dGhac4QJIGD2SxGxcn5kj+0O18hWqvNTP2ZZmQ4ti9DZXS+eqvy6adffHX/dPN2GmNwckZeYJgL23MqmmCBwG5qjWPt0jJjHEeeomZTyfbEnp9LmIB+cjhhIQWUIKs5iWSN4DMgyQcMaelw4+edVy9X4gAK8ZknoiQQ1kIK/uB58+PPBxcvwNN4vs3zsoQSvDccnGFgrh2jMS0VmZgbRC6HSnQ1Pk+Apn23ErUDrMGlg5E/02Uuia6tpGCxqhDEpIbGkkVvCktf9m78/m8+P+82P3sv6nT+eKvsXUj7DvvNi8zzD0VnfnVPwVqHCp+gttLsHozla9Stb64Z/UljXH/70qXq+Uw6hC+vkDvHZ61zqaQV+GTes6nEyqbohPk4t5lMnYkR4gNgC4sPz4d26eJvfWNnraYBpHM4654ebm1wUWxxNSy4Q6q9ywFcpXQ5P386plxb2Z/GeiqpodU0uQhyBCDkWPT+6fCZbTuK7zhWhp9fWmOfHVlsZBy1TA0I58vVworXbFv9iZ/knaSmuT5AI1ySi3nTSIPTouEBQpwP7GFnFFis1MzIx0Bl41KrPx0eSwkfwrM0gIbH9/R8PkV51cZU/GoIstCkq4iZLJktpIDH/Uf3Tv7639qSU4ETid17Jy8Pqbmtta7rvhi9mAlCEqU230wNh/bZzguhyofeiwMlCcycq3x5bpl2T+V+2+lT3nKKVgZ2++F++lba9DVtpOsskGV1e+8k5cCy7AWA1xtMYNIo5iv+GiTafhokuVcvgE4OnyvlWzSdk+10NqSXsgn2bOfL2PU0s1hTHwzN+91YhG0jGO+rczzeQhjwZiZ3M6mfg1RUfP2VS6c7B7u7fZ5JpgJS66lwWgh8Vpq6m2tEL4/iVGR4UcxGCijlmr4ny4amxWbSl4fA5dc30HI8ZkTKQcdtf2ZNXU7AaRZdGJPy2aDZOsdTGEGnz1rZ4d509e/UhK+V3ZOd1p/u7taN9HoVPg03GfYnfzK5/PXTIE8YNDpTdN2d5WNYT42Sq/z+F/U3fB+M8x3b7k04e2fKoNirNPTnPxxeupgUQyyXSM5fXAnMuOqPsBx6XD+iBipDxZhswphYT570GuPZu29fd5uacWxHPZcqDyMMMu3wyTORTs4ha4vDiecAMUbmYMkwpqx/AdRaA6aSCatvwoSQryIwNWr3LT/FltkSHo5VX/zqfn99O/enX9Thmbt5oXY0VnWStAc47xqoGQ2fDF772/M4laVjXOU1XlPJ4ReNwGLe+K0NigLj82mzp//V73+rg2tjW1/M1VjI7vebRQZGad+U+gECsVVO9rAYBMMoeNRoManY4tpKqz2Y6qoHB4ZmIS0GxJ6bySAl78slfawtz3dte4BOz0cjFMW9EAIhqMaSlECGgUf1JnrelRRXbE0dC0ecQIvgp3utSRrJ1CgesMKCPXPUyVl/z/KLeWBrPeumOfjMEjVv6gxHq1hJT3n7/ZYtldbzyrXUy4eNpKOzBJrBglRFGJ24mdDZnaCe6yyu8kiEUmtrPuB9fYV7ukdLZ2f3/kRJg0jl5mJi2aTSRZyh5nC4iejnqpGaY3ClApwMRz09lgYXNlbaMfwkZ1yiWX0WyKc6MDbnLifbpu/zcI7keaoo7j/vGuiFm3MBiDoGNJvNUAZYLwERIEOL7OyoNw1ArgZM2hMsT9ihRs3crMAeN2VSgS3FKgvZ+nkXHMC4dS6SsHBnifl66cEffSqo3vgI55fng0QCaZ/NxbDDFPLV490rOE3wTqmcW9kuaRHwzm3oRw+Hl5fiw82Nh191uBjhk/6nHF8AgIULdqx7GFvJAzCcjAORfspAYywJdlqjuADTMcpPx+KMufu8SazgccTXpkBuJe1iTuPwGCAKNDZMasEkh4hxKrexiC2xUwsoqqReomAoKzbvj2VqKeVlmBLV1c7A/oSnxs/rNVKStq/MJkb4H87BmyvxEkU+qmvCuD1frtc9VMWA5YINi0m+GEuFMRVFy5UKQLc++SKqFDbm14dEDN+4289MAAEAAElEQVR92R6gXs8o/I11db+ZX6KskiD1+hFuYRgmetEf/28/23rrwpdHre6zw4HqwB9YtdtVbXN+eq9FmCpJ4SoSjMXeXQnsnVbSqsWUmVUqMs3oj//sSLiwcNV2G/pxOByGM/DsZ332zeUKibBx4cMHJ6QfnWdjyOWyObsXTAGICZliguHPNTvXa7cBu5r2dAmNpUGJuLhxDpcOPr+fiDh9MBZK8Jh3Z9H+3hGq2hBUQfas8VUZupgAZtsJjJ8jPtTi56MzG5hpZ/PFcmbcG1A1sqmYtucLBD7h12BUTPBoGDFsWCvgRkI5VmzhG+x6TLD3O8N+K/fm/G6azjkFbXrymVh/ZI0iMlva4h+Ktr+xtJQDP/zZZ3l72j2TAIyOffNSDvm68sVXuscnu70+ZJ7W2yPhAsQb64qPkbQ46GNYjVoF285IPBjkj0aL8zH9qQ50sboGHvH+Sg5UESGTYiaF1FCZwCeSEnMEB5V/fk+cAgFFUp7kgNBMCcJhtzrG6WfuJGmkYA0hVRuxfAsOJRSh0vkMebSW29W64s92fn09z/a12CzokSyTiREdoz/y0FhWPbNpOhNpfiERwpOZY1qMUDCnnh6YAI4L+VyuxH5neXk8Gav/8aNMeZ7Qum5kcFQQ9qyX9fbRM9BoPNTXmfQ7rrN36nmM+c5Cp958aVFXkiEjx+L5xYxONHAbruCXKlX1bJDILHfPrFISnA4t6lTKXIheWxeUSuFnH0g3Xou5oZOJZ9876W0g0//4UVc0wXQunZsvbSVSQVxQTgwGNNhnI1pT447TKsWKWxfmZ9qDDw7AuASHJAkZe5a0UBVU1V6qECwFjKVpslq2tSFpIBnKIqQAp2JrN2jT3cn19sL+C8iiKz0IdG0ABXjUNSsYQmm2aacvVD7/dH9tIX13+8rwwXm5jB90taVcYhLD4lhU7zoozxkwq6FshUPzq4sAjjTOpmBvgCEkHsihb6hyv5KqJVVNb+owDxkZEor08cSwMBwGYNLzSpXMrGFgJsSWalF8Is4k2uNvv3pLjDTd9IsZFPl89xkmAauvZUNfWFxguieqFbO9CMczkI8SaZLPx3DQCmeyTZI8m3IbihSqejdC+UQWijxuOJNMCadSmEcH/VkIR3QS0hdThWKC+LS188uGArj5GA5SyFgiRj+XOFUNc1F2czX/68UhBuavpTfmq0ZH0htuk+axfFyewOsgL/bOuofN6nIun4r3mlYkhgzndnGtMZlqYLmGJ8BYYnkxMRj6NT7U7rWliMUvb21cqSoFZYVw9487W5t5TJRmO+Pd2SyiMqVVLF3hcQbTxxMTAB0ycdANA2ih+EqGZGI7H50vrmX8zhghYG4r2/VBu92LlVwgwY9mZWtGQrBKmopla34U6SBIeSEVoyJPRK5gozFyr2tvvk4j1szXDN7xPRNESFN/2ewPVNbQE3PMxWomdnXeetDf7eqQZSTLkYVbxhqTmbuWaLGjn/bMT15or1x2CfeNi4QPyEjKQ8Y+AMyAhQQg92eizLF66JOopK9dEtyhfVI/I3EnW/MHxihwHcqztaFdy1M7ClHYTIAT8+jTx2eEvHl7HiYxgZ+D7AAPkEbdT183sXkuOI8mkaWNjeUqCFMzdyA7anfS1oEfnsBNLy5NjGcG2j5+fOLEc25pHqApWMQJz4QbR8rpBw/zSMwBoWFHE58/Uo9nm7eyqB+e/vmT7Ca7+e21/ofNZ7vd8aPhG9eKjol+8FnzxrslLUrKbTebnaNXq+8/b3gH6u/+pet7Tw2PrH3jdxY/sn7ZP/JyAkrmAmE+I4I4pqrQKt09cf7k5dHfvRkjk1Lvi/2GkcsQSrYQjMfq0aeTBJBLrybSSaNEMcOmdPXV+J7INMboK19b8rrKz/6id8CLHsqTr+ExMVrm1ouWYHjD0TSQgAfj+qO1xUUiB+9w52cQugp7nMAXYWfcDQolMhNiIueUqumAwMq/vg3DBN5ARTRukbrCG7zfbYsGjSXarqC6OKcQjiVSRBIcyQlcTmQ93WqGHIXPgjgvFAs0rTuFyKQidLIXKXT99lz2y2d2LZFdAaf+g3Pm6tJFrKgmva1V/OD/e3LP/vnCG2Ut6QnztOpDoO04Jl+JR4Cb4HlaFMfhwHKRM7yYSX1DaNP2ZOLczELZrRUINB8eS5VbC4V18Kt//bBo4JBmF0lCnlrh3iBQoJMvJ9jR5/Blwgf9oa4IjOexAcKxE5nbezi6tCSsXkoNpFHfdHgMebnX0/poOSXYjW4iAEKGePRk1p04FIGKUrBUIGUt1OxI4LACYTp6yJdBTcbUjrRY4ACOVSaxccgLc9CpocVc0G93AtmIJURgnteCuNlzJZcOaCCV8Sz+8satuXZ/HB21F34nyeQo82NUb5pNnGK8aPNqpj/x7bH+k0f6b//e5gNHFS5CQ9SQmsjlpYxYl//8jw/b3yvULlOTl/a9F01oqtcuJy8Ic+bTPQ0Ev/mDG9ibyWlITnvR+KvnnqbPAq2lMaXlWhiyoKfLE/vypdIP/uq36qqSSybbg954OEi4GMolDQcT2xYJInHAzKknah+yQT2ORxMAzqUcE1GBJA0eNEFyLfSmgOgEcQvZXsCTsedNIF3OGCC3cXOr15bKuopl6CRBjVxNPG7RaY6DXUsK4/Nl2w+GDzsIbPRTCslQuSqdzsCgKc+aCk8hFqC05WZ1pURhRL8/6dVneYGHaAhK+sOZrg6syKEoBhViQYja+sB3ZUvjdJZJIpjQkhsURSBe0ilw4ccfnC+XYdWIVZh4LE/LIrBAQm5cyHjoINR1F4ZjjmUgLBSW2TQEYeeedtI5I/SJO2WWrtyBri6Hpl5FWL2ljc/NWtUXB852JTaXTo0tt3s+aXH+6gV+Iw7pHzQrl5ZObqUh18qcAhcB8gz19KOZmheAp64QOCJk598sk9nKUNEVFkf6MgZC/+KHB4WVOGf6r73+yvPAa/cGQsfkX1lAN8jm/tlLEY0mYvYCYAMJOYwEAn+OW+mmiY/UpbzwfNp7JV5mNWNhJdNuS6SLZ7I0PgsIkJqkAK9a6h88T2UiBQUaUDrHhEslbrzbvYbC/SBBDXDzxGUSbCWJO36DiSO+bsVyoHY+zZb4ARWdDZEFHvJnxqCumE2NzcJb64W95qx2OdsXLaA1ggkexBgbjdfS9P20m56L9Xf7w4dPXQCZ/vuXwI1XHmL2X/z/XuRIuvL3f1C7mNZcZQ/urPFXTwp7hSgBAGwI50zZ6B2BvN6OpAAgzz2Kvv9wlojjUIJIbyQmNuSz0Ww2W+fDYGrPHh1c/ms3yZh98Ce/OHhxgnoEMU8hqdRWHKUmBHOgN6cvPn3vcbqc/43fXo5t5Hcl2VYUkADzrADc3vLmOq1PrLnvbGlZvPGL1mV+fX1z+9HZpyakuS5UTEG5crzXCg3IySSt7HfWI0NQn486Pzz3ltD0Avvgx89oGqktFq54KaOnmQv51KVMircHbXP3ZT915cLTl6PF1crccgFQlPRiDUH98//0HI+T24vUxRIjRZIYtwcDiFfkws4Y+9rX9tPEj37x4Q/uFvXvX/rR08D/46ED2ZHhrV9I9kNVtcd7CGovCpKKZVJZ25/JLPT4Sfvs4CUPUV7RJ2aGN6KjCfLs5JnUp3oRxPXNi2/k79yp1j/avfx//A3/WfMOQc2SkDKyCUeOZRYcjzQnxvfjUv8Xp3/x3t6Nf/Itn8aedGE0EMY9O4lheqhq6VIRLqV5UJqIakwybLkMVSEDSpntw6OwMJ4GK4lyKY2HIs7DHUtDpF5XVJJbCbU5Xp8r2Bs13gbS0PTRe4OTvCVlgvq93XQ5uvUOwAKeOtPKnoUcA7CLFwv02I2a99rrC6G9yfQY6jqeH5+AzRAgW8iF7Kux0vOPRsBmvqL3n5YWcmxhXvnsw9Z7j0kCWPyd69D2ReTlgO51Bz0zv8THirH4WkyW5BVMna7kZAx0/qhdmrcubVOBJD060cV7ammNt+Zjq19Pyu1Tp32++0hd+96bqStvAE8TAQQZ3XayiMSj6HG7BW/NeREzFkEunk2kIsWwYwXWwRFVMWfTgUkGijxUBlbJmy2uCWDlyqOGvM4R5tAiL2Uo3c8ugFYttxrPlcMc2gWCrwMojL940BEo5Otb659+fpjUIu5w8tOOc2UBX8/n1KW5LJA4+dcfC79596B3/PKJVbuU34zRj55rfFi+u9h9iIK7phgNh5cuzL183rq0wX5RxDL7Shzyd54fuKlUhaLFdk9wpPNCepVGCxFGdLQvPUzgKwtr7Pxc0v/apW/uvbR+eqyTCYD34WFAkvgM42KHCiQZvRCml+HWLNACO3VBUB6pcY8hQEKdAASBR8UkQkkKMN8roBMg+ejjx9WCx8b5Jiu2IGKL9Vb+xmL3rDUetSYygpSpuWo2w2fKK7BsTDojOyfJHpZZQPAeYNlJtyUPnSRBDUT7wAHEgM0RpAvojsyDgXasu447l4u7SSGIogN1jBqmLjlkjBdqid295zzDeJIyRiwkF7mcT/3T/3lrd88dHw+O9mXaiQAS6bdgrzmeg2E444IUN1NkGtNHti64tDWNaCFMx0PJiYZKCI8Uuq3GCaJnaEsLSZwVP/t8SATyn5IeXIwuLnEroyCQbLGjA+yMddgwS0RFvmEhaX5yIEM+bAdFGsDwxQJYfyCOUUxvyRtz9Njz9g+HEJB6/d3lo14EBOLk0EKLplBLX7/MQkj9xS/3gq05zA3Wr9XOd2UBx6aAeDI694AcjQOZ5Qy1SvNIGN/rUGpoqKI+yaCOyXk8oDniEGaYCMJxrT1wNSfPeuPJsJBJCR4CagAVMoEzy/IJaeDPr89jOKAORUmKcFOHbEMwfNMPAWW6WoDs4YiPGCKHzkbBxbvXrQpTgr3jxoNOQ8YSsfl3V5WuWXdcVtYPm9NQc2+8unoskOJRnUf5tL4tP5reC7TVq9x373znjdf/HgBYzmSQzxcpABhmc0boTkaSPgXXLy4n7h8+ftjcXIx/9u92/tl7zbf/0rXX3r3z/Ohc1xzbAxGSRHFU8/Fy0W896uz+9GnAaolknIMiRXa6xzZGKS4RvXHjmtzXLZPdfu06rMD9l6YYtW0yWEtBs2K6BSaAGIIeOiboWPUdaRfhe8o5Vs6cjdsf7aFJ6NmB+BvvrJahWXIZDbJJzBY6EzPSyIMjo7YaW9tazOf55s9GSJn69GR2t1ASQ910g3wJ2vlYDISUEwMX55xnP9E2FxanB/2nx9q3/hv0/PlZ/VTc/M5qlomycWRyDCpDN7MB+Q7x8z/pwMPPvn5n9el48UctLXwtw6VYoD5w+PXYXDW/USH6koZrQJZlTf3aFtk96YEcl6zdzBRiuldYXiztGkMvsDNULgUJn5wPXMPcuFTLb8Sz274wGklFDEZovFeBpUl5i4AjNJyFF1eyk5HY9rgTAfrn/+n4032P+OPn5PUKzWcQNykODA4KA2OUhm0wq3UO+obrcgmKg2gs7PUnqMGGgatXkZR9JpPVSX8cZF/dwOPE/lcjJJnIVq9icFQ/1eavVB3FlAyL2aJgyM95zNKdm8b9nfO67r0pADgA+qTpDC7nMtqJBiejAhU1js+BAVPm+caxGplUcExNOpYds8U6px/16uMzLlPIb29NpzqFLH1zux1F7rXy+tR1Rwl8MJQDN0zMFWMbKWY+ow/kqC/rX8hhqC9VeXA9tXglJR/vaA2dI8Dz+11A9E+JIOjVeSwbgAirMeY+bmposZw/a/UpF2FZMk0lbMkKUdwtZpWIrurwngSCWSrJeqytAQkzNoY8KAHTVrfnmUfshTeWzGGj/UQDZ0gcIhTdsePU5sqiETj4jADSWed0iij6Mhw+eCol05ff+O7t3ffOZJj6269UEZpdWou7qfK7Rer/+aNH8G5zLctgmfjRs8bVDKQ6jiH1zgdqAu5u5AgxErQX0WL+2vItbxKzcnUM0odyGbEFIMzEQq9I8EwqH283Rmho0MkwTeQKrDAYSg/lnn6huMBDR7hDYCYcyzAoCUUYReruWAc9lwD8jjXW87l4JbJBQwtmOYyedNttNOVzdO5yHopcB+j5nkPOhctO/NFPDwhmfPVqgaAUM1BJVS9WUWQz0R6wy36J5aokxCmdhoSYUd6PfGKnubeQzYIWmmcK9ekJR8bIMCY1+jgcqmMSo9FQ1SzfoVCSL+WFUnY8mWAWQFhw6BpZmoAZOgTBoa2Jul6LldTZDEGM4bNv32Rsd9QyS3cvbyVGpu9Iij1X5oZQYKmR0cHXIknOuSoCWqDWd+WAIAa2MeeZfpipZWCYT4JNJUVMzme21dHxBXr7b65qswFmDt295u7PhoaVmF8PAhdic9x4OMS0+NUpv3U4GLxWQR5Mes9UQZYDjM7GePNWCVEhX3POP92XJ6Ls42nU+ugrY3ujAtlm7605Z2Dd3Aj7GOMJvLgn2p8ZxOWIJ3yeHrdPYuteDNKx2ePjDEt11Nl/wXTgvP+dhYWpb9mT6cUVGDWi8cSDAZWGU87UuwTPjh9DeoLRoTECR6xhBQL88kgPkmAEQwhn5kNCclSza2i2ThdwZdjcyNPi1I8nsfNWH5CzCMK065OFwgpBlKCe7Y1DoESnGKTXGMfiuW7Pj/pRhoYRwDx59JwoLzz8dB+HzGACWJG+eiuvStrawP/yyZlRdAEAiDpTIFfzgMlO1HwTJyV9lpcDNJGPuiYCZnNTy+zDWGzZhUO92RxPlUSW00SbxCnftIHA6ysjL16aGW4YRW7bqNfbYDxX7NuxSi5KBTEHD2I8uxG3f7Hbl9W1u6/lMFbThk0xaI0H092zdKaWa1YiSeRkTZmIsAamz/f6sPs4X0Y3MmUqxr+zHjN77R/V9Vy8vjOuwsHzE2luhS7eXiPMqb917Sf/7F+GNvHa126Mfno+Y0hhOff+D3/26htJtOQCN/LRWO4/rL+yVA6MIOiMoFuFHT06fyEVLHum+i4J1/f68RCxVfTxi+Mrv7m4tpH/Zb39CURKIfqnP1Xn2qe3bi7Z+UwikicjNV1R8FVODR1XxmJwqIbgsDVc/fYaghamY+mtv/9r0/ZkaVYg53CaFRCnufzuItqQyAQru7NoKA6wodXqNSP/2t/aRj5vIZdSjtSVn9tIe6ZNw+HxQLkUzr1VffU1sPHffPcUqAP/+ZF2OkPJ+SAT3GQEGRX6TufYaGU2yjOzNWnqYgYPTVbwzUJgI2zgsHghGqNrNIXXlH17i72mrFOeTV/4Bn4+GErnDg4CoaxOIG/oWNNoSIdo8iLZawC8kLpL5AfN0385dJvDHchCM4QsAHxpbdmY+e81Zp4jbPOxq8uvjIwRmi5PkgworHjxVLZMBq1+UpXopSR842bci+Z0KNQsEFUiBjdeTaQ8RNPD3t4A1qcnuzvTGVFaEKiy0X6yi7CpWEeZQOg3/tH8jx4PfTFABtG4enH/vLPCJH6juCoDmU7aOelaXYGhx1CNDuLVan3vsVBOyQiHStE5QybKBYroJdHkTB4q6vRoPE6uEOxmNeZSja+0flstMfPBoxeiAzO2IwjJU6OdltrPf/YX0dINoTg//sLiIDV1M1t4dWn/xelSmKhw8KznZIrYDIjqHSX6dE/hwJt/+9VHPzwcnO7XrqysL4GtGDCBYLy1K9y9Gt4zINLP8HS/Tf7uP/j2+b//L9TbDDfPTJ9p/9XGzSmaqqXwNHthqRDokfHwwYsXLB4TVswY/mCmTO5rYfMZstSVtzOlu2/Dzxsn4xm/ULEs5cVXpytgmQe85vGUf/iQeevtkRppjE1z2V5rNkPC9DqXAULlie5d3kRtHzt7VveQ0ptzrXkBa55gA/1GEKsHJncwOC1BOYBxUMwNwKmt+zACkBHi0YhmEmBkk/jLaIbOwOWQF3iq3u+gA7S6lRHytHgm8pLh+DBA0DjDn0xNnIWyBjmdDlwCBBHEQpg5G8P4FMmnlN6wUilbQhbhhfl5A41+Jd4tCaeAJq1At9FF8diNHMmCJIzk2TRv+noMEsUTONAQiAo0WWYZbdKzZwqWq+Zyed7FCSaGFLEAGeKPH5+VB23SD/EISyaq5wUBMCnZkumUrw1tGOC9atyi6Sc+Sk7OqDmsyhIf/69jVJ09puPAQjUXq8ja4NGzQyHG3L64FGpBy3RwKDjBidr3LvX+zUHvWLQuJKdsrFSN+6HX6RjnHli6w8kd7r5v1G4Yk46iHrz8sm1Wt6uJcr56Y+HwZT828WAa5EDflAezvpEvALtnpoKWRgQIRhhqz0DKm4wGCMxFOGkGQTyDqWONVzVNVMpJuO3PNMtFOW+gilAc9HwUyNAjOczVsMOpqwMqRVI9KUAJb181dNsLKAEOYa+nSycDfyUJAcAIRgVpNB3YCVbY3lp7+bR+2j6IZ+K5QqZ6aW7lxi19YHjT+KEm5VdTM3CEAywLDyY0DUKGYyRLG0wyUTzeGVTLxb//d9ctsP6kC765ySVVpyk7KoBsraWGXchkR+Gm3zMm80Lmrds3TCglZFpnEMDFYO988PDLpxHHHX1pIlxg7EjHoFYsQPNmcNyXVnnS1OXJhx9Ph4P4UM9cmueXSs8mxq1vlMEYrL5EzZ12u6+OePfSaxl1Sm0mOScWW3gtACiJP7UO/2x/7eGzzGLq9o0boyA8froPz/m337yAJnlhcQEj0f/yn1sXfi35cGBfoSInCkZOdImBoxnM3b5ZDMiTptJnB8NOi3HJy5fy0zTX5TDOFm7kuYtzW+RqZT7fn2BWsDLpjNthP8qXErviVJoM4YWcoqL9wbQ21VGA8Cf6ei7/pKk/+6gzao+2C6sEklirZJoXGMaL6+xhLAsmxuZhwURqm95xKZtJzc4n4/0xRAWnA3BNLdt0bi4XhOHssFJMvvLKxz86vAjQfa1epLQCgNpOCAbh89MhlYBQi0CmGr/qC3mC9HnLdqSZPvZVawJ4JQDI8UfLBkCMBZXiHPCgP128dt0Cw/unzXbP4dRAQMJsLBrIBqC6FEvLI3yFxxFGi84sKh4FS9jdLIschdD8nH36XG51ZM6+sTF3e6V4qKVXr1wQclW9oVDe2GPnUmVUEbt7P7+/XalSgDN+1vHzbK1MW+NuJs+QPjDlNR3FsSJG66p6fzz8sqfGyMXtBF4DO57Th2fKYTdSghBF/xVmS+XUm4UCswJfEAoHRDnjx/TY5WPb1pEhPB+huuGR2BAgOR5n2STqwOuE18Pdc1VPYyTgIJyAKyxsueD6lcTefs9TQ6YmJK5mn3ejKhsv/v03s1a4//CF3FJ0CTk/UkA9d4XN0QHyRawUqn3tuZ4sq/ZYMp8DNgXNX904c6IZKvkj95Xk0udf7JFcovAbLPQRvfN5o5LQzYbxW3cL9x/NgGkbnycm/ck8KugI+8rdxRvLv/WP/tWvjI3itd+7MOnAhsGeD8JwNJYc6PigX0iuLFwuUH40PTgQJVd2omppzqz3H6lhYTXJ9hEEk0usczBQBhI8t5L3Zz3YhsuJOKDZZugHxUS7b7uWcOlu4SxAwQAnSCoL4pohspHPAu7Bx73K1tXK31uRd84khHYcNzTbMXlkDVnMA0VVke1DRmCRIBRw3kFcEgWSE3TmzBiGch1F1aeIF8Y4jJips5kO6ZZpogGBggyVycZZreUOGwFB63gaYjzLguYWa0s0hVFRczDFaKpRb9AAg6g+iH5qICB3r+64T5pBDHphd4zSpfJyMTOL9Cly1juVMW9b4K+no7Mqa7hRgXU6YZImHAwtEzwybbg4iZ6MxQO1/u1L6dtfKw0fnvjKgBTyHJSu+boz0m0G6kQihEySi6kQB4o0S0GOO4bs09HTGjWdK6X0IjmTX5hat4ohu4EbW2K3ctZUPlguQKDv/fgJt866jMXcYLwXATbiDASSgrrls9JJk/RV4e3bYDxLvnffrVX431wD7cPGi9NXiXgWcpr3DjCfJFz6bJ/OYII9sOWGJKK8k7AHyIRk44Fp9KYuqYIUByp+CIbQBIEWIApMILoappfo+onIJlm8MwrTRGeipKIRLRBZlNir15eL2XiB7DaPFy7iUaxmmIYqj7lrefXMslt1p5Bn1vgERcDlYnq9EtaHZSispDkt5NliBZdOLq0ljz9Xijo4XcpROIlkoJ5AJcBRod+H2C6Er7R1lJ6GJKzvtvrUEr80V44v72bXWHWK/eQT+bRTE+AYO48eveicS0Qkmw8GSiVXHD08UPWGXpUz+g60tZqLs3JDnZmhATjZwN7cjM9aIyplMwyhAWqXYCQURzEUXrxw8etzzaMD4svGRIWPd5pJQt990TYhIXsukyEUuzoHJaU90fF6CpOJe1NxW8BGXclvymnGld4/+/7ffeXnPzfgTz58+0amsID3tDGT8h88Hd7O4tSXnYVXlrzXXzn5s59f+V9+f2SY5IFjwbNhWfCdtnQ0yBXRC//4N47/94MvRDG/wP7kv//Rf8dx7j/+O38oscf/ubOe9bu+HvUGq1lcaBhXF7I7gTv78CTpdvyFIkzHR5Hl40B5MrFGdtGFzQd9JAEEyvC4PonKSIB5xFcPKrlI9spxiOFFWc8TIToF/+zj7OqmMe+Qevu3X187/OePmqlc3fU3Qq4zyvQ/1ZaefPzVLecfg5MvriwsvwE3fqFEefjRwE86krqTrF1aysjKlw+fxlfvCrl8Lr7XObAdvlS8ydVDlO411xeBnWdOt0Hnbl5y3CzQ6oQO5YAInaCouN2VFTaLBJFD8sapZT/pnK9EUO7KJSufle4/L03I9Sz3PAEOKpcvTzc//+Lk5bmRvXsp1+Q2cOeH9ZPEqC+GsE660BD44NPHoTcMXRS3JW45Gxf4tjViYW3Yh0cjuVJLo3A0engOFxgbCNBq8koanXtrMZlO7jQbeWHuXufJww9Ov79K/yAKfzIQn+8emovbgoZQGkJ+7R0sN88OuvtdjHO0m4jtReRUwzBSoIrV47pLuNF2DNjp2/EMHKC58RQAYXhnx6Rr/gYNM3LD7xak1ZW3s25TQc1pZ+dwtHvw+K2b19e/ttD4qvm1128Dh7bsdnJ3CljPO3sixkdqaSUNkO5xgNNZrLmrcGSQ0dCx2esuc/5+i94Bs4uUkeI6w7HyXwbx3Cbw9q/1f/zsrX+8/h/3Jw45WK3oP/zZjwrzVNCc5P/LaWupkslmvMRid9yypdlWjPjltLu1litvbAqtY+kYKZph+rs1Xkge/gd99vnD8UEnzQspHiXcxQ2KfijW9y47sONlMvIezsfdbgaFTw4QJl8SKgkq5kSirIAxiSBpAJBMIWBQzA3kXg9R/GiES8kQwl0Omo+ps8CySmzh+WwKSJqixxIpno4nVNXsBv5QngloDvXDXm+kxblMiGoBRCc5U7WAyOg7TjJXRAnEJsLTE3HqhGCacTQvTnD51IIZ5zOZDHjSH1ruwXnPgfQCw8i6ifzqscj35bkb5Poiw6GoZXDDEG3utGeynuOxlbm5yKH6rcH+A7DHhmiepAUMQch4CCB0IsGyXsCs3kmPRGUKoGkC+dmHMxZj7/zghoco3c8aRx8/dlCYhTEjZrW74TpZCALcC0fGcDd5C+tNjDEh4GPmzq8v7nUI9fR0LjrPxcKnsxnOcbGliwvgtHc+2FrEjGV3h3efHUj/7Uat+eR5Z0+pXFne/M3Lj54Nr5U30aOh4yX4uxeIofrpf3iuYr1pMrr2a1dLJrj3YVOHeQ4lmYhPCqylGywUJFbA0eCYZCNnqDdHcp5iqqQntxRHRCAsmERufiEuWsHSSuore1ykKELASJBFcHNs+eQc0AIUltIISkkG5mQXW8jGzy0FHUkr85XTCHR0+vL1bbkrSfXzRCWIeLw3DatRXBZl1zCq8YzSCQ9Omm++Pf/+n50+/qihT1DrDP3+71YbJxOYTt/NAf22cmFmOs8BYNVdpbhRMMVW3Pagyb9Re/bIR4a+0u/eXOJvbJIOhZ4fu6HpcAITIQTDx2gbGAIL3/qd5Mm/fX+kOc0XytHps+yF4kKCefN2eWShL371XHIlRbMD4nxqglduzUOud6NGmiOjf3razMajljvtuBfXU11f6e9Karf/63//bYTN//jH/dnOEEuKen9cY7Pnj1uT5kGjN5LQZJxL03eW77d61JkUrcTy8V+Lr4bHh93mTu9WPjEbEHYn+sZGQkWt9TvZX/wn/psmeIiDj2ajzO3qQB8WL+GXS0XBYxJDQjOMLx8MmN9NvfHXr3/x783e/+NB/I2768vVydnOlde1h64YFcrIAHnw3oOtv/Y6+813Hn/yRRoUCTI4PB9BJKQvhKqroXbIkVwWiiyxw7hu74UVL1DicIi7MCq49vBc78ODdpyB08cPzuYuM9lv1Hb+3wcVPFy+fYXiGONg/It/tltQwKEvb/zz305jmw+A07r8snE+TacS5ByKaSH1Upk+1abIXJLOzwEoP6bMk0kVS25f2JxqGj/247QXucrHv5zMTqFKajuFc/f3h+usENNmW6g5G3sp2AGGRhE0Jr5QmV+ASgLUtwFQz1iAft51vWgynZzuueWNVDh2H/XtPIc1OkM3UU+nFj486pxLMseCFEhiKNDsGfEUXctcCj0jyCe8LC5qSkdpMZE6bfdcaSbbIpyghSXTjPzEtbhXZYsAb9Npfd8NvlDKyeAiwHL5cqkYPwxZ1zMUnvjBtcyLB3Yilbu8uDRum+gkeL0Etp7OMjx41h0ns/jYHuICFS/5CDlKpqDqyI58jqkWIVuyAyeVRzTLjtcgfxwm8o6uD+weTqtadzSqlZDSO4uYj0MBwNTyZ10xYfs6z8xfdPxbC1IhUy3H8azQ3T3fkpywOQI0M04QVcj75csGdz0edz3tXKu3MRAXbq0VzxcmB2cnb/xG8S/GB6iSenMeNUy9cXQmrIP37/XTG3hfheOedDDFUbuZAcFWt/+ZSlxcJvQK3cS10FIjFqqU8kQ+JWIS+Rp9IXUjcrCjfjsdOeFwkrCRnhGpqinUADDMUAmyewbPQXwQsdlUPKDQvfrMhjxuCbMwxNNsPvT7TzzHp+LJVDjw9Dawucj0nthS/YxcI1P5y2gpnhIGUMJiNG+synmSChCKIUAd1eqjo61YKqtj0lDriEEsj4Oux3JsIKGQbYY056kuIpsEiIYUHto+zKUXagXYDCJnNj2SZ5NeJIAqNJ4ZwzhelWYa8s4b74YN8ZdHJ1lqOFTAUj7HVGIX1Whvqh61ZnsfnCYX0sycnbiZ8COnJ5rZKAhwWOYZXfKYliTBVrdtMGmH3Kxew/nMEmBK8u6P9x8TeqLK//Z//Urni6nIIR1uxldUyHYLE+D63kf7mTySI1Q6iRSWvl64LJQuMjqSoAkoIptnMoUST+47HKve/Su1G4rXOpKH9OIqaPMGJUJovcAdvffzO0W4+SU13dfmf+/1LtzvfNx+rTqjapmLF9fkTv3nZ57xnv4IROj84tb85v7+MLOYNwJT0fvBJIgjSOLhIw8r2gWIi2lgItn3TAyKuyZUSnkzH/cix3fxcKJfqmE7opYCQ3M4pTUzw2v7llnCW6RpzlvQOoNZ57uTKJ9LoafSLEfKGgwBg6NgN7W0vnaCJcAibrfOxs1+mV4sILQoOi9bPQmMVmfA8csz5tdW7Sd9KO5bYEbB4smU3+xbO+duyWPCtUvuoNl9giegYZ+wuWzAEgB+eqQPs5c3dJjheABUXdIDzMZEYwG+domPyDQaNSETer0KOJXFGfvl1b9+FXpycnI6DQT8fTX68lfDSpXWItTjClduV5u7R4INl8uJL394OqMivaNqVjtPRp5pH5+PvF+8bFQQZrFy2VG/egkcfyVaD19cir2VT0TjckmM8GFE3v1L3wr6jWyNftTxOGgs/PQY/ujyq3/1+uDe40/hZLUjFa0pW5xnGMEZi9bXFv5cHHz7GPre36j95//8oDyX1ubLSToonAxAmIv4VK/r/fxf/xGJ4z/49ruP/+jx9t+8yP7dhUmPZUpF+fr1Zz+Wo23S+NURvjtaWU49HWc5N97ETS9m0pHhyHYyAyIcNWhOY8RMg1ASGSaYRPfMBJUQqk+B731b3cfHL3v0lXUg7SUbIn6hUruxufP/+eX+Hz1L/a2vp7cSjRc7wHLi8w8+vXi5WP3r1wpkf8ZzYmfPme7zh0/WsRoVR7/CGKLXyhgkVuOi77OpaxXfM8qpBFOI7X4q0ySakAwhDEaPpeMSMW5AFZBZKmxX44X2nmIj+u70bKzqiYt6HsDOpyHJUI5DdYFyODJylzO9I+JzZ/LSBP9xrQoG2iBEutpO9+FhZ4wWU2XVBFfnLiCoAI46CuhfhOI1LOx1TT+dSiDCxsXVoQkOvGYmLlmzkWZAJAqox7M+ClJZZoGCTVyzj88pNmP1xrK/0pbbgVekMAK0LL0Lk7Xq77xR/r/Py+IE/kd3F4ePBzZNkoGJLCKx7czkdGSz4mQs90SDomk6xxlxTfdpE8Zr5Rh4MvUyYWwxZppaPMePJHNgG7Jh9XA9EpV4QPfvN4X5SA3g7hSS4aErxTJlanevnQLIVxY3RgMY0poJIRwfHCpRfqlyzZpMlL5m9GdIpz6iYBy+qB1q4IVM+VrOOerrnkSD5OKNoH6/NewCc1vk6c7e80O3+M7i/q9mN1YyfTpKXjW8oFGM4FStoNgBu1UidTQGcP2HzzB/0DO5/RG+XU29/LyJ79/n4+U0YE2+ErmoZQIBdyvvrC3Enhr2TxUilvKH6qu32Vk4Ts0wIAnwAaXSpjKRC9/c6hMa1Q8pyllcTHqGANQh5SJeqhD4btR/MYgX0IPx0FDOJ/dQPBGLvXILLAnSyLFVS5c8ZeeIFrKjqXw+tWvFWN6lp0hyimsv2j1OA4a+H4cCtlbqtHUHM6GIoRkosv215cU7i9Vmq/X+vYcrK+k+gg2sHoWgumrjJg6A/tQC5wDMK+Sd2dRyVGSrmpwE+B/+k+rB/QFIO4fnfu/YLsfJtbWaqlXjjHEmTlrjaXYriCPx9dqW53qEo4p1FXMjHAXW1/gSBeyf9eqfD3QYVUg2ToTxFHYRIV62Oz+W5au5bLXACgjelKHEcILptpdFkkngAy0SaZJwghedM/F+mI6SomqoevscCLO3Nt7Zikt99b2PTvMZPb6Kn76kfZtbOHQavWaigpCbC+hqMUYT4Jf79f+y941r2T9/Eo536/kkgpWZhQvXvncazV3NNnzfPDfcfbX/1UFfduDvAXQMhAFEP4MDn+Mjd3zSFhYtE6vtmbGNdVpRMCSPoD48mvixQDpTwkQGKKiRcTKZoy1PazUAQGIQLlsBkE5j4JQMF6I03NbQAgWO3OcTaSYUuEJ+MpE8AB6dicSpg3lQpGHGuEPG08lkFkgFDMNftFJPG411jPyT+14m6V+7HeN6p50jKbc5b9J40A3r92WES4As0OhJDDqFW9Ym5c1gdL7mWWHEu/7pqdXpiMw8ubGRyOd5A4Ax31UcSO+gZwz421cLH/nhh//yPpnGryTxcCKVktgsG2pjKXBi3/qtS7HVFZhC3V7ry2fKoDEdWSATwcLWCvP6Oj6dLpsDwg+XAsJ3ohBC17Je9bXrJywcf6UExtL8xMpEtDCX5+TeSR/7VaPxs5n++qXsnXerUkn94tFfvGyNX/+H/wD+ZdQ+eQ+QwONzMQH2cumY9ade/bW0m/a7f/H5je/fqsTRzrMuqgZtyzoWj2UyVb61MFsDiRsOOsn/bORev55LssLPf3SIPGhq9fsUDF8ykyf703gpxc7FP/zkWPEUYByhlWh+iVWHUMgkDSZkMNLF0YlixGtZ0puWAOzYsBMGfCM+Z2PTJ9NOcjE7eH9ceN4y8+DmK6mmXYSHYXbtwv2Hny5eiBdit/qPRVuLgwUmfyOOVpgFNIrLrPLB+YOiaaRmmyiVfKk+RsHq71yZWE2nBXsRXZ/YuQsrAo7qv+g4UXg2lMa2H6fjlaVbbFg8PmkqEc0towhP+UXKPHanbuAn8ekINkKUj68UEcv/VU/7SH3z3dW736zsf7b/+E+OEKzvB6cbNai8mfBkDvJClPA8VUUxM0MncMDqdYByGTnXfGExq6la5IaLhWVNegnoLA9oXXGcyfAXannApr1RezSTW76eoqESPUeZDpziEVnHXsnn5l9HNOOLP3ukTOSbF6tLSc740MWejt9XrFpl+Xd+8JcwIOxZI4K3C0kaRRNffTkTVrEIZWoC0XsxXbiU7aaLZwMe5FHF5udhMJFKWDFnSM4CUrZkacLHpYbZffTMLxFAZnVlqXz++Ew+oudZ3BvYJ32RjrsYz/k5D/GCsDG0O48aR924kN98vbLjGfsn3noqoYOje7sH17/7alemIGf4/LNOllfJoqtBfmFx+4Js7b1vzC+TbUw6H4qgMwEuJAPS4XQC9AANIq1JbA72EpDVgcP1r105cIZ2lzC/mmx68tmRH3sb6ENAOh40T+3WdMZeTISAx0X03EYM54STFyYpZCs5Qnd8RU1svlZ68VjqtYf5AvzoYbdCxFN0wtVwPp6yIUce6y7ubs0lmz836RJcev0VUMnp0/OjqTpvDbBMlMvmOtLkybOd/uenb96i5ufz/ZC1FQMl/XRAag7tZwiEsXDHxxw2mpExAgV9NUAAyKV4OOZZkBhIFhOJjob1GT4NTT1TJ2wcYjwkUIJgAioC6sRw1EIQmsaRk9ZTsBJ/eQT98IvpnW8LAWijhWD/6DzWZ0tJYu215aCwOjppxFu9YatpjC0iw9MEaDFR3AfEo6mpm97acry6DpyOINvnGepLT7+zERP7ZjpkgPZwNJqefeUsLztyX0kzDP6Ni12HKuwc/I1a9k8Ty3gEnrzQyOnBjmiRiEoHI9j1Zq4C33iNsU6BYbwRWQyGIinE1KzwUUc0NPKvztubteGHo/zvbS///q2Df/fTjw9GLCKRx0q3PP+JLlZmE2QQwO/7U4Uic/GzxqyY09YubdKY1dwfJQE+oEK6umzel7fjnZ8d+/S4g7B4+XfyxWzm4+ZJ6fJ29Ljpns5UMFQn1iKBzYxgZIJiD1B58ZvXqmkeOxiBuCeNZuF8Mj6QgbrNZOcoxNZxxoJwp9EUJVkONZEqwdXKInQiu7gn81AezFVI3/ADS9az6Xi6L926OjeypsOW+fEJkHu7JDnG8NngguABApCNQx2RQFCfsmG/D7/oiAuby2wGfr5zNrOm1XR2+Vub7WEXyybFmVZNJ6ZwsCtPF2+UCDI7Acjc9eLRX/wk7ERHmlBluTbRZRwHKAJbVap8seK5xuPPDyukFyxXi3cX05xQqQfnCKjMKGQypZdLIJH7wdrik+enMYqatGEu8jMZIHF+3IdA3wel825d3NWbamYV9FNT4OoK6ti+ZpnvfZB8o7j19lzjz+sXe1q4tdn3vTEF6r6e0mI3b7nw08/kby6+8jevnXzyPP1X737wzFgnY2vf286hjGZFiiW+/Hwv07cvVdYO0tDDH/553EEBmDFlb4mZJM/9zTfK/zzmfXnveWrhMh8HcukaZQwgtae2TcvSCsgaBRB0MsPeXCCfnIWXM0iWiD9T7uCsLfvpy5VT8XB5bKW+ztxLaLTtUR82n/cmiK0EzUOU3nCuZqXDyStzm3uxkTPV1KNAe3v1neZ+A2W4r22UqskPf/rlNu37k+EXnzX0zWQoTYizxkp+C6M829T588HE1hgRHdrgZYwSFmrZYgkIOEBTumxDVfSXz9BEGC3nCswiZg0mTXPW7ybiirO1GcCcVV1O4o+ybiz74SdD/1Pz+kVO8gYjNzdtHA40tGdn3l7LTFWjCUBJBzBm0wwHBB45mTpgQBijx49Px0h6oab3+Lg7otLzNFdcqHDWqKVZoDPy0nBKz9XQDMWgqkFafNZ0x/2e2fugla3O1coUy2Ht/e75P3n5wkXE9s006l3/7uZr/6ff2o2omPIkhqF9z5887ue/sXnBJ0eGB47khSuCX8uZM5dbvHT+7PnVy9fbRWAcACwcmWEsOePHroTwseOktvqXKsbTlsawF7gy49taDevPOiXDR3BwTJBKW+ZKhQQM51I8I9lnnx3RIVJY9LAksVhklE5v+PFPzM1QmkyWPuEjOwbPV1ZyzkB1wKAzY4J8aK6tRf3jjidO9JWMDyQzPdNGuTgBm81W6TcvpIZw0HPitqyCcr7IdTfK0Htnqz3j8X5jgwaJVwu9ZZQ5c0v+EtiYBWVuGeHb9T5m9fVhIKXWM6+VzJaUKK4BB30Bd2E7hUdyZS5HaR7bQ1t85C0mExw0OB0bTnJhMTS1JjWXGqLw7FwMt+Y2vrE4Aa788slfLH/uOY/aasJZ3Srw71x7mk0eHxvhvZdhMQclk6PubAXBZRXomrPcBKhdWU5g0LP9o1CcGkwiVEQiYhg+o6rOgxcTngZxLglGSMKIavGSrI5RFDZ0i3V8P+HDjGCNZ6iO5DNLyHQkp6Fg1M5WUslHPx3ORHNpnssj0cKFxTBV7WM8AFrVOwWnONjQzZOXu+OmGAagDvvm1DIiB4WWpy0RB7OJK0tmqL5Uzpv1uvZDh0uk51eSCY7oqgaAuVrJCDUlaFnWT4/BJH468KY5e3Kyj4PJsTwVklRmDhdPRZAwyyFBC7YrHE85dT0Jfvrk1DcXkOV1JCb6uS5FJFA/8XqMyaa0nfebG9+vNL5RG2Xm4kD/7MO9xaXqGkjO5XPDBD7cVWsCOjHdy1cSvo6kUjnNATGGNHDh0Ri+Us6aZ+2JwneB4c1rCxvz2UnHU4+6o4GxfRWiy+juswkUAxzXbc5wBwxQCHezyaV3t0YL5R5Z6rSVSg4fN+UZCxBh6I01hirNZpxDEVQ8QXtmbW09ypaOuk9++EF9db5AcpmANoejoXwUAYYmqjYsoF+0jCJTHevmaCz9/v9E75uiJjsX1jMYIA+76sGOD9JcIU09+LTJxQGxTY4RwwtQMh9nSQbwEDhDzwtlVIjPLFF10easJznanBWsJrV++9jCNGY+Bg2g4qUNguIwr0dhnK63lWPnw3//BY1OKeVsvlQFC/JPH0uKqOI2CZHYgua/eHmCxiUsa/27H42mun6hlMNiNjwZDJ6Yw8mYnQuhCBOSVeZGOvs/LqbKxOePD4+OO8GDF5YjRULgopiaqYo7fbRWEgLCGrjfTdudTvnpM+zG6rIpHUhDpXSz/PmPjm67wa+9sTT+RBz8+YlSW0ggUT6F1ZrAi5/tD7+Bk3dqlu9uv8KFKHs69GO5O77c/vm5zNLQcGzdeI1Kx7jT+yfhUI1nEG87ZnrQyYuJfCBvXlrTwZ7viPKxChjeWWPCY7HJkwZckaglW/lgDH0ZK9SqRoT4sewClh11x/XThmo8qixSzT8d5bYxX6YFgcO3w2bv7ODLHh0Dfu6fp3ju9fKC4fjjPhqy+avf3zok6TJYmUPt07YphNSkbMmasijna3GBUZjx2NBcNL0YJq6wGWCBSqbShib/uN59dqQMiRDgtGIWSNbW31iCUNZ02BcPDsN09dXv307HsecHe0SkH+6cJ1ZTYWqtkkgHIxJx3XwibDstmMBr50joQolE4DguqfZs2MutIBAush6Wq/DzXB6wTdxyDdUX0KlujKUROTlVhAwwtRiCpR2jj3lijGamkk65jcEsGYuYSemyQEWa6P3Xv/mXywsr8NJGDeAfOqeSCuYFzBskmSSy/8lY7WnX7sbbe3K/LhaE+ODUX7jgsSSq1evX52osDIw93XYAFDeJ5onDoiWBF7xQpnx5pP70q0+LJH3hlaK03+/ZgUGG6VSASdHGatLyZNUihRi+fodNV/J8PtkbaHyceOvm+vPJEXoz/XLsyhDG0gakI/N5iLPc6ZShyQRqZhyWKFzSz+4fsoHOw6n2/qHAhQS8gJUXJkNAhyCUmTY4IHCAME0AhjV+AsxlmTe+d/PD//EvYNBZX7l69OKF6cTYFH+hwiyxm/bxce9lcPb+6YQ4fPU7b+1+Il/ewlWESgs8BMMKKFSrKd0ywYyNsDRPB8HU0mdWrBY5EanoJkrSl1+pPtt3MmPq+SfDxN3M34vdrNScsdE/PeuMPQ9nczjIlou4MZAm3a4rymR2jksGZZLNh2SmULuwvXx0/jxGIUA6RYIoF+NQNK6ZJsuBylDBeI6yJgTKwwim9WXDM1AYh1wlW0yTMARilApQAejQTAxJkAV91xd1Mx0r5NeL4LYxOJrBuQR/d8XB4vOiiTV9dRAZFo54IV1O5ebLTtfoyJIuT+FSfvHGCk9kQs1v3Wvbcxi8o1zDBSYPTmGy99nBIM2wAK2nsT4Z2OK03wLgnrW9ubK3kO1bKsVpCW82XFuubORgKj2QH49N2iixmGGiPdu2ib26auox5+GZ0JGCjQKWQXlQWKXB5vNhE/TVRv3oy6Z/1MueddLvfK05lwIPDryT6akpLb1xqbud1jUPGNYlm3cMKndxfqzqeToVnO5fi5HpQf3zjhalpLlXqi3E/PSgUzjsLJDV115n9actwQ2u0KxoiFNA5m2kmvccgUFzC1x51jwd2I2H8ylYCJJ4xTEcGzWDyE4yZCyextqBVcCZ8VQfy6ePDqdyXKqWElsryYPnKmCiHGpZIFTkCTxZiZBo/R2+irE/+sc/rn3/IghVod5TuQ04cdqRTiKHK1S5mWrhpKWmiGs3keXX488enFtUmvfCBMohFMGBvhvHJ65O0Gj3ZOTizko1WS2m84nCTrunyOSrNxbKiXhrnHj+Xt/OBxcXyP1JLQfYPQQasejiN9Zblm/0xdxoFsY5L+GcnztJgc2+Uh08M2lRDgKZKBLjw+cOjpIJ/9ar2Ude0lthp2Fwe/NyrtHc2z395U/Majj93TRDc0WvVHnt2+Z7vzyY/OQzkp2cFjdI4PrXFlzbl2eudC2XqtzcamWqj148s6Vo6+08sjOZWYNJZ8RipLeWPzp4ufnQjS9meuF8mafDC7eTqczeh19U5oS+KHIBMGzWEbnFr5ZurJPqoy8TtAs1xSqbiy6vJOdjxyZryAahk741NjUXACXxoXdpac5O5PNTV5EVz/TkGZfAEe3hCB5brcmU3nYnvmeJ8iuvrh3tzkAVRi/Sn7U9TqBClF3lcGlPosf+Ri7/6cftH82hq5kztBmCAJckuVGXwBY5oTAwotnM4hHfN2cWHBRTTG4o+/bAPj4L36lOd0KKUZwcbBP87HTY3YgVmNXwuBAgHS/vaLErwUBrVJkiWjSnD043XlvV6gZ27/DRozM0oU+krjXAlhPLoQJdE2KUiXqQCo8cOsCzKykM0BUrsjiMuLyeUkVwMgZ4NOtiHkjUX3QhJqBwCimgmWzmUp767CenMMpfWFvu67F03BUxxj5oJCyYubrsVbn1EKSF8ruYFLx+R4agC+51wMC6MToCouWBqaSrxsnAN6DK9WzvvedQFEpdee1CpqsQ1RKlBjoyOLj8Gv38nhRPIxrMtQYnRME9/2Dsarp7bM5lMf7UaTR1qmv7wjJDxkMcQy7kJqfj7CrFcNn5WcIaY1/++Xtzt5Krt67+sjUuT1Smq2YsG1hGpfnSyu+trJTn5uqK0jdZnpS0EI2dizv00kaMxnKQosQzBfNrWLfZr+GepxmXbl1f4GJHopbfKibliPeR+kwHYyGf54PzKXMavf3128c5EmaHiTuVCRLM/uho7ATVZXhro3pkay/+7UetkSpUc7f/r2vT/RkmiddfWTnYk+Gi8NjCVnUlILnyhVTjsJ9zwRMCFWLQwS+/JC69emGl+NQ8R3ggZLH0/Nx885w+9S4kTPnjveGLoz/342QcY4uJ+lhxTiS7WtqIk5ATj0GoB7NovNiKVBkEX9t+5VJlvmtNLAuvXXwNZqnWkxeCbZ7DuqmbFSMjWJCFGnKggZI/zwud0GjKnc25ZVWJfC18/EwsxMP5jeUQJkAEQ1KpzGRqiLqn9pudoZ4sJm69nvcysa+0Keb1aU/gAA0JqXSMAEIYc+JRCGMpbrGUCraSSMrwKGw0C7IcGBKh4Rm5bD7hmv14JGF+5vp22Ac5Bc3CjqME1aXKoDdm0FrkJ/iGzy1CIY82RyYXd8SZSodofrHoGqiNmhFKYlZgjSZdC6i8cylL4dMHT/ePxeVFXowjHgqs/v7ix1+NkacBFpCrKaL5rx6P6Ye5K1nQzL317dcGXx4OHg5GBVFKk0tV3nzshrkLBnOzHBvyvZ5PlnBPolcwYnsq4qnMlUWz099MYJUY7atQOKUgEAn7Htofg1Z/ruqzZGi6tE+DBsEc/2oEqXspe5INHMBHj71YczTLqQTAED3bTgZs6EKYF1vNYJ8c7ESItJGlCc18+eHx2b72zm9f1qD40ipRQjO6Gkog7beHf/jTX5i++7e/cyEEhtOOd3E7J+lkyOYpGAzT5LOfd6k7MexS8QizKzTrJYalElmNF5URQdHU1HZwyK0/Pt2u5lTdzRTIph52piGfDNiE0+mIDE4RGVRII9UV6MHJ5IXY95l86Y3LjDFpdRzWh543WhynX9sodB2rIzZy+cyHh7O0bMf8GGhbS6Q+G01qG+jaRvG4r8qwKIfUfB4wjMD/au9kr792IWmHwxQFJ0Lt7W8s7H7URH9lQFNgbUt4MtNe4rLbO0WLFOrMxIurC8sX6SgnEKdVgEnpgeHGmkezhbsrdCn/4lfNS5aRKZaarYd+lb3xvTvCA0e5f6qO9h/++Vfxv47FQGjomM8aJxfmY4hqi7TCuLa9nE9XlnU2nIbd0ctZY0CBlpknhOEoOH52cOlWiVT83kCELHaiSkQujvmw2AZ9Oj7VPSQFUoSsk7BjmS1NlPeAQiF2JoYES+owiYRogIO6l2a1sO3m2DHAbM2/vZ3Z+1E3BbJ33rg27J9hGrGMp+R299yQkqlYgFCASbLJWJ+A9U47alqr+SwsBuVoyAEhnDNmdLR1Pe7/BKiAOcxR0UXN3J8YDx+jDpC+UXl6NL1UI0NWOT4+IA0zVnZcFlpb/LrcDAd9YWuJcdQJihDnw8HFzGIKT5uRJM9GAQpFGCZwJiugog36lDLhmSBA2WUUD6DJqK921U8HYm7IVS+VKxS/Es959z1kAMya/bXa6tLdq2vba9MAT6PJYN6FMczx7eBgEi4Iuj3Nur4DkTMZSWbQLgjPLQXHB5IyUBdraVdxZwbh4OxzMHHrZvXlvUeZIXlxPdcf2OvLquwNZnrbT81Ylsy/w+mAbxk4ixVW38xSwVIiYMI0mlm98L732Uy3fBY4byskAFiZBRHA/12jy7EMxTGD++dkIgA140HUn7u0MPv8ZP9ee71GV7ZrLOjKdb+waMZurN5NrX7+q2OQpctJLPra15mg3T6QNl99o5biGi8fDaaRYokFELsyRxyPx2goMIWi2A+9+cTEDOuPxtU7G7dLa80P9ItF2knSH52NzdHBMk7duZgGLluVufD+nzYfteFv/aDyb38uv/OXM5IKHPsYXqBDjJOVzlypsJGNnZ/JwBx395qw1+kCkJhI4obSrubKzM2vP/jiVIFicUwPkOqFOD9sD8ZEdP36pjcyBr785L2GpqiJ/Mar7yx2us5EU3K11PH5mXp2aiAexsQZBD19eu6PFQt3Ylky4NBeX6lwpIY7iIBIu+MhSRhJziVCPVBaMzkLA0QaG6thwvRRAKFQCKFx0iBmxeV0Es/VkADQul9+sZtcqS1kBaYoQAQGUUkaDPqiw4ZeHMGaMxOB0VI2FmSJqS91JtMlMvX8OOKxcYERFssx5XgGGWGVhRgHQhO5MschutSz0e7RY0lhLiwVjrvUWHFuxumhiRTkaKLpvDTbs5sbawUczwctLY8joxC3owRBwLpPxd/IiWlZ//RcmllxjCItfyvn2JuXnODp44fN9LsXVv/nlem//cUSYZ7VjXp3cGUzkS6vLtWfvYijIognPLX6V76JohShiNqRSVSEBEiw48/ubsdHFTpWU47rsh7whAC3WGOGQIWA0EwVhiHd9pI2PGoZfDEWjZWpBIKg4WjSpRr2h+Opbyjpm0Q1m6Df08YjO/y1lGjJ+SENZ2kzB1gRm5ER3VbqvR4VK1z59mbpRiXUTLl/dni8Z/SCcyAScup43P3eX3/T5HK908dh1T6AIvhxK77GnbSm4P5sa7EMdwfrteq03YWmJhJmqtkCBpKoaaieubujXN6MZ1JZwAwGRICRQGjB6njarZXbujrrKV2vPpxGJLW4sMRfYEqnw+FgdBb8dAoRs1Dyi7eWtzYE0eeO5QgV/YzAlm7FUx34y0/O+AIkgzQDwqUaaQPNoUi0KTdHQHcYXNmRmCHKm0q0UjwL+wRGpWvm0yf71D1ZPucPPvbU//MKdZVOwrmKw3jHuHYmfdFs3Lqe1QbmZzsPeBjcTjG4hjRZLP5ry2yZ1VPLNSnsPtErS/HFX7vesFzrmQjuzxynTi9DV99dP3o+yP3lO6txNEpTg52dYDA1eKg01G3amObIePu8bxIrNHPn0puGircP+9BMfev6FsAB8nAYNoL4fOZINhc0N/Cx+Dbv+gElCmXam6zHbIeN+XECRV8OxLjHzBMFWdcTQjTzrAUA8mdGALqON9wZAMPZLNhNrWzFdSGGwr0DbVDlVnFZG3D+WpGVGrMXRBoP4psI8eTJUdkyRqcvnoQXGcV4G+G3NmMfot7ZKRzvCXEI0s3O45NeftH2M/gyTGtA4qw38nhgbEH2qMlozNNG42puzmcTdU0N484qiKNJrOMDOEktxHNogMGAO+rUZ36zGkuBEbvz//qFvZhY+0aJF2x9an/VhRddF0fpeRYeF5fRQuH48Qlcvx+QtQ/A3tTJFXvt7Vdf2/jGNyr4sm9ZrCcoHMiiOAAApiNmcgQKemQ+JSouZAPpIg9oEkJGRVrsBLPlNdKxDXuVhRwzGxmKzFMpfWlr/WBgXaU4RSeniqsdaUcH9xIZpKtbXUOKkVtJURqmUlypCA+tvcag2bS/c3HlG4WlR0e7/mORh8h8ht7IXXniN4Kx6R5POl4bri4PowlRSSQ7Vuunk/LDs8DD7hl4Hxfp5Fzjaef0fz/e/B8IvxJjQNTYaz/r7OQr9OGkjkIwAvqdQ/VKbC3sjptVVFRcvgPl07G2NkRRBImVm++/vHNlqR0Sw6kM5oP4SjJ+Oz0+b8rPHm+vQt2ZeNAwnaeHj2TgN6voEzYzbIDJC8kvj1qvvnb12R8+f+s33hyFXiHHjqRoquPQgOGrC5Op+rwVbr9S1MyABnzB7jYwNvutN9XBw9b+CSJRXDqWfSXTBcPmvT15qJ10ZvEozL2S49LxwDfdRj1EUV51ZXdq40mYoGAo6J0fis2OEoOr5TnMCYsQiuS4kTrwRZOO2BlpqaEDytZCNcGZ7nIqFSfwzGKWYHEjdPSZEjkAok89mMtfytemDrRRi5r1QIil+xBdypaah4MACxMcNJIBk/YVLfB0KMKwyLVcZTKdtKdZ50yCUnkiWeKEQcANZW1qY3P8Qorq9fzWy/GioIsw1DoZVa5gw8xNYhoe7xDU/JwYwSfiGNlD2YgFtI5nY6sFtinOmExQzqT1s0FXVsIgEEp5P+Z82RkMjGjplWJmoiM0ggHRj//g+dB7HiEaNDNensOZ767Tf3PFSQNhWUiq7hNpQpJUhSkVpt5pc5rEl34ntdUFuvI0Sm2kJpS/P5ldFW64l6Jj0V6dynaGffTUiYeqgEcZBtZEhMGtKOaPhwCDp7QEw82nmULqwb3RfIZ9+f4x2JSeqCMD9XPu/DaTcPbGSCyFEcxs0u21epvz8UNpNJZ8iqhkYGxzw6AyyRmDnD7c6ez0szUPBsLcK0s2IiRcA1gJyAhiwhmBW5ugN5wZbTgQKMSk4EQMIZL2k19FN4p8pLiGLF7iy/wUPDsaO7arRmiChNI5HG5je60OM0/PNJsZOCxF8ZQFvbA247A67gW234+ZRBjLZNNhjig0GVijAJZLr4c25r14v19biSdTMY4LXdc/+IuTIobe3fChCAPIUPbShVX+bAy3bXUli7saOhzOxF00z8ZnrGNonamhZyLs/MXs3dLccatLvbPWOBi9Vrn8UNwd+9NyO13DoH6Bn2OywBge7342Lyyn5/xJHdhtiuWl1PxypS9PHv7xxzkI424nTr7qRkcqBkcng4PKdi6+IUxT2MKbt7QP6oLl7v94f2kjAfqLZI6cezOjvGyZyCzU3IPPLJgmrc1E81mfXprPzvOP/3wfSCFpD+g1TkqFCuUZtRqKEmiapc9kf5AEZhBozNwi4sozsGPRjEcSYej23LU7yY6KjZ6PL6zNrxRi3fOBNxsQi1l+mWcl+/ixFvadwb0+9FeK1dd4p6cIKDUg4Zc2Gi9lslHytYX5w66We1e3X+iTj5WPnr30gHHD5IPsWj3lMVeYxsksnkzMcuwldglU1dZIfGmQSCbgEA6KCARApnUxTFKVrYK1Iyuel8qlEdTvH56FeVSoxdr1rgGiV7dZ5RxQk6GnYpihfvYHX16an1+v1oQSuzPoY5CQT0a1MOm5QDKe7A8t+0jfDGPHJwCoSUFSzdJgcv3Wt777TwAAl6VGgMUTPGoAwEkETLQoAoSLSS6IAgMAZiiUhiVlPAxpDC/Engzkpm7mYyQVOIQvu6hLCgRJOvVZlIujyRTRHE8JPjMC6fz10h/8yVHysJlZgSwvmiIRFfFXkJj+bNTo2WEBUkHEmKhGsyEfK2AIvvPX7tQ1edYeJGHOkKD+yzMixm5dRMa5RNsBobbo9cNzj0wsxcBkYqQrJaob2XocBL5FRFbz6S9+9nk8jfZ6IxbMI67vksLIVmw1SEKInO4/rPe32bKDszkhPag/2emd13JGLJEiGRCqzOOEHtJC5SJr1Ucpqc3dAb0i137SDyVyY2Pt8IvhJzagdewbi+YdIXr0RL/+NcIgCVQcjA0wwQoa6DpGUL2C9/bBDoXOzSXyURwkbRzGZvJ00mokrrK9L1qtJ+eXX1+YkbNzmRpNTQqBrtyuAqe50CJ1RPcDq90Nzvq97Mb8aaMPUiRdiZuj4fi4qcnBhesrk4KpRcEmU4Yjy3X1juFApsu4gAd5uq0nEzbkQIYeGXACJzG1KQWFlBcPLMfVdAWJVRPJUp4n2UDmVHLWSGUZf0bOojpsFwtpWIcMyfEUj5gifd9GANTs67QQFeL+5qqnQxDmxVhEfRQB6/KseOo5nPbShQ6+OEbvbILrNjWYzWfJzeL6AiPUJ0X07cKgY6ojFfDxYW9EN+vy1MPvxkyYTyD0tTTUkcaW57mBTPFgYGqO65VtjNeMGuSG+43zqU/D4Hycvn0j1s4Snz6xc6XUFclAfrzv235934gXcv2mU7A1a1t7YbjzCbL5Qb3239+1AZMFpgpFERTbeNFY2UgNTRrO4WX7mSLry3zaXgE8BK/mJt1TMVVguBT7xQ9fpGvs8HA4TwPgWaK6WFq5RBFilGfox586pY3twqZ9Ws02noye3m+++w8ukDO+5LGHsPX5o10ew3OE7mfdbktTd8XZuVW6bkSIh16hZCxwR4E5kFgx0gOa5zZjFrydmKvvfxybWF63awFkS4can9zPvX5bIPzmtH9xetE+bqS/KWhsYvfPdmKVrIH4HKIlM8yD52fSiQZfmNu+EN/9N78oVMsgYt7rjYOyyz9wuM3amDS+xybqeyPjYJhaZgupzGQhY0UANtkdH7tvXc/Dpj1+0T+HXFAIly7xPkahqjQZGmysdOvS8szFsQVgeqrb753AtvpkAQ5ZvILIJhoHNLk8lzBbnYE+/GKnf2gK/iHAABiqc68e9+7RaDUsz+PxTkdbQOlYHJR4ABwPd0/wfCWmn4yDoXJm6xU4gp86CDRlFucGGbz7pckt5b71u9sG6hIeMPvF7ufAyeqVud2vXqZ3O8dQKVZjV3i292RgisrqZeKsp5sp5rFqBmaEyx42aqSIyLdmf7ijznlkJoIKKWi3vY+nqrnBuEckPVy+KGyn3piv/4vPnaaykUo8h2BPtTCXijT56bPTQ1djLQD3pZ7YzcTL00u18cHJ5H4DyPMcDnowSBfZ997rlTfjouv1rDCaIQm2SGaqqTH6tcXtjQW4/bj3Lz59pJHK139/vT8IiboSxaHFuyu4g17K16CpCFEDAwEkwKZLQg1M9Awf4uhKvlgBAWoxMZp0tuaWp/qkgBMRhUdUVWGV4732rbeyVZxKohwvOkcD7eWXZ8rgvC2E5yPgzk2fyTPYZ81Cia4g/ksExOEZBFM6IHAJPgcohkwKt6q7MytIVr/77t+9nLoMADgQWbAGWSmiMfF8UfOmQH4OhADYDFSto2CrhUhWbcCpFfnGOEB0nWXDtW1UedpCNFflDZQLlH4UsHF0nreG3Eae/FyXN0eigiPzi0uv/o137/+D/2W2D7z7u+kuJnPhTLDxmGoAeVIH1CKcmfkHMjgBfLuDg41IMdjAs5E51+vDYenWDSYKB4eTlEwIKN078mCELF2tBVUP7nvTc8fpyf4U/dbfuQbc2NR6vdLla7BWN0/6Ox+ebb+ez2+nJ5aThiESJnjba8I6jc0weOYSBJk6KAYetBTahPywe5xPCSAbTbDhoHUec/qxlEfOaf9u9yhYT75TyCVqkv3r1z7+357dGkZWmdTOxA3C1A7rKO1PpvJGvjCaKUUXdcx+4IHAYpot4BkI8kCPA1GA8EIG1Fsvzp9/eHj8oLRRRfLMoGUmrMiIgHgxL7Yli6GpIErKqMCi+WxCLORS6SKI6mGCdSxj5tlIEgsJ/7AzUYeI75nJPMMmknPVNBFjW7sHlmpImp/MMtlYzjMkNwkNFR0aWaDO8i1dxhM0R5i2hTgxQrhT+OwzH4QjxUKCTMweBNPnh4igcBcZ34YAyuMwlnAjF0TQNOoTU6nXa/RCKIOmvrE8e8nbpbw8xAkvT58eaQhmEUFxO/N4dDTreUT+dY30Ibkz7RnDw+YLsY0LLAJhoRC7dyKlFXklm10iMuHYw8YqMAMEzABppLBI8glgPIAoVZk0npwfQxku8F2/VOVcy+42h493qSu/VcnfEbodKTNkUzByyqgnZ6dLfCj4ccHnj348IPN+6GB31tj/7vK7AeD1RhKP0r6jz61wPuRqGCHp+OZrm42nMMrwkCgftcej9gzwsFtbUftoaMh6LMBtx5rJ+Mz2e+91vUizZfM7d2+4G9T7x8c7pw0hC45b+M1vX72ysfj4wcRwnGwhgLZrMMj3HxusI+ji4VI2pZjqLFI6p/LahYrh2DGaIsGg+/mL2vZqkckLSBKyIOlER8Ip7sWSrk0Flk/5OU6yjjWnP42nZ3tpszULEDPo92bX71Re7IwtzGn07BAm3vhrG7bnfvofDqdtf/62UKux572O1BnigoDMMTjBjcM4uY0aZRCHIeMUzpimGhgsEwe2IzwG95+F1RomlGN7Ln0CS1gQeWNxfalA88KwO+03DJPACnx+IHheOC3MGrU0JR8ouUpwONTdoxlsuBffjn35TBOuF2eTInC30rlV2Lkf28qhcUUXGycLmSLCc497OxoR1a5Xn/3Z/tyt6/g8+rTT/fqGwDqlt3/7htJvnj95QrDM27+9uHdmBAAC960QBjYWCv32aAw4N36wnPpu5ccfHvc+77owZ6C6qrmdWHFlJT97OL29VhNS3rBjMflad9rDs4m3sjQmSYVEWTf+/wThBbCtiWEYaP6M5z/MDJf53XcfYzOpJbUstK0Y4sSBmVBtslO7NZPdWZjUppKZCkyc2LHjGCULWt1qqdXcj+G+dxnPPcz8M8N+3ySYx8VK2Va8qXXXqTBstkfp1Y3L1y9/+MXdloN6HF5mPJKLkFvalFvDhzocQG2gD2lk5ay+8OJUFnUZoCLbULfz+Fml88Y///rjP/3k7PmJkVDsYArZg2cC/p27E38h/HS3NFHtd/+CK7WHzu14/tb6y31f7QePx70u/gzK+f1Br+c0MhIghdEZV7IgKqoh+kjTcGxlXDSPh8BrX78EemN2kCZewzJCstLpTYetnR2alPSjk9O8EKgPK1Zy9uWXUiK7PnAbHG//01+fT/56upGkDvaGOIUrQ9HnuGQ/iYX9lggREJI491angM8v4rMW9gZ6FQAAAACOd7UsDVA+r90ZSJ1mjIKHuqZsEQaqur1kpOB2IEMJkaSDTcQuGSXGI0VQcQpSLZdLUxRL0VAYTBFMh5+go6CAg6ameF0iMgKxSvMhA9DX8r/+v/7txuYZC/fcYUIDPT21oslY9oL7uKHZzfE2JSzeLqzQ/OAnz4YfEZmb08E0wvHQ+ZnUk4dd1I8SmtciYYiTRJpJkEwg7jfDZIJSZV5VCA8WDg/9LhChc5lV10pPHPm/OpU2pYoK2r2eqGh8hvFzyvisxTWtUSbjAwz4s3GlPRlEIn4dgjwhmz0WMgteutc5/bCGkLHwq9Fup1k+0EEokZleJBLz71Y+JI3GzYXEhcWlgdBwu2hwmu5JXDiHtEV52BpgqpNMURUFaSu6x0t0+gYI8/EECAGjUf+UoPX0xcDhDx45tDV7YVHELQwPhF1o2k8qsthXRb8lYDABojYMMCTlXj+/oHAOGfJ6o65RT+zzhCecZ8JW72ycQIKRuJvX5LHaJfg4bRsYAZmkPh63WJ7WoLABg7COu1iDjFCiMapULRetmgjk8XgQAhHGFB3I12sNGIb40WlXgzEVtmhntMN1dccfDsXocTuIWQNxBImEO+JfzSd3Hhx9LEhrQLIMMoHx3u3IFfzx1ueb2+UUmXjjlUs3NoQnj/7r48895bov7Y+6PNMmbSXDS3kfZvIeKqC4GRKZP+fLeigm6TV6lZIiiGDc7in0/Ez0FJ5oEOB43bY1KcoaZrDqadXH0MjUBeatt2eFXuWPPuaftiltnCNtFHCexJlAgAm681aLnX1lKew2A2LIqO394r/sXPrGZRDI1o4edVILEiSNi82Z1dB/e1S7EvcVh/wh708HAw8+bw3lgSdIek0ovjR70uPqRyV/FN0fDQKw5MTnIYeq71bp9aRpQXcecRSsj5t8BFboYyXtwnSKlr10ZgE1u707Dfudq3AL0erk2W+vvLA9ocafldX1PLRwzid8AYGSILPBtnzxfEZ+/Vx/mPzZnz9//TvLVxEuFTe7Bk1HFxdN4mjnBPYmACJQ29pbe+H6s00JzXkPN0txn4P4p08qKp5xi6YgjRgVRL6ogalRCcmB07dn9/YrApS4kpm/+96dLY63grgd08KqRSu02nBCHivAS4OJYJCYGAJmVnPPhiMvKQMxx69rU4m8MKFzbaE7tw7G0MaHbIsXMNqlTwA6qLX9qTwWcNcstOObQFa3NSzb7IVVOqZ57251MTLcOT61GT2CZT9//xcvtiv1hVSPsNBom74Y3d5RW4NaggGaVajLWPefHMeIoEm2d0+e1cftsTe1NB97ezE+sqBf/uCTeqkHD3vzN4OWgKOMBn33fOP+E/3Pu+dXAxduzQ18nQc/OZ16JYm7lAWGXqM9Hzzt4liOogMgX5lNGsq55INmc3KkYl4XFiBcQjB6zHsNRsMTzZK7SSNhQnn/j49fRv03FqeO9os9zNKFkQCrAu0nesPjp6WNldl4YrYyYlXMPj7taA0plgi5EXWyECbpwPGTYyoe8DVBlXWOth6DUnRGm85RFnt0/InRfvywnsKT64tXnXZF/MEjy5ZrJwJIS9OLzOZOCQiPcMpLrc3FVUGZqH6vux0Hud7k5mK6/lE5dHVjoA24zefCxnL0wqr8QbswcyGcjS8WFpfefXZGNyg8ff7rr599+fSnW/XFVxavnb+kb378eQ+qnFafD92LJLIlDEO4fLnRvyUQf6wYdtFMDqDJonRz7sUEdBGAQBZgedUTtYbhlRDX645qVq3HMz5i3O8XRQNkjAzu7qLi4yIQD7bwqZymD0OaZHEA7qYoxgtWOttlyQXRHp9+dNQYW3IqBB6gchyKy7bTwz2BIXdyyCvsUcOjpyNwfzXgn/MGaJw4a8QuZ+AJUEdBNEjo4sjN6ePbITocypz2UVHqjCZo2DX2ON6N2TV8Xt57Lm4EF85nu+3JfEfz1WWAUyOMAchWgxOpSCrhIVSL5R6oVU4+POrZhB5eCsXWLzCd072micChygmQjZnMoJu8RNGYVFZ4VZOmIbAQy2wdTVwQBSAM/8U+gCHsoEUEHIleJGnkkiX5V/MGij394tDe3HwltvAiTex2xPE+snANPEslzvaa+Vm3E6Edix94XHUDFt0UGghBDQMIokg4zgDC5Gys4WAqEvWv5t/7KVC4vdROUypvppJRuG8/q1TL8pBZ98ZqJm77ajK/UcipjtkcGJgr5EuiXHfUPe3zKBEEQQUAaS9SPi2LDUJ1EQE6hiI4CcKAz6WrQ4UCylrP3Y+gAMu4PV4IEDuCrcsAbo1GepubxDIpRNBtoVRR5L7ON120eyVnsqZmZeTKTqdria5XvCwj6w3Rk/H74SE8bM35817vfD9i13fME98A6+GTfmtn8IPVa7m5f3p5qLnadsFXVtfFdTHuoiYYbQEOJ4oYa4BkoZANB0edow7QrK7gRCEVk7T+Vn34VGyCqjyb9WmGt9XpqhjmXcTlnon2qespZojC8BQcMQiNA/Z/sF2EnZg/lFmhPW0A6tVVVHFqXXTA5EPh8VBqPTs7g42Al5+02cJ89O0XbgKObOMph6RdEHGg1tMjbck2PB11xRqPS8bTo6aFyN/7exe2H1R7Tzi713n6RTXsUwiKFk1u/8EpRQ0imXnQ0RqnLUAW/EJ/jLri8+iATxzX7MWsC3IZXauz2xhPF4jBQP70s30RQMaGsFk7s+Ta1sMKRNjnpkMJA49brlc33Hf+6uTxvbMQtWhI9NT8ije3bOPJmpLPRmxK8m4941ple/Z2FBDgloTdLPjvfdI1FDkU8VkmsJq2AR/SbBm1CRyO+kg/HckE+/VOIdWjPMRjC/H4AnxztH+mzG9QiZx6vN8KK7yCUhCg9Ajc5/YOalLh1VwL9pypWrvbmw6DA6WbN3yLJNTqJYpnpbHqG/akwFlzZZmS2vxpuYiDcRiYsuJUGw2iMiWpfCxKeBkS6Xd/9AGn+qnpZCq1tHzrn7/WQMZ3PvqT1XwCg3WWO8ZtsmtS/2GTvS1ABXjcO91fXSwMd/vZeRIc88aoPLcqfHEqPB/i5PlUWeanb+Xe/tvzX37Q2N07k4pwIenymN7rAXp4Mnlwd2B19KkIubSSuJjzffihlnk58Yf/y93uAPGcNRYvLJwC/B9+9JPp9Yu52cVfPduPklCpzC2miIFAS33TbZ4ZM9iRFz7Ldn1Yr0euBnCVnCPjtmC3QafSz18iR5sN/aQuktg+qlUU+u/8gxuffrKnmEri4qp51qj9cP/v/9YrxdaoLeKxpUTc7a8fFWUM2Bs3lmIxlwe+660L6UoOc/U6bsXJu4BRyJW4tMB2WRONhJ71tMwGszAX6lWhWlengujSFOFW1eZen0+QI5O/ugDvbZ+onKM6E73xtNeUX70ZeJ8r5XMecRFkYq6tjw4JXy7wt5Py+18cGYqhzDVscJhgYmgkJI3YL7o+p7dxuTCp7z9KI64qFGgHUMMJ80SikHFsAJTrOK0lcMcG1PKoCvZZcWAjAaM6kobbZV+KCuawL/bajNWJJKKXMoTF1Q1eQL0uhxd0WGFsGzapMBOHvJGhXQVxTYDkqjSgk05voLvCPn0C2Gla5bW7X3KMSy6Hurx5OuIILhGFB1SfhgGxj1Ju2CYAFNUcN1GBQv4wfmvBaA8Vn1Pvq+WWKCCn6BnEH47troGLiJF0Y4oBanIk6AUh+0m7guZT7lyo8qhLUajSETScdxgnmqCo/ujJTiOSdUsaFSYkFFUwTZE7/YwR1Q+4CEPpNGqz7mE5GTbMXp9FbHa7IQWWF1b/Vujg4/q990+XU9HKLtJrDeamFnMaaaMrTkU9DMHjai82iwxa9lIE+axjzC4iXjeijFVLMW/NMs+ej0RN9vujEAAGURAFXZbuikUcEAA/+3SyWZReu3lOGpM4DLWHFa6CB5OYedF3wvf4rdr55aWsn0RJn82PjQG7cTNWbAykGg8Z3Pp8GPEEHJk9OThBxNZpSUsu5bxq1GJV3m3pFipL9PXVqyABUxDZ5zUCGYgiyHtUwA3bmjNq94bymPGRSMTn7u+2JYAn+l6WNVpLMQsHYwExnYGjMOjuhc0Bonb69PlsNEk2u6Y6aO5LIrGCSk8IQh714oXpWSabmJNjgVZDh11JuDmNDjaPD7n4dM4DMuhAtRlO9oBRHbS59ln1VAdCHoICIUPvDaykS42pXiealkZNm6TYQdU0Il7K+pQdWx6fJ0rxit89lNbx/hdjvx0ocJx7LiPffTIJel76OlIb2tbDYmsQK4yaR20z7Zhyq74xC7//ZS3pdnuSEV/icrWPBhL+/nGHyXL+iKICbcwbqpbOIpeynb98InVZI0f81x+VkP1DTYZbiuCP2romJvJRUVJ1L31c1xPqiVNuFUcExODnl0RfMD+xUQgxXZisR6np6YAwYGW2W9rRMMZjohBWMte8uAUa0+dnsVdZvBCmT47G3NjhucrAE4/jVT3HBaJxXvUyLnfQ4gArl7SNk0m/oxgCeP23r0AR4/4P9iIhz4Mf3S+kk21Dm47Bn2/Z/gg4xXhPtw7oi/OhAm45KtLhoCS2N1TFPy2+/MJiqrD24PH9o155Xom/+8NKhLbl2TgvAl5IEQb93WpfcRDQDFUIfbp+djkM4/e3/p00ukAV4qEufmkh7w11/+JLwaEOdk+hfaU3EgcauDGpT1+NnZYQOBGizwTUCoyaOscNeQUPTntnlpnkyy/FppIN2NNufvYO4ErqllTHV0r8h4aynMDOra3NHby7+3AcA6RENox7mXtfHjVO29evhESBSUGkwkL2wI1OhJ3iPi9HLY+fHcoX571X3wh8+WXvxAcQ+UA2BBw8ajxscIup/NbnlZ4I8hHqyaPTWIY2rqZQCIOwKSOhKgdnWiq0tDFz/7NfXXhxrjpRHFBRcNSI5m9NQecxqrMjg4rjzHF3nzUjmYiflBAYuby4bgKDolF7659fR5Vwv9cAXXixWmv/4hifjtZBcgXBkMXsrm4dnEnxkVydMvdBcypA1LcnsC/bBWRItcE+5hlFhrSF0R446J6ejtt1bqznvcIh+1n3pUsvZRaT/b/5sTGMQWFEvJh7eG83AKOpcKx8Il++kXn+qCd+wYUuBOZA3+mXx66c+35tEOx0z846BEJVD7tGp//RF/demiKYvW4QaDbyCW4s29Ux7uECbjSDqyVWG905GSFCqksWuvqHf/I09T+9863f+B4AuEyn+FHLTE9b/Ek5gbbUPYxBJYG12We9nVNxKe4OOPzTf//sbNw3nMj3fz1jgCRujCaTkToO4rpkr3o5UMOaXGHRNDRRbbdcWhtEyU61S5MGbKL5vmsKEIjUhWuXU5/+ya8MqmfxbCHo9o+HHQOJGLrStbygYVMwI9uwDWGdCdFh+sWDPUNbPo9zOBS0VIAEXI4MW/zm3U8DtxYyOIWSod6T/aZJjEEktDbrDZHpAAPCYA5wHdRGCC0lomm/KSO7alYN5S4H6kbHaZPA2E+zVuVXnyXj6oBQ3Tm1igNCb4Bk4kets7zlcxMp9ugZbrKmE47MTx+Wh5PPth7s4QHGgBLT6M4+b4ILN642PQIfXQZ+KCVSZNtxwtV2HOEwZl7rq3ZfKFyfRkyfND5yDDh+MXz0+CRhyUAmE1ucG+79je3CDYMkdNGs94IiJT7n+hBOu3E/NZ0oV3zjcV/XW+OBP79qgo3yRycRt4dw4LFqY4Yx0BAEg1BL3N7efPzZXW8y/cqvn4dw/0AQRh1rPRiE3J5nh5+/sz7v9bjGmt6Z9IJIAA5hqgHUGpV+feQRbc4CIgKHHB2WekOdA7wJMhywqTzrCRTsLU+obXBER/H7g7vPKw3OyMSl6zdmwVmxdjoihpwccnmnZ8owHkhkYzMFcoiNH465kTT2xBuVUZdwR+bTIceET2tCR0RJZ6jZ9SGXJmKyztmO60wQYlOeUAQ4GbPPziq0yrVta9xUBi0zOY02y5rXhWZXcJPgLQitAerm5yM4GvrqazOrUFJsT0r1cI3r3nvWun2FbkyBnvVg14nAZW9wcapeEz48m6S+OYVOgHj+Mg0sdBqAPB7OhCctdoSZyp0mvLEYvKc0MiEAvhx61b+sUvKHPzwVEMiXZAYGy3iofpka9CzCgCiHIV3wTku7lGHyEVer13Tb1sI8cSDgfV6FAy7eBColSRHVjRnXqOoEIRjCSBaR3nhrbmu3Odyp0Y4n6BQOS63U8qo/Fyz1u/51ihwhhycTRxXKTeFtzU1YvVLdIlBYDJLBuBtVpQ/+3VYhJS9fy9yZCC9cDm5XuNpz83/8yvSjR/xwq4WY5noQUDQJhGFF6ELWMAGKfV8gklnoFxVxrzIVVortkQchXQ7yfEtTOCgUNzNBkjXMSMazZXQHNkKm5WSM7hbjr7kzRlM8290Kkr5wdGn561dHdUGTz/O7d5EAkPFnZ+aDbgzoknbZ7HpIO0CgRhn0Up6xnkldX4JupsSN82O5NB48k6psyE1/GRAJjehH3V6df/J09NWvvPRYrKlutwUBUNJEvqwMSrVECGNRv+LGdFWcnooDZBftTt5cub7bkl9bSvdTkVFTfLqFnhx3M3H76U4D8xAE4PWG8OP3thTFe/47Nz/+m63xGJjZKABMrt/SYxMGReiIX6txjWQ6yzuuUDZf+kkJ80jIohm8ENeU4lq6m92RZAHoPhcpFxkkvShIEegItg0EYeYubtRYyRizSX+A5Oz7Hz8laIHzg0jv8Wc/Kr307YXs+QissFMiHXS5flZUr3/rmmQ+6km94WnkwoIbkzJUuIkQKkVKCO4RZYn1S0w4RDmLBqTPTGtbHz9iFSSQhvcmwJzsf3xqE6D0zv/t4pM/2Jd7YjTvVQyfOOXs1avV8vilr001n23bJ1Z6NQ1mfMncUoWuV8Zdse4zm81WVbR5Jbe+yIUmpxAnjvg9Tvb7ENzLtIvChy0zoxnJ77zxe//49yxg8PHHP7x0Zfr81Fy55QTjsD2ILiW0g44OhXm9B15fDMnq5E//4MFer3FuNrVyc70wm3pQ6nEGSzvAGs3ykmo3dcsl14xJ2uMyJl1DnkCgMGmdYmgUYy2cAkNk2Ov3+13u7pB35YDtzUqLl6dAH2j52IkIeyEUA/o+zMsOo3BC4mTTp2tSu61x8QDVY+iqruk2AHJs/UN4FQi/evtldtntn1pkFVujfeGEO5an642W+nRSPLjLK8b1a5ESL2pulHXs8XCwxsfp5LQvHh4ohLtjM7I4DePPu4I3gmETtDkZ8W5PAvE7FTwb9is8CO71rSbRNxXj3rOOpeUKUQ+C3n9aynz/IgYFXHfAFQk6uSOx00j3b551z9Srr87OZFc//9kTUgbCFioKVn9CjVoAkBNgN6pzusmzWS/WqI0sP550R4KzUakvh9Oh3MIS4g5Lwx6poLNZnxSB63iNmWCQGEleClh+EgXMR//9js6pGy99nddBZTxWJZ4kLMzCPCQwl03PfPtti0nOvXxB7nAnu7vyZCR00EwBmptjaMQKB4kxx1EeWzOGNB5N24yHzn7a5hTTljHTghlEEcee+RARdOVoj6+iegXTzaUj096/+MloYTWFJ4LugfTiDC3PJERbysxOFQL+E0GrCI2g3h8ICWLGPXm3mxXQKNv98kpyJtJRcFkJQuYHjZPDZsJveq7kONhmUNnRSWNs7ZoeOhSKocAQ8/aLR9rzkeWBnMJ0jJP7/ZaC2mPU8osiEkn0IdAtDJsuOOqiboEBCPUd/+KM69690xBuFEjsShR1t3YaR8kjQ5ztC2Bmfoi070mm3xUNIoteX7MiLuKzigEYsK4BCgMRMQj3xyKTe+Ne+VhGoZND3u1gW0+6qnkWUzlyPa+YkvKsU3p2eu7W+Y/HQn3z5DJmL8f8PVV4/+7+BNBfi4SD0zCfmBy8a6zMFphXks5EjbDO1vEJ6VUCUfekbeK4okiayZszPqCvi+abi7NrF9bzrzYe787Mxgfvnd378eF8PHrWs9byTErFlmYpCg6grBqKhry0QwJsozHRFGvq7ZVHm90+aA9xXKKocqX1iw5meqHd+82/9//85uhoXC33+4AGZ6No9YB92o3+3tvN6XOtwwdB4Ixaxmqm8dZl76Q4EdpdAgH5CTPk7dx5RroZ8qi72ZExy509e+yY94Hv/dp8bsX1QHOLgPw/P9wiQx5i5Ngx/vpvv7yQIk7fnzz8qGJM6je+Ft1DeJjWsFHQiwOhVIKeo50LkaRL3/vRl952S88z03rEBk49n5zUYgl1nKAnzPNHn2Bf1l/93ZfHCWtyb49tsuWToQMAph9+eHr2gqmR0UCaCR0G1EBydiUQdH/RCkGT8UidUPz4CFwvJKJxU+Db2WxUFhxFU+auXM2eW2tnkFq9cvt3Xr94MSoA7qhqPuUMyhGe0GZoITJQiLWZGbrKR7CQKxAJhMljnmwccJO9Q+mhFp3x8RiFOEmKb41ShEdPzk7PHNcrnFuTOdHttVKLROf+dkvRBrDGnRoAMiaY2J0fHry3U12eDwEg9KeVdhEAok92bV0IX/XXDvTjp4LnRnA5nO2yQxEBKEEJwiJgiLKhW1CvH0r0n/UaKHHpG9dcQ3TyF3dHzerit29q7935/MfNiJusvrcz/I0Y2Wy6FIxDM0DAMB/XGpwyv+RqNppTphO6lFEN4EzVWYqfuTh/yNW2PjteEYeBf3G70zfBD3XrUXFvZjY6g6t+ugxLn/z1ozdX+Jb0d7sHz6bykKCYyIPqVN6j9Sf9ImpAsC0TvCK1jqGezT9T+j8+e756Yfrrv/+V/NJN9WgnlE9pjmb1pVJJ8ye8YZAOtYZ9iJIQIJciDgdSsVj1pnKqZaiUKm3tMH4fHYvsPnpyd/M5RYgB0kQAZ0BTHdEIoTgP2EpHiU4QOO3zks4dbKiYxGXaWPzGShGxKpsDjCFVSm4+P3GOhPsvb/yf//w77KcN/Wnp01IPOtlD+hHlAaLg5mphMXRr/bhZ/tWw3ah0XJAnsRz3UV4D953SYf5wXKbVQZ27PRctV+tDU+Fg98jj0mNufaDLbvx0k7wFY+ZQ6LaK4ZsLqZVv8ZVtfHKiyooe8M78D99sHD6xPu2RZKSPMMrR/qANLhH0VlGonZ0RqW9Hz1Uhk9kCPAYCeHOY3NNVtpa6MDd53rYOWsDlxWQh064dJ5jIBIE1Uw4m/SEqQFWMk9PO3FoCnOLDClrsjKoTNy4DsAekMMCudbkB71/MP7PVQqPjWLLooxZ9fr4rht2BVCyrAbHgzKwKe2VHj/vjknrWbRXjbs9iMMk2+toULMOSR1BV0O3YRNJvggB++eIFVuH7kBGKpZDC+hIwG94/HQ+qvfrzdoi0jNFwOhObWjy/+Gs+PeYkv1FwIcLDh7vd7Yk1402kQ7Vfblc6BODCbn3lUiiQMDVfdNHSdXqZHgPFofvsyVTcg532br1+zn0u8XBcvrfZjYiQOTKFiU55nbg6YjRNOS2flWrn/IX1F+cJ3aydbhUM2o64Bu1GFzF0tdt/pi36UHjJf9rWQzbKd43uUFJCwIXXI+Om3rmjV87F2z30arG80bE6wxrbxTpEG8ylUdz9i0+fnt+4lkymSQhAIoNCjCi2nLNtjA5AG4Wpp+8/ffPt7OOjdvl4VBWVQNqHwzCDOJXdUXG/j2kwA4JEf/Ds8zvPAGAqGgnlsGDGiqlA7DvZ3sthi49OX8NyTEDnWVJw0jEveDt3r7LVaA0RUYvptCOiv7q7m19yB/7xS+jMtARwnVr9s1/94kSYUoeDvGXUeyMwxFy6Gn76Z/vlR+PTj76Yy5KRArF7v+3oo7Hlv/G95e6IvLPd/J1vzTQ3KyfHrXTQF/fy5y+ma8fPtH7j6HFT6RhXriRhDCz1PMlz6x5VJZTGlVlrUNNjqN87PX3KcgpiTn87qpwMrrrmnDb6hN87qXX9uFesjrJJZ5kD8LcAJ97f7piPxoHUAkMetiRaamJICfVOpq+26kCw8YVPo0KLBpGUCjTiAGg65eK72AGPCiAK9NulLSc4QeS5JQvqV72a0JaGCd/AdoJeKgSYr3wz8eVH26d/LLq9PlUum6DdH2ormSmWAuBQtjruJcD6HUkCE1jFaahrGSiIdnDbG/ZlDNfKAmGrTigdDMfDS8nE5rOK3/ZOn8sEMvCjzd0xDy2vxSsExo96TgxrPHge9fqjhQAsWAlMZ0LUuG84cY+TR6rhqW7POk8G/vu/t8AhMDMR1haQWoufOR8003AENcaV8trNRBWJB6UjpwebEzpy9YLWa37yYe3bX7+kHPMAOmyVu/aEkCRtz+12z8SiFbHT6NkyZ3y585XrSy2G7O7v2W4qlqb7ph1ymf4QFI1e7cLkVmJT5K2j2iSRUnuO2NXB+Y3FomDiDdHlsXYeH7z1tSXETiD6SIH0SXd86/Xls4pr8+G+3yu5buS/kGsCWHW9e6IIzvLXFx2v3b9vQYxSbLQPCsM3mwev4LMPIm4oNZvDUgukm2P19bnws3OyLPTgwXZcJQiSQGSyRnFmj+/XJhThHgHAcCyV+oN2mb06Q7z78E44RL62PJt10872R+JJ2bKHkqO6TGnYFiHYRXkgvqaBKG7BgTImjfqCWJoQSOCoz5vcGdAwEi698uSppMn9QbfdblqSFJ1GDFA4bjWdEAO7PZqGGajlpxjFBvM2dCKzByg6SKn7Pc0DAVR9YKUj6Yu38a8NT1zY/WJpVGxrbhoBeNXBUEwYE77UQsC7FBmdmeyujHVKcZ2KLQTlDHbcgYeW6NQfBU2Ks0fBDbKTACNXrl2fS375X98jHyvJVLzaEFkPc7QHuizWEYxEls450rBeQcwEiDBPPmsMidqC4uvdH2ANe6sAJHPRW1+5+PGd42x+cY1l0SO6BX/ae1SduViYnib++knllRfjCAw82jE2IPcJwHsx0QWaO/uDhXNTIACCQMwKWAdbh/JQWswQ+QvJcVbf1g6n7VCYgBBakRDYHfZAulMbGK4C1cn0WBR1+hYgKGtLs6F03jyssCMq6PKoiCYLBu5RL88GBbvxuDumg0jHUnWPXWW5PB0NTijSpjS3PTLHkm6MOciH+C6eX1NwDcFdCC3zx2jUH5e1ckd0Qxaqi0Sv8qByfSPeRCfh3eEOoiGd0fCs6hzWeg3PXjY8LHagoRj8Fzeno+LdXz5bB4KTXV7zdw+Oe+dimuWyciZrvxHaw7qnf9Xwo1IAgCW2xo5pbzY5F436Jb02tq0JNx2ZWv3u6yc1p/Lxpw6Chq8v0yTiGcYHp/cEnTLGECfKpcjQ67g8OJRNzbweM4d0+wDuAVQs9TyyEuPvrpsIJ7boKTjln0AoifgKybnj+yeedPTr3/77JAgUGzv9NqcKkMEpkReDpYYDlorQSi7MSI3Sce72hfMxle+0i3Wc2z+xey06zIBh5Ee/eiBVj1AAQAHA42cNHpxKb7zwWwssO4h4Ziaft8LzM4kw3pQnmuHu8ACSz3v1EXf66cDUnXAmfSNSAU3Eh+IHx0JX9FsnH416+UvNzRBw/TdSviexRB+OsdTWX5R5xZNZT8Rz50hYrXy856FDQx1azNGbxTHi0BvZ/Gwy/rNHR5EXZ3I6UBI90JEsylh9q1LqAhcL4dkbkedfFnu8mIBpmQNfJBP7tcGvHlgMLu9JBwvfuBWL5HO9xng11yu559xyZmDV6z04KvYC8hEK/B4Dpj3O04m0xaZ+3hvFa/xqIV9tK4yU/f7vvvrRA9b59HTvPutkYrk3XcViQ3WRjBhknCk0TqEFDKSL/N4gjYldeq7eKqrSZIYBtlF3JlmIlGXwrN+LeAwYn70wdXrYdg87I1KLevHkejZCMT4FXyQ9QsiVyToI4Ax0HmwYnUoRUMZKLHPJr8oTKRx27ff7WscHyG4INW6trYMQ+PzB461fjSHb8UQg3uNOSBJtSpPTelvWvC41ZKSyEQ8tMqO52EGxhUTtuYQzLlnn43lbkSNzXul4WCyZ0+CAeZEKL0bQRst4evhFKJTfqnj7XrU1GBscCLi4qVX6C2A9XWAT/gdi00+6Z2j33/zxr2Zf8Nb2drLRV70o4mp019668MF/v196JExIlVmJcyed/s7EAQKIOJGW3dkUlYBTrfrmXCLvyvg6k1L1v3zuvLOaPuft/Lc9QHJNbVB71ZL94fFLK/TxT/ffWQ+cXDr/+dO+GcZ6e3tXIzSxI62+vib88jOOE6ET9ixqLGSzPk8oEqHPLQc/+FHd6pWQ3Cn5wisCDFc/LYMDewLaEQzIXYkRTXX/Lx+lljf+6x98emFKEPJZnIxaCRTw0OohH8ohmSjlaygFQljOR99aWKBg99HeyagLrywmG1LPRSKt4pkH0E76HjsXtdi2D/EFIeQuUD+c9IbNQb2nvXV5/nf+4JPlSKLnGMuvTdszqfYhgYZoVhJN2y3CBJgOwCM1BpAThO3aUlSgPFywx8RkvsemCKYOUTZmW+rhSXvBwm5/deb8hbXPzoZep/ngkmcOkVOJdDtD4eeTSSTMnfDHVRrlxrQ2XD6fgjX4YQA0LBg4rtkDXkjE0gyZCPtgGjOHulBVBTsF6iNAIEKExsxoi4lY777a+Ph55O+eZ29feLjZSPa1w4cNl+VYCr+wRkMTPrKSJY2xMWq8+L0Xjp5+XvnpAzTPJl2xirL3xtKlGKvrE0LerFlWr0oVoLLodY/LxoAKwKOg34KQfCgWA4DnFSCTcAVduUR+kPMhQ8lRey2XamSXE/rTmkLSmcR8eaDaqugz2BgpLV+7zCPD/fIoDSEK5vOBJAX5Cwlob7cks1rajzoC2z8ee1Fr2Odtx0RQmNU1tsvRFmjVdLE6MWlfdzLuhRUIQiiWzc0WGlKJl8woGUJqjsK3D2gNgUMQZPE6K9GmVhRqm6EIjAcsDM2C2getvfwMkZ2ZHp0O3bgZzxBlGLswHWvWezGvlbBks1c95ER3BD5RdaEHt2ZgvdM71VnJReMUIWxKnsIUHnLyy/bhl09xdizxRHhuMXs5/aS4+8m//ixfoC68fNk7N69aaAislx8eNErDmXkvI1PhY31qifDjk9iwoTbtXmJStdiF+Or8zdVjxEFb8p0q+/K1SLbOwjbVPpKRUHtwKH/nm9ddINhlD3BYu3YpXW90KM3efVZZyPgqgBA7l/+k0fPkA4vn3O09c9SAgwiKrOTCGU+X6zR6k7tCdWHK+5X1cHYxOxIl7fle9A3KvEy1dh0KNqamQ5WTpwc1jLwMATphtHYj5LQv5Bm9MGcpPFEDuJ2mqsv9gZtleyRyir1BZtbxQSxqFvUGOGADwHVmZq7vBi/kRhbNqj5EtEIC9/nHh+/8xq0mnKxLzkCUv/rqwsPH3U8/xiwnk2Ko8kE7y9iBnrMRyEMkMx0wZs4HD0v9zb1WctFtW8xYjI91ENEZFCMowz07mLi/fHL/tEvZZLYwfbTT6xNGLJ8I8WDaDY/LrDoAqhh+pKh7KRIa2bNb3e4eEFpaWY9e3qYCn/0UNfonCdrlBOnEMolabWA4DuAxQwljUxkTtmFtKHOw2+WBvDZZf44bRBRXpM3xgsfrj9FwCjYzkTLbFFtADKGtuZAiW4auk6mlmIdEQWRtKlasnfBRc8uQEiASc4D0jfhJGdV8NDHRW8NhhMaPAY7HxrZE2sXebqM94cHQjGckiJArsnEl9FjTUMyl/GqUjoCVurhCA34qAGMBhQ1YskzSUhLHOMuRwrgxcELhUZuY/f4/+3u7D3ZtRQPmJddV3y8fbalPSxEJcCf0ARoa8brIkfFMBF5GDov72qfHr759WThuvpL2gywWXIv0/uFr53735sP/10fYYCA2G6eHE+ASq65jx+WSetYCiz4f44WRDODwk8mAGph2R3z9qlHzXvI7cO4K0/qgfuX1l/Q0UdocXnp1EYJMX9j8zatvq7iX3etQMfgLD9LbHrH3Bm/9+lzzK3Hdsvb7vbkDgEOome8t7P68JUjc1ql08XzW2ljYiIXOX2hrqHmwfYL+aCcYjN944RwaFSFzaF+CFcsvSWLzV0XGFZx6MzLh5UeH5fUZlm/zJn1CJWPtISxqhmXUj85EwJJ3tzqGXUnnz/nc2vPyCA7A1cP2Wa21ei6MRdBq/zno9FVvSgD1ym79Ujr+18O7J1DnGpKcI4GbNxl3NCmI1DyOPOlux20kFo5omk0bIMFqMEcE7CAAAqaOSyzGQyalYuYQCUZQLx5qSTUrA5It9vD+o+3nz7/5978mwIxy3SM3u8iIL3606fLSUDKRCMOoaGIBjc5grX4sdS1jB7R5Fc6B2Wd7/SN24sIxJc3k475nJ9VkZhZCcBojiK+/M3srot4QCambGk3/7m9qn//vQo5LjO/2u/unISKdIuIABSSyi74potKzAk3rbG9M4VrAFDoH/elQYOkrcxEtuSPiJ0G3HcxneG0gaykSWvHApxTJSkiz0jB1A/S44SEUJIIYCNQbgDdMI7hMI0ScDtSwkZaA2xMjOABgX9AdVouNJyn/VBz3tjt8NOyNTyWHFWdled6uc6iqDE2DVAYer29pmlId1ZDaR2cd0YAPJkIyk8Djc6ibPxpVYdhkXLOKhpfqh0GaikX84EDRVDATTw4rZZUCYC8qaSOk8enRmdu5tQ7tAP4F/yDAeOUGBxUIX4TuI2F9ctTpWOSEBSNBQdT6LUUYKM/aZzdfXHKqNf8jRXQnat0D1KsmvNxJlwyo8BVsxjrjwxIUyPlLDsrYk0iQ0kWgYTtPf9wOCTIyNz837VHi/v1iXfrs0A075791O7BYgMagb2zdGSgXXr9V/IM//tGfVTZu5Vamvd5Jt0b6q8My5IoUkNbSVMoXofmfV+bWs5dS6FrMFn/6bq0DDtGpRa+bA2MzUHItugEAlKxjk74hIBZguzCYiofaLrs5SLnyflT6QWV5NlPeHEhlIZykpb6iuLDyma4a2vHWaYoO+hAQCOdJb2TKpbGX0Xxy7dmHfdepE7kFUbfmQVgKdNv6EaGx/UpbwflHVjwUTwVjYBR2Rt3qJATC1kSOXF2G3bBjP4uNhciBnCYL+p3DcGixXrY7SiE6dHdVG38Flodtbvfwxd9a2hbtTCiJTZqKHzpVFP8qzr339Ju/8xZTfqqxo9nCxWc//WhkYXJXRWkPAwLtlvH6rXxdsexy/9xri0JvjIdBIhnw1Q5F3hQ/rXmqKpoFG/1hSFf3fZDPhmRdG/MyPLFNAfCeT3y8KZir0xe9LqdIQnV48Ivd7gXi2tW3K39ZLkw7H/37jyxqtGrMcV+ceAYIEp/SY+HOkI54e1LKQUU4TbnNUiOlQnEYKJ9wHgqqcgikNUyfC4ZEQJTpTKjxnC+rSiLrRiJpr+NGeSxGIyLmPI4AMxSznmTg/WGpO7RVFIoGJwBfoJAWb+0aqAyPX0zGJiOMubiqJq1Yf8LpcskVmaKsn93ZhVXy2sycrGiNI0kNJ3tNdmfc/0o2bcsilw8AmgzpeGSs4QNJR1zFJ5wdQRxwEsswuNtJKGD/0379o7orAaZz5BGuws0JDHhublD7UQIcjtaKRv5bF4euwPZfPrr2Wm6Ed3/+4JPZN2/w9Wb0XLC908FD/ujLvsZ93c5NTQjh+huZO9tVmK/UBW2J8YSnYSnl+BzlyQe75aHEnV9Zy4RYyIyM2NYxFZd7vEIGL07f3Wq7pD12rA/78nHKnbu0nvjRoTuiAmazsD7VWlqi3t0a/tk+bEpP56eWZ/LIYHD/p/c50L4KoH903Lr8SlRcCK9dsBqHI3cmtYoFW7pTrvWZPhJcjtmFQeu8+X/8x4/Pv5QPrxAL1wLdwQTGUHE8qoi6YwdbtSGlcdmQbYm8AepGaSSI1NjrmuZBlXJ39/dZDRVkOCLAjfv3QqFs09LgRnnGlzm7/+jhiSyCgAQOf/cPb997WL2QtB789f5w+0Rl2TJI31ylcDfTH7enYxFeYvmx4vb4mLELQHXOYnmXK5xaDbiDybG/VOMJcRi9cluRjhq2tXNW04PQ61LO/enn1YFVbQKRMFBayhqc6HRDiDohXJAji9KjJoiBlEAQ1uHBSV+0JBiy61UBbpc8WrjuyMDgk64rHIyDA8FZOnJsT5B9fDzZRhLffHvj69PP/upfezQcL9hHKU8UcFIb68HaCT02adzGlshgwHWk1fy3vIPztKZ1gEZ6aSUFgHBogZw6FetkWOfs+gMpFaLscDQ7QwA8OdGQsW2IGhAwgPAtQHHMtjMiopGFW5G994rETGyVFqQJFI3OPSk/qlY6BmLFv75xVh8yisUXK+P2pLCSqfOnuG1q7b12yZ2azsuc6XWbqjnOpdyW4J5Egg4A8S02D9hhFrBgDFakvc+e9RA+v5CSNSjk9qKkwTpiHxKknhKhEqlICNED3qDb4gmMNjRe4l1uc68nLscpsWYZXX6kARotOCGYVxgPAybCBNtrXiv40tcTFVNmIihNyhVewNz6aEd1hxKoEd17rKTmZ8FZHE9K+cNi/2BgaUA8msiGvcR8zEVaCuKpFnW42jePWl4K9d6e8YDa8H4pCuYJSiIEaQxynpR3CTJTQYxkLQXqs5JsEjgRQSY6HVdIptENW62Tx0XQCvhIeH5jsRWa0pEC3eyOzuxXv34rVEjoAACa7nTAJdrw2REMFvypKbh9KJ7P+iq7JUMBfAGv14+V20ooxvR4DURgAAN7Q/2kB3/j9ZxZH9mIChtK6EpcmwTadQBRANwkueeN7a6IhVUqQUoNLH2dBvaax/dKyYmu1XFWtWenfLgfi6j+YV8WWHcgE1AgjSYm0khIE/leLjws+4eq6isdPbgvurM3XvpHU5A0SGVyrsUV80Ht+SY3HbSW4tqIP5wO6p9hVW+wff8D2R2kQoRpmebNBTp9KdluaArGxvL+zAzlq4+a3KTgEtnx6KRrEHNB8dKNYYN3U2a+gnb6Y6txvOgB82GGPTlEdMXnhLBRwgPDLDE/f63/xd3eF4QZ3ViITVvw5knxaPOoPjIqkw/vN/aU6tpN4rl8RMg6EPXpGiVww1w0FgmQbmUchRi+yyuElwWxcDTiwhA3Dg7OpH1Jn49RWIEEw9lh05pfis9SCbVvQ24XLyJzc6Hhc7WodrI56nSoAKnIwlemprjTz//nZ8HC0DVta9HgUp5wqoB8KMoWoljdQprY2qkJJoeksbRl9lt6OOHyyaSPthLXon/zR3UfYS2GvKShSxAJZx0RHsC2CftxDw7128r0XF8hOSJEDThTYAIiYGQkprrbFJkYE0S+xMQAHk4lXOqx+mhXBi0s40RbvDF4OXpyLHUplbUsbqKlBAl/sMv2hcIUfr9+yHr9G9+5xO02pnqRv/j5nelXL//um1cIsfbeL7cZ0GkhFJ4y7315MucOwAzZsce/KCOsYLbSATmKNwZqY/s0u2zRWYf9gKUEjEqGogRy+J8+He5J1769hkVdP/509/Xp1fKRhmqg/tIK6Sd3PzjOUlbwa5Ex3noSnBuL0P/2oDQlWFMiNBlCtsl/SfdEF9BQjPYvLeOT8vJvptf/JenyGU9+cvcv/qb5zTdmVA8uoa4MQ3OCuX49EaTzDa0y9ou+gb11dxvoA4moSXChkWIoZ9Tpw+OZW9OWKztyCX3KsLyjAEnAlNod7v7yzv6VG1RDtH7xgPtKLhFJRoqH2koGYk15fsE7PeXBekNTsXtNzbF5OhcxRZgdYDE6kp5GjnmprgOhaFSKEuUBjrkZ7HraMvuFl6bkz4ri0/3z16fmucCmGRCQyVtvvHaYEHpeoKaphtoHj5xqryPq4zxlO66gYYJlBs0lZiH3kjuPjcS6BRtJFrEHIy0oaejAFw4HHaX6Xt1nDI8OxXF1En/nrYdDoDgOeIrDWD6yLlIerumnKnyJ9dDuqqb6poNgzKUghLsQUIq9EI/tPnx8I5F0HTeLm23B1EJXg775ZdAwGvdbjp+GfQAjUFzVvno7aptA/XRwbil01lAiaSIAqM9EJbys2XF/OqLf+VmLrfQHA8vnBO58Vu8OupMBfP216Xn/YstuEIBEJ2BTp7EgwdZbW5t3YB0KB/2OG1iaSbJtwOiD8mQUCTiWCYEu94Tlq093Qqj/0rlMoykcHbVqHP/2jTUCRiETjzCBtDsaC6YQmyChGul1TQQfRFKa6Uf6lDbSUzNQLFLV/dag7e2DavMTccRnPFN4ormDui9GYoMYyvPSvRGdchKaAuKgV2sLbYdTRmEvjW3kQ17Iev58JIq0LoL5ABomR3EURbDP/+YL1JtDDBHNOrm8VR+HBiQB84Ob8QW2b/Qgy1ugaq1B8uqiS9Ub5crZJ91pn1RMy/Ep4FhT+CBVONG05ES5QHDd6tGaCyhr1p0RL9vj01rzaHz50guBTK7ZGpoEzSQj+GFjYKmRl/NgFzjZbh+xzG3MMJ6WPLfn0OXsePcwFMH7LcEjCWPUjSOg1a6dz1FwwCcBlnHSDCz7OMogi5zc6MzNenkQfHrAx+2K7fgawNAFeE93LQBDkCmfosmM6RD+9PR0mp9ND1on3d0ac38CdlOzL8sHSm8xgh3tlGOa/bMw0kkxL6BAcjZkByIWjqQwr3p4aP5ixyOI+UQ6cmtBH9WHj3vaiCTTaaYymJlNQAHi4JPDSCGdzeIHbW3Utv2OvbSeevjJQc7ngJpeIfDZ3PLze19CD8eD5Hju9rRPFD8esO0PDy655I+OhyOMQcwOhQCS7d2fCnmAOlIufe+mf28YlFvM0BDRJI5/bf5rU8mj97oHggx76OUrU/O/szyrm6N7fSw20xhjI9BUjGbt0+dHXigxG508aiFxiwmgla3T1Cx11uBEedJzWxEQFJssE3RhbkZvG0BiZiUFNjBIOa3uDZo7tL0yj7pHzg0CXzyfe+9//eHNf3Th2//ylScPt8sG29qtZ6OBaM5tAwbgGCIOTNpP0tZEuloQBPdCRZa88YKfoBDPg4+ac69Ts2+hj+88y2nAzz4RX7tInPvONRRCwNognM6LEjuWwe1jjMJc8tFoOu9JsFK1Z6puVfWHEoUkmkVuLcTsZVrtAFC76Pfqsi5zhydjyXDvI/IEVuaC7mnXybaQmJlSqaBBeT9+d9er0+G5ws/eO4T2q2s56tvfWdrdOtj1cwZEpV68VTrcSxgjxbJHzvjZHQpOpBb9Pksq+4MuY7M4gYJkPkm2gCf/6cnvfzUPff/KdteRvzy8MJ/PhN2fqnsffvkwVA9eiCHH/+Q/emJ589eW1vXy4Ad8RCS/CONrb70w/OAe/wvhne+++kK/0fzkWcRjlqpsNKRPruT40Sk1ZBE6QVCQWRu+dyb/2qsF8OX41IMv+8UxbqC0Cxy5vDMpd6B6dtIydx/so//j/MLqVDDsokiLHtCKD3la2jfFzsp3L1+84L031HIw5A44pNGTUCYZgaudxuVXVzvk8CJE+zp6yApsvHnheEYMrGTv/mJ70n7+XOzCB83s/PyVi0u6gtKLs9hEx9wRTlIqnVHMRrqYe9WBvtgZSEofIkFib+jiJ/mLy86F2Cf/n/8+NWW4fG7/370iFsuyAV2Exh/1upZlt3wZzJ5EQdDThYA4M/XiOVIKCzLnphcPmpV+Q0An9llt1BFUzcaJPKa65aLCgcSCJUYYlfD6PYB3svLC5cVz679s7u1p4OZZCdnro3Y/uq+ur/q7trJfbp//5sygJIDbvDzpUYABz8wN3ayUdF2fWRw3ZcdSqzKU8On8hKOvR2OF3O7mzjIEQJ6p8emOLxhNL4YBACiIymAXDqzgpWdjn0d++OzBmdK6+9CIc8Cl126ydiV8DWt2dAMGnnSeICVb6w3lB0A4TtAry47LGwtHuCenpkl3R61xs3JSbvsDwRl/kKAIi7J0C9NFrD0S2bBlcE2uKMkNg1UV0yCebz2/urHh8XpoGF8srJ5VykhztxiySGQ6djHoadS8vaPBeZpUxtZIHhqKztMWpYtdBwDDSM2a5ANBWJX0523wWrM3MS+uefaP6xOLV0wgz2n0pMJzkyvfvHa82HxeZ08mFZKwvBksMpNMXYre/eiEqAqOMJjeyAA21tGEYVGBYS2ERdY8BUCQXSTXhSRWHnWbw/Si53lpPGo5LgAjcgsvXMHtsAjY/lgQo+047x1+XutD2jg6DEdbg8FBt0V1Bnc1VrcSL/5aKmzYmFXvC2M2MjWbiEyGQ04oNUQIwi9eXepUh+GL5PnlRQ7QjxtKPEeDhi5wSDaPlJ52uBP+3LnAYWNr7tZ1Czd0aeTjva3WzmgyGg8j/t8K8Y8V1zaMaB4Eso1SsdwZr7xyY+qFS7940EiFSLQvnRz04kuYez17YSOsfVR5tlkdXWcMiNrrwUgwMmrIGVFyjfq26QroHDOIO5unVsI/hjxYEu5UyHjchieDz949LCQiaHK2tNeFeASgdGQy7kvajRezcpXtb/Zfef38YWnI7neShmlKpkK6xLLpzAPhSKiCQw9/UZ5pi2uXZqaziY2vXoIrrY+2xkgQ9sVj2miQDIbW/LH2l9U9TZAx24BhIjrldhVML7LbO8mPvIuzePJi7HGVtgjVv7C488UBSbl9FKi1G86Y7VWA3tBK/MYVu8GKx3IGj4dCQJnjg3yEb/abdTU+6w1oACaOcECzuoCtAJ17Z1oMs2E3DKATeCzbPYaNwpojO/paImr/7q1Pf7l1YWkqPx3LmxhaB/kO1yxXIn4Y0sxABLOCVDohPy52VAGj1qYCkUC/1oVlNTtl9+uyzapYRwpMR1+8ZO0/2cHPoYXFAgYysm2ONTsUg0rVoyuXgpICCJKqyubitHv/SYfn2tFImIEy+h6RIOCtA46wIHQtgxCajUJ5xBw8YzM0lloL18sgOsa0XPRQ6ECUB3CjJK/PBiWi1xqFhJ89Pvnev3rVPrcofLa5++nB3O1TFVG4WE7SvS4QK+SlijSyjHoHoNfO08cNWDmpUEljecpI+7N/9bFU7p4UzqUBYozoZ3ogeOOfvrj50cGgqpFqIEC3tEuOuebb+2P05Af76//XW6AX6v2oFOftrdKz/i/Zb/xP36we9Y0QvgxJFieHGGT3w2Oi3I+f99NxBiBjSQb5vCKjKWrxG6+Y5Qku6jY/upjE+o3uk63aqWK3ZycvX0eP//OY4twv3To/JEtPpLNJ2lpZD+AB5Iyv4AieDE0tTG88FlquWFDf7gMD9eZtVx1LBBD7pW/Fam3q8eejzS93wsuV9uNty5j48oHkay8kUj5b9AN6wII9tUrDN8WZKtIYsIvrfrM1aFdD6QBIahAk9igicGVpAYeyL1yK7lxfYq7dGC6krSLb6O40DzevrOGzKf/K5di9+yMI4vEAOZvPKmSqVG3kPYwuyYhjgY7dBuDzc+es+pNgnqEtyHR5gwzZ6w8nhuaLRcZ03Dfv9TlSpaU3ayepxLJ1bvTR+22zfHbh+nkd9j+uco7PoXUDfFY1KsM5PKYEk0L7tFqc+C0N45WeYyTz1Lyb3Dsb1n/e7sPm1d9/E7PxCotQK7mrYRgDVz972EvQ4AwBUAvZzpaQciYL6yv/2//xrk0CiQX33/7+q/t3jjkcy/3+63ENqfybbZ/blFeBUczKuaFhbzSugP54rD8YaSXUaNupsD+dT8yg87vlZ6La36/UspEcAhKnpwriI8JECMVFxCNMT0fcy7NtBW1O5GyMZmiFInHKQWrN1lmxjgRWosFG8/1P++ernovrxL2WMojyQ0ro2EEaA85V9vRFIGoBPKjU9vVuc4IFIdd6osirtqyXG6d7FgLGkYJgHil60EJm34pgjKg8KFcPudthDkVTdc27EPaXf3bY2huvooSTDp4Y3uR1//CjR0q9qr7wwkvnUp6u60uhXdRbCc0168JoDTk3k0Og1DGLRd3iclCcXJ05OTtaUlzojgXLaA4iBRs878OrByYtBi5fjyvp2MPlIU3PXr52y+7IdT/EgShSE8SYYJFoQO77Zv2a4zgapKmMLxmcNItnx/L0XCRAKr0+qes2V+ufHZxibhVYw0wOVrotN2M1GobinJbafMAERidN+Cn6qkO1IaFVr3oyATjigsfm5vt3vzr3zZs3Fs+h+tb+hOpY9ARu7z3t3szgBVwq0eMdu+C1FYToSbC3pEQDvsmkz2oCP3SvTnuXV0MDwSCTMD+ErrydFfYl4UE5GqTds966hfC6CddFx1KePivGM6Ff/NUwSgJGD+Q7ZZmftIvAue/Of9ISqJZFuHQQALNJYi+Y+Nabt043HzTv7BkaMBLN0W7r+//51/Prl+/8xX96/K7RKNaHm2woHs+tBJ73uFx45vLFjUNtdpbfHoPQg8/3FCZmMKOx6VzJDJX6B66BOnaivCCMa01cN7ywLhfS4ohgf3JC4Ci8MFOZPPH6qcesXZb6AuOoFDaHq92eC0QxVy46HDptvfuLSi0amdOeHa29OfUCElTbIyvqomHk//H/vvNP3l513Li8ZdiEZpDiPoKimWCQrfUEawhZPol1Bsz+oWvmEu6+nBX8oeHmhAfVkUx6SNIPWxHGv3FjvnTI5t98RUuOW0eaT5YpQtJ5lMMRqQH4sMDRg64d9CdgCNSFnftNARD9PhEH3c1adcOWGzhFm3EdQ5JV6FGGl2ic6fCFlak4JtVbtVGLhd2RCN2vSJr8dDuQKVR2Kie7at4Fxl/Pfnxvsv/lEwUI2wE6nI3xrBbKTc3ZnpnUUumXh+9rbL85+NM2uRKgn/+R+nfPJ4GwufvpUR/zr15I51WB9CGFGHNvt8h7scPmRLvzxJlaDK77UCe88p3l9/7Df3G3VXYh3nsrWyvVz+WnmMNmaYmGri2jffbgzz/rsZNUJJXJJdjHrVduz7WFV8CdMycSMlGsYBtBii4flTu7wBlddadnNUsTmtYHz07+9D8dr93OLfzaUuDZ/fomlIdsGzF/9OOnREIE51shcUQE7WKJTfqxOJzZ/2gPh402UEr92qtfHh+QuhzYfazNFRw8+t/+8pPMucs06b2QoUuNcopXpRl3PhaBIbcTSHhibqBvZb1iu93WUEBNuoSe6DZczBhsnrbVW9Go0bnbL12YMmM9X6Td226czL6Qg2Zme5/dbX/2eG3FVay2pMo4Zfmj+0fG7jjmdkkRBl5CBtyo3quEMVk8kRQNleKZr52bjnjsiuSMGx1QNIaK6Sak5eXzfizXJ22QpBqdYrt1TObWpmxUPO42TiZrX79CRtXrG1N7XKD5pHzyy300oF3wJyJH0r0A8/3fu3j3D1pI2EcrqkYQPZ0uPulUB0ripXPnv0nuaHyjPWltnb6xMIMHtZKAzrvBF69EVK6iHIvUueVL55jx9t3lr7yy8v6fdlWyp+kYQhv9QXBpOumNl0Bj8cWUFvJCQqs/0THN4sYcTZKyI6kaYA1tJB6EMM0VC6gjqbCeVAWIGwwgGh7zrC+WBknYA+Bz0SDt4u71uwGxk0nOkRFX3E0TGNArHkxEzXIcxuVBZhz++eeD3/7n6xwTLo3le1WV5OS4n5QPJpAoDCdAnwS4MpCxIwmMGWy3q33jrVsBAJM7cr8IKBZFdB5qo0MNykVEt7vTbBD7JdvlmZ8OZl3Byq9sOWScfHHQ9otmAW625X7Z+u55gz7rVe70164vTb1+LuX1PN65ZxuVlWg6Ek2zz5ppb5wR526c97y2MPPgR/dOj6vgFyYwThwXjXQoGvfCx2W1obOxVBbDE9Eg9NP/tnnUeIag/t/5x1+lySk4a9jlIS+ChAx+8LC3eDvM9eQwrKu87vJidkdpKAAvG3gQC7uIB++fQoQloWSj2JQthY46Gqa6AOzkwb4H9wOwq3jYkQRwZWMqzvdbn3Kmn9GFuMGzek8NR0LT86HRxCiJqIaPCMjILpjlQ9XnuE06irdgH8J456UAgEBNU/I6FmfJCCkpOuSnTM7V1sgk4X1ygrvC7qV0+ud7d2LEcNTiCZ06X4i5iPBoZ/Ka18AdmVWUgMegaa7XkTCc9Oe8W7VKLu6J0ih3VL2Q8vm/szoasMMu5/Oqo251bs6SAtCXD8p8fZhbWSwsR/Z/pvQgcQRHcy/BZhsKazLjwtxe/5sXF7pAsIbC5eP9bq2p44Iawg2qgpTs5VywZom4dPq9a7e6e9RZWwmsLs+sBBir+39+VDJKByzvzObhbqWVx1FIBU9rgt+/EMFRmO8qHiQAYc82ucBiRycSqI2bTW7eu3f/+NhcpohF6skp2703vHw9H45nP/yFFgd1JxiCLaiuG5QfTMugAabHcYnVbVvkyaaUvnH53G/f2sNqj//k03Nt0glRMR+tW9DQVruSEQvlY4UeV1JurSxbAqYj4MleEWgeBvJMbzJKeBGpJzu4sivKqQCkuwdiq6GxRBNFR7qELwmtCur3Co7D+2TXnKLWjtlGVb70TXBQlg6PJG8Kvr2I7H/QemMpvT8SSEu5NB0dkkTPyLK+oPcqj1XElFg5ZAdBNPPCP75eglJbd4pP/9sBZbYIAiVknWr3V25dF9BueSi2ikLmnSQ3sjp77I1I6MlR6/BhMU97zjEZN9o+/KvTwOsuf2gasQYC59750ipgvbl/kl19Z+3jf323+nxz+XrMk0A03vB5UwPRXMuHFcu7UHDtb/b7PHxxY64sOw27++WjojATTcCh7JuXbdHe+ZEDdTokMTi+c2a58N98eeHl/+/K+1+0iLEVGJqI5fNkdXgJKtueHhKoqIPm2HGBulpBS8/HDqphhFsOR5nnHeX5JE4FJpTPS8zKUS9vcTu9xrwLTd5aVeS05zv2BMdTTaK107N6kwfFba7NpjHGm3dX+XHelbEY8afv76MYs/TSTHXUGUlj2oc+Uau0qd2IXZZ1r5BgJo5ODKHGh2dxJ2w8npz5AYQX1VicIdAOiM5HFxnYZfGthB/Z/Phjt55YXF+DEW2eJHafNGqtFqUI45bF2YQ7mEAUP0DDXsriwU5qzpD64+Lnv6DdUwBNW0hE9YSCL823VNVCeMabyV31Zy+5arUOBoD02Hzvf3lYqoNzG2MSD0ATGK53by6towwlOtDu4w4Y4tyUPbAMkNFGR9Wc4rDuOJVMEa6ss2g77WMwnvFPgwAQmhiBv/5J8dJLKfeyV0isxTMBXtRHp7za0xxwEEnkcJ4cjbtgnErHfX7c41cFllGT84HGgTlsFwc9zh92TBn3onFuQnRl6cp0TDJ0aWwRNM5LE9CWhqBpibXFtWtemhxKPECGRuW9qbkplCaQslhGr9nbo7NPSk+GYCoaJZyzomT65o9adQEoWRiW9GjUYKI53/valdrXiOK9ql0dt0rltpe89MIMJeKBCNrBuMAy459aOXvwS48BUBQzWcL/7OHezp9wS68FvP/wcgiLwFvSsz+8X/iXr1C3U/qPjpfWV4Vpz5OdmpULlY+L81MYQmJ7e114KLkNan/cDce8zrj2i/s7oSTjP2E1kO8XcjyrmiTJ+BNLutXv11VHOT+ThWj4wpXwxdfevnZ5uVvuA5GZQj7kqzQEElYY37jWHTQkNOkyVa2ryiADYUPHHQEFWW4+r1EuRWcBJk4/2R1ChER6CNWGcl6n5fPQ4cDsK6nnfz5oMiEgmHUXou29bn23Rr2wmLiyjPc7YEQ87goYC2RP2cQlyq0rDdXNJsXtQS3lFrn9yWLafb8LsAueoDwiAStwHe+ULKMCy1Lf04MIkMpOwRHMAYeDgdGPusBhiS9kC/A6BIQDwN2tPAn5Gbw8NCDGvfBGvLHXuLZBOLo7FaRg0KXLAyYSBNq8hVHWRA1GAMBpDRz7ZNTf/sNDzQTTqZh8c1rlIPPVudliF/j4tOXysFUxPh5AlwLjKtt6NMwogDQ8FfDktWCgQhqPDhpxZcj//gu+kH34b95/8x/HjBcD3f/wJer+SvhcDNvgPr/70342RHydvvLpoL9ElRHPnCiNLbehTKya6GfBc5eWnED4dG9rdWkqNzHZyciXzkABcoaJywB4pnm5d5+8pab5Gge7PK5saMlPg8XRPbgBjkvVkTIXJ0abZdzPoCiOnY5lC1kOpZmLSPC753ffPz06eBQFgUiabho2IBo+w9ZxjEUNAe0hKLsj28mtPSu1cPnyFV8geLb/iQgYojze7xp+kcFoDcYmlT3dIgHHt3wxH23IYLhTOvlimMvE+c+f/pLS+OX0d4HkYR3Qcul63v3xUSt9PU+tBQ46et/ip915NmsPSWgeZ6YyF4Pw6GGnfc0P7FcabWncrQOxlzoDSPH2x7+7PrOrRsSOT/68lqHACKedO2j/DU+enfU8Mt7ie8R0cvL07v0Xr0+Shoigoe9e2pI8/cOK+Y0L9PrK3l89NvbHdr/56v/vne7jUvGnd9LJhbe/sVwFsOPGUfTJmLJQJeB/+qT0T37r8kefbqNoMPlO6L0ff/rChdC4PUwVyKoL6vaEaDr55C8+x2AcdXNHrT4c0FwkgN6YsqOBz374nt5KeGh/mNJdKfDMmTARPCk7sjGqpSQUk6jHwK498XvPX77JNPYaazll1VUKpxwRdyxvHulyja1GYJ3BxzKF6cBkDI76eteyeEcnAhRDDhtDnYSxQqxb0nLz6eo2+OF/fviVb53TSaCk42Np3Plk349Gvvb1dSKVuTdQjw+qGOmfscwWWGssTJAb7uf1btsAZvK+yXrWNR3NkwubnZqEwLiHEevterMm2VBq2b/pctIoNi73m0PWkDQv42HzrlgqBsBcC0NmCPcQFWVDZs+UCL0SnRIlzqg0yiTFzHKCea8S0tko5unAhEFS9WdnpKXNTyfNRleu9uaJ8M5/3156cwNCJvN+t2wj93daQmngxaiZrKcdt8Kri3ofIFV/ZZZ28JGmRjZgwoJgOD4LAiBIXwRM6PKy607d5bNOSp8Qv/GtjdMsxX/ctZ61G41y8lx0Mbz6RLMJKhnJMS6XZYCYOe7DQL04kiSaBHTZm84UGLMBucaQ6S46VRjqj/uF9AyXIbqjrrzbTgQQAISpCTfZOmkClu6AYdCJTWV90Viv00T0MX5+xfXow5M05Xg97PXCGnz1XNOPhAIbRz8+rX3OZ60sEo5MpcMfj8Wpl2fOg+Ft+RBLk4uzBcU0I2h6ZsN9iOwIhjxuO9AgEEwz4XVXiZc9Z9TfetPh3lrQVtP5itqr7ibzzO9+9frDMiuG7QCBegnZT7ohmQueiw9RTzLm8zWKvN03UFSyeKFSr9w5k4xmb0iYvdjSFJoLqelZ3hB0oQYOT6TYsrc8Hr0r6eGVqRe+fft67NsgAFN5FQAARQPcLsIZqy7Nxm2A9pOyIKKmjI1FYwLSLtzmHENx2n0tPhfGfXKxUddYYWCEs+cj3XGDiLmJOWiQxSMXfL76qjMwEbdd5bggRTQnhM1xyfNZRxggBPDCTU/5C8ESuw9+xlt+TzILKXpv8EjHp2F3lOE4GzgZQKIgB70PPy05Pz9xrbnjqQx+hKdTsfFJA9CGnkBJa5kE4vX50AEH5VLxMa9Cosy6aVecZw2IIQIOBIxBrZAOulAQx81OXfSDIBb0sA3bYVC2Z6PSMIlQ3cpe04X+2jvJbS/SklHGZRVWQjKh7dkNYpebcrGeQn5koifHmlOSLhZm6wqw9eNihur3zcEQha78Dy9D88tbP9qZDgTOGdbHosLtny6lqGG3s154cDDAy8/Q7UopxRhTqWXP1cD+FQXComADsnS1f+osBhQAcfUVUkbNBhgrhINxlGmUu+xul/G7TZsYwsqr/+DF++9//sM/r88vMdd+e9GF2A+fvc/prHMBwOrqWg6mu2A4S2magU5AboiuLme8YKKwFj36+OzZe18kGXV+3X/QObMwD5KhykeVSSoygtU4ZEmtLmMLkcRsJ+ZsqdLLK9GhGBUhlMBt86hnsAJR0fxedDJyeMWe3kBrrWGj0fD5UFEyPvyTp0QBWD+/QCXh95+dHm43EvD50k+KyS6PmE4ym94/HrpSDBb3xwJTJwfbIsad3SvdaYu6CxBAs6VMIB6KxqMzb38DEIn+1pkClbtS0RQJmgEetFA3Y28HdA9BcL3AqzdTf31WkToalMifv718ueD64n/fxO9D6y/m7nSPdd1C4Mn01dDsi5m9z6TJ3QPAAVnOnoqPRpKRDid5yfS7kKE6sVr7MUThqoLLgJ5/wr/9D6a3yq1qyFg5P93qA2txU0OdSAEY8la3Mmbd+OLStDgSpi4FSjPyr8rH53v6axfjTwD7k3I7lgYgt1Vv64VIkPKboYbrUJddqXPRC/PTuN96sidXW8RX4Cd9cz6D1Ce23DyeSXh076QmpuIY4ZhdR3IodXhUszVBP8108wTU6uH0tG8xGx843bsHO/F8YFpf0T30VAzc+nm5ZZiX38yhdnQMg9s/3eMUVm31oh7spBYAemj4N735717j/nAzFIVpMqBiWHcyUKmsk/DdP2hdSM/s36+UW6NbX73Gi2zKptK8I3Ot5TBSMyK2RngLbrefmkgyjAh7ZyVYH/gczWUjAmGrGpViGJ8blRA+czXlgLjQxzvHZRWBZjwE4Y/yJmgRiBx0I9+Zmjf11AMfFcp4ArHemLNhVBHYcMgVYwJZlNSO26A9PGoQL1+ffjgy0h4/roo1GaVYKDIPOqcAOBMAEOCd3/vzZ/S/AsZHguYTUoGJKlCqL5I11759S5AmFozTbM/lgkiIbgiS18sooQDjcckFjFGoTnEIAr6DOi8jhgNhsgZ5CEri2kePlNmrMwAwHiSDli1SgoLryOmjzYkNqDAphxhNYykEpTAamVtcPQaGntdXiGpvwZAlQMmH8trj1rlzOeaVlWju9PRGqPzFpnhQzpjS9r9tiT8vaVP5lTfmUBN1St1rly94InQ4HxxuPj8Z60zGM8TMuYGsX51hbqH+maFfRettuEACX/Z687fmaiDY+/kWRUpjiNS6cD6kFztGU+NBF9C6W2n0GzIeuUWHrWh60jNyPtRzebU7Yh3UH3P48aNe19VhpX46MHN1/Woil/vay1MMRpJm2uMBVACp26OQ5PG7gQoO4EHDJbsRxRwpId1RB7U+r5m4o4A23JxwEK+Q8dT62yuwqDROq2UZV8a8P4RSoE0KXZzjXOnZWEfo/3JHq4m4wQiVdscBPQzBhQkP1yKBuOGXBxXJwLSR7dCOnEjmBp3+wbuPsSSwtrY4fvhw1r8GIqY3Qp9wJ6++dj6FUtBz5OQ+xxsHwbz78dZEjGAso/TKZpBx6Vi609tF/ZRpDdBWt7kFupK2oKJ2GwF1EAAsjMbSltMKoYdjMIGBHOKKw70WFuwgrgI5gXWs1gbB/MLux8+bjxouXwC5iO3s9JkfH16fD7g9MAXwH9TEJU4IXU6bV/BKl60J9tKFaTrqGWLdcUlji5yy2Qum1fUUOf6zfT1sS37UHCLxhlY+Az5N8t4ISW8DvzEdhwZkdlDEoXC3yqa/fan2eB9bCgcX1zzvaQKFT0ImVtUjZuCsZQcp+0qO5oLJUp/LzlLb9WIKM37jn71592f3BQxq7vKjRlm7EXB3vYOntl8GKQ3i93vEPHPSdRQB8dCxKTUUzfsBaXL88zuzF8lxmP7TJ6XQclg57pyyLJ9Bx1Y9qDCI2wONkaeKI4e9V8/NPPmgLWXDHOudmP0YptuAAipjNoLWiuPwaioUjbCTwVgPwNllAuq/fW32/bljZSSvqH6xrf7yztmbF/34dOzZf/n5XDrWykCU2v28UgyYaO0//5e5axvEjro1GYEep+slF+yhjVAzU3Mqw8+9fqUHOYWhs3Ypc3YiExA7VLq2y83EXY406XQJTzbp5k9Odjqh2dCN17N/82/+Zu8vf/UR4FQr/cvDRst5651vTf37v9qXf7nln/HZb+Yvv5b6yf/9v2IwEro9DyzG9p88iz5q2dPujpvk+pCLYKnLvr842F57waP96EgoeWYvzh0ePn3jb93YLp/RrYnMgGJAzX5vdabIP3vSgc5ashn8stwyq0NXOgqtzt49OEAL87s1EzbV2XQoG4LvgPY8Zr85uzaUBHnhxf7TrQ8/OJolJjMJCGvQjCCQBtYYjxiGeNTpFeaSARIHeLQmKEBfROI4dSO2wOCl8qT05ITBwo3PdyvMsUX5LW6SYqj0lenNn92BktRiMuDS7LrlJgk7wjsAiNiYN/KdFGgKBFfnAbf5aFxvw9jiTGHFrbONgcLnIj7qtJ3AuY94DRS7iXgaz2WH9YErBuH7UhXxfapVYpIvNTt7MTo7ckFjXQw1DQAEO5QLJcZaH919uGObivvKSjzhtlSfZz1y4mimiLgxMXFxPgM5jf16v8SWEzbSBvERaThCKGOocVQ6LvW9N1EfBZyICQXw5cPeMNAtDdmxiSIsOrPWPztMUWGa7SXzCMAQPYSyAMWaoTAA0AEAqz9ZuJlC5jZKLbv3wR2AJz3hYIcSVhm+2hx6ysfc0MymghgA2SRTLfVgXBgBhNugYp2hasrZqK1pxhB0hhDkDwgL0aisifzzXmcPbvWrrF/nNIFptmKxdR00EZxyY76IB+sO5NJhNZFPIpyOINHshev+0k+2NXUUmaG3i3VNHj+xWiYRM/0hj0r6xsrotAouYmA63seQKW0oblUpfzwZSpb2DsTNQQg3Z2fnUcNV8cATcNJG0aFCii6fMQMkKWbG1AGc+f4/+uZJh232GhevpQ4elaApymMZmGFGUm43C4lDZC4ffWzppik2D2pxBpmcGm4vEV/Mpym3DSCwyhUsP4KYy5d8gF1Yu/qGFwR0XSUkHKTNXr3tTYNpy8L8lgOgDGvHPMGmJZGwCfmwUUdfng60dfXwQHLHjE5ZmIoxOIG3T1nCElleQocTrNuOBDTUK5OzZqnZt6oKXgO8fqrXHC8sR4Mz8z6AsP09gLS4Cvf8pw/nr8w022M4aikOstOSg2P4/I2NiaCOcP76fEBuXdQD/paqL33lXOVP2P33WPd8zLWC4Ca6/37DsO2Ql+w0W2LDYRZ9ijdIOCDhELkINe4P1Inh9SOQ4+6OWVhSKYtt9OXFi5Fh22KLugumDAMZjGuGPEnNGT0Oqx2KsQgFygEY9+OKWqmcVn5Rlz/Bvv93bgwZ6LSq1A8F9zkihMiDnxzhMmqC0SABP//5rpLsE1Hv8Xg4C9uFAl45fMqeGbfX1xAYqAnipZdn8UX6x+O+sBEKFdK8oAz22XhoHVWMDl6zzk44zvId/HJveyy1cre+qm1zNXphJpgmy1801lfoDg5KIHzSwIOJAI2RNqii7ah0xvdcTdQVf+O1nMxaps+U5T4/wnP+kM/l7YwNMM6MRbIF8GSIXIzgAUo9NioWi04Q02uB3Yc96UAJQ0OIh7AQzLWdtYyfVMO9OyIzDt2YjfmikcFuKww41cpAGnQkdNCGxCDlPDsR9CbAdQAcadF0JFCIZWdm55OuH/1nvlzk8nMLAcXnGw6Bc5Hrr75lK6wLpefeuDbYPZNlYOfHzVh7OH8+/sN3Nbax/Xf+2RuHT0vlABw6t+G5f+T06kvvvGTMxlCH7W33ep0R3x3stIUgre6U1WQuEMf53r2Rdo7VcqpFUsKwsf944Or00zh9utlY/qPfnosl+D/77M49sfEci+QxvWMzQeyHf/rk0iXJn/Ek2ADpcx0+4WpfgpYOeSMBk0HnXcbeh9szl+x4Ojf41cgq0Ye7xtqycFzrPX/3M2xgrV9ZBEHzw0+eEH1jrzlmYBJCISsoetPLMQUasRNyTFYG45kZ6/xqxouQ3bG2w9aolQgKLYwrbkJZBeYugZWO8MVj6yrtu7kgu/Bmpd4Zm6M2ytwwI7DV+7x42Idi67NWCN9YThwMMW539MVhaWF5iopEvC6XDYDQhhdmwp4Kc7JdU467CUoeSsD8Rqb8rGv2Ry2ZrnYDt19IlY5L7Z8/J9yIb5r0hcOuSS4+hKioicDdhJs2JW1YNGOUT4yHoni3WNduf/U1nhzf+eh0qI2tgx4QgGPzSdqQEIPjBq1GbYxiGFfvc6hfwg0C5GM+7+Ub84O94+O96ghRAcLpboPZ9RkDc/a3DkDLo0c8hRBn6ny7zwUqJOEKHf7yySRldI0Yt4+9fQsjYdBAoNzl9IQEuoNBz0WSmdXZuWAYAFBrUTUcAw/sbTXDMBm2IUCk+BSAIgAKAE7qNfd/fPfZ0k7y7RtQFko0OQmCUJe4s6lOlPETuUuiJkxD8lB3e2a5sUZTfpiWm3dqQcq9lpiCJFjVcYgAOkptWxqQqptmIouL05Jk9Uf4pGdYnJYPTmcKCa/P4/aEhyMUh6BkjMEoRLJNZD0319DFmO4hY8SP/+A0fuM3u/UnaUjbIMo7ofBILBMNAoAUKEZDQ4Et+Kg3QqQrPq5NNFFY+bVzX24+rX/4BRNgri7ccNoy93RCF4L4pG9xNt6tt4MAlfQzElsOW6YNM2bt4KQFK/owHGptHy8shDRb2GwOUyEjrNqf3NdrzS47rmaWb7+0sfH77yxHp/MT0we12haBErCqiqhpuDwZA8RiOgD0x4JX6IKRDCArkYzXsQECowQBYYwJo+mmZLZEK6QYx0PcAQixWO6IbHs4MuojlzoR0ILZaZePXZEFt8410HYrwEqpmZg1ZCVY4TXB7wZdfjzmBswMXilVpQHO4x7vPETQdmADGPdAnOfMroqTEBrwx6cpxWSMtOv2V69tHRfHLRW2LdBDYNGAHfFd+faN/n4/BDrNgz50OcO8EGkdTyxZZBtG4XzKX5iZDMfFgzrlhlRl4HW5mxPV1qOK3tYVNp4knxWtlVcSYtJDI41FMGCXOyyNJRjfs/tdTGNn0x49CpU5AMVlu4FWBM3x4NMbwWJ9sPeDZ8zMuWjSR1nU4YNt91wSiuENB0zgetjjC71ASB15PKndiodbe/3to7q3EIokQfbxAX1pWhcItlv2MnEpRskuOaiBzFhwGGAs0gqoxGWiPnGI5WxFtCFailqiWedOHh3lltKGYbrcyiYGtgjPxoidHI/ACezJZ1rpWOR1P7h9AKjKtSBFT+Tibs/jONMeGBKMAdfhgXFz3qWbCHLA41731SVSUMHmiINAjySx1pzVNfp9QQJcQGMomEh4PQQpY1VTlX63VpS0r12arrdA9oRTJ43klfyJwc0YtB+1S0PHNpp0EEASQGEG8ET8yKg3dqj+5lPwZmL6YrL2811fbB0zzcZRtfx0b+7lNY2McKPhr3/3xR+8sUI/eVz8aOvdO+ryFP8v/uql9553S15ne7cvuPR3CqvRb95uiY0sPxycSNzj0qIbVy3jES8xQfMhYfYuiK1ehUx5UNRJGqEihxpOrwgD9FfmT2Xt4jmmW1LVnz093L6zcSkzfQ1478nRhdu5/R8crcSmvd3G/X/76cVvLf7iLxu/GY8tvhNyZvLSX5SHfMmfYJJX/Pv3wqdb/eBYN1J45us+Lzd52oeXXrgaMMR82rwvglS5290dxxbyyzdTLVNBmhNpu8bE5zjYUOocTUPWxmyrWw0jUNQeGVP5fjfA31eiOZHlrFdrinbzfOBfvfjj0dB6eP/Zh+bq29fnQj7WOXXniU9/fJJyAedW5qgX59ygUZegJ52JXpS8puWOeBkYEaMgG6MTuen2QMVrw4vLeSUAPy6LvpYQzKQef3pcmdReXZ1veOVqVdjZGiyF8kiGavbPHt9HX7wUIZanXnrhJj0+3u5YDsgRhqF6pLHuiTwfhbPTI5JGS0IXHEYzGNhwvDm/pDOUaCS8+bxtIyyGASlUHulDTXZ1LZmzXJRGCeeW0e4by5M7bZvkbJAmCUKtFN0SddUVKDn6vVbpzAYLIeRGYlYQiF5eWb59eUocHX1oIyhmTK9w7uTpx6cOKLkR3bAVe3aWsuyJAJz0gPwU4IJAv+U4oTiyDwB6w5pwVHjJgXUQREHXJKp41p48YElq9Oi42qRHh3ddqy45Rqi+WDAW0nDgSB9a9ZqXgldmMgNOg23CH0kgrpA+Fmtc+/JUQm9MpiMhKR/sNEqgzJIUPvQwhRuLAO/0hhWSoYbdSdQfwBns9Lh0MOpZsnn71ZdcgIE8fvrYh+N3j0ijM2FpwM1Ohu+djp8DFDcWlq1xRGA6FrtZtRkI7GKuu4NQwqO7DPrQGpUrP/+B0vPomXN5FBbv/uqBVpHMAHY9t8grcIi2HkssqwAMTT8/6FqI6RYx9BrQVduwCS+kfF5ddfY4JAgUXAx7UhsdqV0nHIiuvPOtv/f93/qHgIMNQEC0AZcMoJ6o41KsjkH6qLGgo6gfAoBmRYp6BYzRAQI2h5IEwm25GPcwpoAKFK3qOhzwoNRE5TwpF6kOJwxqkC7UE0odPx39/MddPH9KKvr8tXkfl1eAIeAfOxnEwRDHIUnObVq6ruPIjK8zNAK54Ej3DXXKMB1fH1DrI3/KI1R6R2ctcuQfmC4ENxXePXvJ1bs/mMt7znCv5LEkxXzwZ9XQSqGc768n57YkLB3HKAp7vC9gU95lKxgaiVemLq0t3GJ7k+7D05WLDBOPfvDLg/kEpmNYPGqJsqWORKMjvLqeU9vDP//z3ZtLVPpbcWD169zmXYzTlwqh7ZO+xIOXz8VoE6QZcKKfjBv3Bo0esxTLXZpX+2zx86KDg+FpZOjipWYdCjsTVh+rA/Gu6g/Gr69PyyNQmcCghUgDLnHet/7ypZOfPdp//7knFOTKtfN+B/oSQCFHDCKPKoAxAi+sRp/ttOQmxSNLiI3vP20EHDhDyY+eHfdH0BVDb352MEeK1fIksChaCBKIkr4lmC9X+CLni+KBjahVdUKUu1hiu7UWS2FnYwOap0RDngalaV3Qt0xvZPrci8HdkuZAgoSIOB30B+KwUm1uS1EJEHAiFbM7GqWVxIKNAzAWzS7gWcX2eVamIyNeVTtaee+ORuAUGZchO5EqCFL0G/+LbxK0xeoJ0QJADQBNR8Mme3eqM2tTS28sOjgC8trV373oPRyTmhIMJwMx5wNBUS5MvZxc/NbfKjr//Odg0tOnSA4NJnKzb/1dV6s1zKH0vc1KaIX+9MFJEgMtr+Mgwl53PIF1pGKoYzA/j1THg3kkUDJNhR0nrLxvnCG1XowJVA22uTuZTRPdx+Pp7dqj00M0YUkXqJIG1HRra4RGArPqnWNpX8vMuIq6ptZ5zwTNTfmrJ0ef/aSO/V+m0i/kJo1cOA1wr4S7pNvHm6CBumB/q0LStA2AIup1vfV7b9GRwOBJZSBMemwNcngG4v2xJCQDbXAwPu3nQ4G8n67WR5BkZVfXGqdnJ8ng9EZh+68G1F5pZvXK1/7kbf6fgkBzFB04raZUI+D0iwESWbY1Qg/irUrb3eyBMiwEsOycJzu/7HdDD+82y3XW1zFowC7v716Zmdo/HFe49mwyYLHBXnNoMWi9rTUQw41bV1ZgU262Blh4Osp2ectLIOur59+8pAI+9jnOPakSWR9gArROcl0p5QrIW/zcBZQMgZOyMWgMcF4JxmIhChzZoBIcn4mqzWt2wFZGjo/E5dIwmegjTJhznHdPhtF4MDFvejXTnJCCO0BSeIBkubbHBUjJJbhyKpN9zR51FZwWeFNtsoLqhANTqW9e6/TAwzsn11exVIIcdhEf4VFMO0aI9dNQvweGfMAf/RR4ZwmcXodpxrEf9eEQjVCOZikqhHmg1Mavbzz962LtCw5DwfSqP7KwXCrVPvvkyI3VX39xEfEhg/LQ6lk6elY/3vbmk9Pzqy7YbtSHXi/sTnsVG0BdEGHiUl/x5dIeCOb7hjbhUDIwPZ1DIoimKuxIKYSZvgSTHiwbCMsSOBR6uq4iHMthDDyuKvVaK/V6HN0pAkMAnAN+WVGc53uLGcs3m692ATDjw2N59qf7iSU3fW1uOGYHkI1WTqsoanqs/KUkRpk1FR2Oeu7jOsQwPk/OWVsoHE8gVk2ez3LNs055yH0wWMi5KV+kt8OChnH3oBV2TTK5ZbcT0b1C/MbUr/2df3sZ8IEOJoMABAA25KBuUDU1HAaRVMACbL/bBwBIa7fjd1ukLwkAEACwVSbOqtX5SAE3RDmClVSHAbzEZMSMOqiYgDSgX3XE+Uz5J+/6E8nMSzdfmEpW3v/jpwYyo3FCvUNQ7kZx4JO0wfOdhRlGw4Y6RhlYYmEgbXuX4EIq4xu3K11pb6gyxKjMdU7a4aWAGg1YON56sH0+4x8b3uLAAkjrJx8304tUbUxPbSSe/7IZm9TJA09TGGenox6FVRb9qa7aLU8In1EZiFffjg/dqOFGL3/9qm3Wxn2+EPGCmpUOh1s7p4I2Dk4TmO3rbavczz65ub6UaAO/+LPjN78/Hdt4vfUn/y6Uzxn7rVq9FyVU2hW0crHOwZhjuXZj4nWshXAECQacK55y81hmzfk1f3Hb9JiUXZeCgezlfzb77EF5e/PstNqeZpjYK+trqyF1v7zzlw9i55eYuCYOj+l/9YpRGQx/9tDyAJ/9y1c3riXVv9pVoQG5hhIm7PMm4Sk8Yzsa2nBWotzzcuyV+Xp/kjc0lkdHQdKrEpOS3b/XvPDrKxxQVZ4Ayn638UkZX3+ZuxiZPJm88Z0rdi5R3TvY/OSpbAdcGd/Rbms6mx++MvdnrTa87XiXQiRm6NWhgkx79Yh4dKJ4oGFH/ehTYHWjii6l5WLTjVoE6X/Bh52esiol2hjtTEWJSh8eDSoWDFG+JGnlX7z98mJ4++kuZ2EEzD1gWt4YxT4aCcbkyclmMBkEeV+8LbOeQuBWQevYT/fGVvGAoYAYVyjtsT86Kea/9RKECZdPT9GOp/tB1zXrKnjM8Axj7RdDNkeF5Wd9NREOSyfAQjZS4QXYaSxOYJjFcDxWarHkheTp2WnqzYtSvB1pCyfPRRhBG8JEWEiGv7VunNVjXaXe3iGDLuRUvv1a/OHmztoFGt/wcN1TPkv1tttGlbi4uqCTIrOCB1qCeIfz/u61a1/HVNFV6LMjDKKejfqgLXaOwZlVbmc3/UYWAa3q50cV3vYron8+lo2ueqIDQcE/u7/zcjoqAeicN7niMmperYfj7mEHVQfpiLeteeMjTFI0ZICE+uXzXhRKDj8fezb3oE6ll40q+gRaWcy1Hvc7n40EXomm8EQ2YMeZnR7bfrrlxEE4G4gHMI+FEXFGb/qHt6ZB3Dj6j3ur8cvLK+4vTtlEJmJ/tnv4890uBaUcO3sxyz/6cqsL0Aa18b1X5DC2/9dfPj9DeHs4LrbnRLbd4jNTeaDfaS1FxUn3+HEbdSWzmYVENvqs1z9FgVzPnl2McRzUIXmlOKK36sKtjQmGBxhdUxRShKavearFQX2rNAV5y6xrLWh4MMCGiSZvMH3RHaV8AK3htj1p7TU4eKAaAf+ziboE+3NrOJb3Oj6AuZGmSHP/7rGXDkZ1YICFdVQ5OnAiLzjjvVF27+FZ4AIwiqKtsWULMQeLWgPcLOFOECBxgDXeWIl9coRoODKBgnSEePHiomeh0npcrlW6cSCCGCQZ8WBJtz04bfSGXONzDEbKqpa4OO8I7klzZGVpk2eLw35M1hgsqHOaD4lICl/bLetUz44x0dfipZNxJhxi0lMkTndPu8PB0IY1JDITa9ZGhYUk7gDVfQghQQMHPfmkboNzhcLXv5p/fOekPCm/5L7w9evrX56xPYmXd9pav45SaLaQIkdI9Vlt3FPzKwkMhxfCPr6uGKhes4FLF/ORhfBJr+cMA/ELdH5N6J9WK8/6/T6RyhSwXJRMwsV7m/qHtem3r89891uvvPm9BSAMAIDU4umkmwIABwAAACBgr2gpAALrjsPbcu14cGE6RBJuGWABWy/zY8RgPG6fCwIHkGUobbxr4K4gZ5tOCLcYaLTbx2f1BmR+OkQuRoPfXM85NkFuXPSgUNYbV2Er7iGOobMxoF1OQtdv5O2+9+P7YzyVyqJUGnKPTzCRdoWpFBoSeUKZu5CsHnEyj4XmwyTpSl5Yr1c4qw84grayEftg73ABR5AJyUXsmVfj7VInrQ+fHWsTbNgAzWTO4425o6Te6Q+O2tCvXb6WBZZIwOq26rhNHJ2IV8+FSs8aLlJSbGVuOrzZEQvewCc/LEFi4eWN5UGHs0rF9ub9+MaN8GrOghw470GtcRNAMx5S61vSGBN6yuWL2XJTGZyw4Qiuj///BOEFuGz5gRh2/g9TnWLmqnvrMj1maFCzoCVLmpEGPR6I7c9jr/ezP2+yzmbjbBI7WdtriD3xkEceZUayqFvN3a8f833vXYZi5lOnDvP+ftrbVws3vyxuH3AMjeT9UaSD+GAweTR5KRipNpE+Ppid92bWGQmzP/tkhHWBRRVVE6b98BtnZzSG9n+jI5D47ogv/+BL7WYDz8isQZiH9dAbzO44mKeScgBbWFzUKujcUrI0lDV4/FeVR9lcFKKwk6fcO0Wa2z46PuLOXMoR0+iDnw3tyIhhsflZF+NmlHYHlEZ/4/y12Qun/+g//tQWx6dePf3LGsHtGktpBmMg0iR4YGNA3ZhdCiz0Dwd4MEFcI44Q0dbaxom3F8yXUuUhhNedMOyim5wbajYFKhpiNQbzE2GvO4KjpFmVb+1X+qV+2KcKLEn55mvt57bWgWQymClQZ4L6VF2jUy8Gwvs/fxBz04sBk+93mKCH23nR4wlV5oa3xtEEO02d8F5DPmgKbyXMgW4wRq3T2tW53GwhmwzastDaP+aZQwlCCSvAQDQuykyUDu42jrKw4/HGIqTUNuU4a8/FmIgns9UuVyf4rX9zkxoYv30tTQukdjh1plg+4TF9IBp3xS8Wtj68EZSd1LXV5w0TozPt9r4QQZbfOec/P9u7lP9luREUFOqzNuVI2OWQgUhPFee1k8qNzyXws+NkEEV0+eV/dI5gYtoxv/np0ehB2WU7l86kNEF23LLbF3g4lNt7FYMgEjMhpdgn9Clhm7Wu4EQdPMz9xV8eeAxZOhhJPFe4vLi6ejEBW5X92h7fsFQVEK70iSS0gDzYaoRFyPHqreetEMn2APD6DJtiUm7y66fn7n5RObPm/f2vvvL4sPj5Vnf1wgXc8IgyaInBCxdntn7+8OHjKgDgpApyb16o+BFYPDjcV4M05J3EgcdpSRNFHg2OGa4xqfIDkgTBoFtVm7Ouwox70Rc5vNfprQStniqnMzE6QZG5dO3P6pVqzYzGltcAbWYnO2NTRJ10sF8H4UUvNKXrTYR73ETix4yPUoN0OOmuVKd4E1m+eE09L6HGaLsMf5WhJn++L378Ig6n37h+8TNAMYqF+920bYiCEQ9NLQmcXuruHHRubb2IpSauaGTZ5X4E1czlrI8O8RPH4zthajwGXCA3T1njw5/ehSmIiVjNBnJ7q1U4P5d/Oabo9daweXC/unp6fdYfLCaHr69lPvrhLbFtpDJpylLbLTNgQLO5Gdw7q1frequB4ezics5R9MNyE4Yc0kL7PaAE1Zqj2mBK2BTCa4SbCON+GLZRH0OIp3KQj0n5TO7GQYBEsMXQ8qkzq7G4KWOQN1Xmd3MzJOq1nhQHe1MrRITbj4/LFTW/HKTgQCZDmvV2a3/Y6bYOUM96GApIEIQKE0nqHTR9kUhuxs0f7OGTRKPZ9ZFJ7vsnZ47qrsH4YVG7nCdPrl5DSQW7+Mr33vqDEAB9B7ggQMfdAICuAaKYbAMGgiASoTlblWQddBozFIIRbHfcpryWZjJ6Q1hYxjAASeokRHLdXnMhEaod8u6U0pXgeL8/PkF2S4NEjsmcP+lBEIZhr1207r+gvIE0GwXdUWvnxWYfUk54xnO7UrLZ066CqbGRbhjIQAQj3Rus7wVEejrWENnoa1MWSr65DCaOUqlRSwXJwjTTjU370L39zWZV1uy9e62r166XeEVRIICgpUEvejrj4mD3ZNLa5deS+LHbE3YnVpkE7nMHAApAdyhy7nk2bEYguO2DzNajXQwlJo2uJpuffPnzD2vmQfGxeRPOnT5xtRAZ7ey9YJjwjDq5M2LdcNpGJV5GTNnr9Z05nf7oU7fHRZ24FuT1Js0oxUdVGXetvLryYn+3sqcYjSOlPMXyngf3Bq0UWM0u5V49KYua0+d50EfmPJFkRuYrvdFIP+in/j/igmF88sFoJkzOJXtjxnV4aVYj/cpuu/SwzZ4LuWsNbhyMpy3s4624P6LKddL2UnT8G2+/ZdVr/q671OrOFRLE3AreOdY2RX8u9NXXTkgz/mePnvR/diyN1t9YDkU1DPdSO/wwt5JZuD5z0EWWDTyYzQTSBqSavWK9I7mDOAMZsDByXJHEr//+SzAr/PkfPwSVbRFCzWddWALqWEEsP+RypT2eIAKVdAuN0hNInYxrKcD09apfRamYJaK2PWlfi6xuGyizukATccwbkYp8rzVmLyzPn6D0xu32DvdI5SDWzmfSExhZinpiV3KDuieka33adb/T+IprQjiUGIM+uffMrLRdi+5Jp/fi4YFOgLQ3gTh2DGA1bkD5fceD8nabNGZcjz/aPPnaApiYocWUs90xwtEPbu9p1vCdl6/bq8Gjm+Unf3T7q3/y/Z/+5MdaRYDOJtdQn1FoH16E8u91Emp8ULGzMnH4V9vhmJp0CpfOXqVy8OPtPvxnT9hZL7PM/vzhaO3WUeN0srZf38LQy6eC/EbYyXjtP3kBH/f/j19szrdboeVM4dfOP+tP0Kup8V8+UbkhcDjU8S6FE2Lj/i6iwcu4KUMwyhAlG0LwwedPYbRz0GqLYzWd9PR4T3licoLskKA9skDc7qNcUATEz3sjZ0q75PTLKeE1/5Vo4O5nPVdUHjvwoyePTi1k0XHT2m9RXztFIpFAtFD3kGcT7D/847/but1K/c1zlevLv/6TT0QIO33SszWL0V21kGO5s2m37r8i0x/Z2tPhdJbCLZKaBGB7ypOqynfQhXPBaqs3A1zLi8uKDbPlcVtAPQZq9Cj2dP7U/5AnfvhB2K1aQ72S8IbyBKereIT0P9eCd5qRtRORPHbX18I9OFWXYCiwfyQwquJlKbZn/UW3257F8x37ms/76M2X5/oE+PZrPRAMWSAkOtbSPPL82W7WHY4II81Fh4Pu0ubaAePfIE7HI7f/y+dq1L+R5VOepHLQsagoQkccoILQmrX/CJoPwf4IOtV/53dfedRTaneejEU0u7ghoMxBUhBmApDGmkYwEEW/9+uZR39Rjp8IoIo7htDiNteVWkTCSs8z7Prs0Xj0rF1fgWIhJMh4vZY1BLDRflLJz89CE5HSCIeAcQTTLXvET1AVIZIRBCI4Zpa6vnKy03esKSS7Q1MIMozRpO/E1lKxVRxGyInZD5/KzMXnjd1EIPw8F060YSgwQ3vRs+JRzQ5OX0/O+3AqiRLH6DQgktD24e6XO8oyi8jK4Gk1z8DHm0Ox06ZFZ34+BnipdvMoTaxfvfDOhVO/6XYAgAADHBpAogxcDAhNAAgyFUfui3CGIcUeGcLEusLkCqxp2yzmtQRQq4KZ2KJlORiG7t+YnLzADKcEDbEocEEc5oNlGxnN6KNqVdcjc79+NUz0W+PjMcqobSGVSROCxB+32tOtamolHM2hnZugtASqZ4G/ZcQaEjFwe5NgQAAPA0RojAHJDwJTljVibPBUpPYxPzjclsuaBwmOTAmSnXDO99Jy5OkvS5uHXQk1/DSZuhbYf6SAF0OPi+rmg5We+PweH1m0uWeNl32r38z/BgBAdfhoBqo1JpjO1J/DqUtpZDxOBZW/+nAMAsnsTOD9b4X/0X+/293bungeARDLjQMrQbL0VHBIihAVn+1QAdNiJ40h6mfRTBY9urPrs5j7xao34l+9lJgMJb+gMMVh1iICFHzMC/4LS9/47ondTx4Xy71wLK/D5t0vXlCmeunUMs0EOweTjRMLOD06OhouXVp9y+362WfVa7hv4VwIq8qCZrjSGfRiy9KtRrcSD1kSGr3zWW/9W9QnN/Zy7sDUSp96Of/+3tal312YHtj3trnCYOwlwADZfXQT+HUzfOp07htrxMYSOl8ArT5p6Gba3xu3pcl4OJM9d2Hx+IvD/mRa7HDRHo5S/tSqjw3bx7ujxMJcev1sM7Z40N0Kv7y4Gso/aRfRksC2BuPhqDYY0os+zhNUJw5GYUmXbzASaZ23xZEBSUgkJMEo39HcYbZUHZ9++bJJOCRM1g56Mjd1TfWfPjxymbHs2qrPXX/8SM4Rfj+sV8ZSx6UIdSEZSLvOJ8ek6JWancMeIZDqcqgIGyvhPNnXe50J44XDMV/E8vOqxXc5RWa4MY3xQWIs9Ub9bCqj8q4Xf15kIsLxfiuqEXE/3Hpmlu5UkLgvFcEOLroajlBuEuFk4nCqV+5Wj1QZY5h3L3p2f9j76aP2a/NLgRjlz/hIz+KRqPIPOD9s2qnkTq0+8trwOv7wQXGFZP2Xoj1BXd9YeP6kHR7a2XR8VFW/99oaq2SebE9RkmhvjRfbU7E8vfDanIVQ3T5EBljKe3KvJoAZGjdYyh2mFiOeo4ZsdIAXT59KGwSxtzviDprpANYlyFdeXjlj8+/94PNuZVCn8ZUF+MzVLARTn773HB1a2avzbgfqPFNnrrh2cXwQIr76P3+nV6r87//u6VrU9/ory5zZH+y16XQI9sY/SJGuP/xm7g9Pv7iz6Q/g7M7RisjSmi7CtUbn+EAInb0aT81mW60Rpwp620hF3Ex/YMkwIiAxxHG5+eILzgKwukZwTX6nqMRVuebUXK9l5y/kPD1+ICAsEoskXS9u7iyeXP3a3z7pfFmr3mseMQN4BjQPuVktjIc0xGNXyniAdA8aylcm7h5hxH0MytlvFs5+7cr5QCa0aQDp4Xhpw98DYOxKpSPo4HCaWPR1RyNXLOJ7hyAisd0JVXOWrp9BWVIEtkY4tqXYEwMEvSSAyfi1b56t/RfImNxtNm7/Z0tmY17VNR3Xtm6PAz7Hc2EJrPufP23rjvPDG0eLbl1JhcZxSnjWM0fEwfPWOuGFDWJvZ4+MRISE3zZ44FK9hNdU+k8el/x5BrIRfmuYiviBrWM0iVOoNAEE4UJ7lYO4gXpNmDeM2WziWdWYCeIRVGhttpmVeDbladMGgtjG8TRUxyKBiMtH5q4sz70ct8aGsldBOWS24KFFxR0P+3Gat6WJLgQVC7jJ0HdWbck3FuGBwVt7ukeyFzEmhXo6Yqvat0c1dPNxB/rVDc+r73hilOOAGnAoCFKtsu7Kl0BrLZTQTT4Pu0Os3a6DhB8MD6au+VzRcBxRC04ckxZdyYDfC3Z5O9kfx+fde7wxU1guPxoun/HuvWi6gh7bIw076jcK7hebO/jpDfzULKObpSdNhjKFlm57K3z9WPVu85mLTRWHFJCpgexj4HlsSRhEs85UNwmdMzCgilCzNIy4kXhKMraFRxWPixsxGg4gSF6NGf4wdjgpzG2wNhr/1Tjvx7dvPjja3FfVDDKTiyGtNBMd3NtdXj7HnTjyTNvlp/2Tf7LgAAABcH/QXGLcqoNHAzO6aIPG1LJUpV1695Ty8+ft08seaA7+F/9TrnIT5irBLjQlmZC62ccngxYsmaapIS6ZoSBzCnXat+o9NertYHC33Y/8+iWu0dnpK154eOOvjqokfmXNO2EJIsRgMR0Jwq5T0ZFQGrSPZpL+aDDYPDp++MXNsDcrBvEXtfbGRnpiiRo1MRBiMeEMYHVaayilqe6sUqKZpK3+rNOPppyPeyE3unXSM+SnXgZpYCC3lL5/64nHMlrPuRMzyfsflfdflPOAga+fSLwVQD/Y9NS9radNuNLTBhBB44eSFBjJJ2B3Szg6OBpcO3/1082niZhvfWbOCNhU2vvwvdtrfN9G/cipqIp1j37wOUm1Bo1jyafpC/MQ3J9IXABW/d9/xwqE53tNngG6YaPd46Cpe3CzyEBpxjAHGj0f1iElkV8c70tKTTtWeBdOVCSbKcRqvRZeGkL9xt0TuCzyrhDTKXWEiXb5t99gpMm//befQe2Ha8Rr64uuM4tZrl8WqwLNyFZzEI2GK6ORHqDXX1/oVDWkPKI1n+UiI4mQ2IUT5xfmvGhPpDEU4ZAJeWF95/aTCYgbEFR5sRcz9UGi2xhrzMNnpE/9X/7ow++F3I+4pozlR5MB9YF40O1VU8mcFvo7L+E1L54/l9OrDZqANu+N4aMHzlzgzHc2GnV/9mBXYVjPxZMZOCQI+3+q4SmxTbQmjjIOnVxpfXLLOXOqpyHOA23wRXcVjuCNVv5vnGkcHQwcYjAU0H6wME/7AgE38EbEVJoPuFP5sdZ+avrUZit0JhZePLkWCRG99taz0oDv7d0h04XY177zKxcm3YfPt2KJ6KOnnA+Vs6dOpExQQifUmyeEO7u6iWTdHvtRt8gpAJr8zd89TUmt53c+nwIds80O4n+l8L2z9waPRYysPO1GB/22DDERGfjgg+7l5WD55ZDxvPdXT1spCM4FCAYiPTaR2Vj2Moo+cD4uFzcnAzZFMD7//nisvzjMuf0miggpdW8wnP6fx+e/9u693eaG3+rW7hBXV6F4FPkPP9O+/0o1YA7OE2QNt3DYHUwSE/141POfW1/KueCaMnlmULZn3RB78gjJZ771zpWAGwKaHa1P6VlSo4HPBmzE23UkEvGgPRAY+O05d4HUP+F0b10/8Z2ZY7/TEEfXRw6acWNuwJjABgAGAED5cpE8vHWD8ptPGg+nVH41pINkutfqPr9zZCPN7dvuMO6QlhOM092kfuMmtxbSluK5rcoYD8P8cTd6MjIXjILR+Ami0x6qerSNoeTq65fwQqgvT7FgwCdIveNeIhz1wVh53CcoTyzkQ9uqWK9pp99Z0AdGu06seoNuP85xo5aqecb9dHRmxmBlG/A+h4JxTXC454Nn263lc9nFl2dZgIhjy2f5XCGBdBO2IQ1l3QJwGLi4mloZq56Eh4UxCafT3yNbByNQyWx4SdVpUoZED7nfe+vib/3dvx/MZwAApgNwFYQp0CraiXm7Wmw68USrIdJpJEK5JgwgcLNBuM+4gClDKkUO+irJuvIeSDAhBlbayiASobW6RmWinVFrEQMybDu65Yl7+p2phikUNa3uPgY+iHDoUr2mAgSL4k+em8W+MHDDZyPmsBkInAlNClK7rsKBSIyr3h04pjcUkSRLVQCJsrN+j+2aDgZdaQLHGU+etbpOLsw2RIMAGiA8tc9bsYybwCAHDp3KnuAEzCTYmzfqGwuQg1kMndImmCXA0Yn85mvgd7723akDDo4Oz82xNYd0pX2TNplciXfbdiDn06v2oD6xh1OxhvkTKdDKuOOW5PMEVlywznb6XFlUXVEM1m3AazQ6FS0G1nXNMFTWWfj6rFJp6jXReTBoVTXHL/qC7rPr6d4hhK9ieEzf2ZcKtJzeYJUiITzrjDR68ZWESqCaWSOB7ZtB4ToIG+LixeWdD4//+X/58o1znl+hvjY2Uk9KD2OEtLstQhxqNtqn5r3NNmhkuEIWDD4bn9yI9CAQdfc328ezHiKB8Pxhh1iDXDkxSkiffjFlp5h9WMlfiLMiXOr5znuDY8lxWey5SKhVclTm9JXLMdo0X94A6/On27bTmo76Dw6UmqAGhcLb/l/cqIYlzSuJIIkwYeSZmz4oj8/4E4XZED61ouFMbWIed7F8zj8tN3mJZmi3cyEVIDThaEyYkj62CECbCLrw+kn0iIxxjXqlggfsVCRGzGVBTOeaYuWL4VzO5YYkJB2QYK9heUMvF/4gSv27P/xXxtY+lTgxkCjFHYZ5lTs0Z7oYhGM+xBU57VY4wOJqIM3wOBbwL9tAllkY9EFXYkJeeKo5Y4emcJ9v+Ww4TwYp1MLD/f/444jWfWOWLopgqbAQyTKB/c51Ujv6rOIlgmvL66OMxW07IBo8//sXcNhAgSVpqjrorCfZCZXgxv1fPHyRXk0iJBaEnR5D7JTHEZy+6MBGnz9xxl+ZaDoNkHzy8UfPx63Bhpv1ffu0x0wVPyZyoUz5YT9dwG1XEDHx7hG0emoVTyXZsEx1XHt3H+YWnAvzcyIZMyI+q4cbT3vyoBdBkUDem/S5dNFpQrDuECdmz4QCBKEVK/vD81fzcUIbbo+kRrXDAlkXUjTSH9NM1/qkyy29FVC6cjQHAldOObGk4TH2Hk0DwyBWOaJPFJYQvFEaoVNbihkaIux95HBkN3kGh32YtCMbAhTC/G6t03lyjF7KRQrU1xfO1I/L733w4fz87MxKol6htUGv3dLlAuRJufufScNuiTnlVetC/Ys2Phs781snWn9UV957NPB4EqeSkxJ/9EKI+9CtJ1XItFFVdECEUcM2Tuy1tUjkwnf+21/JQQwAAACgcoPkLISOWrKSFmzCE0eVqe0tMFpRzsYYkie0sZGk8XySQMuGMEDZgH8oWjEUWC1AhAHkABsCMADzr14/5J7HA+JudU81uZahJy1mOW8tpteb05AvFcYI5Pnnm7ofnuD4hT9wjXR0c2d3NjV3/qVTtadtGwNCk4cdOGiCIIWg0QDA2cFxxx7wqIHYiuWdiR0/OVpPrLkpdtKpcEpfsS30lcLKHathlkxNDLlYj0wj1f5Utkx/OApY9tkvn+KCRqfi/URAxh2JUfD+4OR8MO2YwzYP4h6UVnVNgaP+GoYRFhWMQDutkQKmsgmJjqRuGWtuutTW5B3X8jJmYs0P9vVhXTvcq6R94TOn357mXxE1kCHAAxgEKWhigMXUEDjJVI795G7tjav05mEpEg+nMXdnimXWgpQDSAg0ntU9UccVThgQXD+s2baB0aitaekAxDeqG+cDvdK9wsxie/+Y9gTSlMIPpIbiWLxE9rmRKwRbCK93HzwaMdaRPjnKLgE0fPjktroOnVSM1SA1LAS0TyVOzaEuwTtxA78/gAlGB7ZNEm3RoWa3FKxXReBBYdaEUfV5PeRV2bUN/rB7b+cwFKcCZ9Jhk3YQOOV1XokztcePaucNct4VVQYEOe7uDb1/eBI4SQ7sIh5SgpaAw2Wd6Wh81KlLiM+a7plBZAaXNReMDmEaP9TYqeupEJhDrdpYG06MtMsKBYx2eySOnUIkMhLsKYl2mkK3XBkYaGsoLvbr5c8rLm1yZSWfWvG+IFE6msT3mnzNObsWE+sHrff2BHJA2PTa2RDndg6Env/V5cLldLUytIVRjNcelF8k1k+Vy0UAAO+Wtz+6y1hs9u01gfGLY34l468+HDIB01hFbx31TwRIZ9ztq3CYUrjt3uyVqEq492CoNVRtlSqUccySTrrjccrYs72Uy5Vasox7zcNIQq8cTPuDhwNhJeXXm8O5xTdvfrplDLT3a5vLr6/jyhCHXMlVX8+YhkYW3RINp6/FbV0a6zkfNzKX9ECwNhlLHKRqd2+qKuTIbSnk6ATGkjF3q2OSHRKPBGDK5/b1bL5yBGntDu90pikHr4eYg/54TjbHzxx8maZP5DPaeeOnn2lCcaevnT81G8vne5t95cXmTCr1f//HX+N4E1g9ZyAciI2QgihRyIub/ojWgsBkMp3ydkRBtyCYsKQMItb2Oy1Zs9owS274e/jaHPVGPte71waOybeLZDL01v/4Dc+8VP1yv/v+/ToDfX3kvJQMQp3a6m+s/k//38/vXFxJfO+lTLdVPXiepvi5mx9CCyubT6QAPJGaBHE6A3KeXlH0sbxc42wP42aCrmFzZyytvjqH70rT2sHOHBI76Yfb+2cXPfHAJWtvaFaak2e9ZmUQgVRvwzxv+3zBrC6Lg3bv1XcLAfrkl8VOt9KiMQXEdEcZHsXDC1FfZ6QjFHCFEX8oxWagYuXQ9k95XRrsVXbb9diM99zMVUVCQquJ6l6t3W5bGJyRqEXSiGFyJRYOx0PHld1Y1pCkipfg8MW5GqZ/9qfPL510fSXglUfqUZjSu+0NHzr2RqWhSXYpOjSPqROuP+4/rq8kZ1Acr0J21OeOxz37D/anj3abydDWB91rr8TPXt84uruHd6S5jezuU96fc7WebqXAyiDt+fxf3Fz/75ebpE3PhI//5DY1VOdeP3fj1pEHGjd+9AT346+euDh90utHQoqxL0/2/Iv28Eisfrn/2q9+563f+x0WAsB2RBW4aB7H5N2yOR0OF+fzFC9xHXW5IA9KUyLgIlA6h5lODPN1RuoY8UUolw+4CbgyhWUcs3DbjQFgA0t0YBb6+rW1p38u3xeUS+kIms96k14UFbIwMwWumakHXS40++BlPQSPqod8+/zXZlrFUatXmWKTSlCwZ4IEA4/krtDmLr/0io8l3Ospw7bLP98mfNgYQggS4cpKJjvX4839WtUfyTgCz/d66OzceiC92Hrawnheo1XUrWvTIc34ViLJarM/CgKRxiIZpuC4+AHPQyqUod0hV/8ANO/UBaVVqneX46nl9Tk/az04rK/NIDOEp7hdtBzUm/LbfuwwpJ5LwdwX3KMPu1rCf+5C1LV4Rl2OclQq+Dv/JAcAUABEgMsAAADGbWeyk2rg5uqrSy/UdnEIRJLu6VgkgJFTJeJgz0e8MeTiFxgW8cOOfHj7McRQenQO1e2Yl5G7red39l/9tQsffVn/yjtump60+iwTiUiqhXY4hNK5iTJuPNS6omLJLmsscXWjB9zLGYwxCRUkL2xgIXfrOTV2Fau4xCiedu04VSCjVIBgI5TmQX0gG4dtbti/O6rujk5dz4lhd+JryUfHB0GEm5tz4192J7pp9vnpvN4ZjvoN6trVZcljdBG5/rTnQI4+B4YU8d98+w8AgDJgORPVOcAdH0wQRpuN0rzuAMTEsim5kpQpf8Rb5yG8qniu5FC/SFnOIDjhG3tKx4vsl6uxrBdFrKODpgNBRBJKRc1f/Khyan4tlGVGvHj2ErADATQdHsT5MDZ5OZ+oO6g4VLmROR1IWpd3Rzxm0GOueDN+/+iT/Yc//8XqkstwuRr1+hzG2FQBi2ZX3rWzl/gJBh8PYb0jfyvlNXL0lzcGT2tQHw/EfIneFw+XNnwwYjlRHPj9oKny7alm9DRHLbjCKMqSbjvmNraJeGItxN3bubIYq4uj/X0uBNsn3MbeRMoWvIxb3Xz6uHM0jj3ZJCnLvRTEgvHFb51v3LN7vziiEbbT6e1vjVIz+OHx2FEdRO1Bz8cR2A+NWlua4olCYtq3dD3Xao0/ffzcbuu6hBauJM5fPCsXJ2pLzCdQXlCNIlRYcI/VaX9aa9MIRXvnfFD5xSQUtDNdvEKCudlQJEk2diYLsQxEZOPZLOk3dJV574Mbq/MOxDAESu7erJ28MsOy5m73GcC8z54fq8HkyIJcCAnTyaROA2hYf7KjVU2vB1pYf1WLn92YC3SanaOtSv/BnVPXlolB/75QDjRdcJoMfSePfxtNS55SaZRCK3VYWHL08GL86vwSWxpSIqca0428vFMbjA2pN3HlfJDMAm1YBY7ic0MJlZq8KEEzaSZJVjetnJ8t9SwRApg/lwiyvf3tvdvjwtV1kgS2CU6dpR4N5fk1QkRSd1703n3pYlM2Cdz36rurkGN/emeLz8iJk607T7emkD7mGWnPYh8Thi6UreJKJjzseJ8168gCeTjqxwyPDCa5dUNTlZ27t/Y2J2///mlOMB/1i6+9fYHGPPwu9+VmKfMWUlhxJNpnI4aol90p/05F2YcbueRpv+ZD+vZcjNIds/bTdgRSiXAWpWACaB0FW1j3T46m+1tEzx6NcNuaqspEsk/lTXTAPx2xOFpYIT55ei+97vesep//rBny2mrM6dYHatcc24Ozl52G3Tt8SjQB9PLbhf7/OWp9Wp1AAHGjhzeV9WyEC+JqR6TydASwnUaOl1ED90+kycl//He+++bfBwDwFnBzY5rQi885HyZCGJFfdxE2R7thUUWaQweLk/Gk0+2KXtJoTv2UN+aQTn8KLAfSUBBJQEBw3IGpXmnhmWWUAcAEAMW//y/+m+OffrTfHL82v3yrtznVJswuMm72VbbvOTzWQPDl+bxiphgcufeAP9odrKzNu/RohxfI+pCPmZlzYU3FZa6+d6tx1Xq11uJ2thovf+Oslw6gprh/9wihQFk2cJIhvHQ2nVw4sYqWnj0MYjMzkjOEPUxDqxDTGObipighWRDihJcy/lSo/osdYe+Z7Uul8nTYDanBLA6XfDDMokCeYUzTqj7rGsNObSjZZjQ34zoxk/VuZB4/27ucTu/uc/uWsfrtxPyb5oNS697TQ1Ivnypcu/juPCM2IX8SeAHXfMRgpzFPlUplyJDi+rMBWJJfWfb97P3yK7/59qjIRWhOEjCU6AY1oERIHPGUJmPyqBTRlI4vzrpRpq0N9zilN0FVY6s2QbML//UHw+yrG43No4zblBQHdWP7LxQcGh22kOWEiTUrd552nfo49oaPYjJHW7cuxgNmXuQVp1rvh0NgzsSjmtE6RfERj6jhFEPTtictqXd22kWALm94/SZSc/TRdDKD+NjNfnWDqQb8cByaowNZ1M139LAvbNaND+/tGawh6TDj4gjeGnQby++eugR+dwhA4+k2FHCv+cpvz/ltNHqwOw4hWImDVoKZQb0oJ0k6w/RK2w6yOqBwCCEebJaxjqWYcMWB2fnApDVGxVGAIHgBbW+X11etjSxeOj5gYu7YFZcj2lGPVyMp+H7RnTHHRwf3ikIGN0gINODudBVLFDzeCTRVVK2muDkHHyLDj6erf3eF9VLcph5mk4VwlCm3twZS/LUNVhk2uo07Pz0ksmt6lcohmu6jX+y1xjWQikLIweCopcVojGccDJaH/ISQJb9nsksl5k4mjHE12eEf7xzaz+r62YDvK6HTl3NBqpod6wGbGjSHnjxJvDRzxWdUuSmvE+P67tKk9tl7VjRPlpotFkhxH9rpcjIB+MO+FlCrPLgQMpIbXhsFsZlCNxDxV4fbX/SzAfVaNhEKIOPhMIj5tir914BVMun6F8UKS5IsvURTc8aoPkc4YiDTmew8rZg6qajRmB3Ci1JPFLGoq3XPFDXr11I6YzK79ePry5LxEik22se99tzCfO5qTOaKd251O6L1xu+l2JM5qwO1MfC0hcUNNgrimuRokw6OUNhSGD61iu9RcnW6ezxG2lMiCLV4eX88te2y+ggPKvheSfWGQOqrq4b54oMvJdxTcFqalo8kVtYKeH/yRLoaCN19XDlInHUhzmyeNUmFJ1RxckA2gPdcqH0sE+MwsUwipC4qlSydkfhee+vo3PUTBg+kj0pnf/9N3IX0H2+OmtKz+WzJlrFBe9FnsX50JfngWZFJBah7Xt5uSlHK39ipu11qgPdqlLKc9kBDiq1XpkkvL6Ltra6PsfZlLg8nNRcNVCs1kwvlwuMhv/llMZH1iAdFxvCIE9dxafre53f+we99zRpOhe3qn310L//Sqi8bTWp4t2XWWp7ZtLLkpxlTrY71UVdMfeVU4ZvU4PPicVmyfZY9TzCC3j4SKTddKNBa+Ug1fPQMiwrG7q0nT3r74v6oOh5/55urz4Hr6F7ltcsnjAupwzv3r/3dU1w6RtHmzSlZu90/j4dWZperv7xZEvjcO1eMVn1yp5K8vs6ei0ulnpV2Pb1XjkWTA4ro8hjeYOf96VRy9vtv/OFDB5wDW/K+D1qEIcMIbySrvbGFhTzSwBiSxSkvi0iE8YRiNIAsGydwj2O9MJJLKHDBuyMrJgK/CwEEgAgIQN5G1ivLh6vMArD1/adHrcbRd8kg+87av/tXf4lRAyvCrND5wTl/h8e6JvDXpp1pfdhp8gWc0AUEEodKIOqlOAXp92umpay/fBbXXMW/rnb2Wo/VZ0a7ac+HK8MRzvHxVDwxG96p9zHgBD0EGSIEaSTyKtpV9b1bd0Nw1J/22ayWMOGWNq32K4ic8m0kQmEM7g9dIrVxcbFKA4uFBqpT+uggw2Jnzy4S3hVvr8Hi5vHNOm9Rr8xl4+v+tmG1Bq3y3aPxzrQ1stKpNCd23vvFXbcI0l4mF47slwd7g9FFa5T1WwAA4Ji+xHzpWZdrDqYhNKd3/BeZnzyoXD45dIJ8CDijgDUQNCbC7Ff4RZZxW0q13KFEE5Ixh/F7YEVrtNWePh2OI0E4tAx9cvPgyivJ7pcy6PcD0MQGkiSYMI253RNOAMsL2qfvb/cGvRI/duPEcqEwVWUPB8YPR0vo51F/pm3WAlikr/oGE1HvtkdkW/SnlvJRrsmVp4Ktty+fTfp8Ee948rRWP/68qEG2sGdTru7cpYhlsODRZATZm8KUdsOzDlMrdU2YTM3N0264WiotXnn1b//tH6MAHFTA5VOrADgjFdOfcdEVd2d/b+l6UkaIg6bUD7GRGbZVh2x3RBoZR+KAAk5VHUY8iYVZZuvRID6ZgomG8DKTFHqNQUuY4m5fl0KOa1wMw7VLHtqABEXX6gOSogljJJMCXpCKT3tJ2s2e8Yx1nlTloy3eb/sWIywW9aZx4EYwQle8BGmmSKep2XUT8KEwzh98XEGUlqZayfzJK988PzgcNbe3SDcVy2bnvrf88d07xKa0eC1j9eGR1ov7PEQG0ppCHYBTJygkMHn00dOFbHItnfRkkySkNjpKbdBKGmh3eyfkp1APc/OoBDERBWee1g9OnzpHpzKNR+3u548VManTdb42DvnSMO0QBKBtZ7AFKBIgYbBdHcQjQT7KDdpq3oboVlfoMT44HjoXTsYTqj4q7vV3MOR+94BwI0wQm4mHJy5fR1BlAaA9vVqWR6r70vVzgWCg1ZNtCwKH7ePqJKREsxm/nsN3kWGd4v7tX+8HU9pyMpvuWYd3a4ooe2R1tuC+MDej0vOzM4X/9PFPeF0ZW+yhNwA8hOwPEtFFxUBcidBgMPHB7M9v3hq2xy9fmG0XI4WXMma4z9+R2dJAjyUEG9p7iBn8cO2rSRqRxAfSfQ52rc9PaNB6WJc+2Dwzuy7GZx3aafRhw1ZQwCtCOR7VEAsd71tJxNcTeelRq+eoZBLrwvyJRHry2FWXxSAKXLPzYT9zcKfZL4pOSHhxp7J6idgrSUpK3TjJPtuZ8sOyK4fMxqM3Ptg+v3D5wx+XYrlwIRYX+pMJxFxYyHtWE11enTak6ovn5AKb130GbDiWq9nse+N4tYPQIeS137lmtdHNTw5YRnnp+up4oB4+bz2/+cLQJVJHe8fTZEClTaK9xXUtLObNZzkiBxGk2/Zksc0n3F/961+uzATmlySvD7JlUHw8TCSCbc40UcVPIk+ftzOngDYWuWMHsa2ltbD8N5L+ivjD//B07it5aRqQxk5sBvv8r8eNB00IdXUbo+hX4GZd3qqSYlIPv7O49fEX5lOGYSKL3lj7eWc8mQz3+ycCgIoytcbgwoUzrvRy3y+7EC8zV/BCECrUbCYVXgJD3dwp1ueTxpqP5UQbFjGW1SHTgjCE9DmN/fIIMjPehDJxLW4gXENnU/isX0ZLugNYOIYDCADLmYGhKecBDHBQnPOpUpXcee/p1VBidj3RFUawofoC8cGoPrBkXyFyNZToliqmAeRiR+3bC94c2YsLDYqEzMKJEwO5ibRIx4CFEZrcOJuYX+1sE+qgs/3hA7E6Xb+ylj+XPTufbtaHiRiNkSjfm+j8BD2dje4jsXGt12zrrrZoePRUMPXKiZN6yT6sNWujkXeo5JOpYQ4/fli0hqIr4w/bqgaw7WNQSKf7u3zXaKJMbPZcdLRdHB8y7ijafVB8lAydOZlw2YYwboPacVof3zuG4xHDwEmFJBa/9RKCgINpMcaOPWARQB76JGntPmcqxx+81/3md84nYrnqzuMLX70+UGrKdl/xWJFgojAb1KdtVYZIcWQBXHcj9UZrfmNl66Bq3D/M/cbZumMO7vdJT/DRF8/9Oax863N3Gnk8slwqYu6PPbq916h88KxhVirwbNQzmZ59aYVO5tU/vyHrqKtrHhbcbk6xeBTjyIADlTqAsNRshu2OtD4+FgekN04tebOrC6GuBFOqhqVzc2oVclPe1LiIw7bC+gRlaPb6s4U3Q6eArehVHp5Qcbcz0tH9hmRZJJo5bwLCApNQyHRsc7/Uzya0o1CCOjyc8QEDwAKvPvy0+o3fPQOGtQDl5hTxmOfRiE6SOiQ4j59vbYfxeN4zeV48u5E4VsxnB+X3PmmeuxTd9fT3gtGYA9EZn+1SPZtTnyz2jjDXvBdLB13OBJD21tF0Z37u3IJf/tMH6WXUyuNRUoFUa/9BQyCc195Jfnhcx4ERPXNeo/n3f/RRIqDFzobVg5qoqcOuaN/d+0IY9GES1VE/xfgon5uh/9abSzvgQDm34ZR7aCTrJVrV6mAuEcjDlML1+qKzMBslfERvMgBtp7+6CGn9ght1my4ZoveLgynaoi3U6pk8iTOqy3h4MKCGCW/IxIne/njWHd8eSN2kNhk5YcKZWdu49i20OyoNt7vx88yuBwoy+gUdHn1WT//KKcuxQU2MQMjhaOiIg43ZhP7TT/qIMew6f4vMs1CYhaPReEyYWNKkRRfIpTd9Nct+sl0zFGexwLh9tI7FOy5t6A2OGxLfqX7r+szBuHJQbxwfGglX/qW3Lt5olevPXsBOwLTdozvdDWNZ36O8bjsZ8OUL68FoptvvTDho2D4yRNStuUdlHnVCPptTDiUXla4+1jMS4Tm1Nh4cubgE5V555QokcsHCTr2vB0MU9CTCokt+7tFe7cbnjm4nZthYZqNTPvBPIVW0GMjwELEXm8WYYVi0p+dhwieiBy9ujOsSvegq1bvykIyHHNoSddKTnEWE/WlelQpzvmFi7qAlzQ8Gx20NWvYHGc/p5zdd/8+3f/aLrVlCsmyneOv+V/7Wq89vl09Dnfx6YlIb3vbXiVQEqwAYmJdCZ1Tg5GO+zqhV3p/ADuHOLtbub56/trR3Zxvcqpu52UoCPYPUn37RX/61y9k3MsVPjyFF+I3vf3u412/fE/p1J7a+lEnlEadp4BNIgo/62uwsHolE6/dLH45kRfKFOzqERVo7Wt5j7PeE8PfPXE1Fhtuc0Dy2VYjyuubUgKwrq988Aa2WNNmmMP1+q7v01dX8S8E9vnPy2+/WOuWEa4ZhOwOlXJRSh7Wp0CCw/jB6belh+YCTe95sfOGd1LYAsjqnE+hf//BelOTObCw5Mcy5muZA+xQbNh3yuHpoeIA7DkCDB37IJzuW2ZXHfsWZwDrY/cU4djK1enYOezKWZaxtGJEFT5e3iU57TEB5TwBybMiGVNsmYdidiDx5Vj15AiZ0I3A5duMnOvLzD7xX5kk/6Ued9s6hf272wqF280GTX47SJy6fLDR0P2S5MFG0vSo03h3ZAQbxY0a5u/1pLR8K4JYJMHqv3M8mPTMRTObHZUnpiYolloUXnMYBhRuJpsZgjivoRquK5KSjs2yQHOu9juHAXqBCOKWxfjuyOzIMwkDosS4fP2xFEA2i1dFozATcA9zsDY5kiJcFvd1w4mF3KgSNVWQ8RGPpsHvuXEbVS08GcGw6CpPIjNuXSy+fRE5jcelJr1scFpi8izORdhOOngA0cXzM0XlvaPFVHyxyief/679++P2vz+foYP3HEzLG+SAhlI0eqGUaZ1Vz2mzy8bhrUJSWUkizp42a1bBP3iOOECfeflzv7QwvrEvPtqqJq54+NiqWISPs0kUURVFBGBKYkAmIE9EJQYInFXQR7R/+82fxoXbxnSy9FppmUpV7z429XqvhjRcy+ZPLfalb5yuU5agiTDqQN4w4iHnjo0og6ZoqsttLnLqytF+0IljAXWtkSjARzJAv41VhIj5RiZitnkjAUKz48+JL57DdJ8KF2eW//+7vJAHggdvFDBQAjfsTOhEhfXBJ8GxcjPC8hExGX3kDdrsG9x+0Ai5EVTAPHlBVFxEj0awqjdDsGssSekOzVVIfIdOtIVdzM6dfPqX+TTJKMdP/cE951nDXpWHVIAJ2iMAbwAQTzBR66kS3Cy7E56lPSCXNKV+b1Z4dTB9Ne9XqWJKXT6w9xIhqYEC2OLxRCiTC+dej06NStTEl3ZYK0LX8gjXQbgXU/Dsb0UQUtMfdh92uAwrBMQOh/kG/2RwtrcJuxjvjx4JumpO9roC7wZUIHPPIrCViHbtXcA+Sfs9gZFEM7U6xwVD4y91YSoRQ05gYWtawFWHaawoleZjMEqloqF+cOjpGucNkWBrsdTZWX59/fcn9+I5Tf86YLqJP+iW58oun3MBYnwYgP47Ox44HUxyY9Z2O7hrNwHSu3iIIDE2yddNJWabccFmTkkRoT3Z6WHeycW0Jn53e+vhp5Us7kg6OIZkH8nzGRwbw7c16Kgrr6bkzZwvHP6nc+exu1lgnwrE5uK/sIhpkB/24aYF3Lp7qSBjvAH6k98eHOMWokNuVWw/TmPkl7db7gT4E+VHP/oBZIPFpqGsxndrUEBDSNZoNnuCnGu5W6iYCbK1HIqPmmCEpuNs3EPvkO2d6s0h/UneA2K+3lHFX0bRYjr10+uS0qsWT4aV3cnJbkPt0pwEiCsxKcdDqYTDSlnlPJka6Y/3RyMWr5kAzHCZtE9X74PRsocWZD+9vAc5OT8nTvnXrxuSiN7x5aysdrMUymthBfQWv5tVrH2zHzw10l7vgxYmpDiMmIGnKnVo8GevLpIVHTLbdOepsffZshjPOfjPRNOLjux1YN2cp/GALTi/Odo5GAf8ySNpkn0ZT/oJv8aBvThlp7N6zujiChm7dG67EIgurAULFcRw1bRVW7EdPuPOnU66cLb6ozC/imtlwp9nUemYiIR4yUuaON+8de6j+cKQsLOCTnap8J3Y2mt65tR8+bvK25myXc3nfrW7nGlwfrrPJ2LJ36gvHzIlKxVIn7Vpb2xvax2BbVD1vx6GvxHxi6PZnzzfC9q/iIcvRao+OkIU5UaO3bz3TS/1fXV4DqyHw5AAkVVTng0lm68NyDJbVauMf/u//6aWk65v/5h+BLVFWnIBrNGIUG3ZDDBA51eWmTAMWUMBC0OIcOWx0sc6g02t+47UIPBRgBBvKlIRwisu0dGX1yoV5Cj7od5q9foHyr8YDrXqdGI71oQbkTjTjbdRtBPGzpNAFKJ1KCTYEAbs8VR2Ln+jy4sU5BTWfbb6AD61IKDjVXJPpuCNyoG6g2FDwahOlbEzsfIDxVpeCLCcVP2pJBtVjI3MsZvN2vcYZttFaCKVDHufAPDzkOL8WwoWDWz0DR9jcqtTu/tf7vfMbmfGogZl6jA1gHJWIEM8WkLU1n79vmt2G0dN781NiTlvy+vFh46Dq3v7xVv63Uyln8bq3jRCFnd3DDyuN6197Pb/84sXuaOXvrUmfSg7c/cIx1zkk4rKtyQRIViLv9QSI4WZ/CmWjS7Hi3pFnMaRGPb2HDVfLGEj2cXVqJsNfvpgQtn9YrEWm0rgPtbuT+sELHe2LCGj2xXJfTAOQQkAUBrkzwJ8Onr587v3H9V9Z9j19e+H/+PfS4ueHgTfmT39rg/uI9+Mz0pgXNf5grOfXgwgx4WxtUm1aB7Z7mYYEnx0IWqY1HemHlcHRwfbyV66SuqS0nRzVkdZPiBc87fJTkQGnv/6rMRAdHDj7Pmc5HDGs2pmzl+vNm/pkNEDyoOOwra7DksEcWioVUb/fHTONHuNlqgPVzqSZM0uJSa1D0t5i53ga9j9tiSxudMPLp6+7q15M+vDIYYhUeCI3NR+Q5k4Eicl0iPlJvV1UoIw2nd4H3lX/WpKiDidP4Hyq4m2KrrxW+nwyKHlx7/hgtcou5OJ3flBzKGb5MAABAABJREFU0sXEy0gu5Lgz/t3aGEWNKWFHCkEtQ64GMf5weufmJjFQ4xdy8yueg/u12miMT3cTZ4JIRoK16YIqK7rDwguurrQxJnq9ERVSejXV5dKVx8LWiHLWYxAt6Dcb2SQ+l41xvCmQxFjSVEUOLPrmvVCxPG50hLZNmjBrEqPtFyU84A+TZHNvZ5SmFVGK5hKWIeUtwy4e7DeMhSgYqB1CjEutgUtRy10ZHXWT3yj4chvXD/M6kIu6xQ/6CpDW83kaxoKvZ2sHjDWuPfviXiacj148KeRrh7uii7DTEIoIYLLgc6X4nd1Ds6y7v5kyU/q9kx7H3XjT5/hf2Sh+wh/tPklfv3bTnhRWCmi3l8djFa4qWfakVncsaGGWJjTdBxNplNnvPqpuPZwzVOkIpOZSwyApJ3S3m5pP+4FiDJFxzx8Z1pXFkGPrcHRKpupSP0MHfPnHUcPbKJ5sTX1H1cpj0GPAYRWEylzot+xGlDobz3L39n762ZNkjkZUcrskuJuUfylKpDSoPVK0XvPmgFhCo/Oulqbkl9j4vYO7z2un/l9/l/zz22rAVzyRu/fvb/ydNxZ+8elt+N2rZ367UNreDfzaTPvn/OyQP7V20uiZs/ea4oWCb31mtGsclZrqsJ8xFM/Jgj2h1K5y7vVr/UdfXr1ytsqpOBkADQjY1tL31/2o2nt4lPnVc6Zk3vjy3nwabzTB4qlTlhc92p7QqmzCNhGEXs3TQHBGB/2ye8KZtF9QDzhsbeVkNAee1bnlWBCTxj/aE/AglgszLQ7kfVmRYguF8KR61KiMupIn7XUxfh2+f6CdRgcE/oM/v/Otr688PTxW1/2hr8aRKpxy47XuVO0r3S2R4+3j5v1oYY3sdYjEyuKl0O4e/6TWJlexXtw7R1h8r5UP5/2nA5t1/kFN2PmL/7pcmKm5L67aLBSEYdV2mFir2pHNNTMw/OXtx//2Z/9CSH3ntb9Xx4wpQUVsjeaO9cAVPwDAhNu2mqGo6bO293ToqSqzVW2AwC6702vu1OIedh1SbuztJlKsQWE9RD3a2Y2SiY2s35Whm7v7IZiEh3D72RESirivrTT1MW0Al4ucWU6Kpq/Bk0E3lWfJUf24dFxPMP4gnRqVDr11KDE7D7kgQVAzwbDM0uXmIdrgLRa3JiqVyeC8W6v1+4meklZ5V9rZE6RWl/AYLg8d9WF86WBwKIsJlorjNiaJpG1SUR9iEH4CGSLi+Ki8q3TH4uhwO/T6a28tvpGQZTgVcJKCXvvgqWaNogZWbBzVtqcnLp5voUR8SSeHhPTi+alfZUFNevoXf3b6/GJwkTzavXV2MSBPR7vPH9Gw9eBuVdB18hseRfHvHxrXLie7utB+Ol1IEpbF1WpVYSKynE8eSZsir5uhsWE9elL0pH1ST4yEs+MJ8sFHD89diADYW7gQ7TVqj2/r53KgCAOuBHAFpFe9A1rqOfjWncaTjw7s5OTFX2/5rfTS6WxyPnV6JtRJxN0+rF3lRg3Nx2mTB6I/zmp+T3AtiEvOFGX8hRmXJjfeV7Swmo656bllXz7MkcMAbH30k6F4+4tETt37YfXb33/jV979TQBAaB4KOXDTdt770fHrr7mJQKzgU066oQ8+7FiqMXc1+qBWcrrj+XWKb0KkX9AtcnBs3HmsN3a4CQctk0BUtZ7Kvf+igeHMc8QCUynnkRBIza1IwULSc4FyOG7Ym6LPDcHoG/MOQFEAgJcBheUY7iI/J1RnxrUnwggGt0LhlXwIY+VGrT183mcPK9OOMx+Wxp3+s0fjd19fcGtq09DYhL/Et+L+7P6flwN+xiTR5VORCjI+eNgiB5P563HmHCz0jJrQnvWAIAWCMDCkuoPE8OcyVHEePx50BZCKA08OQpIEZZqq5Lp41r97PN774TP/fMwGVDRulGWje0/iBkhqMUhCrlKzeeXUclESTc2IrIVS2ZxbYQ3IvrMpqJB29eXT6LBz8+6L2UIkc92PnF4uFkeU171yJjD968/6BqweGa63C8cu0RiJMjfxGRN7nxvQ0lh2D18YE8Na8pCNO5URjEaWM+HVQnDVDR0Nyvd303FntNOZnQO9iunK+EqlEYZipzFXXlN1o7dzQ8p5kq0EFgtJXmRoFbVWURp7bdzHhkL4kTBVxtPu1pDElMuuNCtIpxaoxNmcvbXfxyDJmQSwlFhCVBw8HvXhtU7SH5RfPI54Udsx6dnAXAJzCfCkpaNUOBmJu6uPCK7ZGYMJC5oyyJ4O+wKeGz+rpWbmnpBVL2rDum2b7OsLhU9Kx1aGPfEr2VF/MHncXZlRRF6e7iP+JEY4OvTpptEbh+cp13TS/LxuxXz+y7nqUX2XtywvL+5vff2bJ3c2ZcrAIqdC3J6O0Gz2N66I7x+D5tCK0hjLxiO9zz6+xzF8KjBeDa0ddiF6zYOvz+l0fPissSfSp5eTt+stude+fu26ZXP2SNl6vDlpcgE0cFQ3rZiWp7PnFrBBUZB4VR9rtRH29ctrzz6oNVoyFE3E58YPft46dF7MRezPbh8ykwWW1mbdzKjOHffFk9fo6bh50KZj3sVwMrUC8qUvHxxUUDbnlilXC4eXX83NaWD52pmuLPa6rhnGKT1ukmWp3dTISHjQkdkVdzgUZmM4Qs0EYGQow2bLtWIFChZCwMqcbz7sjTWHsEM7uYwHxwXo0unv/erXPLGMo8pQahZS+jiZFN8T5qhe/Q78a2vh/wGAjXfpkWZTyIQtKZibAlLMb3kAACwzM2iY0VzgdMIZdBlb5XpViUbYydS3NJ//4oPNT352uPKOdyZG7wzsdqOWJiN7hzvEyK9TgHDY8mCUz8ytzS6YYFThDmAcM9ysEXBGCkEH3G6SCPqYRrEoqSIUYGEIdrsJ3ROcnYeogE+VDQ9pjPmhaVuQg6Isw5gOEIEtYQjkKGsWzHUnRYEMDinW8HuS1vCA8wDYgfsQTJjAbhgUpSEMExH9wuqpU8pW98XHO/EkvpxIqijy2veuHEfQiqSVPr0/vHt347tLnVWfcsrdqaiMGpyhoIA9oQu+nxZvvgEzF+PhWzdu/WjS+713l3DZqRXHB5AJGGPrbjWKoe0nz7PrM7onYpbvfPijfzPz8tfD3gXKmUojnhQcgxWmUx6H2/4wPq1Wu6UqGwugHpsq7w4ExWmOlUpTkZq0yjPszpPPd078Zvbk21dbVWKYOYjQ/ZBkJ0+aN4aYKMLZdBbOQT/6/MOBoh/LYaKFp+OI9+L6Es59+s8/e/Di0ZnTUdf6agoG9S+/RDUDKnixpRkeIeM8zhqcwtY+L1WZsDw1UEQcp9Kzk+3u83Lt6puZxWtBTQf13jNnBlz83/7OIQAIADEIdAColpzvffflrgNykLpz4yCbAxGzY53MFrxxtCNbPscfStZrJYvDMzHASvp0LCBzwdmw8PDDz5hXC1JcA/jU8UZXYTzH9ytddJl1VbZk52ndTJHhFGHUhgcTwFhOACXYDprfM7cXUd4Vt35azImoQbHJo/6UNP2pZQ0BQeVAiIPD2wDDQMEPT2NW4Fx6eSb/8ReHuKQzi5GgRNcmBgS5shfjuzzvmQ8Od9T5mPVXW+WEz+mEWJITrGPl8lmX2xb9DSA4AGtJDaZHTHTDg7/1Vl4kxS6ACTISp8kA7B0PfY46zF3fcM/VKwYyuM8Fo2CNwKdhtmo0qtPpWvZ8c3S48/n99Imri3OZ1HJQ4RvI9tG+RbsvL0j7025TzAdDzPe/OT6VM0eVzns9QhzFzqe7w3EsGnRyKQ/tV75o+RSvPp4SURHimlzK87E+OO33BphAnCPbx2q9rmTmIBcImLcHuJXEHEkDIsRAiGBSQ1rm402rJrrDb8pdL+x+1BIl2JnJML2e4nf725+VqoKIHQjwlTMojOKWHcvNjliDbo240kN/Fq/dmcz50zol5VcjzNw5yK7fqTGupcRV0O1Dw/2t/VFL7rTRieWK+zy7JTkWiduYB+cmYyqx7g61jnTQDhq6g+WYlbwr5ZLd/kzOQivwRDOJ1k+2qN9LJ/4f55NH3LTctiEHfm2j6UeFg0ngxPq42bUtX1iV/CV114cFCOkzl7T0zbPaZ5uvsvxfrbL3vtxH9ekHjx9Ib7GpbPqwL8dChcCe5A7HP2y0xrf+q7CBvPLSyWLJ2y9t95yAUK0Ouhw5ox1AnNrYUawMfLCPY0KSUFoz0Sk/Pt7vOZJ58fQZS8Vv75fDhbCViWXPBggVCY9UV4Du6wrBoayGaCjrc/GD/sGTzjB9KtRqDfYOxhzFWCnIrezrMIqFxyp0PIuEgzDtJ8GhYADD0TEptehKuMj2zXbw9Y1Y1I3fv4cnQ65siNPabGNgZ0798T/70TA30asjomo4qs9y+a58Z4Fwuw+fVf0zUWxc54qtpjefYAS8X4sbwVP/+OuTdpH5+Cicj7pIF2J1aSqhCZ3GLz4zL616YlkARhDPObTgjDUY52dTqdEv3ksI5B/900dusDhquIzRceKq5jhAkvqZJQRy8aaN2xAFhzF79MIK4JLYUhTU7rZebI8F4ejSNdfLf2uxurhUeVGuHux60Ni8N2jihDGHYQhpoeqIt08HwlN9akCulVDEEGXVpR1W69J+M5LIsrEEPx6M9hsxyuKpoNuPQDYijKRIJORDosedpg10t8eHShpQ9GAwjhI0Ggq6ScYh3Xh5W6EmDdHA0ivLmOSbU5X6k6ED8cGIuXlY87DZVDCMElYXUIkIJloId7uqjoerOdQTYq8X5veGqltxXtwtw1Qgs+7T5bn+SH10PAqlw3XIPrc0m1aIBDQ8gmyDMg5rzdyyaoWJilD7o593CdIBrbyKw4ghuo3JfD4vTWaFvlzjmmPJghvDTPZzYpn88tmkpSirKbgpHAHK6Ax5TSbkqUzGbMyniOPtrf2SJ01rmM7ZXUo2wzOeS2cWj0vFXrGKtmYiaV+iZ4xL+ilfAAhcbtbvds8FEXrnSckC+vIqtXB2Jvd7S/udRrm+T0ia22VcvhSwcMU+Hq26yS4FPBs5nHbDXSOXpCeKrg6nk3I1FyKyl2e4RrPXEcz99loqFM7Sgx1upEynVhiOJP+7f/IHL5OvYwA4Nrg3dIKMdmaG8MDQx5/VJbwq5RYeKPT1NxLHY+6LL4oX5tDbDwwyLS+fiCqKxB1NcAKZXZ3h1Pqntw6PGqVvpRYaHR52cQuXC0925XLGB59wqVN6NYKnR6Pe/uZUFKWRPjwEtge9UFiPdLXpw+rrkYSi4kW7bq0EDb05GhmBlKeXoQ4rnaiPocZ2/jrwEfH+OalOu6q2ay1idzu6J+mDdWTaMmzC9cWnvet/Z+UXP/0yN5EnvOGEiPl3oyhktBV0RQ/PX4qE3EL9i72qC4xpEJwBJKzMXF8PIDhgiKcPDzuChEDKdGpSlMJi1c0jDj6q0SbUDfm8Z4lyiU/oLjKEA0TvlNVsCnh8HrE7bIu7crl5VGW4/igUIttGx8achVB6vzcsH/fNJCbZjQhMpFLhUkn48f/2fjTJnpvPnDi9PhC0SdtJOfrO0YHRGIx748IV3EhPy3vcPAnQiafRM/yzKy4mLY9iGudeDCPvf9pkYGDSFk5Ig66WjzD1h9NcMATNBhAlzPdlZ1MJkhA/hoAc4fo9NkDNLEQzfkrhBjQeDfpN7taQHzZpH89p1CjMxk6Egmsxge8/3+umQoHolWjH0I97enAjBykC45jqSL24Fh9XhGgwMG6o5ixNB4OZMGU2UbQ0xtGY60LMrRWsEfHK6RSvKtp/LV8wpNqwgSR0Li30knD5i+lpJDYJOBgN8U3oi0+5t86479xozr6cHLmzameY6moUMLMcGmkq9z6uEAP5e5fx5/d7fgBqPk4VYft5Bbn2rWh1+NlfPnb9Gup5idDbY/PZeHsx2MVzZNl2Dfa3nlbTK8vkCbky6IV0aR4DU5ujQglUo2KCYruToWziVr+3cSq7+eUAdrsxlxsngxrdIhAdIFYkFOryvKb0DJvERXzQGuRmmIolpOd9eJBe9BH72lgKixMVdSXs+VXPdCTv2EJsNQn7EvT9mjHmYguJZ/f7AU8zmEygaf+rUeN//Uf1b7wJ+VwUqI8h1ZiIjV7PcS6scDSy5A7+7je/dnvzWBCGo6Mp2NeQfrfZKa2/EZpL4tO2DrHUtWsekjva5fG571x5c/XCsdJFp2iO0Xsmtfz91990JwAADkCdSBIWeeC3+89ikbPg/T+O/u4/IegXyv/86plzr7+Zv/aWffBCZSQdR5gFl6ko0+EwEIuFGawNuZWDoeOEKnYDzjALCHbz9uZ//0/vrl/IzryCLI84H2Lt7u0ZvgTs1rO5uCnxDi7bitEo8b5k9qjYIQuhSChC+zygy6iIz81Q0FAXa1MaQT2zydP58PZu/entA27YRVFgA3dqMR9JRkx+IIwlzZYR0oO2Gsd1PYIpuNNDyhKW04jZ2Qiia80pPD5q+zS5kaI+7hgrM7nmmGwMDJ+HzF/IQECAjkqlbTNXiEBp4sXAEpri7t6AlCTD1YnOMKQReGMRy/3emS/62u6d7RnWDdfUv/q4FhSax5eD5ixz0GpCioipMXXckcJhZCRsyDvY1TNVGQ/KfH+//EzRqFkaxjgWudFVIo1P94uM35XeyIZdpsKXjW4fMgIGbk2nI44fTjW3LgsD0ZP3DofiqNQLhBR12tepmWPdWogYtghctYEawGIuciWT3n9Y33bA2hU0YTvtXht0+aQ/EQ/EhnvS6P6dSVQcPGzvTMgLa5nwSzMjztEOzJt362I0fbowf2k+XC1NNZs3XbJXFasZik1Gio8aYOKs/frFxuZkR/Jev7zq61exnWG10Xrryunrl14yAVdzwjNQFw9Hw45S2q3P4+15EsqdcinE3OPucGRjyHYRGTvD0DlfwaxWGwvZ0ybMNzpNOsoatiKN+Ga1LQ/U7Vu7I0fdLg43so13Ti8X+yLq8vRbnedV/uzJ7MI/+CrdGnQm1WbquRaMFBLkg9t215dYF+je5lYPnl5sHu3yFLBSgbkY3/YltoeJ9fAnz+uvxS2GJGaTy4zoXdznvnzQax8Pq/BkeSZEO1FW4pb8+RTrOu+J7vz05pUTy8Jz4MvOsHgdCXnBUNvZ7n8imec5MI8BCQOsD3h+47Kngh//xZE4MXpix4e5k0EnSdq9CKn76dl3AlzZcj8UxwPdxXUtmN4za+ajylQVXQQ0fPE5mvZPXIZT7RoI1+BFEXHchBf2Iag0DidZdXXZ2uxSw+b0p01iPUb70PlsGL8ud6u1qug4wyjG4hom394ZavZUa3CUz+rTHU1H0QE/hmMQZ/vi8Tiz4CbCwYlei7mHXi2ccgGAbPXHI8qTtIyTAra+6p/xgEePDIi34Cq1uBzVRxDMc8FE/qWlkLIwK6fD0z97ilKGcsG1+8viLMsdTgYzXvLjw+aMezoIRwIw9MP7WxuhYEn1R2UeISAiRHJlAfcEPBRRHbZ6u6WWFsoABGhIgfAbfloZ69OecFwRaFXpo5Q0KM9cWS9Cx8dbRX+jstjqZSPQVgFVWL5+p3ipPdksdmJzCTOECvbU/3piig7JtJfJxOKxSOU/HwciQYsFjAuLbCn0UkrtmDd+UFul0/v7rXQulmdGtCLsVsZ+3FmcT2z96xvxf/n9o+Vx/5djbatWOOsmF7Vyy3393TeVqL9Z/izYVYIm98lebxwPfvM3g/ztg2FFxddypW/QgVhkv6RQHgLta11TPrO+xh0r9XF17KY5QbZMKkRMKpIakCDGFX5aPUzwOhW0LETgxdDJ+MxSwWrdrvgogw75euaAGHEG0svgrMzb/UO1sIRmacly2tFc8v6f/DQUGXlT1hE32biUVaxSUSCPZtiCkOuo8Gsb85M/ufkv/l6pEmDQAPmVgO9M3llYiNfcPihhI3uT1oR66zcuKEe9Zz/aNpcuRPgIAJIxsVMRHFgmwtpAjcBAO2jdn4uucEPHj8CAICKXxa3PjpbJD4b/UVx+ez3yjeSUiTZ/+KmUDczMegy+S8Kk2ZOD3qkq9TH8FKw4ERj8/B7XmJa9aA+fjBeyrO913yzqevSjzfUFanAxFl0pLPgj/Gb9aWMgjSaEYQdyMeCjEINnKezG8xcbyZmlMOQy0ejaescSJF3QYYVCXG3Htuq8H4RfmmcHc52HO0d+NuxL+jXZmYlGEMiOOC4URVDbHRRpZsZ01R811rLBldWTsGy/97hIJmXUNXAtUdGQMLllSP5g0O2nPYRhG4qpFkvNkE5HlpiWBs3PLrsizsHzQ77Nr7y1cOrU6YQ7WHuqvv9nzcWKuv61hNYbGKJSPuROXU7A7ZwVghQYC5yNOlNkIeD5s//x9vy56Uw2I4jKB3/8yxMvRfd3NCifOPudE02psn1wzN8Vf/pY/P5Xwouq7Yy2GoilDN2SoDXHjZ5g2FNdNiENGgPdo5CaP02Yph3U0czl5ZY+aHA2ZyH1vM/UOKs7zTbDC9G1kaL253urZ9NI0ULHU8NwooW53IJfbQzvF8vt55wcAl95ZYbcWB720OrWcLg1dgby8mJ8YzbkSMzm06FD9Ccy2hkJ+ZR3wc9TgrjbF7iOFDjs9fuTuQvhvirslHopMnrhpexvfPPXEtHrACABCByOWMVxCC9eAAaRu7o+BwEAvtyUs96R8LxfaQtzry0hmuHH7GLV0CGQZ9kvDodXX0nsDzh+zF+5nPiEGx80uJlvpNLKUfEnW6i4B/sNkQOxXCSzmNm8u0khSTrOeF8+u/C1M8/b5uaNbT+ujFjnX723n1iRGBPSHcc8kCO/w22O4POx/Ocjr+OEXG7bydtAdJoKjqPe0XQIIQPWB/IBSn7GdyizZQxyq/DDDycxldiSQWdf9IWjQdwPpTyqo61iRrfROj0T1BVU4IYBzhgOQe+Xre4jXqiYc1e9XptMOinWTzKEh7Dp3XK93TZfPXOWuOjMsNr9zx8V+63508nhBEIy+cHnnf0fNU7+ZuTiay+XP3oRjTLY8uJY77nCljSp52iX2etkqMjYFeM1KBDBhE7bKNqLL8emJ2fCp3C143TqNU/iXJ+Xu2on74uwcCBxxpLiZIxCav7a/pMhYWdXN0Iow2ZcTOugfAzq3YNqaMgHwj4a1UxJjBPI3oOhZuP9Stmaja6+lD2zmmVnaJxEEH0cYt0G4D85LD764t7v+rxQwthvPiX8HhsH/DGkmNQMazfX9f3XpJ2K+IVctUmcxZiRC9kvTZMRL8KPYXJMj20/oiKKzaI678jhSNBEXCxElSudyWBQ7xRhpzOzH1JtplnFCNlrtsXFk0nS1zFIHQnjGjWj6Vl0vk4dNJvv78YHDh/EX3n3/EAIbvxGSvW4Jk2hNoz4TswcOh0claiKMTMTk98Jflo6DCjYAHYt+GZpbDQldM+4satPls4GareBdWuYZlhBAPyD0e0Xz1dejWgeIDb76cSMkb2CxVbFbsOZHOZPLwii8fxwnGLI869Qn390993M4kAdDYUB5PaLA2rv/9o1xLE3hbx1KS6NeD9k18pTlPa1eH5qiU2FXj3L9gCVSnoIjbPbfTG2RK4Yh/Xi2axDiJqKIA6KunXcbXkgxYcabi9r7N57RtpaIOgGpyO/9Vp09wONH5E3tpEhjmmindV15mhIt2kmGYsuQMuv59l4Pr9X7x5US6Fmh9d8Q++F05kHf/xL+D7zR390j9sG/+y3rph2/rg4xIDND4nn3Uo2SrT3+/jy7DyzBIHgYFT1z4SE/bZ7JXaoHn/7X35Vv988dXM8fFDKzwbp0MoB4BGnqcgmZHswAqI9zNP72olLEwSCN4uj9VWl8qNxx+Qe/PLmK5dT8RBVuLakXdZJj3C/VoPKcAXqRBn3xLB9+aQzIVQcxENhzLA9LB9yguPK6Gm97w4EglAQTHSWNZMptzAcv/hwb1qzwu7o/Px8YSO8YNmx2PxQMna3d5tAjXixQCJgOBDqY6MFlJ7CpndpCSXVT+ucNCKyp72HLyZxMtDvEgSCv5RN3tt97k+Bk+uzGE3eKT2dilaA8S/NnalUG5JD6OOBMtUUF/b4k1bkmUFsQMP29MKaCxnh1vtHA6w7m0oHLifxRe+RHfLytVypB3zZcFC0+93kaugHP9t1w7vZFaxZNdvPWt/9XjCYdRtyXdx8ZvcG0TS41GHPRBLdB6MGJk0iZmwKowM76pEsRuLd9lA3SNIbhPGShA/nKI/C0OfcXIjwW1YAq07GfTMIsin3emKyV7lHQSkxAiBaY3vHjg0EzGU6XoZwm166c3u4/PXXrv6W8xf/7q+rex2mRE72uE2Reynpqp1JVPBi46DEpLO417UOUd5w2Edqk3u7rR5PoTbPIZkz7r2FrLR19yu7ozEr2JBFyb38t349Hj/lAGTbHs7BQdZXRSrLIq2wcwsICt0FEHTn41dOvISiGK/Kc29vtAejZZ8meM0QXR09CwutgQ9BRj07tp5+7wd71TstXnEuL84zij8w8aVmzT3UnwoomJdIwSGL8eXdFPTcAkW5dDyIz3VchmvKqEUvL42byjt5Pcj3/91ojIHmCqBnpiOOe/+LoSd1dlASzkVT407RiUKM0ExoU84xod9fOd1C0c8mx7zOIzKSIdCCWzoawxP1V/67b/38BztuS9SHrvjiykwh3PnLT1wI1RLp/YF8IkRFZDJnD6Y1AcJdkTnYeVp8SkOat1UxHdYfOUt75qJ4vYE2P9p33Ex4I/TKVzfgWy+kW+LjYS8V8pw8eflgFKQH0kyIvFs7huOp16+e1sUQ29vxRKK4ZSOKNt17KEbXpCwWDMVKj3U/I05aipYP5t349out+GquSkIDJglzhE1g7myY0Jv1x9308pqdXrVdk4FGHjt2oNfulluKbcFezBL8HOrQBhs+nrqI/rgh8icvvX1xPpWlXWfOQNttNzfYivVufLStNfk07ofHfaTXWguFhAjerQ1FSnK/svHF+09g1NN6JGqHzn97v3U78ZPhTXvdBaxso1dCc/FETnak0ZA0tI4C3XtSJbOUrDCLhWjEH64oRHaKGRNr0efcJlyTOut0KqDSXFxbNadWpbqLBO2qPPHYEKJBydyc3wGliYwkscXXZ5dvHxRRSagc1f/LFAyJ2PVzaZy+8+Pt+csJ/HKo9yc7CcGs+lNznAV9gpz69ivbf+//Sv/2qzvAzdzYV8+FCdKEqoeDyMaFd5ftcS95LVvJBcKZnLg16DTk9JwXPXBG3ZamdoLpmCcY7X2NwUJkZgy2vAF0I2XwDvSiP/HhhK/gjaZ8BJsUXZGTsUeffw73R66Q66Dd1H0QCABYmiJRHZhyXgMxOuj4EMkxZcF223abiRi1kY7AgozwhiPf5cWXnPLh2K6rjlSF98bcSC/jk+DC2sk3rj/74Z2DD2qtfZn9fBALOWMMPw/1ozEytn717BjhFmbvcaP2rS8T8o7qC6ZWUEEnkYOpVC0+haYtWPj04zuv/r0Tj7cUXeVm3KO9NsdyrOmeHvcrBpmeLywN6s1jeZh01f0E4iBRVyYPJsVvvxOtf/BFs53NzbGtXTgg1J8MvFfOjI9K+UiMr3Q0ZmIUov7FnP2Tu7e+e/nru8MvZp2Bm+240sEvi8i2IJqk17Pbf9zvOuq4oQ7i46528qorYJIaNDasFB1MJRlAUU8/v02o/FSj8tH0gNEVAtSO2pTLjQm2JWuThtzZGif9KV6hFAd88GILceBgrDBbyAYhqH1QUq1Jo9OzFAP1hTyt+uTFlhxLw35xkkNtxGvvbJbnXEk258dz1F/+yY2zkHbmqxszc5H8TOyzH20Onx6tXvX3RE6wtcXc3J/+9YfTSoVxjcLJ6MuFc2ZV14aGn2VcQdf82ch0LlXwLiaK7PM/e1b9ePvTvvLNKxG3QTC4nZkJN9vI/++fvbUwg7z3/o6lOyfnkbjJJM5/PbwA8bKmMjmLBbZr/WvvzK2H2Yd3958OqpFl2GYZDGF8hMfHMtkosuyJEorn+d2WhnLlvf6CEIkUComT2I//9Mt3ovRJxS81ZKmlvvDrgShUAYcTPZIKsczAq+S1lOW2eGcy1Q+fHimkWbqzndDDviZ94rWc0PEuzzJzmaTQPs4GduqHxqwbD3sCpOWM60ODnwJ3u3+KDNDeAK/u/rBWq8vuzw/dOHowGcTPZakDG/eF3o4l2v2mN+JJAaLY5iB6yR1zXHRw4ICnztQ8bi3FPDSD7lShPT7z3cWFzt59jUICOV+pncoEoFs32q+/skpG0CFhrEaR/W5X6LY6Zff56zncF5SL8rkLCZPtS1NheFg5qhadkMsfTHCab1JT7m4rtLsfPMuKwGOeY9Kz8/TDKmqMGC+VzIewrbrrHkgrg+U3h+NdPJX23+FwJ8ia5Wbpsy301cVuMASbS+RGD24ZK1GKWRJsmJeGrRfPIcw1hoMxIu7mhT7ukOvnLoc82if/7N94kAEgdEBQx0W7wYCuxMmQlTAmfg34ZKdVnRBJIGl8EQoo2QiaCYxHJiIIkztcKIcloosAdon9kt3XWWtycoPvTBGYJmbfWQqsR01HG9dax6MJnQ9PcKgw42oPeupI6isYFmYHURKEfegQ9/FQt6zNFTJyxNvabvrSc2Y6rKrmCFJqGpVeTj1/KkM6kV3IQpiEoJrHb42aU0dW1a6WZPyY22X35fC3zsthsPT2ag8JrjycDPThs09vceORq7e78+PnqmosphKQMUh7Qc+HcNbkeAqFYNBW+p1qo9VRCRV3T7x5v82sT+7+oe07BqnTgHyn1U8gO/sdvuo7u5ARD/g87KKDcdECobNXkv5E0EPpzzuT8jPDE9UpaCaR4FPWwIJm4mwkwlIeCQ0zHKuPEAsUkqVWX9VcKZNaVZu1GpqJe8ZfWSQX1l5REHRz96g+dJodRRGA3tMSmYaiRULBxmGlA4pWPN47Nn5r5L6zqwf3e29/b+k//iAQ34YiwYiOx7ofljOrqfs3B1jKd+pr70BjTZOI3fKUjPujF7zN4ZgrjoAmsj7viVwCG1nDp82LZ7xd27l5qzudio+OBa/yfKA7CzPpE7PLODm9fv7EfgWawKN4CgygabE1XvCQsCUqgppj/WOY6oiwN2UYx1Crqm9MKY5nFCi+Zyh1XHLC4KKBbvYHNtqDRahXh+ZfDV5cXPmyVJ58KXXee8S2m2Qul4ylItfcSTjz9RhiFtsUM2nV1eJ/4vZ6XH6BfeWrabsP/fy/3HbPxIO09WgknnBl1s+fPf7rfbJcf+mCZ1rfPxZVdn6JDhdavep61OgdPlKhrqaojAsXA97s4qxqj4QBHJrzAqAreFxnkIqibx53KBdBuVlL8Fo4VtpR01kIgo1HD2vrBbS3qXaiN/LQUedwAkOyynVzocjLJ1Gjp3FII5uW1XSShObdwjgSXjWHY+3gUIKstoaR9MxIH00Uy1a0UDxDzKV8ljppdx3IgaYiSuKEgyTT86l4Trdc/Z4SnwlRhhnx2Bg0IkQk5kbhRETB/ChOSZyIPnm8aQH/fASeC+AT1RYaAt1uerypaXpBh1TsRu3l+RO4Chchwxr3O8XDVlNMuT245Y2f8DVaw+mwHwg7+TApGpmTV2ZSCO2KJYsSiCTpYbP717UmuXkwENS+97QoeVZDfmYtNHtSn5QpyON/tDdydPkf/PGdQC7/5h8s4g3FfQGJrJ3OloZOsyiMnCRhyHkjWggjHPR8q5L8Cis8pnVNBH4othqmYVV1e1qDyYKEtmVIwgMuczh8urN1+8ARmNljHPLMFBnK65YSjFZdEFoDPkk7IWbaXAzNecxip39kwvbOCLKccMLTkw2S1W0JG7xofetXruDLkaCEpd2+Uu/o84PS05tGyg8eono+UJ7xLKcKGcqZHESoxq6UEZyDnpK8nttguCsX4vdKwR/7Quu4nZY8756/MsMuATQBHMoDcbDXM8QcoADFADtVezld8s2uo0BRwICNuaNe2AagMtADbrGQ29DYA6Y2uvLOqSnlCI3W7Vv7fbVvUBAxtLZK7YQwDQeD7mLjxe2nKir7BdPji86up4V+szPRPMuq6YL9OuGqjFo/fc77/G9ePpnVqLZIvfjtMyMsPa8Ye39SL9cBeQVMsowLMX11KZP2Pp7aSK21k3MVUOj8ZyWGSeu5PD/eE5JYiESFzyt7DZrwkuNu+9xvvoQ+3KFRNaBMsZ1JPugx6Fib3xOnQHFJD/2wPeePV8fuENIrGuUpUDrABQFCBIt5yITMzn6TRTQchWVeg3zQM4kxJmrWcrlCBknLXRH4KQkh8EnAwVQp4fHJYenFw1raj7RHIoUSgqjkE3ZN4fHBSEjNzF5L+VtMEsfqvIw2G3CAgRQrYU3StjaQbP+AQz2qRmqIjFE+hyRiuG56VY1nLUdBMrm0LkKGKSoEkg2nEjCueY0yInh/cP+nPzsypkI/iS7GvEokcEXkqNCaP+vlNT0wKu96kwMDbY/6TJS1H1ZqprCuO6fPJPb+qjwi3fn5uf98XBK3zX86D35uAH8Zcn87JavDhgvfe1Y5zWZVKuHh7jFLPgx4ZI5r2wbnmD3K65KUoIdkDO9Jp9b/zptMkkI/eAq6tsVoGlsjXe4Br5MaRy/YC7gKSb1mG4Kfyx1Y2kjMpYRuG0Uy381DgC3dry5//5IYVD8ZH6XXfclcaAaFGw+e5SD09p8fz50OW4JYbQ/sRMCqbctNQoyB4me9wBsb6BLT+HRv/h8tvrits9cL7LPmVJqiFrOamAsVtVapzc8hkbaL3Glra8HJirn52cga9//G315pkYh6s7oEQ/2jx+/dv+ONrEVy81CO2W5O8ogXqozmogzLmJBkCUe6+hZlhBh1+7mGhDWYnkt5Eto0ALFFKHIiFn22W4FlMHqkogorRtDFWaf3cnpXaRz/ZVPlmML5yInvnpC0a5mziSc36nMuEt/vfPEXDyqs0x9L5xF/kMm8eTJDgv5f/nC7cGUxfHVhVYATF9xaGecjk9JQ8YR9gyLAKxPvd6+wc6GDFzq49+Xeo/Gb307sNwbJ81FsTHXvbmq//jYJiDyEuCEA7u8CjzznhYztgYgdX7scOGwIEv+Js7yGezMRdvqLm/2LZ7z7R9MIAgdw9U//3//+m7+2cX+/sXouIx5X5310hCUHYMRn44xlzuD4cZlnLZRddMuEQrxw/IzFLKAI0kWH6umNVc2YRBYzrYrkonEyQNM602tN+U4tuODFIzg0AgZsBNJeR9eCMCI3ZUUZ9rVeo6/ABGDD7kwyEwqF0fSpHOr19G9Lw7E4FFUWASjreeX6qpZEb/3oYbk4PX95JpCIIYSpD7neXiXkC2dP5xt8uXqz41ieMR1eO1lYzaxM0GEfRj786XEGzZoBY9i0VMl0wdi4g/pw78tfnWmu98c1oBSbW7d7c+l4vWZ2evL1r0T0bYWGXbIiZShUrweApOz3R9JYTKb6OBUGTPyZM2FYEP5KgLw2d+pZ8PP/5f1pRSJzLIugugGmHWjrxT3Kn37p9VcGInuZsc0Iunxu/bheDCMkbHsFjQ04SNK2FiDl+NEWjuPKxOiwVJ1H3UmUimCTtlB7cURaLsrNvPvqtQ9A32KoBw+7MZ+bhyzbIM9vZHPJxGIyUG+WB6q5lJZIb2/oiH5gw4aktJpHj8CJi0s9nvyTP68hHtqTCZ4OF979ze+fOHMJoDEAoIljKz2vhgJ3EPKyAAXAQ8HcKFzDxkFWAi0h4UbDbUlasV67SHMGVbWF2UxWLIkRF14fdbvN4XhQDeedjTM0urI4WE4IMej51sNMWVu8HtAzcZQKIhb1wU9KJ+aikVAIdan75VLMMXOsFwYzHc7a+0VdituFzKmvXQzvd4fWwQhfXZ3PDuEkTe4oKQ2SnaBZFShDtG3PuZOkNQDaiIfDpmq6mDiLGkPrSLh5QyxiyomlOddSem0l2572aZ2GOX33xufnz6Z+7zcv/PwHOtytqmPINVATQZMTAM3yqo7KmNnjQRUDHopxh+JnFiPq49K990unzrtUm3hcmvr8uXN+ql+q22DkMajcOYSN+wVZ70utFOvh9kcTDGgBoqI7NEHGGfe0yck+dxdxEu9E+rUBIprNMTP1k8eVXqg7NQgF5z2e8TSAHnNH4zAaTWUoDVWr4wHCexRbS2gODZm46BnoJkPBgik5tgbb1OGzXjXQ5wn+wS83rwbcb5z3o7+6BClT8YBoPm38kkHpgrfHeoqP+AJgP7nTDeouCrZoUxmWVBwL2G2X2jd7xfHaaxGFZUvlcAMM/qViqAK8lIobT/BKjMFnyNqnzYWQlytLMsJ0SjYa0126QleHCxliJgAL7QkCx+eCtJpJGry51S2vhICt8vxEBixPB1gm4N0fmi6gGxQsSM2EPzMxUlZzNN3Rd235wZ1J9lWMJu3RSDi9HDDJSfmPNsWYK/3GtfVUWKx7T53Nf/BRU/F5Xj03KwD5+qv405+xQ54hcBxmJgePe6lgUgUzYQVfnoObAzlOgsJSbLfGj/204oJkE6FYrHjcxqfahZnQfnNP+fS28hxwl+Y818jjh8Krs9lzlzdu33vGHQ8c2OVRPRFcczDEFaC8PlBs9C0I8af9Pi8pjFTGCgFvGCvolZrOd0qM7vWRlvXAijdOj7EJRc9Cp0h6Fi4aXUuFQcOM21a+sBE5d4VMc/d+uos0K/KwNRSC5NhhbP/ZlAesEmww7j8ZLu11Wh/VGNgwM0PatMxd5f1H9YPDZrzpzUVz6AmfdSSMNiebyk8s3Pb4feVD96/8rXdxf3T8qK18ssfIklSHrvghC5jDpmhx9YO7z1bOp0LMnO3d224R/ohCLcjNQ31Y5c3+rb1q7bhndRuG7ZA/aEMhT6do9z6+Xx5b9Vh45s4zrd0ZjCXTH/Mm47YhwA1+Kj5rDwxcKH1KhHV/mrBplM3okx3BnCju2aBAo4NKlz+eEBlfxO+qHw9gWnHc+lF9z2wrqzMFF0EAC+oNGuVSORR1GQBxdBMwqGoaQn+EIHTEG0CjBFmVIE/AZgx2yo01QeJmTsy601Cz0SvLgfmknAhiguoVyGJ7aGFUNBpqjEU/Q/UEJx5C+ZCvedASt+X4mbzml6LL4XFVZ2G+25sMG9Pz60tvXp39y4f9R79oTFrNhVPBV95YuysPdu63QnArkQ4PXlR8Ewwypro+ruz1nnemacIGAW9iBhWKB0tBy/1qcl6Dj35yJMeY7QA1HwaFX30tDJMdAR+3xQTq9sf9UU8Q1WObm4Nu45h2hmRyiZwNvbaUvPGTz9BDADNYuTr24ZgZxk+dn3/yfLc9ckaPt7fi3pwKwj4Sv3jJ80LJwux0IgnFcT6Cw1bfLw93W4N1zxl/LKr3Br4UfU9GUywvlszyWG0dFNd9LKHE1yG77Pe8ucw/eDaYytxyuvAb378UX79yKr4Cz87zHD+FNdkU2w2CLbAuABAHHDiAmDoiAGfdQWXCW4wC8vFxrR5iKAIJ7fCdgk/Xq1UMg7ssHOR5N4YfaXZqKVN8eKv0ZEISM0ppElKCCdODSJ1duY9/2UegUC4FpeIkSpqUoOA+cJ2mm/rkeXWyHvYmC/mHLw539d45f016MLcgwxMZC18v9IooQSlOc3i73IufRG2DLz5rpWfcrydWmxJxFFOebB/mYnQYoaGnYlW2WgJYORmNZRJ+E9U+PPQ4ZDDB1lVNFgAK4RODWP71+e0Dv3c6vF43fIp230cYGA6YKawbAEyyF5ilxaun4iKN0G6GLQ07J1TRygVcssg8afmvLNjZEGoRdibSoGI+ypDanFBhvvnVVw4f1ybPqn4iZBfrjAfRxS5AEcAaFoRXi8ekxrCQbeOAf1IrsAGQ9XRkzdvTSBKtlAXLrffiFGpBXM2xLao9kFmK7iDADaH9nooqxU9KjExrBYxI+RIKOvRAOqEjJ9+6dHJtRhsr+ue9g82dGIosBEkSYFu3d+KNsZqI1fh+xjDn3ZHIfKClm88dPprBRujIv+A9cyXlcyvc0aaUgK0j84s6WI7RcSo4yfkuzARqz6bx8wvtLvRk9/nyUlBmKbS2P5aFgD+JTqR2C87l4+qQ6hnAk1nx69xrlFNqm2UDyXqxcI6knZix0z8TVG7Z04Ehpr1y+7P9yNuJk9fTnaEUzfiD5USacHUnmto4+OP/KCxeip2IwW5R3bXwyY/Krk31Vszir/uFB1UBdrkG/FhVgl4zTUMTb9o/g4+qVTXtMCxT3+yS8261NaTHWPV5N5DLqlGKwmJGGUSBTxweFytFcpSbd3lWcfLqrzB/ef/mguh6PZH78j/cwl67lNmIgQwcPp9Dbw+yONZod+Co/VDgwjFHFgUZpsKQxzoYM2Gfq0f5RVfZbm0+7ccjWOLqsgfWz166GE5GXJKWQ/EuQrgsxsuiIOXpJSGo7Xzy0/ftHG8r4OBJt30o4muUNxhX5LZH1csj586n72de+JhEtBBUr1262CtLdb57q9IvrXquXfSH9MAxT6l9ySShmt8PVQ5CAWNvu730xtc1vfflH9+7UxxlvKNnN8d/8x9+a6fXh4fdnIp5mv1qtdPiG1cuX7FdTuXZAfVS4TSk/fjW/o0fP/2//c1vbna69+8+hBKes6dze59sXnszQaegysGt+Ky/s3fM5MncpVmiIk6Kjcc/KuWWUlTc6/vbq54pNP2wfPDlYfzNBRg37RrwmSzvo+sMQuvO8dMdBffwgm6b7iHPMyg2vxwrSoRpy4ru2KZAAYVBYNuFTRx0NhkZCboxlgkbkSSjdnTcpRuoKsozUbg+bPeqdiAVd1LBC9+fM0vVT//9wxjtPnct3/J7uzdHaVuFx0j8hCcY0R48Ps7Nx4nldHtHkLdudIdd0RtJrc/aAnbp0twv6o+Gjbrf51k8n+8azKfbTR81QQ1gHxz99UN7+eLs2ndXhGtZn9rrlwYIRYTnvAdb/Zg/YF0mX0kyJGJW99rRtARkD/DymoCGA8lGUPriGWfq6OxvnvWe9lJDD10XhwpTbzcnmrZwftmfmCt1tz0R1KkjOw+OvR7v2usXz107MR3+/3nozy5LE8QwzHtzjjfneyvnrurcPdOTdmdm82J3sUQiGExYsEiKsqxDS5RsHVs6NmUdmbZo0iQBkwRBgCDyAruLmZ2ZnZw6V1dXrro55zfn5A8+x8+PeHxJH4FlAMTkZ0fKzAzOXLrXczcyecL34QH04N06WCbLdJZfCAUIPH5+Vsjgio1dv5GEnuj+R/t9gsiVYBCGAjrGkLtnn3+ExbTAQIndq2tRck5ce/3N1bAFfwXLIaTw5jeu4EDaACBzjiQjQO6AmXJ0NrBvrBJSBHRNYAoD1WdALgUuLAJChKNQfE4lfABIlnHTNOgg9KgYzHORNMWiIJkI+pKtA9ZZs01w6p+8fUrKUoLv8WkYQ8KV3WyfidKvxaefnNqnkxOTTKzxGZ4JOzak0prkegkHIIHjY5kW5fU8l4fsznAMcuipzztGbmll1YaJlqUrqiTrKnY590ErcFA+BX96Mo1HWAmHx46OuMpMg0AvxFObv/h3vxdjAxXSrbbdvWguryeaX/RrrkGmUJ/d+vDA7zGssJBKZzTrbLxhxgvnXQU0x1y4zq4RziCzFY8qQgeKw5EUCMLtm0h/7gNpEMOSy+vI0dRffaNcP50kSwuWghlNi2PxXi18d+IHgHnUlnYWBJ5acccjOBdr9fr0jKJgo2cOqTir0BPBlfKkQIqoVU5KUxScuEq7XxuO49t5tAAdzRBGjC9oytSyQ0JB8WDe1HA+jSYoPgpBUwsRV/ZHqd1cPE8ELXSB53TJOHp2nOH8m5Uskgi1htqErNgCwYLxTcQZFPBre/le1dnvNNNd3+4CUcHOYJyk9mMFGbD4TFloXk5zu0tWUolt7pzHCnycnKCoFJnxeEyRLWGdphOoN9STYKiTlmKFdg2OXdvBYmndmpILrmw+CyBRU4FzAaUW08izqPl56xbvA20AUs3t563ElUn2RU77QOu//aW/UPSy5iyIxRb4kOdiOBB7802Yc3uR88J3/5pW9W9mr/S4zpCs+ozr+gyaz9UjUGDFwkqhdgb6Gv9CofxFyr5xZfXJg8nUcLNluKoZg1CO2d5of7iCsXExDfrMwo0ov7TK4nS1ZvqG2w2NxN7u+jevfE9tAYxW0IMPVGjVcc//ouvSxGo5b/qyogZ8PNXD6go0y8ACYuLpPO9EuFPkzADcLK5K+wYOwCvXGIQEnCALkv4y61uSddGZkknM73pNIzSNEb4TJwU0BD3Ksk8drbhUwmgyo0LNXhiLmvv16cryaqwMMhfext/i/uRfPDo99u4CBJxMxZcTUsnNRlbOFULN5yFrBoB9PKJ24snwxp2X94qAZvS8w798dHE4/MX/za5fRxzHHZvzTusYphJxBJw1nqK8eSEpyOnTWII9V+DppfHr//QXf0N//+//k89+9nhALOqIgAsi5phujIV757PFCmsWs844aOpKiAMeQPIxkl7HC2JKkdwTs58s+5BiZTe5iZnvmyGN+Vq/v4qlAh32OqKNYAaIoQkA8MOLk7oYo7GYWNpYAcxBSIWT897sVJ/Xa2xcvHVnp96dew6BEQTBozgOuJ3xaKQykIdkUoXL00cX/er6la+ANnUOmd6Bgf7FJ4k4eEnz/UGX6PRRnzE5Ir2WhTTzk4uTyfmxogdf+1s35LjbfWdqHNtr3y9Gy+GDf/8T/FE2t7Lp7O0sCElqMPd31/74PwCU5q7+2s1rX10R376/35Wqv/0TZKpDX996qULO2+CJDDsZSgNCCgqldldSxqrHcmNsJfsSRQR8s3X05aXKJ/7uP6x8gpRqz43uu62swFy5tdfrPQL6KsJQrhNMQSNaikPx+OqNpc1xNOpMfv///vtODpdd3J72RVnXdgLxVlo+kWEd6rW1G+vgqsDFSwgg7GmPJMO8fOvULGzFgt3cbwWwd2H+atNf/MYrIi3++F++51B0IujefmXdjFnfd3KT8fTv/O0XKr/0G5v4dwCYAgAgvAsoAMACkQP4ODAjgAwomoDsZYmJyvIJKvQ7Y9Y2EyZ1H2cyi8DNNEABwMwCohDSLzVxBUNBnMOQj9758N7XX1OqFzKGG4BFqI7vezMUPT69IGw1z/kHAl614Vc2876ADw9k+d0hLZq4kErcYWGulDWmGgvNabTCILWz8VxVt8QFeIcnPCSA4HJohRqjnRIkOJqadFmoaQUWMBt6Jr329ZJWm+1/US/fRpubGfiTccfwCiA9nTPT4RCKl5eLOTrDpu5UZj95UFdlYXNBxpALQ+7XxjCoOAQ8cc73rpfK73/SP3DWdsCnq4IKxVoHw56GB/2IB59KmkeeTSzUSecT5tXFCQ6KzwSlLFJJsWDqIlzK5wVyJQFH8NlTeYWl22eD3ZtFQrGo3nFz1+NBDz0/2IhxZ3EvvUVJu0mlKcV9PjZmkNawB3C7332jkGXms2jS6pSopIE4dBxPp7lCmVNr6gxBlcCd6ZFYzADT3gfVgRBDb8Zyko66EVAB6Xk503mkrAFVAyf4EL/8c803ZO2G4eM0K6EYSGBLZK8m34rAkUVXKpw6HJx9Ovn0z2fsErF8uxIXndFQDwYWcHMpLjLhwSiMm0vLhZTMj8jVluffXWUKEdP/eFzmYoUQcEk+e2tpNJo4Ian645/XoltsJNxag2xrvj8o0tGXZ6dyf7q9tOCj3Msq8Ti0z7o9f+Jo/YcLQXnzJnQ4nLEzt7AO3F6L/vxnBixacHZ+dtZcf2Xn7PK4MPKLr9yOw+6yrxxZFiJL+sf9zZiQ+sprXMJUvnwyOWlXg/HKt697+sxJChOLSy9m1vuqnjCWrqy0ftSWn86IjXRqsSQgPX9c7D8+h1EIYWKoK1cPjxN6uJtLJscKbbpyhxi/dTjZwCV4of3+6es/uKL+cKP/NpA765795HMmX0gRuBwbtvsOLBACH8HJPAiG5uUgzdETfiG5uKIM+1+5vWWS1PMPP0Yue3Uu0EchbE8/wjy3E6XCTGY5OR/CuBNtmbBPSs0KvXDRio8UuR/GFC5VLq1WonW/fZpBfFhVR0MMuPPXvramRhe3XxEOO4jc1vhRx4UdT5OCdPZ1sf/4xeUAzLKPuhYDvPXB4wI+H9fkr67fi164C+YNdyrlvv311kdPGkeTvWuLYJYZthtaELgw9LzVFkbswrIQovYRAH0Rp1Z4PZnQL7w+GXTS3M1EMmBLvGbPh6Mom8uiidAyzYzits9nEkfzy5lkAtcCqKLrjaOZcdmbYKvbL78UomDC7fVdddSSI4cmDf/gpJ9Z5qhKunn8jDNUOsVyMe7k+XnXmfqwlyHpRBZVJ5gKhhNvFs/jvYHmOzJhE8tLIhXHhYAolUqI6keBWMZwOANEi4K3IqT06fgzub5YWb9RTkp2708/bdx48za7mPPqo9FBU8RCBCMJMHz0FyeO419fWYgVYuby8lggv/F3vs3OQoKLGlNfHmof//5z5Uf9b353dzaNOmetE31U8PivXY19+uA57QDmxBwavt9zozw9bVT7cDyb42+vJY9ndKenNafudowdRZ5jJg8GHQKBns59TCTtmpmKbzvG5Oizg9tLFSypSpHvAzoGdieHx/Va45jAXr9+bWkD/GlTI0EMv45EI0+fe/1A6lT13SxFFuitJUGXIH1otXxjZSW79lJ6OpDq+7XqxwPxvhTnsasZPkXxF+9d3vjurW/98jWUkgdfaPs/qs9p+c07W5tfvfvNX/rvgf+/CIBAAAcABABDYAgCFgSkABCLgCm4UmEAmEJIN4HLkyDBBb+aBXQAMFzABgBNisoZwIgR08sxs5LnUCRxOzecVeVBN1dM+AjscyI8Bdjpccqeduo9NI0Vl9Obm8y8JUcDYzUTfqgC7r9Xxa+C+BJLB2arqw2l4bzvrZZvXN279uTB42efDT1aXsnwe6/fSKdTT37yNFTdlsAh+PQvBjOBTtA5AY7YkpEIJRfZySA4Xv2LfokqOjrytD1DEiGJi6hYRK5vTnGv2Wgsg1E6mpJ2MD5tGn0lxVKpEqDCzqdH93cXPScKJrXLx2SyOZ5Z+ig+VZfSOWRxWx+flwQltYiPHah21qE0A1c8z4nkVlvuW7c2UqZkDQ5Us2u9/kvXn/aOE4GdX4jGjTFGEb7lZechTKZS1ygpG4GU9u7Ree7aJsEQ0oDgAwaY46CHH/bmp93JlWJi2jFwWCFJpFwqpRlK8f0r17ZOTvpfnNX4LLNNQkXUD1f4JgTL7WFMzNKQW/3ZU3ksZ1P52aVc042Xvn9t94cZHw2fjqviLEwk2fUS/mfvje1a41QKozi/vpYf5sfxiHrlNaEz1G0w7M9Q+NJOOlWuTIbpjFh0a81e4NigC5RyaLGw6ofQfq2bqpDGXH7is8QqN3JFkAtegkJWCwgMyhVXwgQzH9lANDAjy+rCPMCMnzdJAGYwqBJMs+sV6ObuX/7OOx/0z3/hyEysYkzAnpzHpHKs/O3evG85nXnZ6xQMyh/NYhoryPOTkZtbS3mIc6GNC/ERKjp+moJ/cQGFB2vCQsZkDAM8P9JYGkco7/d+69M8xbcu5txKWBYRpDqhERHl0n7STACy4UKEPfOGI9dGNgr4TJ9crcQ9EjC0UHfaP/qsa51Qy1dTrib2bPvyn3bjMJwUwydul9CDPCawDv3KYu4UDmKkpz6bm5HrAAAY42kca8iGKSZPav7R0w/KcQYTws5IrV2GhB9BFTo1RPMoPe/U8fx0JYlr1fl02WTiFIoQ8sMOyReyr27NCdKN+0nrppD2h+MoSFOdmjuVE1NgkDcVudtL5thwe5OxAJSyLAO51DuSNc3kAmLRvmyaWTCZKWUYOhZYbh5EyDmng3EkrBW22XrNj5TZDLcMlF+5Bj7/olYbjmmQ272yV3wx99bJwZSRK7dLSALE+mFRQMnsDBDjUBxLx3Ldtv34vJO9wfOMiyTFEomYmN+cz1sNLRlLZwIXUYJEaiMpLjN8qd+VyNzVZWx+ZjbDkMI5EudRLErNT6dwfzqsDgiPcVV7NBqs3tmQ0NBm4jkM2lxGIRw7bQ+zaZ6LIZZmwqHZGjhjS8IJOEI88P/0L7/nYBA2DPKQBZt2XcLMcZdecfQ+ej6UA5YiNCx7dy9zbePw/Yf68QmJ4tzLPOiiw3eq1Rmwuyje+vr1GgNkOnXlG7nlhVTjp4/8E5vnUpBqVGfUFSbTaZjtyVndspc48vqiuP6ffqW+AN//f3/iP2oGfvCNr+5qRk+6UzkbS1yrTRaEBZ81ZjqusWw2jqiTkT81rmVwg1oe3uio5DNjUkTDBANUWKoZzDuhdjrr45gRXWK3haBqxTgALQru8cRZ3skquns56zFElF9j/Gfd4cgad+cIA21urWEigxgqbsPJTG6CcmMjgjUPODj2zPmhhpQX4n6o9jdKVhAKA6fCL2gNY/vaXiXt//J/8V+yAIcDAAAAl1FUAcHQbYF2EeVsBSBD4BK1yqx5CBCrgDuI/IUoDlsA5IaAiIBBFDWmvkLD7qifpGF9FogJajSVYgmzktyBAGAyPkinUoODOk95GpUmQP3f/j9+XyUGR4PRsj086ZtJLLwfAi+D/fZKllvPZ0ju5GQWtgwiFFhP73NB9aT14kr5gT22StTLGmu68lufjO+mK6WXcxaGjucyzqZKPFfvO7gSVgoeWy6z7qx+dCGkyq6HKT15PBvysaSme0I+IhwMJdI07U8D0za4WwVOpZzTn7/15DNpcwXA76VSZSjBBJgVRUH69MgMLEe8mWcIFjyavfvlDKp1f/Uf/roM9ie2A+0urPCUfP95BKdR0Th6OEURc4VKh+kYDopoiu63p4vi9HLkDmfqzQo561rcdgmYOYuUP1EQ9h78/Okz9TsLZ4+/8HnulTIzfLqwnTCnM8LxPFPBJqbtql5GDDbghEHHW10ESYd8aOM2gDEVPJ7lYKdzMbcG9Qlg0zTNYFT1bJxZS9Kd6hOTygckfxVgb0PBmG49lRZpcCUNZtnYmT04f3D+XKW+8r3V5cLwRI89fK972qxv4chLV7c6dQNPq7rkoGCEzAhvATCcxNoiDpeoR/UhM9QSKVpESrFKagqT1HxKgkhl+1YhE3v02ZPTfpuRwphHzMVVWpEvLafMwvMkTrQ6Ni+YBmIMJG7en394Xnnh6uvf3lAI4YPf/fPzqlbMTSu7wNr1xJMIWhaX9yz/coiG0fTJ4x6RFdgU5jAUiJXWxZQ2GybzpZPeZR1y47EMq6CbtxMK65FKHAazjS8f1z5+VA/d3/jGK82QHR+3EtveYKSmCbjf9otp4trO2pBjfv5ouMPiWbPOa4w0muIc3pb9RJ5qO3Pfw8XQYih0Ph0TbiRBCOROZaWSXSassFc3ZpXF+ALGQ5G4lNDeqvel+kRvA9ltVrxTSfJCHMg1B9J4Dl1lK7Kp2ALdas5NDuYSaCW0ZJPONWENFiFuQubC3qhKlDPLbP+Tedufz1J2kkKWyvdeZS6An/4//3Vkk1//z19sPm7Am9QrK5n/+YOnN1bj2AuM5eC5SWuOj3oanqHDuoTTyHmKY+MdBXn1a1+cAq9JyWmEsB44fzZa/e5dlNYe//STMFnpNGoRXL7717affHlcDgzmpS10MuqdubjdVPEFMZZKsN6XXz4qpFk9JWiyzvpTFibXrmfUiSGhPo2Lc2mmg8Ei4CBG1IrgJOhZDEESnOChljSY4igiCnDhJU9r/OXvf3ZlO3PtzrUMgRkn04hn8Uz27OGhqyjS6HQKuwKeU7RpBiW2rm37uXhlfQ0aGsZ0bg/lqWUtrSQQjjl7/rg5szzQhhhsYzXv2R74e/v/A8X5lx9dxAL53cvRwvrGxx98lAoszBBTmZRjhF/fW3ro0dyGWP/igByNO3OT+cWFmOrjT2ZzP07EMuVrzIwLaaBb9eQwK/KMzVsgPqBaI1lV8EqbJ2E/mU9eSPMXrxfPLofVmGXGiTsAA5izWq3mNH11MJikmfBOOpUy40ObOA7JPO5iuXnNOHrUXLjNr3x/dV1cj022uiYzUYeoXi2R/sP7J6eKESvGoxyK7HpeP7oBJAAvITXHk/P+wAnpVX47n2ycPb+0J1GZeDOdh+ewogHNy95cw4UMXlrJJfJUtzmJE8G0HQIBxOb8aq+lOEyrjZOZjhAnrqZEc4yCUQEjkv+3//b/nEhlQRJWzQgDQAIFgDACAABAotFRNADGiXJG4BRN8fnpiMrHrUEIwlmsDEIgYPshotuD804xzXqlWFcaOZAbIigcYgzgVg9HaCRcv7X80Udnd/dyR53WosgycYCAnb/9n/x3QV553OzczdHS0BxRwNXvFYPT0bv3JU3Sd2IFNpNKM1HHnmDTecAitcPhDYptztzMFnv4VCteoRVF2SbYx7K6vLHQ0CV+zCUXKlgsA5quqaMb65ymYQ4O53eJt956ED+dwpCpemDAE2gOhp1AVFmQm3LxAgGira6MxjHpabP2uLm+g/7a39u7wMby3EowETZh1ZCdTTh9OZ7NU8pgJFen0MSOiMq8r17fJiUhlSjFUCVyQ45KyK3zti4bbJ9AUZpKx5ZfjT+qfT786OmVZMHFyKQAuI6GUjHdjnwUs0Uok6eMobmfBDMvhd2aUXJw+wBlMx4Vet7Im89JRbS6g3B9AY2PooglGydeMQVlkpluo0MC4PraLde0zh+dCTwKUzEz9C0QA2yEF7E4Fbi48ehnTRU0lm4tLeTS6RLiTPVJtWEQESYEXcAMsMySR3jyJIxYOEVL1ihojVgIX0ple+DotKYarkR5ucLVlDuCJ/3B6pWlKmTbtFuC4UpY7MGzvmXpqntrdzEGbsC2M3yukpwf6arjGBddNc4B4lKxVzUVG++3x0hokjgkYOBqAokDJLZaQkTj/PCifdLsKKYQeOt7ALsGMIVEysmHHZ9ZXrcoaR4MH3/QlC/MUpGDKeyrL+49fjI1dLFylTmxxuIaMXk6pQxy+3psehk4U9/QnJU72R9/enK1uLPx0tUf//jj/M1kuycLRVdk0s6ZFjYc7spqHeD3cibc7mXD5OTsgKGCiLIcBwcJ18NQ2wcS+QxKIC4ZXjb03CoOSGavoXAR06nVuQ0UDBxfDSF0Lqv+TELK+bRXwhyEXoZx0QFbCg4R9BLKgb7WR2NuNmmWGYIxyb7hzMM9uBQ64tCZjMBeQzkJAHMHhyVn6hX9bYqrPvHy5at/ffHFf/3P/vE/+dQybY4FsO/+4uL/8hu3nrUv7utuk2mnE7A0MaX+cG74CKUmMG4SqTkMwE6AzO6KlS6WOhmATiAI/vR5+8b/9lW7d/bx//zHm9evRakMpCim7vNIRDPAKQps7qRSkXB82p9gkeqYe2za7rQUmqzDTgrQciiUNBEjDDwWC5Og33VxnFANj8agaXr0+KJ5O5aLXHCxuPT8owFc4a7/yp3zai8fu/L4J4/R6aS0iKIczvl0heanHgBw/Lw7oyF8JI3RMuXq2sVZZ7dUdLUgsbGeLKUuD88pEAqsgKSwWEHwNUsxR4qhNOU5nadKqdRoMELAi9Y7GwkwJ89saDXAhpfzV9cr0kR1Bg1VGcMLV6xiFEwBQXcrWQfEhMwaME4YthbsvvGVQctvdCy3DjD3cBos35uR4+OBs0f7nuppxgabrVkOUw5R8RrUn9ASOjQwKI0xH34kRbR6Z+d5o6eEej5fKfEIwVNhrQ9Vx1C+qO1wEwm2BCpcir1xfZdYIBa4IdHT93t29dmFIGgU4yj+WC1SLFy4Gjeja5w3qWotFMJlaczmb2Q9wo8pZOLqMud7Yt5+dSF3MG6SF4/e7aSt/NLLL6w2zyb23H726dnKrqC69lz2v7qy/KTfOerO+pNwYQnGHL364fTVrSt58IX7zefRFv/Kr/1nUDxvzywyRzAo0EfAFAi0xkGJA3EYTu8CSSfTPzcJqM3kk1GM8C4uMDgBFcWoa0VJEcejwPa8lWQHEOaHA6yUNgNNeX5JZzOq7l1/IXP2SUsGl/I3c/PHh1tvFt5p9/f00vmzn03UOSVJlmZ02sbSenIVgA7ef9pSmb0kHkXjB/dPv3IH1YTocubxM2yxxGyuoLphBtJMaXuvvrkFSECPBQYYu1PaS8xld6lMRZNmX04EYp5BYB6+PO76gbixEc/a8W/dvPHo87fdW9lYAKiRP+pH2zjsokhjDmZpL2mHaAQGFLT8gyKQiaBg2OycgoijcbFECFQBv2jN1sucAeSRg/HIBEZPWldfurL0w+vdR3Px6JIt5kkQf/yz93WdZzdMwdYSL250FjGwY7iHl7b8NLZmKJmCDgM2EyUJG4sY2gDzGHZcxhOcK3/RfW0b6cjwLYeyCD7hy4WtRDA5y2UIo3faWLl2zMavshmz8RlEY5ijMS5MzX0DiIqxvEsbtXmTDMH8zaIfeJMhqCfpyCFf3OP77cmw17iwbFdwQpQRFDeIMY+q850slfnV3fYE8tuNkh/0H83HzmSgd5dlr/CLrywKxTCx/Hm31qo+8rJRYoGUFaD9qB+21FVBRG7w9kwiBSqXL1uD2tuPz1Jpj4+vrn5zBXMF44EGC35ip8J3Z88xP0mbMZBTHE91QzGM4Dw3d1qN50/RMJH1pI2rK6W9vOI56nx+0ahBoE5tAzgGyABAt0GYJaGE8C5hb3cCLTBX7r32nbuXHwEnvh02e3Po2f5sdxl3IyYLkZ8rAeghAY65UPOjlkcyPEW1m/NYdvPOD+9x1ajar3715aUzRY+GdrUuf/PVRfLO6pG5f/nkGMFi8pj4/E/fLXrJ//I/uY5htAI2kPImZKqeFgx83R+6EonCpcrW4lifHIDTSNLJZqNVusJu36D/6N89/NwH9u6kvr0UN30BLPImbBBT2dCCeWOsU4XNZUpRfVH3+AQPVNJa73i/1cr5RLzIgjAb83ixkEmVib0Q/fio4Vx8CpYRAgjppfgLibQ2wcyzYxkTf+03dh/5qaRuqmfy//IP/ki9UUzd2IQmKnBSr4Ph4gZyTpPVrnazZa/jQNwFhALQ6LRm57iXFtNZpPdZHcSB53WT7csCQjdhJ7FV4o4O8WaPSa2qFNLYP+R0zaUpEmFfWN8D2nPIJrp8IA06cQFNQEkSw10ebygDOsLxWnh7t/JRuxkvpmAb5uVwYzPvm1DjvUty1HlCGAyk5T95Oh5YizuxIuqs3L4yV7tSs204oZURiZSgjifLGzsixIEnersvY85sKZtJ0ThCMbLiPXt6QiNwFAYEQ9XsYb075BAEslRpLqGqTfJBT7Pmuobs++N0nnFNPJBC5moqVdIVI2VWUsvkelJDQxpSYsJyETXbI45gyLI4Cn2jaWiGn/5WVoWnnIvs3oi3h0fKRCoWErLJ0WdwBwYLhMAkl2hIajQMsjmSpsboZKC4s5k7qSSLK4gynZ0fvXMGlRMv/YN1HofZmYG0ObePTyLGChl3rmOGZR5NqmCLfy0xzgOsHSZi3dyq/PlPP17eylAoV8qk52jcZiRQdkItDs2nlwEH4s7ZR0ehZN1ZvbFDhUyceazDtkEvJxcHrr2QiT142vysS6y/nssRyKADT6auHfca83G+RyzG6Mwq29G4FUipSN2P4ykciHVU+NbVr37nh9+7/uIe6EceEwE+CIDRrBPZAliKoOAs3E+5mxkUx8HcDt2rbWIX45SYAi01DNyQrSFTACTgCGV9mCahUAnA2WiesQGScB6eu19ZpfcvZ4NZveyq9cuni5uxKmmyvk7MxsWS99//7k9zeQ1I0i8D4f5b59bjHjABoEVsYSO3UoyFXy1OsnN1CGYI/cqCczEY9B+DiQVR9927L29//JOzyO98/xvfXrCFPzt7JrmsHmdFHnIFdncJvfz0QE4wPcNKsTGKo+RIK6yl92IZfzr/6PyQMMN80sHtlAch587Qc/X5pUMElLhTdpbjF3o7scfiSHRMwSnHVHteCwxiWR5YwmaRlfUMY2qDjbkykL/46ejg/L1vf3PrzPaw2uVuvvTG968/fNyemZ33/2ycPFMzlUJqjUh/T/jgZw39Z9Or17a2K+V3338MYHiCDqZSPcnEcZaduxCUAN4douOAejgs51JYtzdcKcWmEgFYft0D5Kk5dhCOIC4VQryVQn1gA8Kjo/nPfnq0u63Fr2dNWnUT5BzmAc9tDas8m83h5sFxHXQhNhMSpiMWM/0n4+ZcunnTNyP104MWeQaSrVDxZ47nUXZw63/4bs6xhMPRex83ofY0yeErvxh7HJC6Z5szlIGSqW1EnnQfH3eXf2UFRFCorj36WKKWUSdBrn5le/Z41vjJoQtACdqU++PVlb2BPx5ZXX2MZMVkOc/VqmeI5gCI8cr1pb/5w29ESP7df/sBvU3YQCKdNU6eXKYKnB3iKCQ5udDbJArSHoalWrBlE+6aHNiBdvyjR5t3bmx/Jz+bq6Xu8OLDz087s5V7Vxl6KRdzHp7WAz6BZ1i7peGFFFLJcemSDGZ38iUh1Xvvgw/BDii7rqOoMJUbDCl4Nl5NZ9tyYCuzNaa4+t3FMpqGsqgPpoc0GtmkcqkyGAJwEB0npsPxwTvj6bCOgKFIYS//YH20lDr98vC3/vw4mQO+/cIKtRTnBJYzaCibev7hgTx20jjKJ7LxuDCYjn0RMfigjbV4SJ/3zmeDxo2vr5rDYbuuAqNDDSgsvnll2kyk4kMdSdebvew38vPFjJ2KnXw0oxAQubZEkWJap1+oJCMWo21HKI7dG4kswuAHG9xz6fGPHhu5YmF1mV6OEb1wVmuFm/AXB7OVdbFwczeybMV073x9uxN0R4O+Q1A+EFNayuEnw5sx3IsMHEusbmdSMbe23xVSwsmXhmDE8DKsgK2tnYLl0GpnxBZp2cJmEgxDjOYAesjpR2F5OyApb9TCRWJtYzvp1VAEwn7zlcRgNry4Xy2vl+VGWySdarvq+1LvfEIkUKFcRjFINMLq8+MQwhkSEzKxTKGkax7ixj3Z0LzQBqJkBsIjt3VS7Q/qs8iNEEyATIYSItNkNUwzLZikkFADvTqyIkNoMd49Hm+8eGU8A2mMSlw+RtIxHLXOWRGqdtxBL8hCZNxc9l1iQqlkYn4axeKp6IrT77ZyjM4no/1WP4mZNgTjGPdYM9LwaRjFwbLNqXh5hx1jZjJPFBb3FtaT2nHDnl+M70ZYKf/k/YMdwECMqHAnpf7Ki9SFHjwdzMdyBCIVIWgrAP60FmzdymOw3JvTa0LcXiUXkva4LZ80LidNFpF5KMEtrFeo/PPP+2izamDjZMYQhf7D6mnpDHtYoCK3HaJIu4jmHg9fuE7SFtkZmDYO5BOLNuapx0cRzszN0eW5v3zFzUCLPbt5MZ5y+FKMi/3q3/61nY0XUstAFAERCKA07fqhHAH4IEgw8MAH07sQdzGqIrFMiui74OYS0sKzwKWpHIbCNcpXFTDGRYEJTEw1EjVEXRPI1Cul2pFaaEeVe9S/+dFP/qfvrzzRCrVeL6qBRXi8nIzzUlBJLLWO903T9COr9+XpPB7tvLCRusfrF8HQNKxWcP+dM+ePbOClPMCyZ8dzjMa++8LSn33RHVmR3ZVsV7n+m698+eRJT78EROHmzq7UmXkk6Pc8r6UbWALPZJBAg5fj0tiDbBXEhf3n9dOPB5VVbkMsCgm8OT27fHx4fWOdxSlLNZl0Kk/xLEXOeufxxpBk/MCdRDHHHAH9GcDkgIOmun1j1cYQuTvRUPSMo5ws8d0b9IcGMlSMlXjh0raeV9tzTUNkQNrl1/8ORkkK0KtWT+zEN/def/02mwA/fef4/YtnmdxKMJTYwLWya3Qe/NPWw29afvb7bw4AllGHYWPUfQ4lURLJi9dK64f3v6ikK0+qcCFZIWEKbKvkETAb2yGeTUZW5Tu3+M1SmoU3sUjWphenQwcLr2eKDABALNAn0kXHH9KRE5AYLtptG3FCXvYkc56XxzaS3l0vihuVT9+pw9vxT37nkXw2+8ov3M3/Aj056cBHp88+clUuSi5s/sIbOx983FY6k9svX3232j/rR+qTnk7PcASF8USRzo6dxN5G4vT+heFNvdCUTO/Hf/wkVYHpiM3FiA/v9772DTcqZi5O66l8POXKpx8cX7lr3/zFK0AJuQ7QTFw5gXx4QDr7U1AUCY/PDGGkzXQWsSVx69UlsH9iyzlq2O7J56dxIDAL4r3XKvQCBj0+jIP0vNdOlsCb37qLuZx52vRXyj0kJVrcvTde/L0/ayq9d1+9jW7deeEifE7Hlu+U09Tyjly3wqdPjGF9qE4yqz4t5rbZTLwKn3xqhxV8sILqk2EhRgxDazI18zDCL2bv5kgf5AaSVq967//4i8W1Kzd/eMtFu7nSdRsgVqa+v5H0D+fZprFOE8PbBdwC5YHlOBYA6oiR00xXYAMYGZ4/POh7xGkzZC4mtb86YH75ZSetyPvvrcvZzRGPLaTuk8jCFcRJpVkA2oJiCyIJL5fLHlrFEMjBpHgpJqTH0sH031Ud0XltjSB/uIQdeZSLzfctG+OcRYZfwGgcjnPJzdub06FnHR9tbkNBJEXHWpoiJxhBwtH2Eo7H9QlNDxnsCpIqI2OfYSy7ukQGWq9pSpQiLs+G7cg1GThG+zKOA/KlWxTScBcKPVPtIYvikjFVxoHaquvFyNZEoautRWZ9ayPTP55F15PRavbyjy7FYJp+faeydof70ePRpNEWwNA0FyLU1kdTHQRYfGUtwSQFKBfnZlxftiJnrjuji1OpkkovJHmrE0snw/TGwnt/9pNDvZth4ulcJh6L2UGIVANCrU9dnikNQp5GWg+r7E6qqxFToeLO9DEY0GGbdA03DD1HVcbMlZtriAvaTaajWnQqTKR9NwIlPF2D5l1TRZQIrBqrezEHBCdPW4kUAi05FqYBi4UdYoXRwW5devTZvj2RkhlseWmZXUs8eufz+khNkeBht1+cYq8iFEkC8xsFGU04MTAP5LjAdD1s/686sFfrSSBWptIFaCJpCEumJvPeSXNzN9X5rDVJCSmSQShlMpDO7Vn5P/MQpvjoL/cfPpU3WTwuiqs8FUvBR1+MrsWJrRUiIOEIQ9gct5h8BbSxqWTnEWP/g/uUfH4EasAA+E9/8NJ/8c9+B8cBIAS8CGjPAQgF8gzQbPmwNM+zFkPy+sFE0bIkmVMDgAAA0otgDCCIICoRQmwHqHX7PWDhBgWCUBTSnAm1u9o+OBMW4r5Impi6xQJ/sD/8V5f63b8e3LiSVw9nZs1IZVODPH0lufw7f/Tn7S8uXvprceyNNIw58Nh69JYh4qSQE1xaWtzmW5f4aCxD+YAmg5//XlM7S1dSBMjjuzdST/94wk7GaVLwCvwwjtLHREJWag6U2Vg8mw7ynIYm6Xo9xOSOfWrQaU2rG3qo0N7AHOExmhw3hjxD/dJ3NsfNATl3ViqL44YyNi/TGRFI4mZExhEEU2xIc57tA0QEEASwkGQlFAjgILui2221rBpWtvxE0iKBxQNkJkv09XxqkQV+fIzqWvNHAz/BYUvcwhLhn8wVD/ZPAGV0yUtmROjrgmGBfInGOiBpcO7CC9eQcefDz8+dOHbnegK5EECaGaDRZ0NjJy7eb2HfvVshaaLZcypL6k569epS8UR6XP3smZsqrd7e4gWzd94cmwgLGjmeg5MRk8rq6uxoOCTZXMAwKATnHQ2Vxk40yixlm9FI0Sx0Z2X3G2XE4L58+1TqyUwmQH07qwIHf3nhXyOitC2+WkFOpsDImZ9NZZH9ze9943f/4FHrZECb/J2Vcn29+/kzqZIVkQFvy9rUqQ/XcTwXgUT6i/vP40B4vcCpYeS2vNxLhSRqHu0DwGS6txTbW2L8iTUwa/+Hv/+T7//9b1GpXb4S8+azAg50IPT2zhJKEkBITIZGarWgG7PefjWxxHJ3l30KXxWBYW2sxbApbL/TgxdWrtwKSvPZXDGMoeF8ennf0ICvv1GgCY5T7P0nVTu1TCUZGi00H577ffLlO6u9EUdmVlZWrh+hvfvv7r8YJ4gkJ1wH8kDgTUg/xrEs1FVU+6jueYpMsbnNRaiAjvu2uLLII5w6aJdY9PoN6t0/xJ+/tT99x9u4trG1sNtTjElalodnTA1bvVk2mWQussezsRU5WnOo+LPMguUEXJxPRsCsQCNrDtX6t89QJ1zco+IxWyf1OKFpwz414R0+vZXJ0WnZBzqnzwzyE47JxTNkTJdBRr4cG8WjiUkZGktHq4vssw87n9bAqKKl8kigBqt0biQz/fvzngHcKJZ+uL6KT83D3mHg247IQZCu227goaEQ+rhmap7ZMyFWTcczfDq8POEoWaEoKp5PpigBg5maHRoQODXGCgqnCqDtKrkyPFLVdktfXl3JCWV/qgwlYOtGGUCQ9FpFNRjEpvFYeXoxZUx8euEhUSeTQxA9XkjEI8lILSe4MoSkl7ThVIjZnodzAYQQgDro3JcvufTSTv5G8kperTsVCZRrdmfUsYrCwl7KR8DNZBy+d+vJo2NGYA3b5gkytALEazTKVH4hYqJGB89z486cciZkEqA6jQ7J36TJzrM6n0R/PrNyloYhIKahZbECVLuno1qcgFFGDezZR3MsQUmlpbTTmDia+/ToYHFzkdyI27LUPO3eu55fg5nqRf38ou+FAM27Z3REKrIm44zAvHq7FKLBfGrhk4vJnwyfzZXSSlEgpvsawxBc43kPMCbZX7kh7MSCWDI9hU8GI6uulLBUF/UXvvbKq69/H+YihJWs2ZwGZsx6royPskkKPdM9tvCDu0twSVtUn3++34OHQJlJhq8tGDoCCxALMa6pHp2ONgtczGMB2jdu59fuvP76SPrspx++/g++9c3f/B0HACINcFmgDwA5ESCj8MQEswvzi85gqZT4YAxcxsGtznDnFjM1QFoBQi06s8B8HHCP3c6zOqx1Fr5WBibts3O4crOCL+a3svnHZzXiadUtCM/nPnYxWU9lYkVgNEQNbZyBSWYXem44CZiMgNP/1z/7wyDm/+mwD0ylZJbGen09oMpJEHcXqZHibGZyO5QwMZ/8+ckrv7Z+85ep2s87O0lw/nxKvFop/UKFvJjZXC56Wrv9jfVqnn2kJYRJxCUp9k5i/GQS68yb+bQjO/lO+w8Px9vsHWNr2ZQGccNMvl4MifhxfwSpMp8h/Dg5lGxYxPBrqSnvoy4tS8BgMEP1uS0DAQbkVoETF9hBiEQv3Ybg03DQevcCp6C7d9ez26vGlEto2jMbNH5y/2RZBAr0PYabf3SWxKluO+h83s5956Urv3DX/Ljx1ofD4o30SoWZPb44TZCXTnzNqYDPG5t/O7t5+xrhxj7/+ef2I7eyv//kzpVZIeYHkNfrlMrxBwcnLS5VcyZJb4mKjzU3ufFqGcVoPOI//eOHCDTPb4mVtTSB8iCKeIRRvxhhiVDICagMwaB6NVU6e6I/eN6jBQZZjemT6DdfeWl2v/PBf/vhiADeVo07OOlZbPab5cJ3U8+/HMj1I/Dce5BEOaR8/VpWmpAf/fZDx3dKP9yTfr8Hc0zdNtVMJigr5N46MIu2oGBULv/s/U+uIDq8e8VBUvKzLrPBJDmnIaOf1kdBMfbKYq72cKjq4+65GmNxLL+0GdWf/vO/jJ25728MmGhE8KggxgjLbrdnSY4YmyALwKzIEnHyT04aL4d0UkzH4dQTEnfbl3Yqs4wyygRsq4HWYgpZnK7gVwOsbkfJlEsczmKZirkGIdB8SUx8+9ffkL+A/vTHHwOnaWkAFdvHXxiGpqGv7OVZgkajxnA7qnYd3ALwGJcVs4tzm7IIl0f+an/oGBpfXNnJ8k+fXVQfj1kbigW4lGJjf331m+rK/QfPzh63PnrnT17927e8lruUYxMJ/KSnDnsHICFmKMOh2WQBq3b81rNhUAk1hywsJpZ/eW02NaUPJneD+M1fuXcxicqSrfrOqN8NKOJeNiF7WOyjxz8Jm/EDfDX4tm1hX1and3isSM61ASfifIiZI9Jfvsa+ks4AfWDuJkN1dAgZ3I5VxHfgEVWrg+gunRDwT/7iKVLM0ctYgsX7QzcEdQ6A2ZLQB4IIlnIVKoejvu6pXWmBCvq1znaCJBBCJZj2TAb6AzpkAhbU5lWe4hKFjQtjNmpKOEowoWpZams1bQ1Hp5enIJWce0mBtpBZH056kBbEoWBpeXnsTzmBiTDlwYf3Jw2lsJhbu5odXx5PDjtBKtUHqFKOSRPQQOqFQxkDjAE6AjACdVR1MA6paDKQpFG0c4ONUVi9cyxg6PrysmZammbjDEAKCXDrxezK1d3f/G/ufvnTL6dpWHEht+NvZCs+Mn08H2wWiPNPz1ZvLFdbpl6tp2LFlfW1dG4bqJrTWj9ajCS2qWnzkVtY48GcSIRUEFjoyWenqEEuXC/7hvZ4v1GMb//wlZt6dXBsmn4GnV+PHXaN3BfjFBQtfiVuzPXOxVzxoxSuH3/RqJ6Pf/Cte/e+k6ufAxMLaT/pE2S08OsvqbBHAnP1g7oxnlqqXsiwKJLSXSIHijPZDjKgBs59qQ7qlJCHM3neSJHTju1YcxdHFiswEJHyRbv+6bwFhMog+P7fvUVCPBAErY46nCnLMRoC0WeGWQLF2CwqZu/8o//rfwMAQFUCFoUIAAE1AkEA4EPguKca7rh+6PzKixsfHI1eeTn+9p88Wl7P6cU8MPPzvqXibG4Rsi9sXEc03+B3YwzmmGPps8Ph5to2lQEmpgy1B+pAqs793aup40/3F9OIsLwxAYgUYHvhPFXAQtz98x8f/4t//I/v/lLii7HD4DbgIGUikUzFjV7r8L5ExnkFgOIC9Gol9eVx77NnVYxFWQ37zi8XGw8bswUMT4q3r+YeQnHmD8YcaPXXbdVGuOeAWY7zAq7WhvapzW7mmLjwMkb+x788hovMja8uHv78g7WXFs3NxdwkEXlRaLaHTnXatoKxs7JI4lvFmEWlAfLLcb2mjJJyYHeNq2/mLw0s8Ec5i48CcmRaZAYxTkZ9z6AjgV9aZtikz2ss6do193jkuNDkNp+pyZLiRCnQTfq0vZACi7E1QvE7fT5FD8/HlD1fefHKw8/tqzf28AixUtLTE5VaSqFpL3ou08Ohv5JsXs9RaUb/8EEckXStx1eSLXwW9tJXJonN9Ru12WhwMUolqVEbpBYBgEUwHPQdYmkFjBP04EKa6hYixHU56u73kLnGxFkfC7LbMbRCnP+8NTqRruzm5+0m9HI6c+fq9jPoD/7VEzBmoCWhXIxpepMgkP54HPPibZPaKYjBxexDQWbfXBWeqssL6RlGACGun/akAMzj+smPj8r/3WtHtgv+1kNh5JT2EjEMURDSdAHTGKuO2emZm4XFIscN240rYvaNby6lExWzN/qXP/4QWdtaSIt7i2FPi8Ay3pKtEPJjU/yoZ4W6m4dd/zW661Dr2jJTzItM0J9O21/uA4iXKJaIdZZCiOajeTjuJ1ZYMAYcqG3UHO2Y9NLeJsKxExVsnkYQDCCAQTHzhVL67fefJDrS9NoNHS4uJCmInMtoA84hGY1JwFRoU/OBtsDjtBU5sZSJkDHX0etRtXWkOo1sOgObBAZB+7Xx04G6VEktXYntZnMfPXjvZDhb+PZ2EjS9c0CxE1Z2sHR92x/7s6MJ1huSuIFH6MxHRhDAr6SEshvRE+XSrkTZqo6HiXyBNy2pFgQWqye2EnfHhHhY/Y8eNRC09GrAQ4WKC6HLs1GifxQgLzmriyeq1OGlidfjHfmlUgoPs+1+ta0MZJm/+tovQEuFj587lny57c2Of9z/zq/fNcNANWdGzBj3JlkfLuS5L2uN8iYT9XxCJpErMZ1CxZqUdkIdBpxVGPQR78iMLmUdVS0OdRHcd8O9r++eDfvFuePUPF1X0dWcur1NXJ5/8Lvvb90rVO69YvfD6WO5eJcu3xQ+/7BaKHNDw95IZ2TPMIM5oIKBjrqgneB5pdGBI5gpLsbjyXGzbQYznqcRTIwV876LEJ59sP/zabOHgiKQZpwIzCcS6QxleVpngGsKlCpwDEGwMIL8jV/Y/HSVfnDx9CCYGU2bUWZggEukGA+bFCeftqrQLh00Pyqx1xO/+qLyWIMmz8ykcD6RzJG9yVhaDGVjunIx5IMIsBkyLQR+9PKV0qPPToO2H0TAymIxnHiHze7WViGs9uvtLqgdAMsFmK2JQOwLNUHMQ+zojCkSYJbAvk/lo8X2WPzp4SAdFY3OZF7v5158gavB5KD5zunxkjLFGQHDtE4hFicc4MyZSy0VyuanarhMkq8sZhVnqHoebfoIsH07Zn9WPx7hnbOmb0f6ili4VxBlt0sBfD8413o4D++UeJKFQthK1OGNMTQZTukg98N/8us2AFjPLpf3VgzPRgFXQEHDjULd2hDh//F/fPtbf+sHFjJbyJDGodxou4r0eSrc0ZHc4FSKlYnVggE6UE8L7cYREgjM3R0qDdyj45edLjBCEMSn+HgGcmAKCzVv+aXbANSZ9/t0PhGFSGin9ACULjuANb1xj7SeNneTsVTKv6hDUfviTx/oWpp86ZcWFh369D4wbDSfdC0oSYZutL1ZGD9tj45G7E3SiKLwcfvBW+2NH/7ytTfTUg5ovvWUbYPuoAb5rgZrmUKFeRmRT1vn/aNHMlIsleJ75FsfNq3no8Cnv//yrXQTME1wamEwFUvGPIT121qv9daXL1euFbMU1fc90iUKyNJ2DsFQJESy2UWspj45NDfL1DyIZogVJImFjY0QrWzG0HfemjX96fWvL/2dfOndP/jzjy6HRZH9wQ/uzmAWvayBoY/a5mGzFohRDGovV4LxNLr/qEXo+Sdjc7VYesEB4rHEwelkPe/+m8v9V5Nbn2ueUKuuIFdV3wf0MA675x/20ivAp2arFbSUZsqbalEHWVhLrd4QTxrd/VkVg7xQ9zx9WsoWMmKCieGg1H5MhVwxSgDOPJ4DIiIw6cnT8VI+kcgWfYJdLgvtk/7P/ujjHkdktoRr24Xf/slTrd/bWo03qobRs53coC1DcTi9kIytJIITPWpNLzpHnb/5a9+w4PjitxhpDunw+KIDBT/df32pQP7GG0kT+emffvjlWQMr8rc2s3MNXKCTa1eIwIZ30vhyKp+Dk93Puuu7wJUbDKbHP+TsB32mcDm3+BQ/1p0qaNvul5eNzcUsXRLHtSH+rnZ1LwmooxQbSW5YYvPAjnBQaxBAh+wUKJKAy4Vx6zA8GIUwCjB65fq67kUXg87loR9FSMETSksuiDoQ0nvH3Ed+aXkJueboEF1XTaANctF6ioAmwYIhHwB2xjewDDFyEQYAsaqMImD+hevxVdj6S5lxACvCppqFwP7Kcjm2YjV71WdfjKmr/Padb+L7Xy4RZKtnaoOpIbtS8yI86gMIznK5ORVYNJouFVNzkJwOGu2hOx5HBZwUhJpuChUxmuHYbECrEtQdBcXJFC/CePTKlV29T+ud4azC26RMKlbRH/IBBI3bX7YCLQZs3rV0PpiNnf0vjtqmssUYRNJNQPKjH/+ZrIWv/b2/aX3vVfQnPycKvQaGKYNOHPdYlkcYWzrobX/vqqG22Olw/c76xz9uLMph6BEaYq3EOT8iRgKQFMB+D5NqT/tNI3uDTr56VVb0t6azwmIcDRSjNExBGOmeee8PoyQa2yILyzjzveuzH31QLvvFpdJ4MICxQA21Oag5gUPLaCpXUGmn9rAKmBoZY5L3rlYfVq3GsOr4PkcZfpjZSRk9SOlOMRBnM6nYy1sXrvlCbsOcj84a457jArlrvfZ4MrRvX93JL+X69bP+2EA0z3wthk2PWgu+bWVxDYdaAy0V79pjZxXz31Gjpaw9HwGV6SyxwoXJmNkx/ef1y0gtX0/VDmcMTpFUPI8zju8IE8yUTT5vdwQy9/2C/rBP6UCZStT10NHDOUgIRXCDFafDaevdp21NygXKxs3l8YiMoiCJhHCRLOaFZHv+7E9OU5RfeSlbN4DrV66n8hnWCRw2sbWwwFDFSd+G01huLYWC0Fw2FaH04i+9ydkD/bOnz//9xb46WP1qNgLToxVnBNn+doYwIGgSS7jye+edzpSncJLiICYXX0ad6nn/3bdr4ZogO/ZXhKU725VZD13PbHCNLiFQxKYPADYMKf0QJnww4fOSpgJBLxWnd5cz+8fnQAwPWPzmV+NffHE4fO+ztRsbd+6VBrPL02fR+m5Fqs1pRPQ4vP94P8dL1Oo9Pj53TQgIcEX3zNBLl+HmE5lb05LLwgTqWt4kgnNITrQCW57Yj372aDwZcHHws6cd+RSwnwG5xXhSSPiA2q5K6uH5xIhr5uxCGdlD5EqlyIAeXRRXS+L+81b5l3eLX8GRR875u+/9Bc9v/vLGt1+/K1+YzwZCjPbv7z8/+rgGCf4yCg5F2a4CJdDNB+LZsHmlAED34tmvbEu1M1091CmjOe9TPtp7PqBysVRhJcjQT6QBms9dwan9xjGUwrkOv8pzTk+zI/7lH6LtAQrOZdsSxQoD4bRDjqFc5ld/ae+nXz44OR6sZNb5125tYZbyh0rQ9XMrgL7EYg7kdYe7i3kV1tqjaUOGMi9mjg/QH9wtE3Py//iP/sM/DIe//V/9r9cX9i4+ecBojpvWX37hatsa4N7ckV3Ydg0QcObgBRzFUkCzA1zMZabqGc90v1GrSdqVNxd27qRVZWQCghMn6og1KuuYieMs0nzWycGCsJkYzDUPAH2SMobuWILmSoAzZmqXYOPMQtoYUL4NyeMATNxjygZ2eX8aJQU/v+OxwBtZQRmbf/zeI+6V1NIacdnHzPn8UeMSLwTt8yghkcYy9/3/yxvzj56pf3Lw7sfNe4scDYdfe3NdcUYra7HXX1r0Bkq94ys4+GQoF9/c2MiktafNdz/q09UepDCxl5GvXacx2EphGO+wEuYgGGzEoMtnB0Az3HphC3WhaVsTVlNRzoFNCsEYlRRVpmpJSjhBp0X+wnbjOXEzEx2fKwVNZE4Dj4BsGKBCZ2GRAkdSpzMNwS4su4lFQdHPnqoTTbHjWAyJbCBA6oaIw8TAgXw4YmCFAlUnAiWYjyTfnfS6bUmMczhG6wjlR3ZsYRHDouZBL16hr69uRmFKzMZxTLfg+Lmi5lbJuLDYf+5nvQVNdWaWj8B6ImaFash2W3ExhYvszAw70oSAcTaEs7SAu0j3bJRMRK5segQAqeY4/CLG3O4q5lJ+neSJ4WjImEGIEz0EGAcIFmA+Mpn0tfEnspd3IFGgruVWxTUctw1zvrQJFCb2j/7F/tk//SNodz3lAelM3MFkJhtyPqTX5esc9tDxvWP9m3zx/PFFr+RhMQSWveuC2OS5RuAOORTLbZirWO3oCVBaXM1giRdTcpFNbnAmbMVwS+rNGk9OOAxA10Vo1V/6O1tn/9avnU9vH5x4p+fWDGTmcUszk5pGpQWMRlv9YRwiAhVHQRQ1TT6RAFCYitFgggmoACTR1euLz86746mBuDANYHgQHD88RCvRVnHbDMH2CMIotswlskroSH5yKQMAoIk6YqHACw7id1T0c/x1P3jH0ylzALsEOYpcQubXYv3D/ss8EI19NYXO97uTv5oh33rT8ZE1KVyaOqQDOJTNTOc2HlglATfC6qM5mYgN3NBOEuZqNs4QVOA3z8ddgWVTQffjuho1ZxvQ3q/soY1855lED6m9mfm77zaMjlz4ViGgkMmntVXJMRQXyQqaMApzOBLLwmSGtlx52DzrPANW8OWXdggAw0zYOD+77Jja0MdgLFWJpfjy+mv077992XNkcKxu52gIUvEZ9t7EN0JgN3Dz6ww4dPKBJxA5B4dUF9+9tf78zkJcm4DwygIEVb8c8AvbL/3arbBlu4cX2JV02LnA8zhr+qLGVilc4JjJ0QlTSoGGFUAxrt3sI8lRoynEchg2fHAw8KJYWcy0k+OZ1r/GJ6itFRAElXfu6zOPQKvMwnqvNbUkM0ICVDMi2uJvCqPuXHroVCoU78NTQ+pP3Ex68vDo06F55q45J3nC04k3riy0VnmuR1zUjcSoG0x7Po2gX090ukaqZwJxtE95n55M/+bWgu/7K0tx4135vRT+xmvX7r22/Lt/9Pxn/+L85S3a8qfmRJVcLbeceWlJfHBeq384lgDg1j0CSePZJAtMMGItUY7lHzx4UhpKPybEdAygA2B4ATHFlfximWcB0/YnnpK2LWRibTHlwaXsWMNEvMJriMqAHx11Rcmnk74P6INTF+9UYZ7+2RR6IUaKgY3M5N//1z+J30wEvtnVpZ93XPa5Ya7QnA6VcXJjgSKnytgE2JQ4mylTDfvwj/+86F+rhgMAAP79P/mP5f/6dfEOUhkgmtLfMm/ishHCjkc5ljdMVQD5MhJRYGABRQ7P+4zJyYsrJBupXR/SGuNWtwN0pcz3rr1aXvjIOUZt1zmoI1EguuiOCNcCVBEwt9t3J9MS7c1lCAExYGo0T+BTFYZJLBQQtdaPc/Tt22uTL3oGTZW/d3v7evnj3/5CInx9I4SkBRcDWAmJy0bDCGZT5dYqAi7ET95pEw9U5HSCvrF5999dn/zzT5Tnj6wFZuFOsloVWhc1F1qlAfjG3Y2L+rFVB0afP/lXh72vf+0N4YW7wXg/vrzlz2uTrnmetpOMLI/CMxe0B21lCfrqXuYP3609+PGXr/31b8pTgpy5p7yNIxYvlDhY9J9iKQ40lUnEp8bCstxH3Z8dZa4uL14RMAQadRoIOJyHTO+ydYuhRLwhXYncaTi1g4dnsuF6d7IIa4Urkfo8FQN5Mq4SZMvLpuJEY3SpjGBjesgLCSJJZulhq8slcoxIJGIFXjRnR2MIIfNlOCSDmWbLmnT+4QdXf+V7u69uNX76Sc3yhRgvLqVIL14R3WNpmgyJ/bO+daDwMZjdjBX2lsIIpQdMkvBCf4wGQeTNRA7VWK/XDcQ40LKBYoyxiOSPP37renMp9EY5zpBM22Q9g0ULPB+oKpfE3gC1R3RK96XDy+rwCyBf8vbubK1MoXk/sjYLf/cPr9+ve/SZTA0kbpFXaE9p93UiceXmdvud90ubxeqwv7iajRe0s/O5uJ08ff+YL+CRochEDnNttdEvxdNfu/nyOpuHEP4g6pscIMojB4GjtuElYjs/fIFHVILmnzoEr4tbdNEEL+dvf1GIuFExOAc8AbR5ira6YNKXqoSuMyaroCSJ0QJRvr4XOZZ9KZVXSlatZdHcvCMD9T6YJVfSa9pw8uSy5s80RiGLmykHQDaurUxl1RtP9bGZLyxrcfzi+bkAJ+I8T7kAkl9fhx3ty6GtEpTG0CAIIjomtrlkJX2itleprN72SyukzLSePphd23KOWqrcsoWYQAKG5untC5NfplmYxiCyY9grORzWQwLE2x0tLkBy4PBU+l5BrN6v2wHOFpBJffLAo0hi+ZUr6TAXnY+6mzszI15SmyFRCtlUfHMl7560dZ6PpTZD2ejX+x6nzaaoDxhxHBg8a4z7eIZNoGzw4U8ONDsqJrklQAqnEZ4iZ8MwCa0sFTIN0SFJovHpuROAIoLAc8yQaQyx3blzOG3GwFpFuNF16HrPppM87aTr9Z7NwV5vyqQ7n330+BvX99Suhp2zYYzSZ7A1j+IrKXZgQKAXpPitUqKhuusFTOkAQH8CYDQLQ81BPMWzO7kybiiHH7W+9b/6FgBBAABEYYRsZAgPvOhK+ZwSR7U5jQNREyUc07ZMPVwrxm2LGXenkinpqcAogM8+vv/47d+t9eYJjhBeK7hs7iK0yBJ9Z33j3leXOheXtWfva1Pr4LeOMltQMpdLEOHMhPb2VqKIfm5fCIu5bD5dqup/9l/9/Oqdhavfzx7RDcBwxiNl9SpeO1TaP7f7tgVxrg4Av/Bm5Ye/8tX//T/73f79eZKgVl9Zmm0SfCJEAe77FesvP9eMNJ0XeDbNZZPCaD48t2Wq6CtdZzJRoHgWxlk/bcKrnN7I7W0I9lP98z/9FOcigUFXF7OaB/QTrg+0HjzUBxMJQb1R1yQSNh/AxduAHZekk9CHsJDDN1biDy5GFuBRS+V2V3n4pXR3e6mSZry6BTwG/tFXbg9VeT6WUj2IFVPA3koLpvH1lfP2RWC4Sh0je079MRDbAiKantfxg3r14P60FGK/8CuFhSs5n/eD5kxEWI7AH+4PJdzwsmACg0ZgzLdR3+HB7iwPoZOZi5qKg4fDoU3RCTyNN2yzErEEyMG2Ha7kKQx9/386jACbLicuv2z88LV8M0ExhaWABrNbeCgirQd1IldZGw6JHvrgvcMbv76z+nKgvu80H8xr9X7j9ez3XsxLLHLRbR+/PywW+OzWwpPJuFzICKyXTmcqiQKCIvtEC7qau1Ou+BojsiSbT//0c2Dm4vrc/A9/8lflq6I5HXzyqTkgqZuvX396IrXm/qQ3oLaSokib82im62KZ2dzIg5r0/JPBG0UGGrrjQz3O06QznnUuRD7RbskLC6zfCtdWadzx+xD0+GlISRzmMGAWzC59dVWeHv3RJ+hNLJehp3FoVtPgOfzwnWdLOXv3huBkb1QQNs5Fg2cTZMvussN0ohLo9okUorAF2rbDQFN9MusqtM/w8nD45dtv/L2/Bm8sjYExKPEAgh0dnBKYN+lJvZFf2tgIN6Jmp//sQgcYLS0yRTbememgiI/7kuv7tEycPGvQdBxZFdeyFU/PnJ9MGZ8AQw3CnU5DZnKkjiLaAqYKKaoCFWHMvMADCOqeRY1nQLwI3L2VYzVSO3JsmGVN8ifvjvDtUmExSC8DQw/wpSmC0MiC6AHaoye9e79YwrKptmbNe0FbMrPr6AxUP+s1HQAgoOVELB0H0EY/sIeGbJB0OG9RDqhHkqrF4wQgslCiACXEOY3cpXi4q2kgC6wwRACe/tUhw+XxchwgsMYEMKkgLYi8B+VGQ9+CIBrzTFLIZjA8gJyEHmoFtiCl8KEyOn3eTXjw0kp80mntH5wMHe/q+oI5sWpH/TBwl6+uxYrCyVQLzGhzKxmjYtQV2kLlcatjwjDSndrAxPBD3MqyqjPhfCGIxkOKzroNPQUc9qNSRwcrxSiqgQXgPzx8Z2m3eDO24UA4okdnTQ8dTwp8kGqMWaqQWlqrTTRIkXNJtUTgO3dueq49/OhLs2Omx8ATU71eZvI0WtYh6clpVey/8L3XD+rPj5dj115dHPzbz6yBl9tIUBSPZUSRgOaBEpW9aXO4QW2JMN8YdoGVNAyZtN7H+Ygi8psvvwgH7pV4ylvMyJo1PjsDGmZhKcFoHsQGBggAV3NIBEE9N8PzL0Ps0EPnONXwC8VT36gaYxDMAO3RMz+JJ5ezm1BEj1E3m72Z5AUlQmZSb6C4GdmfTKIrt9eAWg1tTZCNojOTSmsxrdkemPAIRQBXm553aF/du11Ab15fFsQIEFcOP/r/zQ4Asx4Y75WSm+0ok1KHtQmAWaDna7bPo6FAkZFiRTgkFsT+R4dw6EmmMW5cfrH/oRbNM2lAmdjm/dZoCt7MO8VA53xfZ21uM74cf9GYXgT3TZLFxqYVxFEXwLcR5ISe+7FSY1ILotS9r76xR5/92Ze17PlJ6eUlZIFsXrqj8/5I9Suv0mqtz22tfONrOQcT/82/exDW/JyAbr54JQUnrtbUib9SmFUxPPFqZvFnblfNpIcalAKdXi44/6RBnU7BzOrtF4uUKqABGFtgoiAAMMOYx3Y3ir07eWPc1cuivpvO21mqeVrbnxqBmxS8KY6Sv/zSxLHyDQBozs+Hzq9u7IJpDrXlmDTHqqcT0d8obShihCUxPyLZwDwBht/5322OXYecgng4c9t+DdIXrLnXr2+I9Ce+D+hyCvPC1eKS1mk+A9w3sdWvb6r/svH61yic4cWiDcQZvLC0uAjN/+oMSqUQc0ImMhkvgUOGoYIpN97TtNlAZzlUyDAjzZa7sk3FMAiBQaoipN3GxccPe+VlMcTtFbayvrowdwI71Dc54qfv1U6eXN59Gl2USy+8kPzWndXHDvhnh8+HrE5Do4zH8+e2tsDXiMHyNzbSnnT49slHWXrc0ZZWMcy3Gi1dmeAFBlqYz/rN/tPs5uF+429sxH91E/2QgX/3qbw+lyWtR+TDr1RyM4kfw3w2I8iuNBuYd7fZ4akGxtg7Pyiro+obL26ZHusfj4AckWZIalpvxYmpDrEp5rkf9k+GBx99/OZLi89OJrOWwqx3CDWDJfjIUDww9uVpj2MoxNfgqRJ4XOlcQz5vzhLuHBEzy6vzVEI+OWw8M5yPJ5CcJtK5tZUtnKCiMdkeTJMUjsnh8cjiLyattcLmck7QWf/CHM6mA6yUEhSI98Xyi/OzxoNPT4o5HnimOaqilAN4R5wf+yYQTfV+gRJvbpbXv33toD4zDItxTTRt9CMrigDP9Sy42wQlfg9WLuSgmrhOwgFp+UupLLSmSo7IQgeb8OYLVMYI/ETcUHA4dC7qDjUdOxyduZLaXKYxnhu5NOjZTi6VRjAUppODIfhg1Bn2L0pBfjGtPh/urKVC0GE66kKR8c/P2JUcSrCP0Xg0nw3+pEGJ0FA1ijgtELhnW5TmYjAw0rTReY0l2RTfhUFMgDRdt3HFCs0AyYytZe5AnnjYap6FFSJGK/CB72TT+LduXet2mkkCe9QDa2cjJ6Z0uoPKUi5/fVWd23wYuzy+UFoex0CsiCH5eF4EEiEr9TzVwDV7TlHo2tWt5MbaeK6YF83U3JqdzOlrsStf2Y5mvhXA0ECBAx+OoGIi5fsm0m22o74GJAyta+MQTICep5s7hNOvzVKxkmrY85mTqJnKDGTwWDbLFsGQ9iAGJ/duvagO3Aef93Uykc+UIY8Crb6sS5bsiGkKU7X+qbLx7Rd6uvH+uw++lshmB+NogOq0MyPJpRg2irCP/uItjNOcAIGxdGqrqE3HesP/3Y/2lyiweGtd1VGfR+JXFpxZQPnGncWcPZkJRKlyK8susrSfzFkLg+aIL4rjrJDIR1af1N6+kPwgHsYyctAzx/PRkKI5QAnSDNlpzqzQH2NQIV/KlryB74aSRe/srlMs1YqcS2PalgliKWYzNAobw8Nofma4JB1fnGCABxS7sBPFbCToJXjj4snp1lpWMXw4gn0MYhkYcGxlrF0bqICQvXj27PHZ8xciOVIIb3Y6QJwkk4HFOCyIo+acgjEgGIcgUR9MskGOQOH+VOFIyuLsi9PHB48mtiajch8iKRMzxzKqn/icOgBkXZkCl1tWPGFju0nj2J5150vX804I6wBx4Y8hAvxqMSEOXTJuyaB7ePTFnAJfem3vuz9YPvviOaipw1pPTOCSBO0Uk6Tt43as8aOT8x6Q3aQ9NPPS1sZGEUiCDJVkMi9ufH75DP9aNDzYfzm+XYJS7x/1WIFJxTls3KwoQ9EEWkyPyS4XkyKqg13QkbQhaiUDzgwDInNjF1W3iXwiXqFnM+rei9mFs+F//Dcf2+fyK9/e1abppZy/sO58fOz6mhmQNGLDMIaOe3Jq+TopWi3DPG6Z3EvlyZkZJvlMOa3TxsBAK7xrzLRJGHokOQOQYjk8hYw3X05dDHDMMeT7HRFHe7LnnLqIKy+/mI0opy/5BxAQaUpGD49/eq4czplosrkrsB4IodJ0ImNYwp8o1fORMxzhd1Kl3b0StEEqph5RMwP0U14KoL98Mljmqc2Nq8fjdmJj7/v/9d+YTsY//YNHmViO4qrU6i49M8PmfJ4APuhK1qz24u3iGOC0cQjP3M+/fHZj6S6Zwz692C+w+Oa1/EBzathc1cKNHJyAAnnm5Tix1dQ42vzmTW+Kx37W8fihbEb7jiUohrNzQ/zo4ekjjc3vvkAY6eUV6ouZlLy3u8bmEmSTt219NEu7FGZKw5GZXACbjoLML6E4bi5B7hwspODGsHr17pUc8gpPeTMPFVIOERcELH9yZnoBqNXUGJpK4HE9nCSuk7BfhDq6eth/XJ+nk/EuIp7VKMAovLwcPMrTKy9s0IvlNsB4F2PERSvZ/Hzonej1RIXRsxi+JJIxfK6qtIMlHRxlYwafcMAOyqLFOy/uN0exvQr7tYx81pDOez6MbRYqcKnczyX8wO+bAO4JmUIGnQwltTpuWtlYOKv2XB3ptfQw463kVheZ4sGXnffrJwtvXMOyUKEc//SzR64jbb9cws6dgQOUYoQczLuAxzMZpJTQ/QALgJux8uVY77iWxdg0gYxl1Hf9QnoN9Oap65nmQCrF0i7t9jGWwoS3v9y/vidOCeDzDx9vLuz9+t/7DtbvPP69t2VbJuOkr5Pg1InK+MB33BmRIODia5vVzuXxwUQAwjqIYLCxnljfXGE1BAMBOCgQkjVtnWg7OYgSxTuplKBixVw+w5Wn2nMoL37488N3Hzd8w8aW1xGdEMGYJzmQFIOMs87YCEAyuwtTSIqKRWlOH9jjp5cjR7U2ciCvQbsvrh06M5yl63UT7VMLfCqxnZw3J51uI6IiTVMoCqMwEHECSNLs3RKZMCyOjzkgdAajylATwfgwRmIVUn6mN04OhynU8sxrxdVIIsE6bAfAAG7H10rpq2uhGg21xgQpJqJobZXqXpq2pdzbWzvqjaYntY1korG44gzRxWLSD0zN9VSlA66Ul4v8h3/6JZuAC8U96fNqikrQN4rEKluuyv33P2s0G7QnQS72+Rx1CEHcSV8YQ94Oczf4WsoEB72oPU8BnkyjziSkpgrwrjoBfQywR3kRDr1OayasZBAkVYKBEag0pVCF3VFg0m336cEJSZFbd3e2RN4bGW1d8nxrdaVoTOsZMWgUPJM3W7/9yTLtYOnyxadn5MaSdDly1BBA/ObMFwiCQKl5xzzrj5mkAFracCjFiwGBynAK/uLz4yf7g2dJIQIVUBCwiGSrrDhon7jd5b1SgsT0UcRl005tYk1n5xKQiicNeP7x/mPNbx/cfyRAMGgoYgaJLRb3L6rB0OMIIA67Op5Kb4F6nBQYlO52kIk2uag7KIxUUqDEYARCRAEJAldKS+OPn40qzKvrpfrUPfznP8m9toBCkTUEIEATWT8FzmnV+3KgAapLXQX8NJDawMMOv4RysRDbMqeDDjw9hxHIW7e7znX0X781yPsASbIYX3JYLKdtLG2U57RPe5IxmzVhC7YJ1QmniowM5qkgmDCmb4XpDKhhBNmXZGU4KaeB7XTxbqkgXqFd3no6hXxKz4SVxTygJlOReCm5qOL5Pru+Xm7L1e54eprBfyOfTDLcg+cPFxVb1KH6J0M35Qq4gLjh4iYBWfq44aJwEABL6eSaMjwDYKlT8tkbK3fATP3DcycGJLJUzTeT61tf4a41Pn7vi0P4zsKmcGMnBXNSc4QmjJEJFpxoKE1iOZhfTWNXbuQ21kfdyUHbsCejMMCWRTacNItCgL9+A9rbTO1Tq6+9eHLqIX3N74xNk92+KiZv7EyOW8Gk3+yOehzu94ZZdyQJHptIE4l0yZ89f+vLb7yy0FaKdQdigCCyJgt5Ae9PAwcCPcB09L94OF1eWqHS4oO/Oisu7JVubXT2n5OT8TJPH00n4py9+dUbtTPU9pc93OBjiRUZdlnuo4Naynbza55QEkxYPAfwOtwt0DwCEszMSOZRyQTxRG5IOvDUO/qjn73yndsoPEsvrNvVShOnmamEz4mtODLwkdU4PDeCPSI2lZTO8dMr11/dTjNp0/BsA5rIqzM62ccwmtn94TUW4wsSWH9bpeIkVhTIeUNHPGgS5gGYzydTUiJ/rH1RymneoJMIsss4cjlwKUwZ9AJCZD1o9GgEL5WLi5lkKT961HAH40ag4hSzJDC0o89PhmkkNkUcByFKLAEbGitsjdz+kMRFDxg3h5nMjcySV/+8Whn4i7HAmM0fP5tGgPQmn0xifmw97Q6mLk+Qkuf2IGglW0ni1YcPlcuxz8UWhXRnMPIRLEd5BkVN/EZnahZJBvHc5qNGpz3lOfjO976C3e+MgdHMJ29cjyVqVfsJtPO1tdhv/d3n/+5HAw8tipTrhIYzq0GJpAMQDkgQF+DCjKFYqBXRDlSgOSEpNMZqhjH0OOLNoiorLO+SSxpWBNUUl1cUuPXeiYAC4bKVzWd+sDo9i4m6EyxtroY8Nno2q9ctkgdX710HHQ+1idCkbVuSdXkc6Ugaz764HPWbzrzlPLMulLzhkSNXiW9ml8SYeVnTXIuEkIihDXemT7WRPqcZHOEIwkLxiwvLhMXtzYUZoKme/umT0avXMLZpxhFyyjpnXSVBLmwsLsaR9YWVhOXMbZc2AOzN1646Urez/zzBuhxluxFUSBAP37t4qjqT/jNGrPBdJndl88oia+KS1puNbAdDYVGIZpOJpfd3rudVEE6EkKGGbuCYLSCBESscs/Pte591L8WFjWBKMhdnYNG7UB7XzeH167kaGTx61t5CHF6Opq5KCKiFJ9lE2g1Q2lJbhudFqklDqbgAdgF9SsscScFCrgRNVY/sWi4H5fLpQXv483//yahhXH9tYzEPjkgrQlk8g1O5WWfwcbWhxQOpCsY2DGU2NPdibGCBng0RS7lcCLz97nBvgZEwRCwI7X4fsbVYyZ/rrh6Bn31xFAusVXY4aJ/q9x22DAwGWXOF66H0ZHBw8uhiBU4keBALOUKE5TY5VfujAAt9+ac/egsDB1Hk7O0uDAwyyWQedU1Ade5kw4u+AScCHCYvZCIAJCzla5KXjkHZBShJpC0c92h6aqkMEf7ZrJHP5cRX14x5W8ukFheDvgY/+elZ4ZuLaB5GSaZ/NGf85Ac/b2deTX7/h9cf/OWnXGTggI3Bfa+naBD+ma2GMGyPZGVe/fP+qHxTMGW4AYqpdTEqZAwaySeIx18coywhgNi4p3jRPBjaY92SQhOPLCyNlJniPGQ1Q5vOjemoJbdGo4eEA4W6pCD37saE2NJLwrNG90KepNLCld0CLyacZ0dRFk8l8jql9waKmYBvb7GTxlxtzLML5YPG/lahtMh5h5dzGXV2bhEXfbTMJBAOsFwjCSkLm9tvD5TICL2+koRQajd77yvw859+qlTHVyswC8hoPjj90CpDwmsvvaQnmXqt/+ion1vMMFDBg3swDG384jpEpIIeMn84bR0PpvKMBsIA7o5ncbndiW0W/Wtrle2UMyWnVT8YfeGNu0CjFqDWQYd9ZYlY2omqT86tYQCZzNZeESEgBpqs3hQ/uD83Ii+KMUc9k6VQ0zcn2rxamxQy2RQfKyaK03Aq7mSD6UCbEmd9LVL9bvOBP298/68vjAfoRBtMDe9PP6+nglduvPoimOIyUo+sl9Uh7UborV/bBCKm2r7wZcDDjWICFPJkszVLc9gE4axWLPLF3BLWOmp/d1H845/d7/4VGDFC8i5D8SJyMUsF0F6G1Wd6JtQbg16jNp5f+mQeWS/fwRtT2ZhubpdG+Yy4lBzCiOkJqVBX1QjK4YO6koEEAs3Nbdm2Gbs7EkA0lyf3UxbNzB0LHjxXK3lKjwbJ5OjyQY8h0kF8wQ+9XAWGGIL1/ZmKQiy2cuOK1Jz6utJutSIGsG0ZA3GgPfVRV6HA0+mM8LyF5a0sKqi+3681Dg6Gz/TPN7bWkyvry2sZZKLIx/Ivf2PXgVSABJXQIXRCM735hbRcZljKlkaooxiQweGczRO2aU7icdD1QhgJkpA5kdRNLOBsgYzFLcdeuLnpCsGDtz6SG1UKpPr0aK4PByBUkMeT/TDz6p4jLAqu97xTL5WJ0/bYITAEh2fW4J3GO0PALyym8lQuHwnyDMMykEbjPb9Puo7vOpwOUd3YdI6GktOQ7Na5v4oDYsyHHDXW7gsIsL6UbGq0OUJwknFQ5UiWVS/YSN3AZZCAwXgp9tHPnjmhVFlOKaCHphJiSVQag8kkzPhAmct1HCxORbo5ncABTgIeGCRS4PxcZwiCp3LNdgNpntVh1+M8Kvvqzsa6/UAjjEMVNqH3DrSNHDYFcbC4fW01CQKp9Z1yPQQmA92cSuNo2uE0sNWjEW/j6sJsbDljez4FaWwZgQt4NLRU4drqqlRVBuJcczALQWM0Q6dTc89AIc2eSf4Q8F6qFHJM+3TQriqJ66llyHv+px95BHj9H76ZKmyAqtfW+1czNuJYriqLKChbumkaexxADiW95yI+pOsaKtA04IOJpKrTgg3HElnCt/kAzcWSXhLU5Pll34KUMTiFMyKHEZEF4YFmWLdWmHVgZSGXS8Pm5enD33/bwNGXv31rA2eyrBtd9c+PxtegqHY0JEgQvaGBVKIQ4Y/+bB/y2A4ICVcS1sBwLi5nZGp9Kfro56cVHuo1jChJjz49inun8+X3WJZHYnr10TFHzMQt33BI7ayuD13+3sI8hphu0weck8OuZTZjcV+QRsDL2UnJOry/vxYvzZbZebdJBbFXbiQ6pdijZyd0LrNxuxDOhhkWou4UYznGqosDZ3orgLxpDBr26Hx8HnLx+igu4UFjdsyGL65nxkuRH3HjamPUmTkAsvX6piu17YR1dnF2oRplEtAm5iqGA9S8qacpJsStkXwqJTEaZUvVj9vuGWC/liBJA4GrT5+1VxS4ut+JJbI+6zrO7LIxTGO8w8I+zYUioTMw7rEU7mptBJ5ig2kUzvQkFcE5xBqMGuO5k0/0J4pijOWyzi+mfcOdQ8r6SpIIHdmDjJNWX4iKe0UC0qHuOJ4jMd8or/Dj0WyImOlVUqQFfC+JdaXTwcwamzAM8RC/WFz76r2bl82xGbKCjdbqvXIWtZeWBcPEIZ+xcXyFuPu1ZSzKcXHl7T/4E1QoJWMp+3yI8pFVGDNXUgpG8w4RtpquBKmKkmdcL54rlZOEY12wbGWRJGlo8jvvXSvs4bDds9yZPVq8lZ+NDb0svmNeplrg975z68Mv+53I6liG9PxsZyvOzPVw3MqzSaBMN9ru4ibHe4xQs7/5zS1pOmu31GQ4UKwIbFeTECUIoByB6QoLecD+Hz/743+zXzf4RQ/a+eHyi7/6NbdLNY6b37v2/amvn4Z4fncJ4iN9pow92VaQrY3EyJAKYFIiCA0NZ7bOLwYYri66MWLq97hEGOJapsBfLYhr1NPz+gKIh4rCLLNnsynsO/EUhm0Bq7czNYaikWK8wQEGpDz1jZmeznDh0F27vVpvhfO+yjKWNh+DURBbTKr6mTZWckm505PthXici9N9CQvwTZEjtSiBeDUsNTobhRZBsvCwZYVAhCcxAgqhEOAB0xjKMy90MJ5OpF6rbK6vpT1keP60Nw4GynRsOBiHRDidnEFhSeBXc6/gJADHQKfqRGxiezszHlvhTDNTyZXtlfLi5KnlK9Pu7HJigPLocoI6QhoQkTgzbvbEdKgYgRSFEQBKYFAKeZ+FDaMfwHgWIwFdkfWop3vl6xzk6gmKQ9L0ws7tl6YP/8VfPbj35jXymuB/9Pjxj56xOZRezYDtiwYq5lbi7qnc7e0DCBnP+KnV+KKKyLoJrGfsR+bw4WMpxgpxMGZSiAms2mG5KdFUKg7ZwxiW3C1JmPHuWZXjwauiry0IccuqcNmfVxv6pLb7nTdBcb3XPtNkT57qFBizzroX+8dYJZmLnGgskRGlRDbBCOVSrv+g7jtMKp6I7Pl0biG4OJhPq63p1XxhfWXVg8HJpL/FE4hiSNbQz6w7rWfvSXo63N0yHYyBrIu5MfewTD6TxXca/TiEOOQ5BJG2bYMoaKtKDzeHj95zENPP3MozXHJ2DvE4KVILr77yQ1+VmSRmNaRkCkjTHJrxukcTZz4qrm2gQKZ22YN9t1RGTo67+IQrpSJiCdw/qUqJ9BwNr1zNTi8vhvXLGAMp5xrFl1durQLT2cdvP15B8zwEfvZeU8BxeBQAMLTzwg5c4eUZMLe1EY7ubWwpkc1RMdT0z+YYAADpYjIdp+UhRVEB7gPJMBpZ03PHAOhULB23TCAieBgqLa4hR8awOpU80NYZGlsqwAjwxcPO6cVZflcokBW1+eyi/mX9eLq0vZpKZbuj+qyhJWiBFuaHJ/WudnzwVGOXDzeL6SCOiesl+AVMy3RM1N/athtnzdofd5avbyM0el6vTh6MNMrva10HcUJcm3lVW5nofQU91xC3EFuJzXbEVHaZXqfUh2rLQUKeMw2c0Fxl1Js2B2wbUY+Uu7dvtafq7PPTszAMEGTn6uYjy4LIIODLuN+GxURCjB49Hzi2Kt6pLP9wkXuPPb1s1Q66DCsWKc5rKnsAwPEgFY+a56atEZWFjOFIrZOWwHoT0PYIMfIqKz/AjERK7w+B3iQ66Kkg/bUXC5qakJQxilPZShoL6Hg5da4AYNBrtBspAQRJtkiWs7w3P/XpOFj6m5VzkEXh2PadNT4GAU0LPfc124uF4N5qUgmVRkeaTh0YxVeKuddYNEz4pgH4SVqeBNJQgj1E8uZkLsRwZml5+eBp7/l7jVt3RDv0E6WkNB99fnh/+fYytb5k9aozAklkU94eY4BiomHozQGSqpiVtReWlh78rP7zw/Odq8tr61dHUrLRumjIY8QBSQnuvXvY7/uWbdBM+m/8w68buvf80dG4JkWzzmQ2neLStogMlKk3enDn+jpO+oGiMoUAKAIWDI/GVoSDKRpLlGJdTYe4KIilpjBxqPgAxlhCCCkTRkWSZDxWiNq9yJypEaDF4iGUE/m2XGH4nuSCLMiHqKrgWAC+9qtvVFudm6XCqy/f1CqlJ+czYKBGPt89f356tj+Wh+Xlkkui8sQvpmA6k6o1BmAMUwlYdX2G4xg/OzobeiuTUgyRdJQT4aapX/ul6yyASp2Re957/MH4lVfXFgtC7jqFpVmWcx7W/Mlcr9xenD3WrJSwt7QyTeQmgGz7IGKHRQh2aMfABDMyGFCiclx71p3AGl+gTuvurfVyxBu0o+umxCatL+w0xpJiDK0M560Tae/6+tgMoUCjsr5Je7bqE6gfEqDUH5JjMp51/XFwIU+M8/76V8uvv3Glc2Vx2i3IA5V0I9cIJzPHoH0kEgrpijMz49f4OQvwFd4wcQ0Kstu7ToL/i+rQA0eLPonHYl4UN7F92GDIIsuk8VmQmE7q0UgmSICgSBJhEDfChkBgooIAN9oSQQMsmc0uBYN6KwLAN37lSo+N7Z8OUmqQwTP1pMpICqcFSZQ+6rtZHi7l0p99WjPq0mam7KaSoUd/Nq5zOAiZeE7SB/1HtMdzEGVPxrBCwjofQWkUARzK92BKZoi5OHs8rkI9PVVGQxE5jtQFUpxPI9jonz/v+e7k2mtvvPILL59cJoypgjAu48Lnz5qvvnA7vpmxYN3o+UZ3el7tQFEIbHiBYZEJLZfn1Cl1eNEUkg6mIYvFlO+GvUYLxBFdGbm2jqzv3OXXiCRsAa0hX4MGZv9KgqnODNJ31al69aq3mHBPQz+0nH5vXIhDOoV0ZM/WpsCckd0ghINBGMZ0YTPDXfbbR+en926vkmOP4GF3EFxZz9iO2VYMEkV7cJhWjFiWb9OCNxg6ELXBkIPPm6PVwt7tWwYwVQyf+/aLQJ4fok2sEddCHSVSWr40USLe8yuLxfZFf3ElTpoTnCu+8J+/OnFQbzQwDsZjUXANi5DpURpUJMANFZci13hyACIPWvJujku9tktAMGv4Uaigk1luOTPtKSWM9gxQras7X7lCYjsvjI1P3nnwoNmeAPH12SiQScBDlu+sffDg0d71dKZSPPu9n6/vriS/kTt4XjOqc81in7aqVyYxEYXurjj/8mctqQb4dy9efv1FME387OSSrxqxdMn1XXDqzMA5DeojaTiZDYyxY9Du6OKcZFgix0auFyBhYhugYaIUyD9FBNKOguN2bjYWzDg51gB1vESGQKR79anvAMZnfrCrkJzKtLWNLA1GoYMgI220xKfnNOPKvqNAsBNiLJlP5bsHcLhvylIqgRKLpTROmlEYeYDWZwngHpmRaYYJNje55x2j2Tm1Iz3Fg3SKicCYoZsigZJcuveknQSDsW6nGAmFrAsvCdkgnaVSROgwJa2qG4GTZSAGlI/PR7ruaUBChSZZC8VZK3ZzwSU481TNhFbjqJpYoUN9SgOdRDkbAPq0OepPzhyIyyWIkRdK0YQyAP54fhQgeGMezy1wAGnMDYOEFstx09GHzb7lKF/9bgViArXfYagSiJKHZ52mo10ppvR5DBvoM+Wx6C7evLpzUv3CR+BkIM8P97tXi+B66lrBQgGs+qxhASrzrfLW8ov2jz4cHDwdAn7QB+IZK/rOlWhBefT7x7wrtZ9fuoFOcmQSVAxdXfrqsvlUbhwedC5nvju8IDjq7nak+1wYyTC2X1e7j3oTRAqUMLe9WRu12p8+hRyo/I1bCR9lkejxWw0yxuuZ1EoCjGYYDYbjviUwrIRboedoMRHVACQHZnkGwuOv/fqLY0H89EnV/vnJwdPR9dc28juJT999li1DlCI7WkrOCJgNU7gIrGUAeWLqTtNzS/HYk9o+jQEkDiMhNHF1Swc1OkDGE3IENMeSbpq3vlImb7+K9PWjL6pTXgC46E7JsxpGYdw5OutQUYbKIljfj+KrkkSgig3PQv3QAGHadj3VVW1yPLqAlLYX4+kikrH4JIESU0OS6r0gAbTZBAEam1iqf9owjk4Xrq45GCF93MxmqTmACTQRXOgQKM4D01WCLBLMel2eSsMCbFvDj3/eHVRoBUktJ2OYAU8Mg+T99Fqs+bQP6SOaoug0vXhrBZUm4LQxnUURgNj9HjIfV2QtCkI41KcqAGDGRpIKMkWiQFw+afbH5tiYvZZGqI111Z/2Hb4gh5LtQlBoa94MBuIVYtpqcS1y++6qNTDf//98iubIE0lNFxOlxbhxdLz2g+vid7jnF3b07MyYzNGtMA4nq+0qoMKkDXDb69fIIDbqyPXBs56/uplOrogRjfEEbA8JL8QpwzECQtUCllW1qfP5YMCLaGEnRkCoG8GKRjw/w6ddD8Tsm69eU2rVRx98cFDgqJQyOzxCIn+7Usi+EA90kC+WI3tajjHGbI6APgNS19b2wi2o3W32L3tkuXT3jRv1WuP8pF7+/xaE30GWJohh2PflnF/OqXP39PTk2by34RIO4IEgQRKgKFEBsuhylf+x/1CVq1zlwHIoB1l0SSLNYMhCuAPucGkv7N2G2Z2ceno693v9cn7f+3IO/v3SAoDzaxubFAceHLjLmYus1GvMEp3Js43r3IsXh1bXlpVQyIocJaRX8hTNHr9+wRZCmiQFnhgvvCSPFMWiPlCHpyrPkrBEByMMAjnNgxsbKcQOFXmwUsVA3PNGizgRzlICETri1I7SnDUNkDh4r8wPzfDB/vGVq+tbb++xItQ/H5Oxa1pz68Xi188Q7m58c7VsLNXCOnFmeoynn786Z208J7Kdx/Le7Zt0Oi+WRFiMLp6xngBglHfw41HctpGt3NruamQQthFU9vJFCiqcm8dftIhpYCdqecTrTjpUliyvcHdyuyRoJGi82eqfPm5acfThO3f/6PsFe4WfQDP9+Glv2XUy6ORowlKIshwl8Aaxtnftw71LuN8ZN4HQUpRYmdkwA3NicdTDUhWAdIGU6SX9RQRf89aZMeYySsu5XLCx3HpwznjsyVD2ZhMfR2MHZ9NFATaI0CM26kHjqtLp93uORPF1gZsqMQdFoSUA+nAwNJgYSdRdIo/OBwATAYsI2F6j+ucDbenAqBVJqUySOH/dzqVYaDlOl6jhyFxJNhYo6AZoJYfPQFMMR1LZ759BWQF99cpjxUQhWbF0VcB5PyEfvBg13skFMrAYNWkb7HcxPbYrdXY1vds/Wx591d24wRAZeiSzOR624GWpkRqfRqqlChkMRROJchxEymBC5m5dC9uxN5yLG3QtW89ieTm6RDRsi6Ca87Y1CXs5lufck88d1nXVuDOc9AwOvPb71cNOzxwEIwxOZimEESESa6cD05wLIUQjVDmf83yk3Wy9ft03vXj9wx1RzIUeYU0W1b3rWIJtaktLgQuFtVQ9enHwTH08eK+2Ity8ctB0F+D88NdfsfGVdG1Td1zaBruxullBVcHh2HCxCIYzbONGZQ6oYAGQ1hLPLxQ1NLAUsXFnB0mzMZv1Kc/y3HZLLQWuMbd5FkUTYtt1VSsAYDDMiSm8iABWKqe7I58P8AJNnTtAMCc3U4L1akSIm5uVzC+++h11zdj5eyX9vhVZydSbKeNywZCUY4cZ3lQiLchQpWs7R1+NDX05+fU8Iji2pV3Li9nrRDfoc1MQVs/aL3wY95ZI0J+a0MKuXMkOmovQi4zIURenDTC/Q7KO7/mw5cHaxInFLIlGpquTjXo6/a3t9sjWKAtJcYfHgxEUkQGATMPPu3M68K5+/E6vN9JOIhyh+gv7yD766mBi98dvXt+ZeBkb8KFYz1OAPPDG41E+v5Ep5F0A4cXw818+8X11s9Zg6RgZIOBg9Hoxi6R02KjJK9ToeTMfebpn01gB88mL03NRjqvfr31+fgKhVrbO0SggcSAaccBAVvtAu33UVl1kqW99e22OGacP92cvxqvVpArjW8UcB07J2RjS5yWIsxArAQNxQCZzKXUhdMd9DHCNsVWspKyK2R62WTmxSzJEfZdGQH8s28NhIpKXJqlWmIh2vTGCieRof5nGgNgAja4SeLZDO9nV7GbXWHT69HracpjmvXEmTQEO+Afv7d3/zdEv/8NBfSUr6NHqzSIoO2e/PC3kEB+Bph2YI1ncIGctcJweEohHh9y5T+AzN0cAII7rKDBOBLyUqK6UXT9QfG/UgeC+m0Lg/HolRhjXcDmK9SPXPHrMZHEjkNWZbHrWWm3VdgNt1G93BgDoprLcem0tzeM0HjZHbd+exV6EMlx6I88rmUJRN6aaVOJdKIghaufGnYtX+0hLDbSL8cR0Sz6y5NDGd1eMFxMRI+u1dSnLG8TULYK0rEPdfjfMT477P/tF7+r1cuBbz34xv3kzfyefX86gMGt3Q+lanl1Vl6mEicLm/bHJVvjpXMdxBCOCQFb9GCYRQM0TOhmDeNy4Ve1nqZMvRpr5Wslh0+oVrmle7wVrm8IjAx2ObfnJJNdIMDz6+uf7/ZFZKCfWrt0oAy6w5hqvW4efTxNXUs5MxkYt5Mb2t//0ZucXTxeTCffSXZoI6MNPE6A1CT1rboqs8/xJpRxe/3t3vGvS808/2ziFVV49wmaO42cscHs7Nc2jA/mCHGA8ARiwCcVQtUxRZNIdwVsUW5RROgXe/u57mrl8+kUz7WEj3xLSxFIA/Wqc/wfXXv35wUf/bG2FRpq6MxpEvnkKyzZYL0wuX7FTy1Djk9lZKDs4m52/eobESGozQ32warcnruKpaAIzXATWMhnOGZCpmUKmmZj3CMwLVnMQPkCWJJ0TTa9FMYDQYISUobyw2FSYKmV+89N5VB6vv5uRfv+OJQOrHoOd+lYoqrAFQ7xLA4KUV9QOkSpt8ubRufZyNk3nRDxVSYXwZbc3YsahGyKsVQHAh6NJ7GJXdwVQxO8tjAXMLWcaEbM3tiSglM+vsm6rNyAQ3AXmgwnH8u71skYSuUHkTS+U6TSkxbff3TSyXXPGzEB87Xrh9JOntqtmY0gJdTwF8HA11pRMhoOoEJlpSx+OEol3vndTMS5efvaUEfDdKw3xzfxlcwocLjIIeUg7MR/r7VYiH5FA6CbZbCYBiujE0lzbwK/UhvvD0emsUEhMcMLve2KKtCHxyrf/FPq6P/1smfuGdBi00kXW48reZJF2uljOARRnY/0G/f6qcXw2+KsfuLNl7YOiSIb79w6LUDJ6dFi/ktI+KC2OOwZD1SkcvC29fvpcPpunYiUYQZKCbG7V3tmQf+wDr0QWTZGeq9nzCSYRa9+4an7yEM6CTcwcJQmFjbON5KVr6NHsleGgwuJl0wU/FbFRRKSKhgekWTTl2tMMElKZdA825vHpTw+PPz2zS3Qe0TUIQwrxb19zawW2uXB9w4kulyaHogU8M+jXap5bAh3DT6UT5EYe/OVDvLE7YxZ9WQ5sqAHCwoxx04TRtwWGDufL6oo0v1iM5XEuJz3+Vz9BK9LVjRQGgZEJTefYQiWePzMRdntw8DWrjcfb6xFPXKkRgyga9nsndDa/0zj/y6Y1m6UBHmcbezsV1QLt4XA2c/Ci552ZZ2fNrE3Akx4ao9xHW5cILIBM2QrQoeLH3vLOWjWfCb66qNxedZY2A0Ir1+rLX76mGznPwmzHGTVNtz/P3N2586fr2NTfH2nLDA7aXdPxGVQfybBLiTQNhfOoXmuM6TVvoKZRG0QQN8LOZFnx4+R2KlQN6WaRxt3R5WAyomawu4cwvg4PUbV5OslaFkAWndhnPcZWfGLirBSL8sTv4ctb335z/rTF24FcTTFjl+2DettKV6E8nMGXITNqjybqJMkXcNKjGaO/GMnBo18dbBYyYBArFlerSWZ6SigyiZDN2CYCsnxJAV54YPdZFNUxkkulaIwoUmCE4t5XnbEcpwsCzSXXV1O5RFIPPN1Q4pidBSHL+OUi51mDOB/niw1uEr/63TOAoBxJJUWJwCkJk5bz9pwgz+ZjHwfQIgdoeKgpz05MvlysgiUHkYlyAubEspgEg+jADpAo8JqXYxde7N7ezvL1oeyuXBU1Dbh+W3r94PWlpBU/lvRPWku5T2BOJsd054gVMmIhc/v3C54DNcdAKpFWj/pCerEwTWs+SiW50Tj2nQjEI7hCTy0rKeQuAAXMoc1nEyqyzEUIqS5eJLkMs7K3/MXfdEWkJiZnooSt1xIDni1sr6SS7mf/0/4sxnZ2ic4ifOvtjfWb27MkF8aj3319diWFxr6xDBk5UkIEzC6AUiNG8oFgx6A7x/EEvyK+vDSurpRWS5XOwBy4RuQtYtuqIPrU8+KQ5NOWb6litQr6rrqcnv6mR4dhPZ31+z6kL53AT9XTY9dqm+1VemvoIrNpvybFrcup7hmiRAJqQOWjKoqUWAIYapVkI3GzOIoGJOJwI9zy3NDyZ5/dn1y2OufLRL1Su75iiBydLFaYG+PzizjFILBFQL7icmK15KcgFkgQakT7iA9E9TeSz05nY5vgMhX0iu2/OGGplULmrb4Q0qtrwKNDFFqQe3wRSqguwubzpu5/o7h1MR8AgKM6Hhk7BkQxNL2TYZRpUMT36MyNz77+atgdZG+ke/vDTTzkUmndzKR2U4BscKQKI3GGwVdXVxvV3PMnRxk2Jbe62ZK69e3f+7T75i+eXsjtBJtOAuEcDi2f5+kUNlNUEYxyb651jizbsJiU+MUXk5hcrFVwWZcf9T93ODkvlUFvOXvYJRjU5jtMlOu/ssX8Hiwb1bvV4ls3scn0+d+cbFIJbi8jZNHx0di8ULaL6ZVK3jhshpa6jA19NCVIIZ5rCI6UEtlsRlzKMmy7V26lvHFgAsi33th4+duOY/qvrWZ86LyzKpweLOxZ/M7m7cHkggrNUlKEXRfD0kyhFpgWOIte/Wp//MmD6//lzVnG7430+tVcpVL67N7lV6pS+t5VRsSaPzkwDYxtTq6uZJ31Ki460/1RGIX49srPjh8rGRG0EEpxknKAztXUThxx4cWPmxmTwV1EgqEwJmwKxRHeQOzP/vZJFguj9YTPZA7OHoDPekJXEqDoUW8KFuiVNzKnPz6rEqtkMYdWFZJc2hrJksySDKGks1iYtQaVCBYybg6ml6lSNuaxeBTFMTw5X6hUZuNO7uWP42sqGMCsQCi65eshrJMxD6saDSIEpqAmAdjL0wFj6Wspqe0iJBIp03mSkCZDg/1YNJaph/cm2ThTvfNmCg9zG+sG6EIUzVmz2WQczQd0E+08Pg5xKHO9nqlxTDYj92dcRZpZNo/HkBnaM+KwJddDfONGupCC48XMN2l7pK6k0QuGydXKdQd+0l7WNggNBc96k9wqEEkgBVtAhhuNPcgymVAfnFzIsrHKwcvuVLRz5mR4+ttnwQKoXfVy9Q2J5m3Lm8ne+eG8lCH4VI4gDHXqTH0ldFFz6JhDT/QTo6EWqs6KkMquRLgPAIocLBwpkQbS8ML1UAywQ90PdcYXls0FSwSa4rknQy9wl5bSSJWDeZhLUQ86yGwRUxTmWvH2re3GYhbY4QYrImvE6Wj59nB+8pPOF/5CWOE/vp2Yj3xWJ3wTm2soHPKhubSV0Az43TKMY2gE+kpozQ0Pp0J06gYL9/rWxsq1+jJEow7i2iDizZa9VoJNgTaq6yYwWfqgT5iKBLF5IqdL3iy02vL8W9e2BYF+/NU+l4oXnnI5mnDJWqmWdWFduTT0nkLHmJBLjrqXjAPXGzuq4TUPXpumi1R3VvJ47nLYHyntOyuspyMFTbuwUAPhOm1HgiBsf5LCwNGqhIUAkCA+vvsBZ6PJUhnPF2avLyeXSp5nqa20pwVW53CkReHlqBR6UI4zDG+GiDSTZ2VvPWaHDIE1DIRAqUoexeLlYevxo/3qjd1rNxhjvxf6yoKCD2YzmnB3Go2kj54k83ZaQgi88Edv0Ezi+EvVZ4z6bey9Sj1nGo9bpx64EIuSVCzxxe3ZF/uvRnblrRsQ7JfXVsiBDp84zrD7vDMo4HRxN+2bzqPnX86xKFaNDoSNx8tygCZAZ2ktxvMFFuDv3EjAorR/qJ+OWsU8v0JJXttIKAizUly7WVb1WW8wHM/6uO8DtrAp8GoQFDNk1rL3f3x/HJxdRJ2J4iEmXCVp1VWsmQ0u5MSKSL+/lo2h+ViFdJK/gs0DIKJ8kRix/TmI+mB+Y8VmHsynaI0BBqPB0wV6cwMvJZTpJVWytv5gr/uVbZ8tTBSMKkSpQY+eO6gWp7bzUwd8PXKSV8sVhp0963Z/87JRz40X9twGLTuouWGkeQwDprk6J61gVXaWfyP7fBPzW/j3s3vj+eP7h3id3Vgjv/jkFEf4+YGWqoiVj99aPOnO5Xgt3+C+9cbf/dVfKBuv/NqNW5Aylk3icnwb5qRGbvpervvvP+fPTQRg0m9cG5QZDa0C8Wg2WQLTy9w4ePdPtx8+6r+9Rx5fTlhnPL3QlRi49S9XmDIz/7/1rt7cA26zRSY/+7IL9tpXxCyQNZyVdKDPyZG1vladyi7UdTiTPd0fyRDowp4raxQvZXIJXIC00EQdVIAIw6WGc10IPeVQSV7fsQ5m381nPu1pGkFhfHDwxbG5WRZLibUY/vXLlicJhZPxnVs1ME+0DjuBYm28sdrYvUrUouVvD6BGBRRsnIV2Hb/5k5f0cJCYuKklADOkSqiXqxsfv7kTo+zi6KwrW8oSioRE1fYWnZPF2NjJZNgL4KKKire3UkNjVdq8UNt4GsdChyLTz5HQeCMtLEdINp8FyD5oRwJWzEoZOvl2ceNX+yfyvgkC4kTubl8vM2HeX8gUmxiY4eXfHq00ijDgZGBqFvpwulQHfMMGLB5dQOTaCkbtTwJjfNnz6YIdG3LVPf8KM7g0Qg9imJWem8ui7uXqt0cJ4uuLUSpdAFvd15dTuMFLECOsJ4QSH/WM2UwjuvC7d3ZLjUqkls5/8Xj+9JltzugwEcJ2EGvekzFKizDmowneDw1UI3pHoygMwgIZTc78zpLdWX2Dpg8fuXQ2K5SY2WTBT+cyTV04ISDiWR9NWJHZ6ybTMwcV7GotnLTgtgeGOCsmDzvUsDmS1vP166uj5unx/n3XxdZ3CgIOnPS89UrOzjtCQsoE4dmD/XCpoyk8hgVEoLRBOEzTHAoFihlp5NQYSPmC6znycMAlydAEUQ+ksBiG02gaxWAInPnDacCIPC+IbloijYlaIiwA3aKY5WsttymcdkfMWdebh1Eeqr21ofbmFM1LAu6PNLKROH816V62mBLpT/tgY43559vB8/aOkDN05NnDx9nNXOV2A/MAsmu0HEArQDWKDLeTiI0iGYxYUjgZ+YZpjMztb+0B27WIxP1TLQ6Vs4FTSjjZYgrHgpGqZRHXIG2GChMKDi6toTpYf/MKK4DxcNE9a1kxWahlo6RzcnBAmfPYCC9hsJzhixyZx5OgDWAiX65vZBAWW6gPHj9tHh8HcYRExtKFvD/7r9/+b38Eyo0wjunB17hoKggynnpy3MwO+1i0Q8W3MSwgWZJzdd2EAQj1GdgTGpmYETEXr1ag6ZmtE6hASUbCnkIRk0HpRDRbaDmzh2QFGE3M97suvnQt/WaRevHzYWDLgu4xWVkNsGW+lC3j10rh8S9b6bxg68tfP5j93hVuc7UwGi+0GTj1wbsf3m6PhkPaERNhd6bmS7XD86bzesrczpMQ+PxkudTj+kLVe5ZlQ47rFiKvkaR/8qulDM/XbxCO51au5ydNHfDIBB4rCxVIiZ/fO01D1Oo/va3U+PHj3tmfNysfrBbuUjnT7ZzLgId/7xu3EmImm+TP2idDy2IYKEkl0IA9PmgVd0UgwF5OlMedZujJ1FEPxWmKEs6XXUPWGm+ggEB66wx17cb44clspOUbXp4IgyKLla61HzTpgF2wCBxS3Z+1JqePS7fqCmINAjWF5UIca0Gx2dWiXz8BDrvTc/lorAMQLB145p+P39kQkqtpngsdXCTSmD0xp/OOZtg6aKoOwmYF3Edd10c0dwnZJxc9tjoRiLpxGeU52sreyZYje7+3nc+dgdHcUiMk39gUJkfjfCkBR+rPfvP4zTc2VdOmADN19S1MDtzpEcdjBOMtUZ9MEtoOgaTA0h/eyI+j5i+D+RGU0cW1WjbUke5oKaxv6xPv5QPMDcDLACcYTGkHH6wD4vcb4zVmcDY1XGPQa+0lt63no+iiNQonlbcScSZ3sjCxLsOTpGfTamfhlyM8h0d8tsSIIBcszuZ37qxwZHpqXQz6wUplO8hxuXS4PHmEyGH7yGVCYrmMhp73RgO5uJxv7fFpaePpy3EdQ7aqlfQOKjN+DCtfPn2F1XN8MS9spqqRZHue3cP9Qq51abNZGL+doQJo40TR3ez1bybDOvG47Y6G/p5Puy8m8sPewgVDEjRhiHt9xBPU8lUbJYN5jBIg6R+ZSQ2X5QVoLROQ34yzAF8RmODKQoU6kY5hW2nOla3S6pYCeDAQq5Yx7yMlPot9lOA9Fj2062v19Xxm/+HrWRxM9QUAFKuV9diZ9s1OgNDWREvj6OtfXHBbSb4Sqk3bszwREYxuYFKRJ02/DpbBGgJcjg3TNYXZzI5JEwOnlm7AMoyUygTK5bUGkMmulAa+cr44P+4DmIRrhDsJM7U8xvsZHnaSxPF0lOJpMYWZ7JZPEyLHyEzu2h++B0DWyUu5UsQAgcCngX+uh8tw51qdeiPPLE3iXJoTqDzsCSKLEzhoAoVSKoAdCBBw2x46ZpQgOMzkSKu58CQWJwvZvOEyvBXtrg0NNFusVHPJN27c5DLMxHZIil0DxfXitwfjRbfT3qkXkOmgv3iJ0BxfKkcUOD4atg4PvRnsQ+OiVLp+ewuIsLOJNRRzdJ2FFJsEWRzXTxaGixnLUwW9VJMlurLX4ClSu+zYpCCw6KwL2S5kAHKmLB62yPOD+fe+WZxoKov5qWp6MZkCJK7MNWhMbFcSHkspl6H6Wxd5M7P5z773nf852X05HjdflNy+ZPGhYrsL01UVqoZ2SIoVQFX3JMGYHI+rs5wbwTbKk4WSF4tgBxwEpm0ux5rFrbIADoLz0AN0zRvQOkMNTL/iGhThDmNrsHC6HUUVijjCksZvfvno+gdvZ/MrQlq7PJjnfHPnFssyYpAGJIFbLE23La+Uqh4QPX/+2tVnpULK8Bwk9KyV7dIqFW1/uItFXTSwkA82n/53X3T/5Y/6xRQCmPardsQk69/ZrS28yz7mWYDvxu7Fwxc0VsjigpM2LDq2SSkm6m9uLNvTS3JAgQE2V9dNVFuvRCETyXNUlK5frxhjZzwHpl8ZzacDkgK/9eHWu//lP/7NV92LL+4V/qgyPlMcoCd88HuVq8locE+ZCakZlCyVLocTDkGZrR2LsV7+4jO2EuKU6TKBlKIkEJeiqP3Vxe3vr2kjQlSnDx2zisR8vfQ3/8PLj67lpDtv5glDtWamgfBHp8sjJIPRbIJdFXNr11cKxXrneDF83sIPsJhP08UNdBxgnucBvVme4/fKiIX3Xh5h8cgZXVp3Nteurqn/4QFPQtu3M6nd9MHLU89aFD/MDDrAxgLzMolAcWxARepJt0pQE1zyGeRlJ1AVejNMGob22YUK63oFduzpa8WI1jYhSNgI9VvIXY72XxkqmV9THsL9x2dHzanE9O//qtso81f3Nj3z8uFkGZXd3BVUXnTeSlYSmdyTgTJuXjK2ETPRO7dXNQTSBkEERb2+C0PAarnSbLvBzDmOjrP+KTQOzxUJqYrJETN/4dX/4Hp22vGU+be/9wZ30kptVi6nPahnSA082MPQgdR5eYbfLbejEbyvMxybWM0O+zGzd8NlrJ//949WyVgrrmQ+wJ8//RVX73P+KmItwRmxLFGTu9cnTz5FIG8mhFeMeCHYPtf4vY/yfxMiIIzlGjlY9fxzQ79Q0KRQrqSn2Gh4PDhfqBUYYPjsqK1kRNLFxLNBaxzrH5VWAEPpqfIMdGVJ/dXT57t4OQ4tcInWubTIr6HVCGWCzrOD2/W9x81uVsiTHtB8MaXrLHcjdTSSxQmyEdD3lh04S8NA6JwpWzRj4cv99rL0VI5CQdhAXAHnwtHGGo/+5L4+Zwh+FVqrFz8QzsXNaw+/4P9fj2RLRrZKcGwuyVxqLVE8p4TQKxeKIsrApXKco9LKbDEZ+ajbc4DBwmUCJ+QBVAVLbh4TXU+MWYKNfbCx0aAk/ODM3NhilzwtgHO8tn74YtH/2S/C6Ty9UX3U1u1eF0CAm98ox7wfK91iPQ65oD+GAhOsUKDJElc368+ffy0hQKJIBAdjCpglmKD/xYjYSFodawECUAwkdNCi8NHSlhB8i2YWAEQkQ0wJmn11fiEnQyIiKFtxK2loJrBtdRx+pUmOk8lyFExajq1bajINlt/ZQQRhfDm3p66URkgBiUbKbBSgHMEoZjFXIW+vkd2JcyFf2brhFoDPz6wCFIWwTpYysS9HRsBj4fBoKaTIExXFLtvJUsKvgIevLnNXbz89HW/lASNk10Qe8UZUIb3fBYoMOlGWJXW+xeB26C2dZSdy9vLplWrSs5Z0Nh3htK3JJOMSIFdA4CENEjy3MCHW81AAyxQK5+aiaELFarItayIV9qdu2kGvvLtHvnV1cOEo6jRfKb08frYTJpEE2B4OdV398pkq3LwKTuCB4n/52xfd8av/+F98w0aMo1evP6ryPWTjdGrR1mIqs/mKUF/n0xaovTyIqo3xhNCrBRoZO8uhT+fZSqUGR3kbRCemDlmA7ZfJ+nYq8YRC8mmG80GkvZAt1YVdC0UzCRIJAlWZWcuZHTm6CaYZrzMclHK5WeyjloOL4kTWoAoV85grRys7pcv9FxhmbF5pwJWpN3YStnN6vt+cOZUMgYa+qznHr0A7HYdOIHCY5bgSyyCEC+ijebeZXMORH/z3XTEPvvv92hmBD9R6ebcBLfTBgzMMRGog13wyCc+GUlGc6ZN8I3/19o4+0eZHrSgRzRFr4kzZTpw2KMK3TBS1IaCN857p4bZZqJYxkTl8MZw6UraYK9Rqd//4D0kGe2xbQxMRFnbYdMiOmR2YXz+brHx68dVzdf6i258uRnvrm/k0t1dYXyGaz08mx/vroCM4ICXWHo+7VBoDSdwxwc5BD4EaNBbMPKC8XSOw1I1sdfd/IQ5fXQzOp/wHe1sr145fDOdL9cr75M07hV/+3bOjV6POyPzuf3IXLwv3f3QRPbgsfvPu2hvbB18dLi+bTVSp8FAaVX775aCYAAVBit9vhFga6uvmcHJYVonrubP2NDSNDMPZFsZz0RBGJJS0AhHkPZyhbRQVtsThizjS+m/8Nzd+9uyi+4WS5GPHZE67zt2N94G5s6A4RCTnAzx3lfeus0izOvsaUlszGavhNQ6U6u/txWwmAIEy1U2UNWt7K1PlfWoxAoql48Vy6ZgBDsIIZQIJJ5+bdIfgzI3bizs3kF4UwekIO+hAWoBIQCilZp0OBxtkYHz+1xc0xwy/7M/ao1yBrIeZ6Qxe/U83D58Y+79WPvzWLTDAW7KSqNS2d988vjyPtyYDdREimBUwB192rt2olWfQib/c2AjGEPtqCSaS5QrBK7pxtVKRNxtccTU0VL/bP/nhiXGkpb9bqGxe/8tXisnEaUlM70IvHuhneijkMBjHtEEAALF3OeNpmKxzhhslBSRJsxgk1QXsgNXTqcIythjIwxQDx7k1MInFwWI45xD6YhQVP7xy0pJ5TgPLGxNnTDbEnm/c4PIUJY478+JKiuENzXHfLJdyMHs2mhbqxtCC9IPL5Eo69+H7lWp9+rL3xcvD4kbgPBpPtMMUAxB302qAgoY+iwgAsFu/vkg9bzES8M6b6a+t2bLkl7kSaqtHD1qNwspGI9MKoJaqYRTtrtKzsROinSlvU7wrkTaJSoqjnLmTFFcIbYG0WcN3EjicyGJEvbC3R/6Hf/kY7ym2PDh+OXFYMHsro4Oq3FfWVoVUA37VvuBRJTKRKFymmPRxtw0ncFOz7E/74RJ1SB6eMLjqcWnu/EwOGsBGKqMt57UUINvuwC6pI7YCSuXtYufVUVwS5cUijKxAs2IkJ13dY1YST58q/WEz0ucogM604YVqrK+t775R7L4eXJ6PAmcpvIm4DFMh8YXnR6O4UGTMIWjMNJ72IZaCBafV7MEDYyvPShhxOVASLkfjRCR6HOVB+rK8WvEYWunIz2aDbCoiZDIVkZtsfNDzMt9xHjehyNQxGPdjanJsrAEibMOuogLe5MnjFnzejPJ5IJW4vVNsy5NaFq/tXoEhHk1wpJsmXLGM4fNxGM0uzTkIiw6Vk1gNAWaLdBVJsamRqjaHehER3i/XiN1049ubTx+PLx6e3PkgKYixw8AaEuZX6XJjG57q5wdn0MW8UM6evFpc3dpIp8c//7dflqpSWwZfRFFul/7idydu53j17SuZtxqUBBOdgxdny+Q7DJ1EmW289QWQ5ymssvBQlAPxXBLXUAl2Qm8BBY73u2WHonDrAFvOAjxZw3Kh4jPr1aQ2mYD2OMSRMJsKF7Yk5vrDvsR5SxmMCI4VENJntSIep2ncwSeX/UajqHrhWNPiofy9v/eN4/1esz8GcbieTQgkplqyhYMGsGxfKCma4Rm81W4W8hWksnrj5eODH92/KGyVdtc42w2f/j8fADb1p3/2jQvKb598IjT4mEgKsxhOMFidS0i8OpNRIY8nc8A0OoxoxTBAD/FBDABjwNH6KnR1De6HEYXCnpmgzKBHZNezbBmYsmU2Lzq6Of60GXK6KbCicl1YConXz5/84P90Ul29zeRvCQ9l5B9U0Tdv3dat3CrRlXAW51/rMzfwcsnV7Xca06MDd+qsX80PI3mmqqFsC9e4xeL8lMDWasnaHBor0aMDAwfkJ48uIh2U+2eXPehqvkjEpKbHBgRdeX8tlVnOXs9OLvaLH++kR9IM9g8uT4EsX3gvm78Ky4fHQGvhurKYISofbhy/mOQocd125d+ckSxsJpLF3Uz/l19yeYRIpyEbRadwppoZWUCy5A3Op4yJEuiqj4UCY3chyJE9HobD2Oz00EQBrAQkml6M369H977wv/5S4VfKaKqkE99MSJcEvV/AP+EX0PfXC7gY/t1+daApeog5dLyUSQ2ZjwfW7OXl+QVXz6+Ui4bL+h4MmNqk2cUWgCtbP78/2hoQZA6c51M6O6EdEayur3H8p7oCr5QA0RIItnSzAUNZ+8Ho/i2z4yyvLnz40/F2oaxv8gBBD180u+0utpOatUr9336eWStDA2MQ+nv/5Obh7xbOxeju2u7HReDYnwzh5ui/ftPttf7XRmXO1N1y4jxEpC9V3s35GyS/ya/VLvDNTIli5Eetl65Jw8xpyFsTUp53sbS3JvTaLQPH/ZljZhp5CjVpEc9iYq/nS5O5IKZKEkC5kz6K+Gt5IAY809FFYnbUj1ZS6Kg7d4H8dgqey0IXufb9b3zx+ZOM4y3t2Su7t5tMMekiH8A5Vnh6NnxyMTnZLacJTJEhXoqieft8mIV2KW4j4cNUffisqEwrQHgcAhEKNCULtmz9+WVFfb1An+aeH5MJ4NsUMBqcZymR3L8MYnR0rhY2N2EpMQIgF4qL6bIBzJDO8p3kLggiZ2GLgecYIXRnKowAub3CBoiOQOWUDD6ulzuHhyoM/Hl/sWVZ5vMxLS3yGzDznzZeHMo7vv/ND68vKhFs48bxLFmDd996y3hyqXsYCDg0gvHfLguJePpXD5PfWHU79q9f9G+slW3QVQYz3QJ6pPxODjhcAEFHgBJonkPsl4MWQ8IcXslyIumcTbQyRPsS/vz4dGXq71XWF+bkFI18Sy/5DPlO7cVwFurM8u5KIGKrU1ea40PZm/eH4u7Vo7/7+TTGpUo9SSkIG2KeImB8a3xY55LdfKoYD/Kuy2eJ/sywIYAaLiWGHMx8cg56B8vdJHY8XdRKbKc3TXEQGZvhBbIjZp9//rr8DwtfX5w3qMSzXz6+e+PW8hzJr1yt/qOrrfC4c9irGiRsxr3LZtAGV4ul1nTGzRZEMbOY26+PX5GAWF9LSGZ3iQDPhtNivpLFi2q/RRV8zQ3e/+YN2aAZLDwxzHs/fLg8WaY5sphOv+5360VSXUYhj7qX87XrK29uJ7/8iydig2ls5OHI2/zDNz7/4Re4Zd/Jp7SuHC2cW+XcbNXdrdfI9z8a3W/Cin/7g6utpppZ5dFMPX0TZG1t4Xi9geuLxqe6+MfFZH4rc3mmh8vL7iJM7csshma3Gz6gK6CDhPzgVA5VN8XOBzoUEcHD/Uukr++trQNTh1NCL8OkU9hyAtMwj0Yp99kJ5sB+zNrFrGcqxcHypRE4kTmeTOY86PtgTSewpKR4uge6lSQtEZRmWGwin8+vIq5tsCi7CMLBQGkvtY0y/7oNrN+qRwSq7p/lSHR7tzyJpbJGMGXhRB9PTYBMFIYOA2hQVSCqJN8+awW2K+wkYYHQaFzMEsPhTHX0WQaPYhZhaKyvRrBvRiqIETpHxZCboxfn+zMIb4IMuLJaFVcK/9nf3yjl3xR+LxEeBGA5enNzjfLx6aUqzJdq/2WvP7xzo0okAYRFBuBiuLCqzhpvgeE84gmiUC4eTgwRQWat5cWFt31dGu8fB7AtbpcqOSya2i9+cZS44u/eTLdV7Vf/pkMk2VtXiuFw+OTXTX9OF+ok8u72hmYHveGgr8l6dG0rO+xp9x4Obt64CQecS816y3EdtoaII5Vot6INe/1l4IsIa6ZBT0MSdgX3ydWq1+srKJpMcYLpEb2zeDuBrYHm8b9+kUuxXj1XuHKzPes++FVzPwcal+0rI3TxYrDyRyUADw9/0005ehrLXy1jYydotaFbBdU6bo1kPL+ae299s1hI1qnEC3hJrzbQgVXazUJIHpgT5iuNRogES+BsaAPxd8vE4tylQI011bOp7EzDNXt9/LVxvVZsWth5s5tiaq0pDujT3FYRZwyO0XvQ8PVnre1r1bBI4nXg6W9mxtz+5t1v+GkhPc7OhvK7H99qLrS5HiY2k4nIoQPEStoCKtxfGndyBPOrlNG0XJ7qztUck7pGBiGM9C+F8kbW38yog17zvInHAbLUIQorAV4SoQwhvt98+Us1gimYpIGbdzdwLPlK3s+kqdnM7TUX15IpUhAFJuPgNhPNMyLZH0b4YimxoUww/WnLgS3bbk1+2P8X37r7+Y+G3R/eX2gaLdDMWBkryt8+u6y/v/Pme9eF1Uo2w4eB4WguhzIOq2FWBOfzA9uznjbhVPbtRnVCAdZMXlThIU3ErKAOfXy5XE2SrpWhFExc20uQA82c4VhOlOiv750Ua6lsci273XCw+ZOXB2wi9affuqK1oe6vL6/d4TquSQygTL5EhQgQxwGJyHN6EfiWFJI8dXEhC0iIUEwRZkSg9Pb/am9WIp+fjajDSfv+L+0rg7V3rp597a1zUGzGN4ny/FFwvg/cer9izkIExgGP4EAYKeVxRXQdeTlWmHc/UrsdCJrNCFBVU1mhOjhpSlKa1EHcjpgCxW9iF4ovj+ZOV/dd08CzZAmticTh/Sfhpd64IwA4kcaTxhFMOrrgyGBTXn3nAxKLDv4Pn6QaUHGPOVnDnBUot121D5ra8Ss8Q2AJi2Oocz0ESHaUCyZqG9WI2EZqlYz8fLyzm5kiebrmA2NFBXWyaJs0RCEUgUinQ4/1I4KKl7QMxLTgMH+vtPI3x6fv/KOV2fGoeywnMomwp88A085z7+xch8fqcqrVMphn+2fNU9slRs+nzLYYJpiVJMcHwOyo482XXTacIOy1CsZjdAGvy/byjRVI784odzH0kNhHUuYoV8RQBFpYsQGXKmV29Gxfh/UEl55rwXqNx+tZUMpmyEXz0TlHLd554y6swSji3Fu+0mI9+5804vzuxqL69GTmAzi4lbEIZjjcx5AMxdXIxFw7nMcJwcHCZ/YIJ6xZS15Ni5UiMQGoxlrasxAGB6ZLRdVtlApExwwNiwFxD8S4CPCmwDqVLn64pcv9eyf9tXIiwbPZam5+eep0ggQOtLpmhSP5DFdxUXcKW+Plg6+aq3drMUW4+mw51QkxqfqaD6oixFhGJBX5enWFHKoIwiOGD4YChZNmfzalRARl4NWPK8vTLjhPX9mo5pON8NoW9pf75KDd093iZkEFON5lXddW7x2OcC1B8ophxxJGw6S28LT5hGJpcbFI7STOrdkVEY51vMKSlhrGIuIYwfLxBKQ43EbEeikMtcrK7ovDw/fefe/O1vbEE7uzwZ0dphVF5uXg6144fPaI4tmNNFthc1p79OXp/RtbV8CRzelLGdLpAmnPfZiGhpaq02hKyCDmvLyXz9Tp15+9SDASxZCmkGYB4ErNbYOh4mBvX98OoseKo/7i6+XVdPb37lbmgQZ6Mb3Q3Xb71dctrMDkSxICIvx6ukRivoOH/d6dekp93YliBEF5r8FxNDj+8cm39hq/ac3yVDZh2sMTc1Cy+HVyrs09kMlI7MgkMhko9LFsPu2fTBI0aCewry+O50o7rLPixfMyvL6xJ/205VP3uzFg4xEs5tnpfDzf5qmTF+vT9oiggBUeG8dDwr3+dvAO2ZjGQaxSjjy3h/Ll/XFQegNlaNgyEzy8ukMdnUyiOLxVrswsZRCG3rOxNSAzZHK1Xt2Hhi1fqQkVb4KsXpUIijo5mcxRO5NIYs05nEhgNIygMThfPPz3nZXbO6ABUp7imcbm1eq96XDBRjpCj37xq+v/0TtdzQZ+rZ5DbW6tKP3lF6O/Orx24/b/b96p9cmLAsNza14ZVvsvAIU2IaK8lhnIKk1Dlxd6vbZSWeJUgEKllbOJ951r7itY/eidndbJpK0pZz8+/P6HV2kGGtpuUEiBde70uJtJlWKR0nwiwXJgnpJNL1jGqynqq26EkECo0LP9k8fFhpuPfElPkM7Jib5V2GrrB7Vd+uDe/eHDw7e//126hrIj5A+QpK/2p6jfEIMXNd4YhkXDOfrR3yg3P5RZMsuvjVt9JZveWr0qXiy9rlNMmc8GWoaSFITxVnOwwkQR2zkFK+s3kGQ1Vd/oO0Nz/DSm8ERAHt47a5+Pyd3KFIdftdsajZR4smpDi5iiEFjiAALCGilUA0nOjalsNc3U76JCXWKntEGgs0Vyh02Sq6lK24X/4Fsbv/pZBCRZGMp4h8tkIV1awaXVRn/YSkNErl7mG+Xc2trlTz6bFr16es0ftlI+GBDUCkfMppPLEQGSWOxA2/lMuHDdSmPuI/Zs2tHcGIGntoeMBpM4qiX4+hubRZrTGCCH4hysKb+32f3VRXmiPP/d+Y4LNVN4kC/aBpiI422Onz4/3lwpaEsnWugL0RyR1AgBGi70npQg+/NDgl1JkK9JbP+kx1Kx5QA5kWn222armULoRQ7UXw3evlmdHsztPfGpo0tBYMeYj/mnTx48PSkejrvOv71Ivn2n5UzfgaKlGmVIwXjetzNEhIsjRfLZEKggIKuKY0eZjPsnvdK1Up/Ghh05iGF+Q6L7fkHzR49eo9VCrs796wgTPGBXx6kUZcgWFzkICYc4nOBFRkpdyxAnXzyFJHBwFnzwZ+tWwNinw3o15UUW7Jt0ljJ8uF4uPP7Jy9o/up4k8ZysBkcm19aMzvG0SaIbm7ADX5yfdX0zfnWQaqzOGIFcqzHT8Tt8IoD4ModCNnCgyqLEBnRV68mq7uq+VWQCBoNdNZobsUAhJ3LgO2xGwCqScPWtlJTkHnz6+J30W1IJjFCoM+ravsflRJwiupdtfasUuzB6bo3m0NRk1tZFS18GsrWyu66uweNXQykOMTHPEGxb7UEArCuOuvRA0EUW9tJlidVv35q8PheTyYUc5ZPh8OXpaDF8c2vvYmnyEJBtRNOjgYZkd967ktKNYGD3DifnP3tg7xSuf6t8beejkQE7o9g9aZo9aL99lM3h15L5mNNngOOA4K0CDnGpwnb53icvBgej9d2MKCaSJJSqriMcnBaBPHUlAKDgjeX45PiLp25ghK9UM8MR2SLN5MUYiTut49ofkokkAy38K9Xa4TGsXwZUiXaZ8qiloqSTLmc83V0O0Qqha3Oi9t7mFgu+eHh4YpiZ1UT1Ri7ujxhf0+GiWMr3f3tPW7hEucrlqFjTXz9rz1o2SprZVUT6RqW+vnEhqxrsbm5vBE3v2UlzY57pHEKNuldfReczm18I6rLkSQVggZETTDTU5zOv/l49wdFLXV4PoyyG6zICS6IZQycmdOXdaiKX/aT3+PBVs1DGdzel4KQWnjivrseZu4z2P6lCJuwnYa7GoZIeELJLetdv494G08Z9LoTtkfFg//Rqae3Vr/eN5pwiIEbEx6DknrsfX02RCdIglb4meQ7HJ6TLRyEBIqFP0ILHxWgq5fVfHuMRUC+Jk4Uq6w5e5F4c9K1Jm2kIHXxcKwQQHHNkyka5UpofvDoiV4Hz8+nl7w4z1UblVoHbpO49ecFRerP9evEXTqKSQi6j+XXQEDliZf2mPg1ys8I/a3ycFt1P7mGt8QAuc9eYzon62aPWhwC1+U+uwBPE/eQEw0WI4Fuv7RRObG2IvnJ14wok4cyXrzujybyYpnNwyd9vI6EOWZE/Z7Os4M/cK2XhUCH1AAWYgEAUdMR6hH+15lo6HoZxIUP3L4+eDi5TCVF72AwQ5FvfuE7LQu3uOpNNmc1+68WrPSSVXOWbw/NkYyvplc4un8SIyfl4JYX1NHiFDDGayiZE7SQozuG3/4s7D7hxIBye/mhAh6TLAdAd8TAuuxvhbBS8Gmi/v1crrq/svXvt/ssWeqEF+97zl8PhrYpYy/IIftEbTk0jxGtyDKqeiVex41ezZOACBBTAeRtJ16+jGO7GMhylgRfmrN0+tWvkapEML8A/+Ec39RXrJz95gfu581Yk5MGO7eyuJhk8O3oc2edBJpHjPDi9KcGAZB4NkUDdEcVAm0BUmF57624ua3amn/wPX++8u+qniP2xlgOQhLd0HoaeHmp6IDWQnfVVwmez7yY02V48UFNXci1HuffTVgYmVv9BvPmeBGeMgrOewcC7f3J9fB4uH1xczBcbV2rtU3pxh4483PylgvUtOs8vvaBWTcbjaJYVQwxqmQrdXE76/vV3G3OP6TdJOYQwJKWUAFlbrBF0msn2qWkuhGZjOSUhLXOKU1z2RonC/FuC9eJHLcoU6kVi3OxFsYCuUroVXqrjFD5dq5bGOHpy0L3oDq+uSo3r5Qhwdc0Klu7aanqu2y5t5vIiEsbDnjYetI5e44m1mu+HPoN1h1N7McVpB4U4kc5arr7/tPn2WxuKjyQSQGQqv3p8uFYpLI7kdJmpX6mMe0BeRFk+A+gRzBAxg6w3uHyF/vLz/V6v5WdqV/foETSc9idSnr3WEE6OYCyEF/uDLE3vrq1po3GcQi7bKpnMtE+9A7lVEgDP9gWS9SJZlyNVD3CARWDTjShGEJB0imOwvcbGs2bzk9984poejiCKaljeIrVSTmynemPXOp8Uy+se6r348sR7PSxvZipXMo7lt4ezqa4z/SbCi0hR9N0IcpdMChcyrO3jmMQmclwuXULyLPSX0ws8ndn+Zv3i3m85TcjSVAqj1cFc75vGiwcnv9tP8dZ4olfe2D0/Vawnj2+tlIHV8kmZBwl+ZsmTngoaAqSoaq8lR/71v3dVAmiGIopOGOggkiifRJ7n29Krw1FzLtR3a1tX3v7WVqdpvnrx7LXbyii+Klr3/8aa/+1rsCim1EigyM13tg9n0TyepRamAYG9HNs59YqzhDrREQn0Yz3UAkKCIgqmXRecg8x84UpJ+Eqqp8jIj/bhj7ad4g1a8uquDR6pWjWXLNYJ2Xn58yO64EMwWSrxyRvrZq95//5pRLM7Hxb30uFrKn0ph/s/6OngxF2H0/WUPjJYULk4Oipfz0vFpNU6t8+UQplYVojhs0l9TcglMoHlAqHTAnLLuaeqZG4l37c6ooj5vg7Y/msSu/r2ZqcPOv3L9/NsjdN+1e9XRMF62tZ/Nyn9yUb6n6y8OgWh6rbQqHv3X1h5CQrBczBXktV6f/jk4cUavh7z0IOxkqvrZYk2x/NeCj/qjd6+sfX3v3ftx//Xv5NjN5ha5NwasXz+ZjY9mS7HWpsKx0WsRGpTffnV0WgtUew86uI1FMuy3j21uLqSxN1ft9rFLDtaKJVKDgfi++f7UNJblcMpTHVJUsThzi8Onh9cxCv0KL1cLwGoANP1tcf/7qcSTDS2tuI7W61/O+D/1ZO7/+EfDxeGeUrCz7Tg7+uzxjsf70Gv/uIJaCwf/OvwOkt/cyXz5XN7YYFsCXP6j9A0Yqc2Wj/7+nyg4SLW2OEdhj8/mZS/ddVYviTCQCIIpTtyKd606SwPnr7qX795ZTE0UXqsYfiYYwsEBENM0BoczodnJwOIU0rltPNm7ZWmM6KEvJ4uAYGs0uRZ63N7eVcsAPJ8YHarfHL1Zv1QPtWenylcZAk0l4puhan+sxeO0lpmrp9fDHvPjhjiTC3i3xWTHUhSljPl6KQnBiHHXblTg7cTiST91a9fD5VpQpUdmMxs4kQVz6XCoycvKMcqVZizwbiPJXYxhEdoPaTSSX5nLadzzHoi8/qnj+tFQcJcT7eRuS2GJKmngKf23GCenT3PIsQgPC3uFof3BwkvRK7U6GoRnwx7r55ZNekyXMhmnHiEfPnzblbQBjfygTF89Yv7b7xfbVapdvspN2USq8ZrSFvxyWwvzOTrbxUSlRzeDoPW1J4q4yQvAgTTfjyLL2aKF/31D3V+heJZi3Hk7vPLJaCCY/T7/8vvqnDWOuh+/ZdfpWl3RsX+ZbL3YpBAbf9mDilrGWpHsJ0l75n68oileNfRm0uGsNXOpFG9a+NMTGl6S+HAIRvY+af9zlbVSzvdhWKBlticE7Rga5fvI6zs+0FD/umD4xvvXb9e2Hn8l8/XPkpwOWkc619/8fgmzeNk9FVraQMGK0iSwL1RjUgSc3Ay8mwaw5lZoMhBkc/1rNP5UJ2Mgb0yPOKYLWip67PAd6wp7MEwhoF8uQbYhMaJORJvJNFRf3RnrawYE5PMTDo9DmcwxF68aIt3yn4q8l9PmXJ5/8vjnQJh9LonR6Om5WUrkvRhtn/hLjBKUzVYlk8CNIhZplARg2oZ6qRpz0edLu2Sa9wz0/4ImTUsAs7A086UomnXchg+JUJ+Iof7FAXzQgkAOBgUczXNDr54+pRGPTSDT33Yv7iQvAB3bdtPB0zqzQ1ctxOmq02PnmYRNN5I6HhkzBfd8RwrsnuFlf3TM7rV29q66qXYJxdz3jQyqQyVkASK8bUwm04iKOV8Yys7/vVzzcZyJFqgsPbDhX5k5Hclean4GBHNlgedeT6FOdaioPDyxB6BrxL1nfUyElDWdIpML+brXBwj3tpGYRWmVvbKnWd9DwiCJUn78ZVNTMwwp/PLZ63mQh5vXU14wPFf/7uXNFwyTKOelqhirkTHYWOJZotfP3Hj2mog+gcXy+5iyTPmFTaHhGpzf5l/I9dSnE8+PURdZ/WGEGHBcO7GtIiRdH63eHEyCDxbK1lQ2nCwIGsy4/Y0wjUY5gqEuAyxtUpBj9XFYAJHXulaQY2FhRVElsGJK25RAPfK3d6w97sXQrLIEMSkUZXzYeAg4EK1NCQOkLRA9Y+msuKiCUiWzsFpsPR5lkhEhNvuDn0QfL/AtlrdRAo1iVln6ZeuRYuBJTH4zVVo9uWJO8YEGOycDeQRSvLExvfEPj0YGWjKTwNJPIOqAR2qOkgLKfhsTPSM/k8utjZEe4o3Hfw7xY1JAZxai+L33ui+mvLrDWdgATiEfbv45786aJ9dND7cuOxamyQusZQ7s+9dTt7/OFlD8fp8qcjy6oobpcTzr4+4UdBoJLjZoEaatYhaGv5Hu7WMhR4du9USO/Z9AwjX764aEZuxncHSk92JYyA5PlX7oztPp7PZ8/20x6yvuGCNsRUx/GT5R3+29eSbyo/+28/e/l2vxTFrH964+3Gq2T14+Hlz9Xv1Wq72rQr77/+7V6ezgZ+Ma3lSgfRxAG5fIT8/v4RQ318u7SgqVdJN/bJ1CnOITZBk0LNM2cUrrq26wqZ9ES6uJOq75SmLOdZK9vRCrheK9VKqCMB+YD1RDAY0rm+mRIhLZUvyKK1y5N66gDYv/8nNvU+PVYK5/CgZ6hentq0/PXp1gFHXr67AGcGIZsPLmL9V79pKKcAIv4Tm7UYjJQxmH6Tpvx25nuB93gaEUnEG58Uy0BDt/tdPmIbil/3Bp4PpKISTFy/lCwao7W00YJGeyU6pXPQ0a94yi7GZZ0OTIaeGTRpKIi0QDDvty73ftdq0k/m4/PQnZ3t2CkISC8pDTv3k3Ild2+jPylfX7t7aBSbOM6AH4pfJQvXpzz4Hh/MoDAmew8p8YSOhtc2SyANvZ0/Ho8HnJ6WI0noQ6huWEIzAiHo3LYK40HM2V6sf/PEHX//m/ufPn/qgd+2NQgdE/JYHsXINpi66Tn6nUFlZXXSaX/6gyb5TL6ScZw8HWZL77Q96pOTF+Rl/HZh/YYt1NIvNfGk0cm2YESZouFqCssMYwITFdMKlnU5PF1GihUGKlJJgSIrzzdNeEjPjwWwHRZ+DALMuHb1cYhjIJuknP+z8yX+xdf8BNu3OgUb5rHI1kaN+O1x8+7297QRlQ9PIJ3Icx0SK2TUNHrj+dqYs+2pbzfJljpdcB5gZQOApVgzurIjD0dQJLMMhjQwsFf3lEEZCr8lB6mIkFlMohG42aopm53O80UPYNNJtGjsb4v6ToTnTWR7+1lbppREmAKxwNfv81/KTnzbXqnHvaMHnZ8kkMpT1yWiyuZ00HazValc2MlkUMJQLkiSGoCF3YjHN8h4WTzsBZEAUspgHEsbqU/vtq8WsFyuUnShvYEUryxGqroaBh1Ik6DEMRuM0ENjApKntP3xpO8q51tlqlLGAfqtCXnaWtEAxhdwMhgOYx/g8II+KHOkgfDYtVtfrz88601434EOYDVXQu/HBNeVoCiqOmBVgxAfwCEGQCHMAmrbk+WTmILENds7GKXiSX0vTUv38NxcPvji8+94OX86cjyc4hJAUziUkQ9EzKO7AHlZNQhw0A+D99qx8O1vfqmZFjDQ4nwgsdQwa4/nRLJfMwwBVKDNne2hHM8zzFqm7u1LidiNtbdJm60w/n9t1sbxZr4m5i/1H/58fv94qlBSALzcQ23Wd6bPOSfT2jcqXR4vzRH9ossPeUFgHV9+sOfuvVmq3ErvO6bPeNp3rTeRNBAlyZbiNsChUwYXL7ri4XtABYv5oX6xgpybGJ/F8Eu8enRGZxI0/W3v66HT+Wi69yWzXvWdH4XQOLL76za7wnlRfqfADN4K5oqT6mjEK5OlzTDGRG6WEgi6nnXkSv1R14PlAFGgd8HYxuX3nptUcha1DIhMHSv/78989BUWoH6rXpNAV69ZiNUG1n0/vOocPmCKVblxNsYc/fZVCCOLEs6X81n9+Jft27f7/+fM6RaFbGUY38X40/MJDkUImnMzE0x+MrcadWrs13sp8480/rLdAg/LGq5YQQO57FK2O5MdfnbAUUElQoG3UlosgRKe9weatnCHHnz06u52e0Gnpt/++jUDmvG9CWRSFsFzgx2nqVz9vl+rFxnptcu80XhAzMzE0J4UlJZGJrq0YNAGlpcCImRxpzEP4wlxNSDM30P3gYoFIe2vnryPq7LxrlH/23dUkPHG7ptmci9cYZKN08Qz9p7npawb62/7pq/tRmsfznqq0Z925tJJD25n8BLN2bpTOHi/VfJEA5Ut1TK6sbNiqkWAPhy7cCsWCMLG9AHIhf9VXDFcz6kL2QWt5671tO2sTYJAEACBGzi2arSRYej2OtHgCp0kunSFYSTNd7NHLFrl9++btxNnfAD+YO3+wW1JlIYbkA9sjWuc79dyV967NHinA8et/s/DWKGIPBZPFEgnC49FXi7LwX/W6X66kHy6Ss7E+v57eu1YTRi+EOZ3rd5afYdowE5bKTWPOwIAZuvvn2pX6Ok2qZwM9kmcuLQQUyVkQXY1mB3oRtXIrqz++3NcvLm9ThQ0Kv/f//vTFq7ORkMsCtSU3z8HJfr2ysVLLvvHRV588Tf7v2nf+449KH1cO7v2kmEi79Hg6NLJvbWSrZbl9ePxquJYm3XzNGMt/P3AmX8zDb1RUyLfOrA9vM/3bzHBfMR9qlVz2rfdr3C/+TbuzGAlpTfIw36og5MGxwYAWtZ0IUOzo05dPuh1qnQiTbn+htb8+nD1pvrO5unZNahFWtHBWKrUvtQeaj5+0PReRJj99sV1hKyWwde+VV4fctsYmoPYRsYXBLEstvjjD//GWlES4s30k1JuhnUyRLsSUX83yOniMUq0XzbXVlYVjM4/2YSnXHxPl70rHM+T6uXkT4ts/ep6sbmuDWaTY6maUyqxvFLznFxPNhx2K2PmH648OZsZkGI0JBENtCU0JYsLEh1zWng8zAeW3rXQW44iUw4UDIr66hScCXEUDLwjTQAwYPkY4eIRmJB+w1Ead3Z8vfBNRIbL01ubrv36IoOlbH2+edmwKgCs3Nj7/8fNr31lPpSVUp2gHsBcL70zvIBadKBw3tQRop/M8zfLl/Jruc11j0O+PMQamuXqF52jFQw08s0Y1M15nGn5Uq2iqsY+4osh5M8XX5vJE4WAHJkApBuyAtEF7/cZbIotCUzk2DIREU5VE9oroH5mAhFzi8tm9V8UqFCD60euXsrQK5cWGkJ65QN/U8eEsIWXplUwKRsIAbJSSZgCxNAi64bJ9eXHwHMAwJIYiwpk3z5eHn529+U0cjZHqbmb1O1dEnCEOe4SI/mZ6ubJd6Jx0DFd78OeHtBhMq1y5Hmc3aiiFLg3F1o3In86Gc5ylcRhqd+dXb2EiUlDcKWHGKyuV2AqmY5sV6UoxhQDR40OQ5xMOa14C4wc/7iIT+b/6s/eOnk04W+6djy9mvdxGNpFOPnuq37xZipb+SiG4HIZHF93NYm3rT97JIoITaLQAJ4HEcLFcsBYdWmBohxiDOG6dEUBAGk/GDE4UMjkAiA5fvww90JTD9Rsr7F75G++sMYXaX/x88OS3Dz9+c+1Pvkf8fy8Oxi+6TgRxddECoe7E9UsS7s0ML40H/cHZzFJQIAvXtotvphIDxx0PTYGK4JsIDR/3DuYJmvnWP98LCVRGsbilmV2vGoFUke7OTIcrVDm0N3ZffNkLWCUlpbd//w6ZShO4WVnaD1/oNhiQCj46vIAwcn2vcXnSyhWw4ZWtJbX3GX1sLo6Cp87BfX3922O/kv/oo/I9PjOZXpqHy6Wp0TrwT/+oePxFPO2YKEe8ooIwni6SSpZGSZ5bsUWnpbAB/X/833/jv/m///LgzOS0gJZJPFxNpMTz6gMjaeGidqbJu6vpbB6ZnWC2mYxnyOXlUnNNAoOMGMBDMcm4vctlEcgI+R28ECmOdjadw4m8W8r8YOoAqSvSNjhrtuYDAzcgdzeGl/RzI8gt0Zt/lEn2tHv/l+GKHb//zrXLnrLUzj8opfcv1TBHQRmwWiiLVGrUaycT6fuDMV4AT2VPQsMcB/q6jWPIKkOeTszyVVJxfEg1zb6ytZs9mio2xG9cFZq/axsylIMj3bMFdE2qMCfL7rBnpAN0LScuDjssc+33/rf//MVXn57sKxlOWi9cCZqnzog4/u2cXc/5FXp4JtQCRhtN/Xx+9/t3VUs3moX7P/k8dwE4RRSnhHdXabjAxAE37kx4HHhtADQ5lxNcTri42F8m37i1CETLIRzMnPVHla3CF5+GdGRNNeLjRgLUgrAXJleKoIPIxzpC+4uysZQdO4zeeG8tQ/OJYubCFsFCalLJnP27F08P+vHTVh3NV7KRkU0ugXpvqs9n83KJKO9mFmDSSOS4EtrF5cvm5Y1/sdv5xWAQ0xU9s/ZW9uW5NZ6zgMj8z/7w2oF26oz7s9nw51+Ohg0YuwpXBTb47WKqIJt3MwsD/+zraSUjbGxL3eEQIaJ3/+F7MxtVCol/8s//2D039L7/6If3qisEv7X7nX/zz45fydSFgpRD+WWy+YvR5o0ins9pibhjRBTj+l0IYjwGibb/QBoIzjJazIAoYiDjIq56qMFmhW1oMEYbqQQWyN3fDe8WUkeISt+mxr92gfPFGxwSzGKYhNICicdEgckksqzHWI/+7jguI/lShYMjZ6TCG97NRrLfWrp8CFJowIG6Zy4Gs4j0BEmkGThrUlzsCwnyvOvkRExTXDsAEAwVeNY0fV6iXGOBBK7ARiPPUmMbLwQMCjbnF6JE8GuQJzhNN4KS5LkS3r3LxUDm/Lw3YgfX3qiODWAJAFvfuDIzZwoC7r57RRrF+Y3ii2MbTgnMDOStWQPnUiiB8d5LVf/w2noypCEgTJvIWyuce97zFDPlepPDwPMiNqdlcjSGxoEF2TqcLRNAmEdpLsWDvqwZpkGl0PpuAkvzN+DEYfN05iBcmlS9KUnZYD7wSBPHjeFoIWXSjZQg5biZvRxPDbGch+JINAUxnRYFVjWXKB6o5pKJYeTw2Wt5pBK+BQkMRWFYSUSoxvRw0ewfI6Ctqy5MkZYZF9KCfHAEBEsDkeokZSgGJ8Tj8SChLyFOmEznj5+9KNby7/z+W1MG+vro5fXvl+pchupp/JF8AlNLB2zOdH0eEE0gGBI6rgjB8J6OZ3D7lCYIK46spe1oQ1wjrqbJdd7s+hSQxddXX3725P4Pv6yJ8FojUeDmoQ8+/tuX41DbyEvHlzoSoirAN+BkeSPV79uTVxM7hbGEjXNcJYmPCWb94zL0ut25OO8Biy0xNz4ehKjvoIXd75QWMDswluORfu1PbifSENMcnoeonWDGvl9z1GYRT1+pFdrY+OlU8p3eRJM/N2KBA2m8+aJXlXDfcTL99rZLw3e30y765RdnZ30XVqKkH5U/YgiAGBEMOrQiPZr3qPbAuLLB2Lsrcp7Wj+cSyE6nKH/Uo7BG9d3bzx9igUWpbYOWxGglu5kXxne2vE749+OMYJ1r30LCG+nTrKgGgaXhyyO/N0YKayhxuw7RLDabsgR7CUV631kaS3Zi0FIKHs0e/3LBLaLViurhl8KsvE0yqe36ylvrRSjV1FFMiR4+empftho7t5JCPjIg2HH8rIkV+cyIUgcLupCiBRIXWUVig+7E2p8Eio0l2U2e/InI0CTR/2cf3kj6j48HFjGbwWooAv15//ln02f51fcMr7W145+9fmojjT+myhH94588sUfuxYX9D4BzOPahnZuFO4y6PzJtgKjkqUJy/a0tL7TbzR6Yx1lYVGwspoBT3atWE/s9kxHgfDHdnnSvru+RFNEJcbHlFxON57r607OLmoiq0OWCzfcWbT5R7CsSlazmVu3HVzCi9+XbjTIXDIe4X1HVRakC9k+41FpFVx7AJJEZuWj1DYpXb9S+Xo7tT0748cPO7+L7d4C36iT5Oizg9DdvlX9wMfYu4r/REWE3dbWQ4WTKMXyhWhwnCS+SwK4/6innbWVFS+paKRo3k++XehLQvFA1B4hxrvWsveW6HQ1VIId04K3vfLMRLB5eXG6z4N/eWa0TSNFm55ftqDt0BXi4mj60LkwHyLyfm7yYaKahJ4W5MW8dzlR9WMzVL7W5fINVNeT+D+ebuVpRyKUPgqQCFnI0s7XTaulTA/AcbPR8bhSq/R1Be3U0W6plhNeikLKzLbZQfBPVCQ+YRGY4jEcA9QdXaxfjx7d3x/Wi8uresx/+j58/mfxnK9UTZA9ybKsJOFP1dDmGr/IZgDg9ao4N054D2MB3hs42URjqDv3WKkwNjIuJNdM0PYtNQXpJhWuEeTVtOKBJsuO56Rx3u3N0K8HbY2G7WuIpUzZgmiaZqoBa7Ow7mQUAY68McugbHNl4b8VHovPuhfbCv7qS/fr+SU2QhhzJ5PC57mKDSSSwmXVJ1W0piJy31jzZnz/rGYNOR0fWV4qiJOYNv8vQAecAeNJngBEIYkIMBcB4MrfmPmTDXkiBkWXIqp20GZZlMWHWnc0vF5dogkokKHdK0ODnnz9KApJ9Y6UuljG1AJ4O87HAZbBRBidhEPBduTtWYofMIfwOky7nidPFyWEzR5KkBwFLW7+SCjlBb05mA43h4d2dxsvDaRCaeoHaLlXdWQiTQC7BIh40HiwZNwoAQSewuWrTCQZnQvezVmP9dvW75d7Jy9awW0rxWFESXAcgdWIxb3u6WE1ZDJqBEC02BA9O0hxJM/pcA4EwisLA8XGRQGLPL6T5fkfhk8yk14Z1urRWdUMLTzO67HeG48pKBl4CiI8ZhtdoCLCYZXAWBMOFaooEAQHYsN1Dl+TO9Y8Qjlp6NFNeN2LXhRNDBIaT7P5xb7ZUgDQGwPCp62XpBFPgIdWEcOJ63p4vgXffF+799WPnyd8qmocQuZU3cy+1FuiSpdxb3ctlNek/sox0PuM7DB1I8swCfIIlIVWHtN6EA3ExVXre8VkDIiEstbbi0f7r58d6FKOb1/AgWnbkrdUVAUAD79WV1TWGAowZeHz0TEpHOx9vT7uARSuOttRREAJCTDWWjkMAjt5EM6h4bWd1YbkfXWWH915nRYHmhOlkBivQ+kot0CHHxztDmsySiAaO7w/NiRWa6Lxrs1c2YOhqWIvU4fzpuFNG0pmcZChuz/Y2KdibaOGyfTFFbRAFExkvADwQ3nvjmmOGhRJKrpAeD6fxrDaQt8z81XLO0ChEFPZ/0fSOLo1ihTVPEjDRyNUmpF2EQdsA+HRapcm5MgMwwfODoFJDqzXUJN8ybfn1vJr1Hn82qIu1aupa7uMVBcLax5P+ZLqev7K6sUtQpEhh/YumDQARDDmw257PSoXS22JyqpqKYeVXMISORFrAfDdkcQdZNG1fSGUyuzkqBzuvx6n56ywQZamAQqO12xuTgqv3Fw8Tzg7gltbWADxMV4gwiHYkOvtY+7vk13/11UwkgQzeWtlpICHgm8BIHY4uVGTsEiSXJdFAAdvLfjqVyab4J89fzGHS8EMhlVnbqUeJcD4Zuo719pUbpEcsfOXGXgljgHopociLiNDyPL9Woy04fzyOm2+Uru75/49/dZqD/Dq3nkkyo2CeYFAguyrk6dPDvu1aUSDUqonGWqYbO9aF18gkzenqm9dUew8YwHSqCPFp9HwJTqcLXMgKkOMK6jNtcT0NsJxIgXxPNoVQjlFoasLJSomKk793p3D6IiyWRY1wsS1GyChkfjy4HK8UM4sZCoSYw8G9x4+aI1nIso+XC2zG5zf2GGaIfuvNt2/OJ0ttty69bh5gfvrV2OKtZOP9yrKrnp/JgEtmsHI8CXWTrYcZ9RmeL6yUV8p0ltjdKNkvWgfGNNefKQ9muGVBWWR/HDmCShOiKCWdEAvyHGE5ZhHsz2fZghiL5GaCyxQTPu8YtLZxK39bQs4evPzGTXp4sHY3CR6mSPPZveGLAj/3uq+mBEvKBMwJUozRo3Yr7xCrrKD5UbvrjDTHFICAFzXXg3kUnsa7iToIBMdN57UGQQG481GO8KeWja1cj8NFTFXpw9mSWaUhGI10M3Q1HrfjsSFVGHwryjH+CMRLmyukFD+5r9l6003FtWL983v3iZIIZiQAiwkfjQEEJelKeQXUZ6/P+zmIphOYroZJ1B4OeyAKEVCak5jQx1DSn48WeLw8vZgSDC/btjIyG0wKAdDBSNYdkygxNEeioa7KSmUlIYhJkiVzldoU6bZPZDdHJonYBn0uxYfnvfnwvEfhrQOrWiiCKIjSejbv9waD096SzOfYVGLYUyZdRQ8BfaF7/T5H5xxHQTnJDa2XJ51kbo3G0JbRuWgtwhAADexyfMHaseZ71YSUz+U8eT7paUUymBseymAYFx8dHHq2huHYyIbQixnO8wUhadou2u9/+vDME6XvXr+rzuVBvysIWR6qxGE0Hs1VvTUfLVISiziaRrHCrTt3UAyoV6qGb3QueqaPWlA4Xyqr11ZFisUg34y07OoaFpoUL5E8ftw5NawZzaQpGBEygmO5uWwCwiB/MshmeBDy4pNzJ4gDFPN6NgUJ/NCPWSIJUwAm5DMVmSSODtskzlOqwcgUiYwfhm5jM80CrGmOC6WiUMrDOGwuW6o53fjOXV8Zab7WGbUwNo2tUnYUeYHG5PMCoqK4zyPRUl+w+bIb+bYXsjxHA9Gic2F1wkwuSxCpO/nqlVyFCuPFzOVcyx1O+82RZ7q1UjEIKXY6cWegPLMn8wAhFIZixcB07y051xq6FhzAFs4RfMIytMB1cIkzKU+gY6YgkDC1sF3btpWlH0dxQNJUEcmspMsRPfzdIXk+iw8H4Qbh8onEBpXyCe58EE1NF0LyCcYgmNiyNHNq6WSSFy0wMi08cHzSiozFE9THkrj9wtIZy1mBd5KL0WISVaDop69flktEOZUEHaDctCWcAwsVudeBHTPF4vl8NXJipa+zBItxqOJPXSITV7nKZo7lhM700sNgPfIsREkkmEQphUZu6LhSRkQYkuMFz9cdK0whXn+pGsMezFLjJwPZsLKJdEcHQCDcWi3ibmj1Db7zhfm0mZwNJ2EYhnOgtJ7G49nQETTrj8lc4qxH48YbKeyeW1txxyczcyOfW6xmvss73ZRlh3pHVV999bhQL3M0aWuyjdgwA9mRXyoAtgeYsuKjBIZJ+Y11ZTaiUBoEgYWpAzGkLeblXL796DkqJUMIsBQPC2Pf9iUcGxyOqhw7P9P5wryalMjOGSST/zzmHzyfSOgzbGObmo9H7ixDBHabFy5lLl1NY2aJLmUWcevp5eD5eaeUEFNCXEC380XAmUN5wRbZTx6/nEYaFlj10EhZBJ1NhwOdSYoFEEommHjpnBg26OqowFrYGMEkoYED2jQ9BXMpUoHVi2cjkqWdJJsm8U5rEA5mvgtWGTIDl1LHhni+qP3q1/dxnAd9FBO4gn765DEQLRMgLlFwiELOWAtlq0AxUCEbxaE2VeWlnD2C17ar+b2yfTHuXXq4rw81zemcho+gDQz9zawdyZaYYgjEj077vO8GxTKMECESZCzqAyGRd82zyyBB+Hh5K8EbF+1mhjXZRYA+64V7V7avXCk3iqayXIy9BKB/cjw8b12urVcRDEnTIs6j2lh3IrMPaJaA8WjMwkJkzDgYSOIEDfoT3A5F18dgHAyyQhz6iLp/bJiKLdKJRp5KRhAnLC6bEGwVEynAR0aa7jgmugAbp0eJlh8AAAXWSURBVMIyU/b7A4kvBaODuUhusAlyPZjNp2KKe3t76+XxoRgHYIzmgnhs+1E06TTHad9w2hPVt7y1uoDAPCvGsNudaUtPhhYcGGmeJLIooOlWPqYcE/HnS3lmPGufxwGRKWezKT6dTupTWfFUNplQbUu5OKVZjiEjR9ZWEmIWgpxnYz2YgZt3Muni8dFhMJwQHO6PDvRsGYIdiIEz5RQCBEhvGGBCrUr2LlQzsMkMaJpzzYIogaBQx1YVviygEjgeKwRC6brqxFFBKDm6Zfjy1HbMqVrPrCe5SNU8fzZHQDxdKtphoNkKELsCTygDZ/DFaUigdDKVJHnBo+TWkqynSma4GCn2QiMR0ZkrQRxLCXYux2CMsWQC/B//3f8GRmDF0PrjnpjmbVdXdWU2m06G86RU2Nu5Y9kuK5CKMaEYXDX1papwAiWrPU7AVMX0HWy9eiWyPC/0aJZAcTgGPXUxjUMPhbAwwp0Acf3YNBeVfFaZLMyllRCzq2vbKE1fdJoIBbMS/uDBZxgKkzgJQFEYo/X6NZoSWu3DzuUxjVGBCS7HUwj0qytrMEpdXp7RLChwKYZOMBzqBGOcxEd9k6aTpcaGFQNA5OJYtFxM4TimcIZAWQylU8lsHAGJhDQYdfYP9jGStj0PxRAcgyAgRIHINR0MI2AIC8wQhWDDW+AsqHsaiGBSstxsdRmG833T9ZeausQQSkgmxETCdtzQCyAfIlECxXCKZqRMynSNp6+/MD0VBsl8tuo5EQwACSnBMzSBEZqiRQHou66uaWEcEiQVA4CluupcdUw7jDwICzOlRAhFuhZW8hWRJJxFBLoACHsXnWeWb8Eoy9JJ3dRS+Wy+XlEtBSMhHEeU6bx72q4X1hJsKgqB46OT0XiUErg37twhWCqMw+50GMAAytCClBRZ6fLskoBQjhUCP3R0DfAdVZ2TNB5A8WnrDEDRen11MVuAKB4gaBhGqSR/cvKq1Wkl01y5WoZRxDItDMXKxcpMNUEAK6RT6kIOvVDgUlEE5bIFGIgjy0LicDlbEBhNsNhCnfmgJ6vyXFUUzShXK4mUZFjaQp4xFL1cLIEgZmmaIhheECMIVDXDdz1HNyRWVBazYqV01r3kGT4npYMQGKgyxlAMjEKGgwFggONaAORKyTA2otCncSH24el0bJlTTZ0mkwnPjkgmnS+sqaabKUhMAnny/NHZeff0rJlMcRurFZZOpdI5DzRC30yIielIRxHac5fH5/thFFzbu+OFgePKNMskpHT77BIIIElM65al2UZjY4WgsOGwC0MRQoBnnZZtuuV0NZ8qyKPpbDQajcaVUqWxst7rTQmasQMHJ3AUxRVFw3Hs+OTVZDZIpqS9a1elRErXrdl0ZpgmS1PVcnHS78Ne4ChaghMQGAUwYqGruusKvKCoFgLiGxub6aQ4m/WevbiXLaaShcLxRZtL5mu1dddxR/3OYjqq5MsERIpcMgrA5XJB0kgYW2JCoqhEYLsoiDRPL6I4plk+iEPLNHVlGQdh7LmOYWxsbKysrz559ni6mM6Xcyuw6ytV1dRGk2kyLVAcKiRRw3AwjEVROo5hEIYCP4qCMPRBEmeSiSyBYDAENi/PMATMJBNwEC3H08gDUIxKZgpeCFqanBUZHEItw+REyYPAuWPaYLRzbffy7NSSFRrDPNtKiEmS5qYzGYJgnMB0XZHlIQT7xXwqDG15aqiygxAEy4upfBqA46W6xHBiOddDFwo9qNvteZ52/daukGQd37Icw/e8yI8JABMZXp1PDU2BeRBlUMcNTDtg2TQv5jghNZ3NEBQiGFDRplEU0ARL4ixD0CIr9DqXKAxIArtYLOYLuVCo4TgJoYhtu61mi2fFUrFsGba6UDmOcyzbczxJFFTVwHEi9gPPc0uVfIQF3Ul3qi5YUZKEHIbQpuaMR/1sMify9HRytJCHBAHsbK9LwsajJy+b7aM7b952dNdSIowUhISE4l4ULQads+P907X69v8fnFNw2V6OF+IAAAAASUVORK5CYII=", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "dream_img = run_deep_dream_simple(img=original_img, \n", " steps=100, step_size=0.01)" @@ -262,9 +296,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "6.457822322845459" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import time\n", "start = time.time()\n", @@ -326,7 +381,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb index dca2bd6c62..4e67c7e724 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "tags": [ "hide-cell" @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -263,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -288,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -327,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -353,9 +353,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation 25 out of 100, loss: 10359594.0\n", + "Generation 50 out of 100, loss: 7851741.5\n", + "Generation 75 out of 100, loss: 6664776.0\n", + "Generation 100 out of 100, loss: 6305506.0\n" + ] + } + ], "source": [ "with tf.Graph().as_default():\n", " # Get network parameters\n", @@ -376,7 +387,7 @@ " style_losses = []\n", " for style_layer in style_layers:\n", " layer = vgg_net[style_layer]\n", - " feats, height, width, channels = [x.value for x in layer.get_shape()]\n", + " feats, height, width, channels = [dim if isinstance(dim, int) else dim.value for dim in layer.get_shape()]\n", " size = height * width * channels\n", " features = tf.reshape(layer, (-1, channels))\n", " style_gram_matrix = tf.matmul(tf.transpose(features), features) / size\n", @@ -471,7 +482,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb index 7a7520e6c7..5539b21a9c 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb @@ -60,9 +60,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

\n", + "\n", + "A demo of convolution function. [source]\n", + "

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "display(HTML(\"\"\"\n", @@ -113,9 +132,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

\n", + "\n", + "A demo of CNN. [source]\n", + "

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "display(HTML(\"\"\"\n", @@ -130,9 +168,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "

\n", + "\n", + "A demo of CNN. [source]\n", + "

\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from IPython.display import HTML\n", "display(HTML(\"\"\"\n", @@ -155,9 +212,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/5\n", + "1875/1875 [==============================] - 6s 2ms/step - loss: 0.1342 - accuracy: 0.9595 - val_loss: 0.0571 - val_accuracy: 0.9814\n", + "Epoch 2/5\n", + "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0443 - accuracy: 0.9864 - val_loss: 0.0362 - val_accuracy: 0.9882\n", + "Epoch 3/5\n", + "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0301 - accuracy: 0.9907 - val_loss: 0.0311 - val_accuracy: 0.9900\n", + "Epoch 4/5\n", + "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0229 - accuracy: 0.9927 - val_loss: 0.0255 - val_accuracy: 0.9909\n", + "Epoch 5/5\n", + "1875/1875 [==============================] - 3s 2ms/step - loss: 0.0171 - accuracy: 0.9948 - val_loss: 0.0283 - val_accuracy: 0.9906\n", + "313/313 - 0s - loss: 0.0283 - accuracy: 0.9906 - 338ms/epoch - 1ms/step\n", + "Test accuracy: 0.9905999898910522\n" + ] + }, + { + "data": { + "image/png": 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Xici1wTAMjmfksMupRcj+iOxsbtHz/vl4uBIT4s91wX4X+wqF+hPs73nN9WFUgiRXbcqUKQwZMoROnToRFBTE888/b8oM488//zzJyckMGDAAV1dXHnvsMeLj43F1LfpZ99dff83JkyeLTBqaNm1K06ZNmTlzJlOmTOF///sfzzzzDD179iQ/P59mzZo5Wp1uueUWvvjiC1555RXeeOMNAgICHK1YAP/4xz8YPHgwN954I+Hh4UybNs1pPq7iXOhnFx8fj4+PD4899hi9evUiLS3NUWb8+PG4ubkxYcIEjh49SlhYGI8//rjTdfr378/IkSPp37+/lgERqebOnM1ld0rmJS1C9oTozNmih8m7u1qIruPn1GG6caj5I8cqE1PXYqvKtBZb5WWz2WjatCkPPPAAr7zyitnhmObgwYNER0ezYcMG2rRpUy730Pe6SMXKyslnT2qmUxK0OyWDlPTiR45F1fa1PxILvZAI+VG/ti/ulXDkWEUo6VpsakGSKu/QoUP873//4+abbyYnJ4cZM2Zw4MAB/vrXv5odminy8vI4efIk48aN44Ybbii35EhEyk9OvpX9x7MKdZg+fKr4kWMRNbwveSxm7zAdXcevSo0cq0yUIEmV5+Liwpw5c3j22WcxDIPrr7+e77//nqZNm5odmilWr17NrbfeynXXXceXX35pdjgichn5VhuHTp1ld3IGu1MuziV04EQW1mKGjgX5eToSoAtD6WOC/fD30jJFZUkJklR5kZGRrF692uwwKo1bbrnlT0+DICJlyzAuGTl2YVLF5Az2Hs8kN7/owS3+Xm40CbUPm784csyP2n7lM2efOFOCJCIiUkYMw+B4Zg57UjKdHo3tSckkM6fopTa83F0cw+YvnVwxJODaGzlWmShBEhERuQppZ/PYneq83tjulAxOX2bkWMOgC+uN+TmG0EfW9NHIsUpICZKIiMhlnM3NZ29qptNcQruTM0hOzy6yvMUxcszPqUUoKujaHTlWFSlBEhERAXLzbew/YU+E9lyYUyglg8RTZymuW194oJfTemONQ+0jx7w9NHKsqlOCJCIi1xSrzSDx1FmnPkK7k+0jx/KLHTnmcbGfUKi9s3RMiD8BGjlWbSlBkjJ1yy23EBsby9SpUwGIiopi5MiRjBw5sthzLBYLixcvplevXn/q3mV1HRGpHgzD4Ghatn0yxUsmVtyTkklOcSPHPN24zjG7tJ/j/0EaOXbNUYIkANx9993k5eWxdOnSQq/9/PPP3HTTTWzdupWWLVuW6robNmwotFr9nzVx4kSWLFnCli1bnI4fO3aMmjVrlum9inPu3DkiIiJwcXEhKSkJT0/98hQxS8GRY3vOd5zek5JJRjEjxzzdLo4cuy7Ez/GYLCzQSyPHBFCCJOcNHTqUPn36cOTIEerWrev02uzZs2nXrl2pkyOwr2hfUUJDQyvsXl999RXNmzfHMAyWLFlC3759K+zeBRmGgdVqxc1NP85S/Z05m2t/NHZ+uY0LS20UN3LMzcVCwzq+hYbQR9bywVUjx+Qy1J1eALjrrruoU6cOc+bMcTqemZnJF198wdChQzl58iT9+/cnIiICHx8fWrRowaeffnrZ60ZFRTketwHs2bOHm266CS8vL5o1a8by5csLnfP8889z3XXX4ePjQ8OGDRk/fjx5efZffnPmzOHll19m69atWCwWLBaLI2aLxcKSJUsc19m2bRu33XYb3t7e1K5dm8cee4zMzEzH64MGDaJXr168/fbbhIWFUbt2bYYNG+a41+XMnDmThx56iIceeoiZM2cWev3333/nrrvuIiAgAH9/f2688Ub27dvneH3WrFk0b94cT09PwsLCGD58OGBfP81isTi1jp05cwaLxcKKFSsAWLFiBRaLhe+++462bdvi6enJqlWr2LdvH/fccw8hISH4+fnRvn17vv/+e6e4cnJyeP7554mMjMTT05NGjRoxc+ZMDMOgUaNGvP32207lt2zZgsViYe/evVf8moiUpYzsPDYlnuaz9YlM+mYHD/1rHR1e+57YScvp+89fGL9kO/N+OcS6A6c4fTbv/MgxH+5oFsLwWxsxrV8sy0bexI5J3fnf0zcz469tGNE1hvjmoUQF+So5kivSn5wVwTAg76w593b3sY85vQI3NzcGDBjAnDlzePHFFx1NzF988QVWq5X+/fuTmZlJ27Ztef755wkICODbb7/l4YcfJjo6mg4dOlzxHjabjXvvvZeQkBDWrVtHWlpakX2T/P39mTNnDuHh4Wzbto1HH30Uf39/Ro8eTd++fdm+fTtLly51fPgHBgYWukZWVhbx8fF07NiRDRs2kJqayiOPPMLw4cOdksAff/yRsLAwfvzxR/bu3Uvfvn2JjY3l0UcfLbYe+/btY+3atSxatAjDMHj66ac5dOgQ9evXByApKYmbbrqJW265hR9++IGAgABWr15Nfr69qf/9999n1KhRvPHGG/To0YO0tLSrmgl8zJgxvP322zRs2JCaNWty+PBhevbsyWuvvYanpycff/wxd999N7t27aJevXoADBgwgLVr1/Luu+/SqlUrDhw4wIkTJ7BYLAwZMoTZs2fz7LPPOu4xe/ZsbrrpJho1alTq+ERK4lyulb2pmY6WIPuWSdKZK685FnNhGH2IRo5J2VOCVBHyzsLr4ebc+4Wj4FGyPkBDhgzh73//OytXruSWW24B7B+Qffr0ITAwkMDAQKcPzxEjRrBs2TI+//zzEiVI33//PTt37mTZsmWEh9u/Hq+//jo9evRwKjdu3DjH/6Oionj22Wf57LPPGD16NN7e3vj5+eHm5nbZR2oLFiwgOzubjz/+2NEHasaMGdx99928+eabhISEAFCzZk1mzJiBq6srTZo04c477yQhIeGyCdKsWbPo0aOHo79TfHw8s2fPZuLEiQC89957BAYG8tlnn+Hubh/hct111znOf/XVV3nmmWd46qmnHMfat29/xa9fQZMmTeL222937NeqVYtWrVo59l955RUWL17M119/zfDhw9m9ezeff/45y5cvp1u3bgA0bNjQUX7QoEFMmDCB9evX06FDB/Ly8liwYEGhViWRq5GTb+XAiaxSDaEP9vd0LL563fmJFWNC/PHz1EeXlD99l4lDkyZN6NSpE7NmzeKWW25h7969/Pzzz0yaNAkAq9XK66+/zueff05SUhK5ubnk5OTg4+NTouv/8ccfREZGOpIjgI4dOxYqt3DhQt5991327dtHZmYm+fn5BAQElKouf/zxB61atXLqIN65c2dsNhu7du1yJEjNmzfH1fXiX51hYWFs27at2OtarVbmzp3LtGnTHMceeughnn32WSZMmICLiwtbtmzhxhtvdCRHl0pNTeXo0aN07dq1VPUpSrt27Zz2MzMzmThxIt9++y3Hjh0jPz+fc+fOkZiYCNgfl7m6unLzzTcXeb3w8HDuvPNOZs2aRYcOHfjmm2/Iycnh/vvv/9OxyrUj32rj4Mmz7Em5OGpsd0rmZRdfrenjfkkidDEhquHjUcHRi1ykBKkiuPvYW3LMuncpDB06lBEjRvDee+8xe/ZsoqOjHR+of//735k2bRpTp06lRYsW+Pr6MnLkSHJzc8ss3LVr1/Lggw/y8ssvEx8f72iJ+cc//lFm97hUwSTGYrFgsxU9/Bdg2bJlJCUlFeqUbbVaSUhI4Pbbb8fb27vY8y/3GoCLi71b4KWLzRbXJ6rg6MBnn32W5cuX8/bbb9OoUSO8vb257777HO/Ple4N8Mgjj/Dwww/zzjvvMHv2bPr27VviBFiuLTabwZHT5y5Jguwjx/YfzyLXWvziq5cmQI3PtwgF+Xlo5JhUOkqQKoLFUuLHXGZ74IEHeOqpp1iwYAEff/wxTzzxhOMX1+rVq7nnnnt46KGHAHufot27d9OsWbMSXbtp06YcPnyYY8eOERYWBsAvv/ziVGbNmjXUr1+fF1980XHs0KFDTmU8PDywWq1XvNecOXPIyspyJBKrV6/GxcWFxo0blyjeosycOZN+/fo5xQfw2muvMXPmTG6//XZatmzJ3LlzycvLK5SA+fv7ExUVRUJCArfeemuh618Y9Xfs2DFat24NUGg6g+KsXr2aQYMG0bt3b8DeonTw4EHH6y1atMBms7Fy5UrHI7aCevbsia+vL++//z5Lly7lp59+KtG9pfoyDINjadnnF1y1r0S/J9X+mOxcXtE/h97uro6JFO1JkB+NQ/0JDdAQeqk6lCCJEz8/P/r27cvYsWNJT09n0KBBjtdiYmL48ssvWbNmDTVr1mTKlCmkpKSUOEHq1q0b1113HQMHDuTvf/876enphRKNmJgYEhMT+eyzz2jfvj3ffvstixcvdioTFRXFgQMH2LJlC3Xr1sXf37/QPEQPPvggL730EgMHDmTixIkcP36cESNG8PDDDzser5XW8ePH+eabb/j666+5/vrrnV4bMGAAvXv35tSpUwwfPpzp06fTr18/xo4dS2BgIL/88gsdOnSgcePGTJw4kccff5zg4GB69OhBRkYGq1evZsSIEXh7e3PDDTfwxhtv0KBBA1JTU536ZF1OTEwMixYt4u6778ZisTB+/Hin1rCoqCgGDhzIkCFDHJ20Dx06RGpqKg888AAArq6uDBo0iLFjxxITE1PkI1Cpnq5mLiEPNxei6/jR+JJkqHGoPxE1vLX4qlR5SpCkkKFDhzJz5kx69uzp1F9o3Lhx7N+/n/j4eHx8fHjsscfo1asXaWlpJbqui4sLixcvZujQoXTo0IGoqCjeffddunfv7ijzl7/8haeffprhw4eTk5PDnXfeyfjx4x0doAH69OnDokWLuPXWWzlz5gyzZ892SuQAfHx8WLZsGU899RTt27fHx8eHPn36MGXKlKv+ulzo8F1U/6GuXbvi7e3N/PnzefLJJ/nhhx947rnnuPnmm3F1dSU2NpbOnTsDMHDgQLKzs3nnnXd49tlnCQoK4r777nNca9asWQwdOpS2bdvSuHFj3nrrLe64444rxjdlyhSGDBlCp06dCAoK4vnnnyc9Pd2pzPvvv88LL7zA//3f/3Hy5Enq1avHCy+84FRm6NChvP766wwePPhqvkxSBVzNXEINgnwvWWrDnhDVr+WDmxZflWrKYhjFjR+Qy0lPTycwMJC0tLRCHYizs7M5cOAADRo0wMvLy6QIRa7Ozz//TNeuXTl8+PAVW9v0vV65ZWTnsceRBF0cSp+akVNkeYsF6tfycaw3dqFVqEGQLx5uSoSkerjc5/el1IIkIoB9Esnjx48zceJE7r///qt+FCkVT3MJiZQ9JUgiAsCnn37K0KFDiY2N5eOPPzY7HCmC5hISqTj6CRERwD5RZMG+XGIOzSUkYj4lSCIiJtFcQiKVlxKkcqT+71Ld6Xu8ZDSXkEjVowSpHFyYHPDs2bMlmr1YpKo6e9a+CHNRy6pcizSXkEj1oQSpHLi6ulKjRg1SU1MB+5w8+otPqhPDMDh79iypqanUqFHDaT27a4XmEqpk8nPg7Ck4d+r8v6fB4gKuHuDmYf/X1RNc3cHt/L+ungVe9wCXa+97WYqmBKmcXFhp/kKSJFId1ahRw/G9Xl1pLiET5J2Dsyftic7ZkxeTHkcCVMRruZllc2+La+mTKlePK79eqmte+vql13C3f4NJhVCCVE4sFgthYWEEBwcXu9ioSFXm7u5eLVuO9h3P5OstR/ntyBnNJfRnGYY9cXFKZk5f8v+Cic751/KL/5pflsUFvGuCT23wqmE/Zs0Ba569hcmad34/F/Jz7f/aCvx+Nqz2+19tDOWtyATrMknVn030SnzN6tf6pgSpnLm6ulbLDxGR6uREZg7fbD3K4s1J/Hak8NI5mksIe7KTnebcmlMw0SnqNWvu1d3Pxc2e6HjXAp/zm3ct+zGfWpe8dn7fu6Y9KXIpZSudzWZPkhwJVG6BpCr34nYhqSqr14s953wil58DFBgIceFalVFJW99Kk8g1uh3CY02pzjX00y0ictG5XCvL/0hh8aYj/LTnhGN+IVcXCzfFBHFb05DzrULVcC4hm9We7BT5GOtkMUnQKXvrytVw9bwksSmQ6DglPZfse/pXzOMkFxdw8bR/IFdG1vw/l5QVec6lrWklvaZJrW8+QUqQRETKm9Vm8Mv+kyzenMTS7clkXjKyrFXdQHq3juCuVuEE+VXSD8uiWPPsHZIv1z+n4P650xRqmSgpd9+LLTbFteY4Ep3zx9x91Hfmarm62Td8zI6ksIpofavT2LTqKUESkWpvZ3I6izcn8e/NR0lOz3Ycr1vTm96tI+jVOoLoOn4mRnie00isYvrnOL12CnIKPxIsMc+AyyQ6NYt+pOWuRYnlvMre+vYnmZ4gvffee/z9738nOTmZVq1aMX36dDp06FBk2by8PCZPnszcuXNJSkqicePGvPnmm3Tv3t1RJiMjg/Hjx7N48WJSU1Np3bo106ZNo3379o4yhmHw0ksv8dFHH3HmzBk6d+7M+++/T0xMTLnXV0QqRkp6Nv/eksTizUf541i643iAlxt3tgzn3jYRtK1Xs/zmGso9e+X+OZcmOuf+5EgsrxpXfmx1aaLjXdPe70NEimRqgrRw4UJGjRrFBx98QFxcHFOnTiU+Pp5du3YRHBxcqPy4ceOYP38+H330EU2aNGHZsmX07t2bNWvW0Lp1awAeeeQRtm/fzrx58wgPD2f+/Pl069aNHTt2EBERAcBbb73Fu+++y9y5c2nQoAHjx48nPj6eHTt24OWlv45EqqrMnHyWbU9m8eYkVu874VjA1d3Vwm1NgundOoJbmwTj6VaKgRNFjsQqQSflshiJdWlrjlOH5QKPtLxqnH8MIyJlxWKYuFZAXFwc7du3Z8aMGQDYbDYiIyMZMWIEY8aMKVQ+PDycF198kWHDhjmO9enTB29vb+bPn8+5c+fw9/fn3//+N3feeaejTNu2benRowevvvoqhmEQHh7OM888w7PPPgtAWloaISEhzJkzh379+hUZa05ODjk5F+c9SU9PJzIykrS0NAICAsrk6yEipZdvtfHz3hMs2ZzEst+Tyc67uIZZu/o16dU6grtahhXf0dpmg/QkOLkHTuy1/3tyL2QkX0yCCnZGLamKGoklIiWWnp5OYGDgFT+/TfuTIzc3l40bNzJ27FjHMRcXF7p168batWuLPCcnJ6dQC4+3tzerVq0CID8/H6vVetkyBw4cIDk5mW7dujleDwwMJC4ujrVr1xabIE2ePJmXX3659BUVkTJnGAbbk9JZtPkI32w9yonMi8OeGwT52vsVxUZQr/YlHVuz052ToBPnE6GT+0rW2lOZR2KJSJkzLUE6ceIEVquVkJAQp+MhISHs3LmzyHPi4+OZMmUKN910E9HR0SQkJLBo0SKsVvvQU39/fzp27Mgrr7xC06ZNCQkJ4dNPP2Xt2rU0atQIgOTkZMd9Ct73wmtFGTt2LKNGjXLsX2hBEpGKc+T0Wf69xT5f0d7Ui/11avl6cHfLMHrHhtLK9wyWk3th1zLnhCgzpfgLu7hBzQYQFAO1G9n/DYhwbunRSCyRa0qVemg9bdo0Hn30UZo0aYLFYiE6OprBgwcza9YsR5l58+YxZMgQIiIicHV1pU2bNvTv35+NGzf+qXt7enri6Vk9e+qLVGZp5/L4btsxFm1OYv2BUwDUJJ2ObincGZFJl5pnqGdLwuXwXth64PKPw3yDnZOg2jH2f2vUs09KJyJynmkJUlBQEK6urqSkOP9Vl5KSUuzaTnXq1GHJkiVkZ2dz8uRJwsPDGTNmDA0bNnSUiY6OZuXKlWRlZZGenk5YWBh9+/Z1lLlw7ZSUFMLCwpzuGxsbW8a1FJGrkZtvY+WOI/zy63pSDvxOPVsSD7gcY4zHUWJcU/A3MuwFU85vl3LzsidAF7YLiVDtaPCuUcE1EZGqyrQEycPDg7Zt25KQkECvXr0AeyfthIQEhg8fftlzvby8iIiIIC8vj6+++ooHHnigUBlfX198fX05ffo0y5Yt46233gKgQYMGhIaGkpCQ4EiI0tPTWbduHU888USZ1lFErsAwIOMYnNiDcWIPqQd/J+3wDrwzDnCbkcrtFgNcsW+Oc87/G1AXghpdbAVyPBqrq07OIvKnmfqIbdSoUQwcOJB27drRoUMHpk6dSlZWFoMHDwZgwIABREREMHnyZADWrVtHUlISsbGxJCUlMXHiRGw2G6NHj3Zcc9myZRiGQePGjdm7dy/PPfccTZo0cVzTYrEwcuRIXn31VWJiYhzD/MPDwx2JmoiUsZxMOLXvYsfoE3vOjxbb55j7xwKEnN8uHMhx8cFWuxFeoU2wXJoE1YoGj0o4s7CIVBumJkh9+/bl+PHjTJgwgeTkZGJjY1m6dKmjA3ViYiIul/wlmJ2dzbhx49i/fz9+fn707NmTefPmUaNGDUeZtLQ0xo4dy5EjR6hVqxZ9+vThtddew939Yv+C0aNHk5WVxWOPPcaZM2fo0qULS5cu1RxIIn+GzQpphwuMEjvfSTrjaLGn5RsuHDbqsN8I57BLOD5hTWjcvDXXt2yHZ0CoOkaLiClMnQepKivpPAoi1c6500UMlT8/XN6aU+xphk9tTnvXZ0dOMGvO1GSvLZR9RjhHCCEuJox7W0dwR/MQfDyq1NgREaliKv08SCJSiVnz4NSB84nPHudHY2dPFH+eqwfUauh4FGar1YgdeSF8ddCbL//IIuPUxcVhm4cH0L91BH+JDSfYX623IlK5KEESuVYZBmQdv+RR2CVJ0OmDYFiLP9c/rPBQ+dqN7MPlXVzZm5rBok1J/Pt/R0k6cw6wT+QYHujFPa0j6N06gutC/CukmiIiV0MJkkh1l3fO/vir4FIaJ/ZefiV4d1/70HjHMPlG50eNNbLPEF1AakY236xJZPHmI2xPurg4rL+nGz1bhNGrdQRxDWqV3+KwIiJlSAmSSHVw6XpiJ/c5d5BOO8zFsfEFWaBGZOGh8rVjICD8ih2kz+bms3xHCos2JbFq7wmsNvt93Fws3NK4Dr1b16Vr02C83EuxOKyISCWgBEmkKslOv9gp+tIk6NQ+yDtb/HlegUUnQbUagnvp+v9YbQZr9p1g8eYklm1PJiv34qO41vVq0Lt1BHe2CKO2n2aeF5GqSwmSSGVjzYczhwonQVeznljt8xMp+gb96eHyO46ms3jzEf695SipGRdHq9Wr5UOv8/2KGgT5/ql7iIhUFkqQRMySdbLwUPkTe+DU/hKuJxZ9SatQDNSsX+briR1LO8e/txxlyeYkdiZnOI7X8HHnrpZh9G4dQZt6NbForiIRqWaUIImUp/yc88Pl9xSeRfrc6eLPc/OyzxbttJRGxawnlpGdx9LtySzenMTa/Se5MFOah6sLXZsG07t1BLc0DsbDTct5iEj1pQRJpCxZ82HTXNj1nT0JOpMIhq348pVkPbE8q42f9xxn8eajLN+RTHbexZg7RNWid5sIel4fRqCPVrwXkWuDEiSRsrI3AZa9AMd3Oh/38C+QBEVfbA3yMK/PjmEY/HYkjcWbk/hm61FOZuU6XmtYx5d7W0dwT2wEkbW05pmIXHuUIIn8WSf2wv9ehN1L7fvetaDzUxDR1p4Q+YVUqvXEDp86y5LNSSzeksT+41mO40F+HtzdKpzerSNoERGofkUick1TgiRytc6dgZVvwfoPwZZvH0XW4TG4eTR41zQ7OidpZ/P4dtsxFm8+woaDF/s+ebm7cEezUHq3ieDGRkG4uapfkYgIKEESKT2bFTbOgR9fg7Mn7cdi7oA7XoM615ka2qVy8q38uPM4SzYn8cPOVHKt9n5FFgt0jg6iV+sIul8fip+nfg2IiBSk34wipbF/JSwdC6m/2/eDroP4yRDTzdy4zjMMg42HTrNocxLf/naMtHMXpwtoEupP7/P9ikIDtTisiMjlKEESKYlT++F/42Hnf+z7XjXglrHQfmiZzz10NfYfz3T0Kzp86pzjeEiAJ71iI+jVOoKmYQEmRigiUrUoQRK5nOx0+Plt+OV9sOaCxdWeFN0yFnxqmRraycwc/vPbMRZtTmLr4TOO474ernS/Pox720RwQ8PauGpxWBGRUlOCJFIUmxW2fAIJr0BWqv1Y9G0Q/zoENzUtrOw8K9//kcLiTUms3H2c/POLw7q6WLgpxt6v6I5moXh7aHFYEZE/QwmSSEGH1sB3z0Pyb/b9WtH2xOi6eFOG69tsBr8cOMmSzUl8ty2ZjJx8x2st6wbSu3UEd7UMp46/FocVESkrSpBELjh9CJZPgB1L7PuegfYh+x0eAzePCg9nd0oGizYl8e8tSRxLy3Ycj6jhTe/W9n5FjYL9KjwuEZFrgRIkkZxMWDUF1swAaw5YXKDNQLhtHPgGVWgoqenZfL31KIs2JbHjWLrjuL+X2/nFYevSrn5NXNSvSESkXClBkmuXzQa/fQbfvwyZyfZjDW6yD9sPvb7CwsjKyWfZ7/bFYVfvPcH5bkW4u1q4pXEw97aO4NYmwXi5q1+RiEhFUYIk16bEdbB0DBzdZN+vGWWf6LHJnRXSzyjfamP1Pnu/oqXbkzmXZ3W81rZ+TXq3juDOFmHU9K34R3siIqIESa41aUdg+Uuw/Uv7voc/3PQs3PAEuJVvJ2fDMPj9aDqLNyfx9dajHM/IcbwWVduH3q3r0qt1OPVrm7eArYiI2ClBkmtD7llYPc2+5Z8DLND6IbhtPPiHlOutk86c499bkli8KYk9qZmO4zV93B2Lw8ZG1tDisCIilYgSJKneDAO2fQHfT4T0JPuxep2g+2QIjy2326Zn57F0WzKLNh9h3YFTGOf7FXm4uXB7sxB6x0Zwc+M6uGtxWBGRSkkJklRfRzbC0ufhyAb7fmA9uGMSNOtVrv2M/vnTPv7xv93k5Nscx25oWIt7W9ele4tQArzMX5pEREQuTwmSVD/pR+0j0377zL7v7gs3Pg0dh4O7d7ne+uiZc7y5dBdWm0FMsB+929gXh42oUb73FRGRsqUESaqPvHP2uYxWTYG8s/ZjrfpD15cgIKxCQvh47SGsNoMbGtbi00dvUL8iEZEqSgmSVH2GAb8vto9OS0u0H6vbAbq/AXXbVlgYZ3Pz+XS9/f5DuzRUciQiUoUpQZKq7egWWDoWEtfY9wMioNvL0OK+Cl837atNSaSdy6N+bR+6Ngmu0HuLiEjZUoIkVVNGCvwwCTZ/Ahjg5g1dRkKnJ8HDp8LDsdkMZq86AMDgTlFaCkREpIpTgiRVS34O/PL/4Kd/QG6G/ViL+6HbRAisa1pYK3cfZ/+JLPy93Li/XaRpcYiISNlQgiRVg2HAzv/A/8bB6YP2Y+FtoMebENnB1NAAZp5vPerXPhJfT/1YiYhUdfpNLpVf8nb7umkHf7bv+4XaW4xa9gUX8yda3JWcwaq9J3CxwICOUWaHIyIiZUAJklReWSfgh1dg08dg2MDNCzqNgM4jwdPP7OgcZq+2tx51vz6UyFoV3/9JRETKnhIkqXzyc2H9h7DyLchJtx9r3htunwQ16pkbWwEnM3NYtNm+hMmQzg1MjkZERMqK6c8n3nvvPaKiovDy8iIuLo7169cXWzYvL49JkyYRHR2Nl5cXrVq1YunSpU5lrFYr48ePp0GDBnh7exMdHc0rr7yCcWExLGDQoEFYLBanrXv37uVWRykhw4Bd38H/u8He1ygnHcJaweDv4P45lS45AliwLpHcfBut6gbStn5Ns8MREZEyYmoL0sKFCxk1ahQffPABcXFxTJ06lfj4eHbt2kVwcOF5ZMaNG8f8+fP56KOPaNKkCcuWLaN3796sWbOG1q1bA/Dmm2/y/vvvM3fuXJo3b86vv/7K4MGDCQwM5Mknn3Rcq3v37syePdux7+npWf4VluKl/mGfz2j/j/Z932DoOgFi/wourubGVoycfCsf/3IIgCFdGmhiSBGRasRiXNq0UsHi4uJo3749M2bMAMBmsxEZGcmIESMYM2ZMofLh4eG8+OKLDBs2zHGsT58+eHt7M3/+fADuuusuQkJCmDlzZrFlBg0axJkzZ1iyZMlVx56enk5gYCBpaWkEBARc9XWueWdPwY+vw6+zwLCCqwfc8H9w4zPgVbm/ros2HWHU51sJCfBk1fO34e5qeoOsiIhcQUk/v037jZ6bm8vGjRvp1q3bxWBcXOjWrRtr164t8pycnBy8vLycjnl7e7Nq1SrHfqdOnUhISGD37t0AbN26lVWrVtGjRw+n81asWEFwcDCNGzfmiSee4OTJk5eNNycnh/T0dKdN/gRrHvzyAbzbGjZ8ZE+OmtwFw9bB7S9X+uTIMAzH0P4BHaOUHImIVDOmPWI7ceIEVquVkJAQp+MhISHs3LmzyHPi4+OZMmUKN910E9HR0SQkJLBo0SKsVqujzJgxY0hPT6dJkya4urpitVp57bXXePDBBx1lunfvzr333kuDBg3Yt28fL7zwAj169GDt2rW4uhb9OGfy5Mm8/PLLZVBzYc9yWPYCnLAnsYRcD90nQ4ObzI2rFNYfOMXvR9Pxcnfhrx0qX98oERH5c6rUKLZp06bx6KOP0qRJEywWC9HR0QwePJhZs2Y5ynz++ed88sknLFiwgObNm7NlyxZGjhxJeHg4AwcOBKBfv36O8i1atKBly5ZER0ezYsUKunbtWuS9x44dy6hRoxz76enpREZqxuRSOb7bnhjtXW7f96kNt42DNgMrbT+j4sw6P7T/3jZ1qenrYXI0IiJS1kxLkIKCgnB1dSUlJcXpeEpKCqGhoUWeU6dOHZYsWUJ2djYnT54kPDycMWPG0LBhQ0eZ5557jjFjxjiSoBYtWnDo0CEmT57sSJAKatiwIUFBQezdu7fYBMnT01Mdua/WudOw4k37ozRbPri4Q9zf4KbnwLuG2dGVWuLJs/xvh/37dkjnKHODERGRcmFaxwkPDw/atm1LQkKC45jNZiMhIYGOHTte9lwvLy8iIiLIz8/nq6++4p577nG8dvbsWVwKzK7s6uqKzWYr9npHjhzh5MmThIWFXWVtpEjWfFj/EbzbBta9b0+Oruth72cU/1qVTI4A5qw5iGHAzdfVoVGwv9nhiIhIOTD1EduoUaMYOHAg7dq1o0OHDkydOpWsrCwGDx4MwIABA4iIiGDy5MkArFu3jqSkJGJjY0lKSmLixInYbDZGjx7tuObdd9/Na6+9Rr169WjevDmbN29mypQpDBkyBIDMzExefvll+vTpQ2hoKPv27WP06NE0atSI+Pj4iv8iVFf7frQ/TkvdYd+v0xS6vw7Rt5kb15+UkZ3H578eBuxD+0VEpHoyNUHq27cvx48fZ8KECSQnJxMbG8vSpUsdHbcTExOdWoOys7MZN24c+/fvx8/Pj549ezJv3jxq1KjhKDN9+nTGjx/P//3f/5Gamkp4eDh/+9vfmDBhAmBvTfrtt9+YO3cuZ86cITw8nDvuuINXXnlFj9DKwsl99kked/3Xvu9dE259EdoOBtcq1eWtSJ//eoTMnHwaBftxU0yQ2eGIiEg5MXUepKpM8yAVkJ0GP/3dPnTflgcWV+jwKNz8PPjUMju6MmG1Gdzy9o8cPnWO13u34K9xGr0mIlLVlPTzu+r/SS/msllh8zxIeAXOnrAfa9QN4l+HOo3Nja2MLd+RwuFT56jh407v1hFmhyMiIuVICZJcvQM/25cHSdlm368dY5/PKOZ2c+MqJxeG9j8YVw9vj6o1LYGIiJSOEiQpvVMHYPl4+OMb+75XINwyFto/Aq7u5sZWTrYnpbH+wCncXCw8fEOU2eGIiEg5U4IkJZeTAT//A9a+B9ZcsLhAuyFwywvgW9vs6MrVrPPLitzZMozQQK8rlBYRkapOCZJcmc0GWxdAwiTIPD+xZ8NbIH4yhDQzNbSKkJqezTe/HQVgqIb2i4hcE5QgyeUdWgtLx8CxLfb9Wg3hjtegcQ+wWEwNraLM++UQeVaDdvVr0rJuDbPDERGRCqAESYp25jAsnwC/L7LvewbYlwaJ+xu4XTvzRWXnWflkXSKg1iMRkWuJEiRxlpsFq6bCmnchPxuwQNuBcOs48KtjdnQVbsnmJE5l5RJRw5vbm4WYHY6IiFQQJUhiZ7PBti/g+4mQYe9vQ9SN9mH7oS1MDc0shmE4hvYP6hSFm6tpSxeKiEgFU4IkcORX+O55SPrVvl+jPtzxKjS9+5rpZ1SU1XtPsjslE18PV/p2iDQ7HBERqUBKkK5l6UftLUa/LbTve/jBjc/ADf8H7hrKPnPVfgDubxdJgFf1nN9JRESKpgTpWpR7FtZMh9VTIe8sYIHYB6HrePAPNTu6SmHf8Ux+3HUci8X+eE1ERK4tSpCuJYYB27+C5S9B+hH7scgboMcbEN7a3Ngqmdnn+x51bRJCVJCvydGIiEhFU4J0rUjaZF837fAv9v3ASLj9ZWh+7zXdz6goZ87m8tXGJEBD+0VErlVKkKq7jGT7DNhbPrHvu/tAl6eh0whw9zY3tkrq0/WHOZdnpWlYADc0rGV2OCIiYgIlSNVVXjasnQE/T4G8LPuxlv2g20sQEG5ubJVYntXGx2sPAjCkcxQWta6JiFyTlCBVN4YBO/4Ny8fDGfsM0NRtD93fgLrtzI2tCli6PZljadkE+Xnwl1glkiIi1yolSNXJsd/s/YwOrbLv+4fb+xldfx+4aJLDkpi5yt45+6Eb6uPp5mpyNCIiYhYlSNVBZir88ApsmgcY4OYFnZ+ybx4agVVSmxJPs+XwGTxcXXjohvpmhyMiIiZSglSV5efAug9g5d8hN8N+7Pr7oNtEqKGZn0vrQuvRPbHhBPldOwvyiohIYUqQqiLDgF3/hWUvwmn7hzrhraH7m1AvztzYqqikM+dYuj0ZgMGdNbRfRORapwSpqkn53d7P6MBK+75fqH1kWst+6mf0J3y89iBWm0Gn6No0Cw8wOxwRETGZEqSqIusk/PgabJwNhg1cPaHTcOgyCjz9zI6uSsvKyefTdfYRf0PUeiQiIihBqvysebD+I1j5BmSn2Y81uwdunwQ1o0wNrbpYtOkI6dn5RNX24bYmwWaHIyIilYASpMps9/9g2Qtwco99P7SFfT6jqC7mxlWN2GwGs1YfBOx9j1xcNDGkiIgoQaqcju+yJ0Z7v7fv+9aB28ZD64fARXPzlKUVu1M5cCILfy837mtb1+xwRESkklCCVNksnwBrZoBhBRd3uOEJuOk58FLH4fJwYWh//w718PXUj4OIiNjpE6GycXGzJ0eN74Q7XoHa0WZHVG3tTE5n9d6TuFhgQEdNDCkiIhcpQapsujwNDW6GhjebHUm1N3vVQQB6XB9G3Zo+5gYjIiKViibOqWw8/ZUcVYATmTks3pIEwJAuUeYGIyIilY4SJLkmLViXSG6+jVaRNWhTr6bZ4YiISCWjBEmuOTn5Vj5eewiAIZ2jsFg0tF9ERJwpQZJrzn+2HuNEZg6hAV70bBFmdjgiIlIJKUGSa4phGMxabR/aP6BTfdxd9SMgIiKF6dNBrinrDpzi96PpeLm78NcO9cwOR0REKiklSHJNmXV+Ysg+bepSw8fD5GhERKSyUoIk14xDJ7NY/kcKAIM7R5kbjIiIVGqmJ0jvvfceUVFReHl5ERcXx/r164stm5eXx6RJk4iOjsbLy4tWrVqxdOlSpzJWq5Xx48fToEEDvL29iY6O5pVXXsEwDEcZwzCYMGECYWFheHt7061bN/bs2VNudZTKYc6agxgG3HxdHRoF+5sdjoiIVGKmJkgLFy5k1KhRvPTSS2zatIlWrVoRHx9PampqkeXHjRvHhx9+yPTp09mxYwePP/44vXv3ZvPmzY4yb775Ju+//z4zZszgjz/+4M033+Stt95i+vTpjjJvvfUW7777Lh988AHr1q3D19eX+Ph4srOzy73OYo707Dw+33AYgKFdGpgcjYiIVHYW49KmlQoWFxdH+/btmTFjBgA2m43IyEhGjBjBmDFjCpUPDw/nxRdfZNiwYY5jffr0wdvbm/nz5wNw1113ERISwsyZM4ssYxgG4eHhPPPMMzz77LMApKWlERISwpw5c+jXr1+Rsebk5JCTk+PYT09PJzIykrS0NAICtJBsZfevn/fz6rd/EBPsx/+evklzH4mIXKPS09MJDAy84ud3qVuQoqKimDRpEomJiX8qwNzcXDZu3Ei3bt0uBuPiQrdu3Vi7dm2R5+Tk5ODl5eV0zNvbm1WrVjn2O3XqREJCArt37wZg69atrFq1ih49egBw4MABkpOTne4bGBhIXFxcsfcFmDx5MoGBgY4tMjKy9JUWU1htBnPWHARgSJcGSo5EROSKSp0gjRw5kkWLFtGwYUNuv/12PvvsM6eWlZI6ceIEVquVkJAQp+MhISEkJycXeU58fDxTpkxhz5492Gw2li9fzqJFizh27JijzJgxY+jXrx9NmjTB3d2d1q1bM3LkSB588EEAx7VLc1+AsWPHkpaW5tgOHz5c6jqLOZbvSObI6XPU9HGnd+sIs8MREZEq4KoSpC1btrB+/XqaNm3KiBEjCAsLY/jw4WzatKk8YnSYNm0aMTExNGnSBA8PD4YPH87gwYNxcblYjc8//5xPPvmEBQsWsGnTJubOncvbb7/N3Llz/9S9PT09CQgIcNqkapi16iAAf42rh5e7q7nBiIhIlXDVnbTbtGnDu+++y9GjR3nppZf417/+Rfv27YmNjWXWrFlcqWtTUFAQrq6upKSkOB1PSUkhNDS0yHPq1KnDkiVLyMrK4tChQ+zcuRM/Pz8aNmzoKPPcc885WpFatGjBww8/zNNPP83kyZMBHNcuzX2l6tp2JI31B0/h5mJhQMcos8MREZEq4qoTpLy8PD7//HP+8pe/8Mwzz9CuXTv+9a9/0adPH1544QXHI63ieHh40LZtWxISEhzHbDYbCQkJdOzY8bLnenl5ERERQX5+Pl999RX33HOP47WzZ886tSgBuLq6YrPZAGjQoAGhoaFO901PT2fdunVXvK9UPReWFbmrZRghAV5XKC0iImLnVtoTNm3axOzZs/n0009xcXFhwIABvPPOOzRp0sRRpnfv3rRv3/6K1xo1ahQDBw6kXbt2dOjQgalTp5KVlcXgwYMBGDBgABEREY7Wn3Xr1pGUlERsbCxJSUlMnDgRm83G6NGjHde8++67ee2116hXrx7Nmzdn8+bNTJkyhSFDhgBgsVgYOXIkr776KjExMTRo0IDx48cTHh5Or169SvvlkEosJT2b//x2FIChXRpeobSIiMhFpU6Q2rdvz+233877779Pr169cHd3L1SmQYMGxQ6Xv1Tfvn05fvw4EyZMIDk5mdjYWJYuXeroQJ2YmOjUGpSdnc24cePYv38/fn5+9OzZk3nz5lGjRg1HmenTpzN+/Hj+7//+j9TUVMLDw/nb3/7GhAkTHGVGjx5NVlYWjz32GGfOnKFLly4sXbq00Ag5qdrmrT1EntWgfVRNWtQNNDscERGpQko9D9KhQ4eoX79+ecVTZZR0HgUxR3aelY6TEzh9No8PHmpD9+vDzA5JREQqgXKbByk1NZV169YVOr5u3Tp+/fXX0l5OpFws3pzE6bN51K3pze3N1PleRERKp9QJ0rBhw4qcAygpKclphmsRsxiGwaxV9s7ZgzpF4eqiiSFFRKR0Sp0g7dixgzZt2hQ63rp1a3bs2FEmQYn8Gav2nmBPaia+Hq480F4znouISOmVOkHy9PQsNIcQwLFjx3BzK3Wfb5EyN/N869H97SIJ8Co8iEBERORKSp0g3XHHHY5lNy44c+YML7zwArfffnuZBidSWntTM1mx6zgWCwzuHGV2OCIiUkWVusnn7bff5qabbqJ+/fq0bt0agC1bthASEsK8efPKPECR0ph9fmLIbk1DqF/b1+RoRESkqip1ghQREcFvv/3GJ598wtatW/H29mbw4MH079+/yDmRRCrKmbO5fLXpCABDOjcwORoREanKrqrTkK+vL4899lhZxyLypyxYn0h2no1mYQHc0LCW2eGIiEgVdtW9qnfs2EFiYiK5ublOx//yl7/86aBESivPauPjNYcAGNKlARaLhvaLiMjVK3WCtH//fnr37s22bduwWCxcmIj7wgeS1Wot2whFSuC77ckkp2cT5OfJ3a00a7aIiPw5pR7F9tRTT9GgQQNSU1Px8fHh999/56effqJdu3asWLGiHEIUuTzDMBxD+x++oT6ebq4mRyQiIlVdqVuQ1q5dyw8//EBQUBAuLi64uLjQpUsXJk+ezJNPPsnmzZvLI06RYm1KPMPWw2fwcHPhwRvqmR2OiIhUA6VuQbJarfj7+wMQFBTE0aNHAahfvz67du0q2+hESuDCsiK9YsMJ8vM0ORoREakOSt2CdP3117N161YaNGhAXFwcb731Fh4eHvzzn/+kYcOG5RGjSLGOnD7Ld9uPAfbO2SIiImWh1AnSuHHjyMrKAmDSpEncdddd3HjjjdSuXZuFCxeWeYAilzNv7SFsBnRuVJsmoQFmhyMiItVEqROk+Ph4x/8bNWrEzp07OXXqFDVr1tTQaqlQWTn5LFifCGhiSBERKVul6oOUl5eHm5sb27dvdzpeq1YtJUdS4b7adISM7HwaBPlya+Ngs8MREZFqpFQJkru7O/Xq1dNcR2I6m81g9uqDgH1RWhcXJegiIlJ2Sj2K7cUXX+SFF17g1KlT5RGPSIn8uCuVAyeyCPByo0+bumaHIyIi1Uyp+yDNmDGDvXv3Eh4eTv369fH1dV4xfdOmTWUWnEhxLkwM2b9DPXw9r3rFHBERkSKV+pOlV69e5RCGSMn9cSydNftO4upiYUCnKLPDERGRaqjUCdJLL71UHnGIlNjs1fbWo+7XhxJRw9vkaEREpDoqdR8kETOdyMxhyRb77O0a2i8iIuWl1C1ILi4ulx3SrxFuUp4++SWR3HwbrSJr0KZeDbPDERGRaqrUCdLixYud9vPy8ti8eTNz587l5ZdfLrPARArKybcy75dDAAzt0kBzb4mISLkpdYJ0zz33FDp233330bx5cxYuXMjQoUPLJDCRgr7ZeowTmTmEBXrR4/pQs8MREZFqrMz6IN1www0kJCSU1eVEnBiGwazzQ/sHdIzC3VXd50REpPyUyafMuXPnePfdd4mIiCiLy4kU8sv+U+w4lo6Xuwv9O0SaHY6IiFRzpX7EVnBRWsMwyMjIwMfHh/nz55dpcCIXzDo/tL9Pm7rU8PEwORoREanuSp0gvfPOO04JkouLC3Xq1CEuLo6aNWuWaXAiAAdPZPH9HykADNbQfhERqQClTpAGDRpUDmGIFG/OmoMYBtzSuA6Ngv3MDkdERK4Bpe6DNHv2bL744otCx7/44gvmzp1bJkGJXJCenccXvx4G7EP7RUREKkKpE6TJkycTFBRU6HhwcDCvv/56mQQlcsHnGw6TlWslJtiPLo0Kf9+JiIiUh1InSImJiTRoUPgv+fr165OYmFgmQYkA5FttzF59EIAhmhhSREQqUKkTpODgYH777bdCx7du3Urt2rXLJCgRgOU7Ukg6c46aPu70bq0pJEREpOKUOkHq378/Tz75JD/++CNWqxWr1coPP/zAU089Rb9+/cojRrlGXRja/2BcfbzcXU2ORkREriWlTpBeeeUV4uLi6Nq1K97e3nh7e3PHHXdw2223XXUfpPfee4+oqCi8vLyIi4tj/fr1xZbNy8tj0qRJREdH4+XlRatWrVi6dKlTmaioKCwWS6Ft2LBhjjK33HJLodcff/zxq4pfyt5vR86w4eBp3F0tPNyxvtnhiIjINabUw/w9PDxYuHAhr776Klu2bMHb25sWLVpQv/7VfYgtXLiQUaNG8cEHHxAXF8fUqVOJj49n165dBAcHFyo/btw45s+fz0cffUSTJk1YtmwZvXv3Zs2aNbRu3RqADRs2YLVaHeds376d22+/nfvvv9/pWo8++iiTJk1y7Pv4+FxVHaTsXVhW5K6W4YQEeJkcjYiIXGsshmEYZgYQFxdH+/btmTFjBgA2m43IyEhGjBjBmDFjCpUPDw/nxRdfdGoN6tOnD97e3sXO5D1y5Ej+85//sGfPHkdH31tuuYXY2FimTp16VXGnp6cTGBhIWloaAQEBV3UNKVpKejad3/iBfJvBN8O70KJuoNkhiYhINVHSz+9SP2Lr06cPb775ZqHjb731VqEWmivJzc1l48aNdOvW7WJALi5069aNtWvXFnlOTk4OXl7OLQre3t6sWrWq2HvMnz+fIUOGFBoF9cknnxAUFMT111/P2LFjOXv2bLGx5uTkkJ6e7rRJ+fh47UHybQYdomopORIREVOUOkH66aef6NmzZ6HjPXr04KeffirVtU6cOIHVaiUkJMTpeEhICMnJyUWeEx8fz5QpU9izZw82m43ly5ezaNEijh07VmT5JUuWcObMmUIzgP/1r39l/vz5/Pjjj4wdO5Z58+bx0EMPFRvr5MmTCQwMdGyRkVowtTycy7WyYJ19uoghXaLMDUZERK5Zpe6DlJmZiYdH4cVC3d3dK6RVZdq0aTz66KM0adIEi8VCdHQ0gwcPZtasWUWWnzlzJj169CA8PNzp+GOPPeb4f4sWLQgLC6Nr167s27eP6OjoQtcZO3Yso0aNcuynp6crSSoHizcncfpsHnVrenN7s1CzwxERkWtUqVuQWrRowcKFCwsd/+yzz2jWrFmprhUUFISrqyspKSlOx1NSUggNLfrDsU6dOixZsoSsrCwOHTrEzp078fPzo2HDhoXKHjp0iO+//55HHnnkirHExcUBsHfv3iJf9/T0JCAgwGmTsmUYhmNo/6BOUbi6aGJIERExR6lbkMaPH8+9997Lvn37uO222wBISEhgwYIFfPnll6W6loeHB23btiUhIYFevXoB9k7aCQkJDB8+/LLnenl5ERERQV5eHl999RUPPPBAoTKzZ88mODiYO++884qxbNmyBYCwsLBS1UHKzs97TrA3NRM/Tzf6tlfrnIiImKfUCdLdd9/NkiVLeP311/nyyy/x9vamVatW/PDDD9SqVavUAYwaNYqBAwfSrl07OnTowNSpU8nKymLw4MEADBgwgIiICCZPngzAunXrSEpKIjY2lqSkJCZOnIjNZmP06NFO17XZbMyePZuBAwfi5uZczX379rFgwQJ69uxJ7dq1+e2333j66ae56aabaNmyZanrIGVj5vmh/fe3q4u/l7vJ0YiIyLWs1AkSwJ133ulolUlPT+fTTz/l2WefZePGjU7zD5VE3759OX78OBMmTCA5OZnY2FiWLl3q6LidmJiIi8vFJ4HZ2dmMGzeO/fv34+fnR8+ePZk3bx41atRwuu73339PYmIiQ4YMKXRPDw8Pvv/+e0cyFhkZSZ8+fRg3blwpvxJSVvamZrBy93EsFvvjNRERETNd9TxIP/30EzNnzuSrr74iPDyce++9lz59+tC+ffuyjrFS0jxIZeuFxdtYsC6RO5qF8M8B7cwOR0REqqmSfn6XqgUpOTmZOXPmMHPmTNLT03nggQfIyclhyZIlpe6gLXLB6axcFm06AsCQLg1MjkZERKQUo9juvvtuGjduzG+//cbUqVM5evQo06dPL8/Y5BqxYH0i2Xk2mocHENeg9P3YREREylqJW5C+++47nnzySZ544gliYmLKMya5huRZbXy89iAAQzo3KDTbuYiIiBlK3IK0atUqMjIyaNu2LXFxccyYMYMTJ06UZ2xyDfjvtmOkpOcQ5OfJXa00xYKIiFQOJU6QbrjhBj766COOHTvG3/72Nz777DPCw8Mdy31kZGSUZ5xSDRmGwazzQ/sHdKyPp5uryRGJiIjYlXombV9fX4YMGcKqVavYtm0bzzzzDG+88QbBwcH85S9/KY8YpZralHiarUfS8HBz4a9x9cwOR0RExKHUCdKlGjduzFtvvcWRI0f49NNPyyomuUZcmBiyd2wEQX6eJkcjIiJy0Z9KkC5wdXWlV69efP3112VxObkGHDl9lqXbkwEY3CXK3GBEREQKKJMESaS0Pl57CJsBXRoF0SRUE22KiEjlogRJKlxWTj6frk8EYIhaj0REpBJSgiQV7suNR8jIzqdhkC+3XBdsdjgiIiKFKEGSCmWzGcxebe+cPbhzFC4umhhSREQqHyVIUqF+2JnKwZNnCfBy4942dc0OR0REpEhKkKRCXRja3z+uHr6epVorWUREpMIoQZIKs+NoOmv3n8TVxcKAjlFmhyMiIlIsJUhSYS70Pep+fSgRNbxNjkZERKR4SpCkQhzPyOHfW44CMLRLA5OjERERuTwlSFIhPll3iFyrjdjIGrSpV9PscERERC5LCZKUu+w8K/N/OQSo9UhERKoGJUhS7r7ZepQTmbmEBXrR/fpQs8MRERG5IiVIUq4Mw2DW6oMADOgYhburvuVERKTy06eVlKu1+0/yx7F0vN1d6d8h0uxwRERESkQJkpSrWasOAtCnbQQ1fDzMDUZERKSElCBJuTl4IouEnSkADO6sztkiIlJ1KEGScjNnzUEMA25tXIfoOn5mhyMiIlJiSpCkXKSdy+PzXw8DMLRLQ5OjERERKR0lSFIuPt9wmLO5Vq4L8aNzo9pmhyMiIlIqSpCkzOVbbcxZcxCAIZ0bYLFYzA1IRESklJQgSZn7344Uks6co5avB71aR5gdjoiISKkpQZIyN2vVAQAejKuHl7urydGIiIiUnhIkKVNbD5/h10OncXe18PAN9c0OR0RE5KooQZIyNWu1vfXo7pbhBAd4mRyNiIjI1VGCJGUmOS2bb387BsCQLpoYUkREqi4lSFJmPl57kHybQYcGtbg+ItDscERERK6aEiQpE+dyrSxYnwjYh/aLiIhUZUqQpEws2nyEM2fziKzlze3NQswOR0RE5E9RgiR/ms1mOIb2D+rUAFcXTQwpIiJVW6VIkN577z2ioqLw8vIiLi6O9evXF1s2Ly+PSZMmER0djZeXF61atWLp0qVOZaKiorBYLIW2YcOGOcpkZ2czbNgwateujZ+fH3369CElJaXc6lid/bz3BPuOZ+Hn6cYD7eqaHY6IiMifZnqCtHDhQkaNGsVLL73Epk2baNWqFfHx8aSmphZZfty4cXz44YdMnz6dHTt28Pjjj9O7d282b97sKLNhwwaOHTvm2JYvXw7A/fff7yjz9NNP88033/DFF1+wcuVKjh49yr333lu+la2mZp5vPXqgXST+Xu4mRyMiIvLnWQzDMMwMIC4ujvbt2zNjxgwAbDYbkZGRjBgxgjFjxhQqHx4ezosvvujUGtSnTx+8vb2ZP39+kfcYOXIk//nPf9izZw8Wi4W0tDTq1KnDggULuO+++wDYuXMnTZs2Ze3atdxwww1XjDs9PZ3AwEDS0tIICAi4mqpXC3tSMrj9nZ+wWGDls7dSr7aP2SGJiIgUq6Sf36a2IOXm5rJx40a6devmOObi4kK3bt1Yu3Ztkefk5OTg5eU8AaG3tzerVq0q9h7z589nyJAhjkVTN27cSF5entN9mzRpQr169S573/T0dKdNYNbqgwDc0SxEyZGIiFQbpiZIJ06cwGq1EhLiPOopJCSE5OTkIs+Jj49nypQp7NmzB5vNxvLly1m0aBHHjh0rsvySJUs4c+YMgwYNchxLTk7Gw8ODGjVqlPi+kydPJjAw0LFFRkaWvKLV1OmsXBZtOgJoaL+IiFQvpvdBKq1p06YRExNDkyZN8PDwYPjw4QwePBgXl6KrMnPmTHr06EF4ePifuu/YsWNJS0tzbIcPH/5T16sOFqxPJCffRvPwADo0qGV2OCIiImXG1AQpKCgIV1fXQqPHUlJSCA0NLfKcOnXqsGTJErKysjh06BA7d+7Ez8+Phg0bFip76NAhvv/+ex555BGn46GhoeTm5nLmzJkS39fT05OAgACn7VqWm2/j47UHARjapYHj8aWIiEh1YGqC5OHhQdu2bUlISHAcs9lsJCQk0LFjx8ue6+XlRUREBPn5+Xz11Vfcc889hcrMnj2b4OBg7rzzTqfjbdu2xd3d3em+u3btIjEx8Yr3Fbvvth8jJT2HOv6e3NXyz7XOiYiIVDZuZgcwatQoBg4cSLt27ejQoQNTp04lKyuLwYMHAzBgwAAiIiKYPHkyAOvWrSMpKYnY2FiSkpKYOHEiNpuN0aNHO13XZrMxe/ZsBg4ciJubczUDAwMZOnQoo0aNolatWgQEBDBixAg6duxYohFs1zrDMBxD+wfcUB8Ptyr3pFZEROSyTE+Q+vbty/Hjx5kwYQLJycnExsaydOlSR8ftxMREp/5F2dnZjBs3jv379+Pn50fPnj2ZN29eoQ7X33//PYmJiQwZMqTI+77zzju4uLjQp08fcnJyiI+P5//9v/9XbvWsTjYeOs1vR9LwcHPhr3H1zA5HRESkzJk+D1JVdS3Pg/TE/I18tz2Zfu0jeaNPS7PDERERKbEqMQ+SVD2HT51l2e/2qRAGa2i/iIhUU0qQpFQ+XnsQmwFdGgXRONTf7HBERETKhRIkKbHMnHw+W2+f/2loF7UeiYhI9aUESUrsy18Pk5GTT8M6vtx8XR2zwxERESk3SpCkRKw2g9lrDgL2vkcuLpoYUkREqi8lSFIiP+xM5dDJswR4udGnTYTZ4YiIiJQrJUhSIrPOTwzZP64ePh6mT58lIiJSrpQgyRX9fjSNtftP4upiYWDHKLPDERERKXdKkOSKZq8+CECP60MJr+FtbjAiIiIVQAmSXFZqRjZfbzkKaGi/iIhcO5QgyWV98ksiuVYbrevVoHW9mmaHIyIiUiGUIEmxsvOszP/lEABDtKyIiIhcQ5QgSbG+3nqUk1m5hAd60eP6ULPDERERqTBKkKRIhmE4hvYP6BSFm6u+VURE5NqhTz0p0tp9J9mZnIG3uyv929czOxwREZEKpQRJijRrtb316L62dQn0cTc5GhERkYqlBEkKOXAii4SdqQAM7hxlbjAiIiImUIIkhcxZfQDDgNuaBNOwjp/Z4YiIiFQ4JUjiJO1cHl9sPAJoaL+IiFy7lCCJk4UbEjmba6VxiD+dG9U2OxwRERFTKEESh3yrjblrzk8M2SUKi8VickQiIiLmUIIkDst+TyHpzDlq+XpwT2yE2eGIiIiYRgmSOFwY2v9QXD283F1NjkZERMQ8SpAEgC2Hz7Dx0GncXS08dEN9s8MRERExlRIkAXAsK3J3q3CCA7xMjkZERMRcSpCEY2nn+O+2Y4CG9ouIiIASJAE+XnuIfJtBXINaXB8RaHY4IiIiplOCdI07l2tlwbpEAIZ0UeuRiIgIKEG65n216Qhp5/KoV8uHbk1DzA5HRESkUlCCdA2z2Qxmnx/aP6hTFK4umhhSREQElCBd037ac5x9x7Pw83Tj/nZ1zQ5HRESk0lCCdA2beX5of9/2kfh7uZscjYiISOWhBOkatTslg5/3nMDFYn+8JiIiIhcpQbpGXeh7dEezUCJr+ZgcjYiISOWiBOkadCorl0WbkgAN7RcRESmKEqRr0IJ1h8jJt3F9RADto2qaHY6IiEilY3qC9N577xEVFYWXlxdxcXGsX7++2LJ5eXlMmjSJ6OhovLy8aNWqFUuXLi1ULikpiYceeojatWvj7e1NixYt+PXXXx2vDxo0CIvF4rR17969XOpX2eTm2/h47SEAhnZpgMWiof0iIiIFuZl584ULFzJq1Cg++OAD4uLimDp1KvHx8ezatYvg4OBC5ceNG8f8+fP56KOPaNKkCcuWLaN3796sWbOG1q1bA3D69Gk6d+7MrbfeynfffUedOnXYs2cPNWs6t5R0796d2bNnO/Y9PT3Lt7KVxH+3HSM1I4dgf0/ubBFudjgiIiKVksUwDMOsm8fFxdG+fXtmzJgBgM1mIzIykhEjRjBmzJhC5cPDw3nxxRcZNmyY41ifPn3w9vZm/vz5AIwZM4bVq1fz888/F3vfQYMGcebMGZYsWXLVsaenpxMYGEhaWhoBAQFXfZ2KZBgGf5mxmm1JaTx7x3UMvy3G7JBEREQqVEk/v017xJabm8vGjRvp1q3bxWBcXOjWrRtr164t8pycnBy8vLycjnl7e7Nq1SrH/tdff027du24//77CQ4OpnXr1nz00UeFrrVixQqCg4Np3LgxTzzxBCdPnrxsvDk5OaSnpzttVc2vh06zLSkNTzcX+neoZ3Y4IiIilZZpCdKJEyewWq2EhDiv/xUSEkJycnKR58THxzNlyhT27NmDzWZj+fLlLFq0iGPHjjnK7N+/n/fff5+YmBiWLVvGE088wZNPPsncuXMdZbp3787HH39MQkICb775JitXrqRHjx5YrdZi4508eTKBgYGOLTIy8k9+BSrezJ/tQ/t7t46gtt+18UhRRETkapjaB6m0pk2bxqOPPkqTJk2wWCxER0czePBgZs2a5Shjs9lo164dr7/+OgCtW7dm+/btfPDBBwwcOBCAfv36Ocq3aNGCli1bEh0dzYoVK+jatWuR9x47diyjRo1y7Kenp1epJOnwqbP8b4c98dTQfhERkcszrQUpKCgIV1dXUlJSnI6npKQQGhpa5Dl16tRhyZIlZGVlcejQIXbu3Imfnx8NGzZ0lAkLC6NZs2ZO5zVt2pTExMRiY2nYsCFBQUHs3bu32DKenp4EBAQ4bVXJ3DUHsRlwY0wQ14X4mx2OiIhIpWZaguTh4UHbtm1JSEhwHLPZbCQkJNCxY8fLnuvl5UVERAT5+fl89dVX3HPPPY7XOnfuzK5du5zK7969m/r16xd7vSNHjnDy5EnCwsKusjaVW2ZOPgs3HAbUeiQiIlISps6DNGrUKD766CPmzp3LH3/8wRNPPEFWVhaDBw8GYMCAAYwdO9ZRft26dSxatIj9+/fz888/0717d2w2G6NHj3aUefrpp/nll194/fXX2bt3LwsWLOCf//ynY+RbZmYmzz33HL/88gsHDx4kISGBe+65h0aNGhEfH1+xX4AK8sWvh8nIyadhHV9ujqljdjgiIiKVnql9kPr27cvx48eZMGECycnJxMbGsnTpUkfH7cTERFxcLuZw2dnZjBs3jv379+Pn50fPnj2ZN28eNWrUcJRp3749ixcvZuzYsUyaNIkGDRowdepUHnzwQQBcXV357bffmDt3LmfOnCE8PJw77riDV155pVrOhWS1GcxefRCAwZ0b4OKiiSFFRESuxNR5kKqyqjIP0v9+T+axeRsJ9HZn7djb8PGoUv3yRUREylSlnwdJKsas1fah/f071FNyJCIiUkJKkKqx34+m8cv+U7i6WBjYqfhO6iIiIuJMCVI1NmvVQQB6tggjLNDb3GBERESqECVI1VRqRjbfbD0KwJDOUeYGIyIiUsUoQaqm5v+SSK7VRpt6NWhdr6bZ4YiIiFQpSpCqoew8K5/8cgjQxJAiIiJXQwlSNfT1lqOczMolPNCL7s2LXrZFREREiqcEqZoxDMMxtH9gpyjcXPUWi4iIlJY+PauZNftOsjM5Ax8PV/q1r2d2OCIiIlWSEqRqZtYqe+vRfW3rEujjbnI0IiIiVZMSpGpk//FMEnamAjCoU5S5wYiIiFRhSpCqkTlrDgLQtUkwDev4mRuMiIhIFaYEqZpIO5vHF78eATS0X0RE5M9SglRNfLYhkXN5VpqE+tMpurbZ4YiIiFRpSpCqgXyrjbnnH68N6dwAi8VibkAiIiJVnBKkamDp78kcTcumtq8Hf4kNNzscERGRKk8JUjVwYWj/gzfUx8vd1eRoREREqj4lSFXc5sTTbEo8g4erCw/doIkhRUREyoISpCpu1uqDANzdKpxgfy9zgxEREakmlCBVYcfSzvHfbccAGNw5ytxgREREqhElSFXY3DWHsNoM4hrU4vqIQLPDERERqTaUIFVRZ3Pz+XR9IgBDNTGkiIhImVKCVEV9tSmJtHN51KvlQ9emIWaHIyIiUq0oQaqCbDaD2avtQ/sHd47C1UUTQ4qIiJQlJUhV0Mo9x9l/PAt/TzfubxdpdjgiIiLVjhKkKujCxJB920fi5+lmcjQiIiLVjxKkKmZ3SgY/7zmBiwUGdooyOxwREZFqSQlSFXOh9eiOZqFE1vIxORoREZHqSQlSFXIyM4dFm5MAGHqjhvaLiIiUFyVIVcin6xPJzbfRIiKQdvVrmh2OiIhItaUEqYrIzbfx8dpDgH1iSItFQ/tFRETKixKkKuLbbUdJzcgh2N+Tni3CzA5HRESkWlOCVAUYhsHM852zB3Ssj4eb3jYREZHypE/aKmDDwdNsT0rH082Fv8bVNzscERGRak8JUhUwc9V+AO5tE0EtXw+ToxEREan+lCBVcoknz/K/HSkADOmsof0iIiIVQQlSJTd37UEMA26MCSImxN/scERERK4JpidI7733HlFRUXh5eREXF8f69euLLZuXl8ekSZOIjo7Gy8uLVq1asXTp0kLlkpKSeOihh6hduzbe3t60aNGCX3/91fG6YRhMmDCBsLAwvL296datG3v27CmX+v0ZGdl5LNxwGIAhXdR6JCIiUlFMTZAWLlzIqFGjeOmll9i0aROtWrUiPj6e1NTUIsuPGzeODz/8kOnTp7Njxw4ef/xxevfuzebNmx1lTp8+TefOnXF3d+e7775jx44d/OMf/6BmzYsTK7711lu8++67fPDBB6xbtw5fX1/i4+PJzs4u9zqXxhe/HiEzJ5/oOr7cHFPH7HBERESuGRbDMAyzbh4XF0f79u2ZMWMGADabjcjISEaMGMGYMWMKlQ8PD+fFF19k2LBhjmN9+vTB29ub+fPnAzBmzBhWr17Nzz//XOQ9DcMgPDycZ555hmeffRaAtLQ0QkJCmDNnDv369StR7Onp6QQGBpKWlkZAQECp6l0SVpvBLW//yOFT53i11/U8dINGr4mIiPxZJf38Nq0FKTc3l40bN9KtW7eLwbi40K1bN9auXVvkOTk5OXh5eTkd8/b2ZtWqVY79r7/+mnbt2nH//fcTHBxM69at+eijjxyvHzhwgOTkZKf7BgYGEhcXV+x9L9w7PT3daStP3/+RwuFT5wj0dqdPm7rlei8RERFxZlqCdOLECaxWKyEhIU7HQ0JCSE5OLvKc+Ph4pkyZwp49e7DZbCxfvpxFixZx7NgxR5n9+/fz/vvvExMTw7Jly3jiiSd48sknmTt3LoDj2qW5L8DkyZMJDAx0bJGRkVdV75KadX5iyL/G1cPbw7Vc7yUiIiLOTO+kXRrTpk0jJiaGJk2a4OHhwfDhwxk8eDAuLherYbPZaNOmDa+//jqtW7fmscce49FHH+WDDz74U/ceO3YsaWlpju3w4cN/tjrF2p6UxroDp3BzsTCgox6tiYiIVDTTEqSgoCBcXV1JSUlxOp6SkkJoaGiR59SpU4clS5aQlZXFoUOH2LlzJ35+fjRs2NBRJiwsjGbNmjmd17RpUxITEwEc1y7NfQE8PT0JCAhw2srLrNX21qOeLcIIC/Qut/uIiIhI0UxLkDw8PGjbti0JCQmOYzabjYSEBDp27HjZc728vIiIiCA/P5+vvvqKe+65x/Fa586d2bVrl1P53bt3U7++vSWmQYMGhIaGOt03PT2ddevWXfG+FSE1PZtvth4FNLRfRETELG5m3nzUqFEMHDiQdu3a0aFDB6ZOnUpWVhaDBw8GYMCAAURERDB58mQA1q1bR1JSErGxsSQlJTFx4kRsNhujR492XPPpp5+mU6dOvP766zzwwAOsX7+ef/7zn/zzn/8EwGKxMHLkSF599VViYmJo0KAB48ePJzw8nF69elX416Cg+b8cIs9q0LZ+TWIja5gdjoiIyDXJ1ASpb9++HD9+nAkTJpCcnExsbCxLly51dKBOTEx06l+UnZ3NuHHj2L9/P35+fvTs2ZN58+ZRo0YNR5n27duzePFixo4dy6RJk2jQoAFTp07lwQcfdJQZPXo0WVlZPPbYY5w5c4YuXbqwdOnSQiPkzJCenY+bi0XLioiIiJjI1HmQqrLynAcpOS2bID8P3FyrVB96ERGRSq+kn9+mtiBJ0UIDzW/JEhERuZapiUJERESkACVIIiIiIgUoQRIREREpQAmSiIiISAFKkEREREQKUIIkIiIiUoASJBEREZEClCCJiIiIFKAESURERKQAJUgiIiIiBShBEhERESlACZKIiIhIAUqQRERERApwMzuAqsowDADS09NNjkRERERK6sLn9oXP8eIoQbpKGRkZAERGRpociYiIiJRWRkYGgYGBxb5uMa6UQkmRbDYbR48exd/fH4vFUmbXTU9PJzIyksOHDxMQEFBm161Mqnsdq3v9oPrXUfWr+qp7HVW/q2cYBhkZGYSHh+PiUnxPI7UgXSUXFxfq1q1bbtcPCAiolt/0l6rudazu9YPqX0fVr+qr7nVU/a7O5VqOLlAnbREREZEClCCJiIiIFKAEqZLx9PTkpZdewtPT0+xQyk11r2N1rx9U/zqqflVfda+j6lf+1ElbREREpAC1IImIiIgUoARJREREpAAlSCIiIiIFKEESERERKUAJkgnee+89oqKi8PLyIi4ujvXr11+2/BdffEGTJk3w8vKiRYsW/Pe//62gSK9eaeo4Z84cLBaL0+bl5VWB0ZbOTz/9xN133014eDgWi4UlS5Zc8ZwVK1bQpk0bPD09adSoEXPmzCn3OK9Waeu3YsWKQu+fxWIhOTm5YgIupcmTJ9O+fXv8/f0JDg6mV69e7Nq164rnVZWfw6upX1X7GXz//fdp2bKlYxLBjh078t133132nKry/kHp61fV3r+C3njjDSwWCyNHjrxsuYp+D5UgVbCFCxcyatQoXnrpJTZt2kSrVq2Ij48nNTW1yPJr1qyhf//+DB06lM2bN9OrVy969erF9u3bKzjykittHcE+W+qxY8cc26FDhyow4tLJysqiVatWvPfeeyUqf+DAAe68805uvfVWtmzZwsiRI3nkkUdYtmxZOUd6dUpbvwt27drl9B4GBweXU4R/zsqVKxk2bBi//PILy5cvJy8vjzvuuIOsrKxiz6lKP4dXUz+oWj+DdevW5Y033mDjxo38+uuv3Hbbbdxzzz38/vvvRZavSu8flL5+ULXev0tt2LCBDz/8kJYtW162nCnvoSEVqkOHDsawYcMc+1ar1QgPDzcmT55cZPkHHnjAuPPOO52OxcXFGX/729/KNc4/o7R1nD17thEYGFhB0ZUtwFi8ePFly4wePdpo3ry507G+ffsa8fHx5RhZ2ShJ/X788UcDME6fPl0hMZW11NRUAzBWrlxZbJmq+HN4QUnqV5V/Bi+oWbOm8a9//avI16ry+3fB5epXVd+/jIwMIyYmxli+fLlx8803G0899VSxZc14D9WCVIFyc3PZuHEj3bp1cxxzcXGhW7durF27tshz1q5d61QeID4+vtjyZruaOgJkZmZSv359IiMjr/iXUlVT1d7DqxUbG0tYWBi33347q1evNjucEktLSwOgVq1axZapyu9hSeoHVfdn0Gq18tlnn5GVlUXHjh2LLFOV37+S1A+q5vs3bNgw7rzzzkLvTVHMeA+VIFWgEydOYLVaCQkJcToeEhJSbH+N5OTkUpU329XUsXHjxsyaNYt///vfzJ8/H5vNRqdOnThy5EhFhFzuinsP09PTOXfunElRlZ2wsDA++OADvvrqK7766isiIyO55ZZb2LRpk9mhXZHNZmPkyJF07tyZ66+/vthyVe3n8IKS1q8q/gxu27YNPz8/PD09efzxx1m8eDHNmjUrsmxVfP9KU7+q+P599tlnbNq0icmTJ5eovBnvoVu5XVmkhDp27Oj0l1GnTp1o2rQpH374Ia+88oqJkUlJNG7cmMaNGzv2O3XqxL59+3jnnXeYN2+eiZFd2bBhw9i+fTurVq0yO5RyUdL6VcWfwcaNG7NlyxbS0tL48ssvGThwICtXriw2iahqSlO/qvb+HT58mKeeeorly5dX6s7kSpAqUFBQEK6urqSkpDgdT0lJITQ0tMhzQkNDS1XebFdTx4Lc3d1p3bo1e/fuLY8QK1xx72FAQADe3t4mRVW+OnToUOmTjuHDh/Of//yHn376ibp16162bFX7OYTS1a+gqvAz6OHhQaNGjQBo27YtGzZsYNq0aXz44YeFylbF96809Suosr9/GzduJDU1lTZt2jiOWa1WfvrpJ2bMmEFOTg6urq5O55jxHuoRWwXy8PCgbdu2JCQkOI7ZbDYSEhKKfbbcsWNHp/IAy5cvv+yzaDNdTR0LslqtbNu2jbCwsPIKs0JVtfewLGzZsqXSvn+GYTB8+HAWL17MDz/8QIMGDa54TlV6D6+mfgVVxZ9Bm81GTk5Oka9VpfevOJerX0GV/f3r2rUr27ZtY8uWLY6tXbt2PPjgg2zZsqVQcgQmvYfl1v1bivTZZ58Znp6expw5c4wdO3YYjz32mFGjRg0jOTnZMAzDePjhh40xY8Y4yq9evdpwc3Mz3n77beOPP/4wXnrpJcPd3d3Ytm2bWVW4otLW8eWXXzaWLVtm7Nu3z9i4caPRr18/w8vLy/j999/NqsJlZWRkGJs3bzY2b95sAMaUKVOMzZs3G4cOHTIMwzDGjBljPPzww47y+/fvN3x8fIznnnvO+OOPP4z33nvPcHV1NZYuXWpWFS6rtPV75513jCVLlhh79uwxtm3bZjz11FOGi4uL8f3335tVhct64oknjMDAQGPFihXGsWPHHNvZs2cdZaryz+HV1K+q/QyOGTPGWLlypXHgwAHjt99+M8aMGWNYLBbjf//7n2EYVfv9M4zS16+qvX9FKTiKrTK8h0qQTDB9+nSjXr16hoeHh9GhQwfjl19+cbx28803GwMHDnQq//nnnxvXXXed4eHhYTRv3tz49ttvKzji0itNHUeOHOkoGxISYvTs2dPYtGmTCVGXzIVh7QW3C3UaOHCgcfPNNxc6JzY21vDw8DAaNmxozJ49u8LjLqnS1u/NN980oqOjDS8vL6NWrVrGLbfcYvzwww/mBF8CRdUNcHpPqvLP4dXUr6r9DA4ZMsSoX7++4eHhYdSpU8fo2rWrI3kwjKr9/hlG6etX1d6/ohRMkCrDe2gxDMMov/YpERERkapHfZBEREREClCCJCIiIlKAEiQRERGRApQgiYiIiBSgBElERESkACVIIiIiIgUoQRIREREpQAmSiIiISAFKkEREyojFYmHJkiVmhyEiZUAJkohUC4MGDcJisRTaunfvbnZoIlIFuZkdgIhIWenevTuzZ892Oubp6WlSNCJSlakFSUSqDU9PT0JDQ522mjVrAvbHX++//z49evTA29ubhg0b8uWXXzqdv23bNm677Ta8vb2pXbs2jz32GJmZmU5lZs2aRfPmzfH09CQsLIzhw4c7vX7ixAl69+6Nj48PMTExfP311+VbaREpF0qQROSaMX78ePr06cPWrVt58MEH6devH3/88QcAWVlZxMfHU7NmTTZs2MAXX3zB999/75QAvf/++wwbNozHHnuMbdu28fXXX9OoUSOne7z88ss88MAD/Pbbb/Ts2ZMHH3yQU6dOVWg9RaQMGCIi1cDAgQMNV1dXw9fX12l77bXXDMMwDMB4/PHHnc6Ji4sznnjiCcMwDOOf//ynUbNmTSMzM9Px+rfffmu4uLgYycnJhmEYRnh4uPHiiy8WGwNgjBs3zrGfmZlpAMZ3331XZvUUkYqhPkgiUm3ceuutvP/++07HatWq5fh/x44dnV7r2LEjW7ZsAeCPP/6gVatW+Pr6Ol7v3LkzNpuNXbt2YbFYOHr0KF27dr1sDC1btnT839fXl4CAAFJTU6+2SiJiEiVIIlJt+Pr6FnrkVVa8vb1LVM7d3d1p32KxYLPZyiMkESlH6oMkIteMX375pdB+06ZNAWjatClbt24lKyvL8frq1atxcXGhcePG+Pv7ExUVRUJCQoXGLCLmUAuSiFQbOTk5JCcnOx1zc3MjKCgIgC+++IJ27drRpUsXPvnkE9avX8/MmTMBePDBB3nppZcYOHAgEydO5Pjx44wYMYKHH36YkJAQACZOnMjjjz9OcHAwPXr0ICMjg9WrVzNixIiKraiIlDslSCJSbSxdupSwsDCnY40bN2bnzp2AfYTZZ599xv/93/8RFhbGp59+SrNmzQDw8fFh2bJlPPXUU7Rv3x4fHx/69OnDlClTHNcaOHAg2dnZvPPOOzz77LMEBQVx3333VVwFRaTCWAzDMMwOQkSkvFksFhYvXkyvXr3MDkVEqgD1QRIREREpQAmSiIiISAHqgyQi1wT1JhCR0lALkoiIiEgBSpBEREREClCCJCIiIlKAEiQRERGRApQgiYiIiBSgBElERESkACVIIiIiIgUoQRIREREp4P8Dh12ykswI38sAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import tensorflow as tf\n", "from tensorflow.keras.datasets import mnist\n", @@ -202,9 +288,153 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generation # 5. Train Loss: 2.28. Train Acc (Test Acc): 11.00% (13.20%)\n", + "Generation # 10. Train Loss: 2.28. Train Acc (Test Acc): 13.00% (17.40%)\n", + "Generation # 15. Train Loss: 2.26. Train Acc (Test Acc): 26.00% (31.80%)\n", + "Generation # 20. Train Loss: 2.22. Train Acc (Test Acc): 30.00% (39.40%)\n", + "Generation # 25. Train Loss: 2.16. Train Acc (Test Acc): 44.00% (42.60%)\n", + "Generation # 30. Train Loss: 2.16. Train Acc (Test Acc): 36.00% (49.60%)\n", + "Generation # 35. Train Loss: 2.11. Train Acc (Test Acc): 52.00% (56.20%)\n", + "Generation # 40. Train Loss: 2.01. Train Acc (Test Acc): 51.00% (52.60%)\n", + "Generation # 45. Train Loss: 1.88. Train Acc (Test Acc): 58.00% (59.40%)\n", + "Generation # 50. Train Loss: 1.67. Train Acc (Test Acc): 61.00% (63.00%)\n", + "Generation # 55. Train Loss: 1.63. Train Acc (Test Acc): 57.00% (63.60%)\n", + "Generation # 60. Train Loss: 1.25. Train Acc (Test Acc): 69.00% (69.20%)\n", + "Generation # 65. Train Loss: 1.12. Train Acc (Test Acc): 71.00% (68.00%)\n", + "Generation # 70. Train Loss: 0.83. Train Acc (Test Acc): 83.00% (73.80%)\n", + "Generation # 75. Train Loss: 0.84. Train Acc (Test Acc): 76.00% (80.00%)\n", + "Generation # 80. Train Loss: 0.67. Train Acc (Test Acc): 76.00% (82.60%)\n", + "Generation # 85. Train Loss: 0.60. Train Acc (Test Acc): 81.00% (87.80%)\n", + "Generation # 90. Train Loss: 0.45. Train Acc (Test Acc): 87.00% (82.00%)\n", + "Generation # 95. Train Loss: 0.46. Train Acc (Test Acc): 87.00% (85.00%)\n", + "Generation # 100. Train Loss: 0.29. Train Acc (Test Acc): 95.00% (86.20%)\n", + "Generation # 105. Train Loss: 0.39. Train Acc (Test Acc): 90.00% (86.80%)\n", + "Generation # 110. Train Loss: 0.37. Train Acc (Test Acc): 89.00% (88.40%)\n", + "Generation # 115. Train Loss: 0.59. Train Acc (Test Acc): 82.00% (85.00%)\n", + "Generation # 120. Train Loss: 0.36. Train Acc (Test Acc): 89.00% (88.60%)\n", + "Generation # 125. Train Loss: 0.34. Train Acc (Test Acc): 91.00% (85.40%)\n", + "Generation # 130. Train Loss: 0.38. Train Acc (Test Acc): 89.00% (87.00%)\n", + "Generation # 135. Train Loss: 0.41. Train Acc (Test Acc): 86.00% (88.80%)\n", + "Generation # 140. Train Loss: 0.22. Train Acc (Test Acc): 95.00% (90.20%)\n", + "Generation # 145. Train Loss: 0.41. Train Acc (Test Acc): 89.00% (91.00%)\n", + "Generation # 150. Train Loss: 0.39. Train Acc (Test Acc): 88.00% (90.00%)\n", + "Generation # 155. Train Loss: 0.35. Train Acc (Test Acc): 88.00% (87.00%)\n", + "Generation # 160. Train Loss: 0.44. Train Acc (Test Acc): 89.00% (90.00%)\n", + "Generation # 165. Train Loss: 0.39. Train Acc (Test Acc): 89.00% (89.60%)\n", + "Generation # 170. Train Loss: 0.30. Train Acc (Test Acc): 91.00% (91.40%)\n", + "Generation # 175. Train Loss: 0.37. Train Acc (Test Acc): 87.00% (92.60%)\n", + "Generation # 180. Train Loss: 0.33. Train Acc (Test Acc): 87.00% (92.20%)\n", + "Generation # 185. Train Loss: 0.24. Train Acc (Test Acc): 92.00% (90.80%)\n", + "Generation # 190. Train Loss: 0.36. Train Acc (Test Acc): 88.00% (90.40%)\n", + "Generation # 195. Train Loss: 0.29. Train Acc (Test Acc): 93.00% (90.20%)\n", + "Generation # 200. Train Loss: 0.29. Train Acc (Test Acc): 92.00% (92.60%)\n", + "Generation # 205. Train Loss: 0.43. Train Acc (Test Acc): 86.00% (91.60%)\n", + "Generation # 210. Train Loss: 0.25. Train Acc (Test Acc): 94.00% (91.20%)\n", + "Generation # 215. Train Loss: 0.49. Train Acc (Test Acc): 83.00% (90.80%)\n", + "Generation # 220. Train Loss: 0.22. Train Acc (Test Acc): 93.00% (91.60%)\n", + "Generation # 225. Train Loss: 0.21. Train Acc (Test Acc): 93.00% (91.20%)\n", + "Generation # 230. Train Loss: 0.23. Train Acc (Test Acc): 92.00% (91.80%)\n", + "Generation # 235. Train Loss: 0.22. Train Acc (Test Acc): 92.00% (93.40%)\n", + "Generation # 240. Train Loss: 0.12. Train Acc (Test Acc): 97.00% (93.80%)\n", + "Generation # 245. Train Loss: 0.35. Train Acc (Test Acc): 90.00% (92.00%)\n", + "Generation # 250. Train Loss: 0.27. Train Acc (Test Acc): 91.00% (90.60%)\n", + "Generation # 255. Train Loss: 0.22. Train Acc (Test Acc): 92.00% (91.60%)\n", + "Generation # 260. Train Loss: 0.28. Train Acc (Test Acc): 89.00% (93.20%)\n", + "Generation # 265. Train Loss: 0.27. Train Acc (Test Acc): 92.00% (92.40%)\n", + "Generation # 270. Train Loss: 0.24. Train Acc (Test Acc): 92.00% (92.20%)\n", + "Generation # 275. Train Loss: 0.26. Train Acc (Test Acc): 91.00% (93.80%)\n", + "Generation # 280. Train Loss: 0.42. Train Acc (Test Acc): 89.00% (90.60%)\n", + "Generation # 285. Train Loss: 0.30. Train Acc (Test Acc): 88.00% (92.00%)\n", + "Generation # 290. Train Loss: 0.39. Train Acc (Test Acc): 94.00% (93.20%)\n", + "Generation # 295. Train Loss: 0.11. Train Acc (Test Acc): 97.00% (93.20%)\n", + "Generation # 300. Train Loss: 0.24. Train Acc (Test Acc): 91.00% (95.00%)\n", + "Generation # 305. Train Loss: 0.11. Train Acc (Test Acc): 98.00% (94.20%)\n", + "Generation # 310. Train Loss: 0.26. Train Acc (Test Acc): 93.00% (95.20%)\n", + "Generation # 315. Train Loss: 0.27. Train Acc (Test Acc): 91.00% (94.40%)\n", + "Generation # 320. Train Loss: 0.12. Train Acc (Test Acc): 97.00% (94.00%)\n", + "Generation # 325. Train Loss: 0.20. Train Acc (Test Acc): 95.00% (93.20%)\n", + "Generation # 330. Train Loss: 0.24. Train Acc (Test Acc): 91.00% (91.00%)\n", + "Generation # 335. Train Loss: 0.22. Train Acc (Test Acc): 93.00% (93.20%)\n", + "Generation # 340. Train Loss: 0.15. Train Acc (Test Acc): 95.00% (94.60%)\n", + "Generation # 345. Train Loss: 0.24. Train Acc (Test Acc): 92.00% (94.80%)\n", + "Generation # 350. Train Loss: 0.25. Train Acc (Test Acc): 90.00% (94.20%)\n", + "Generation # 355. Train Loss: 0.18. Train Acc (Test Acc): 94.00% (94.40%)\n", + "Generation # 360. Train Loss: 0.28. Train Acc (Test Acc): 93.00% (95.80%)\n", + "Generation # 365. Train Loss: 0.26. Train Acc (Test Acc): 90.00% (95.60%)\n", + "Generation # 370. Train Loss: 0.17. Train Acc (Test Acc): 98.00% (96.60%)\n", + "Generation # 375. Train Loss: 0.23. Train Acc (Test Acc): 94.00% (96.80%)\n", + "Generation # 380. Train Loss: 0.19. Train Acc (Test Acc): 96.00% (95.80%)\n", + "Generation # 385. Train Loss: 0.13. Train Acc (Test Acc): 96.00% (94.60%)\n", + "Generation # 390. Train Loss: 0.14. Train Acc (Test Acc): 96.00% (93.80%)\n", + "Generation # 395. Train Loss: 0.15. Train Acc (Test Acc): 95.00% (96.00%)\n", + "Generation # 400. Train Loss: 0.26. Train Acc (Test Acc): 94.00% (94.60%)\n", + "Generation # 405. Train Loss: 0.23. Train Acc (Test Acc): 94.00% (93.80%)\n", + "Generation # 410. Train Loss: 0.10. Train Acc (Test Acc): 97.00% (93.00%)\n", + "Generation # 415. Train Loss: 0.17. Train Acc (Test Acc): 95.00% (95.20%)\n", + "Generation # 420. Train Loss: 0.13. Train Acc (Test Acc): 95.00% (94.20%)\n", + "Generation # 425. Train Loss: 0.20. Train Acc (Test Acc): 97.00% (95.20%)\n", + "Generation # 430. Train Loss: 0.13. Train Acc (Test Acc): 95.00% (93.60%)\n", + "Generation # 435. Train Loss: 0.14. Train Acc (Test Acc): 95.00% (94.80%)\n", + "Generation # 440. Train Loss: 0.25. Train Acc (Test Acc): 95.00% (96.60%)\n", + "Generation # 445. Train Loss: 0.11. Train Acc (Test Acc): 95.00% (93.20%)\n", + "Generation # 450. Train Loss: 0.11. Train Acc (Test Acc): 98.00% (95.40%)\n", + "Generation # 455. Train Loss: 0.15. Train Acc (Test Acc): 96.00% (97.40%)\n", + "Generation # 460. Train Loss: 0.10. Train Acc (Test Acc): 96.00% (95.80%)\n", + "Generation # 465. Train Loss: 0.07. Train Acc (Test Acc): 98.00% (95.40%)\n", + "Generation # 470. Train Loss: 0.25. Train Acc (Test Acc): 92.00% (97.00%)\n", + "Generation # 475. Train Loss: 0.19. Train Acc (Test Acc): 96.00% (95.60%)\n", + "Generation # 480. Train Loss: 0.17. Train Acc (Test Acc): 96.00% (96.60%)\n", + "Generation # 485. Train Loss: 0.17. Train Acc (Test Acc): 96.00% (94.60%)\n", + "Generation # 490. Train Loss: 0.23. Train Acc (Test Acc): 94.00% (96.40%)\n", + "Generation # 495. Train Loss: 0.11. Train Acc (Test Acc): 97.00% (95.00%)\n", + "Generation # 500. Train Loss: 0.25. Train Acc (Test Acc): 91.00% (96.40%)\n" + ] + }, + { + "data": { + "image/png": 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", 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NGzcWS0tLcXZ2lo4dO8r+/fszlfvll18EgLz66qtZ3heiosa1pYiIKN+2b9+OgQMH4siRI+qUAETGxuCGiIjyrW/fvrhy5QoCAwOfam4eooLEnBsiIsqzDRs24Pz589i5cycWLFjAwIaKFbbcEBFRnmk0GtjZ2WHIkCFYsmQJzMz4XZmKD/42EhFRnvF7MRVnnOeGiIiIShUGN0RERFSqlLluKa1Wi/v378Pe3p4JcERERCWEiCAqKgre3t6ZVqzPqMwFN/fv34ePj4+xq0FERET5cOfOHVSoUCHbMmUuuLG3tweg3BwHBwcj14aIiIhyIzIyEj4+PurneHbKXHCj64pycHBgcENERFTC5CalhAnFREREVKowuCEiIqJSxajBzZEjR9CvXz94e3tDo9Fg27ZtOR5z6NAhNGnSBJaWlqhWrRpWrVpV6PUkIiKiksOowU1MTAwaNmyIRYsW5ar8jRs30KdPH3Tu3Bn+/v6YOHEiXnnlFezdu7eQa0pEREQlhVETinv16oVevXrluvySJUvg6+uLr7/+GgBQu3ZtHD16FN988w38/PwKq5pERERUgpSonJvjx4+jW7duetv8/Pxw/PjxLI9JSEhAZGSk3oOIiIhKrxIV3ISEhMDT01Nvm6enJyIjIxEXF2fwmDlz5sDR0VF9cAI/IiKi0q1EBTf58eGHHyIiIkJ93Llzx9hVIiIiokJUoibxK1euHB48eKC37cGDB3BwcIC1tbXBYywtLWFpaVkU1SMiIqJioES13LRu3RoHDhzQ27Z//360bt3aSDUiIiKi4saowU10dDT8/f3h7+8PQBnq7e/vj9u3bwNQupRGjBihln/99ddx/fp1vPfee7h69Sq+//57/PLLL3j77beNUX0iIiIqhowa3Jw6dQqNGzdG48aNAQCTJk1C48aNMW3aNABAcHCwGugAgK+vL3bu3In9+/ejYcOG+Prrr7Fs2TIOAyciIiKVRkTE2JUoSpGRkXB0dERERAQXziSiYkVEoNVqYWpqauyqFInk5GSYmZWo1M8ipdVqkZiYCCsrqzwdFx8fD0tLy1wtMFmS5OXzu0Tl3BARlWY9evSAr68vIiIijF2VQvfRRx/B3t4eZ8+eNXZViq1BgwbBy8sLV65cyfUxgYGBqFixIvr371+INSv+GNwQERUDERER+OOPP3Dnzh0cOnTI2NUpdNu3b0d8fDzXB8zCpUuXsGPHDoSHh+PNN99EbjtZJk6ciNDQUOzatQuxsbGFXMvii8ENEVExcPnyZfXno0ePGrEmhS8lJQWBgYEAgN9//z3XH9xlybJly9SfDxw4gC1btgChocDAgUC6fent3LkTO3fuBKB0aZ0/f74oqlosMbghomItODgY4eHhxq7GU3v48CFCQ0Oz3H/x4kX157/++itP575+/XqJ+pZ+584dJCQkAFDqHhAQYOQaKQIDAxETE5OnY5KTk/Xeu4KQkJCAn376CQDQvn17AMqAm+S33wa2bwdeew04ciTTMRMnTgQAmJgoH+1nzpxRdooAQUEFWkedyMjIYjk5LoMbIiq2Hjx4gNq1a2daU66kiYiIQP369dGkSRP1Qz2jS5cuqT+fPn0618HKhQsXUL16dXTt2hUpKSkFUt/Cdu3aNb3nf65bB+zfDyxaBJw6ZZQ6HTt2DDVr1kTPnj2h1Wpzfdxrr72G+vXrY8GCBQVWl61bt+Lx48coX748du7ciYoVK8L79m2YrV2rFNBqgRdeAMLC1GPmzZuHwMBAeHl54c033wSg/B5BBBgyBKhWDZgypcDqCCgJ8D169ED16tVx48aNAj33U5MyJiIiQgBIRESEsatCRDlYt26dABAA8vjxY2NXJ9++//579XWcPn3aYJlu3bqpZQDIn3/+matzz5s3Tz3mhx9+KMBaF57vvvtOAIiJiYkAkAA7OxHlY1jExETk22+LvE7/+9//1Pu4YsWKXB1z7Ngx9Rg7Ozu5f/9+7i/47bci06eLaLWZdnXp0kUAyNSpU0VEZNOmTbIp9f5E9esnUrOmcq/69hXRauXOnTtiY2MjAOTnn3+WLVu2CABp1KiRyJ07IhpN2v1dudJwfU6eFBkzRuTq1Vy/hPSvP7f37Gnk5fObwQ0RFVtjx45V/3j+9ddfxq5OvjVu3Fh9HUuXLjVYxsvLSwCIr6+vAJBZs2bl6twvvviiem5XV1cJCwsryKoXPK1WfurRQ0wAeeaZZwSALNdoJLl6dZHWrdM+hMeNE0lKKpIqhYaGioWFhXofPTw8JDw8PNtjkpOT1ffV1NRUAMjw4cNzd8Fdu9Je55EjersCAwMFgGg0Grl586aIiGi1WvHr2FE+AGRkr14iZ8+KWFmJfPCBSFKSGpi1bdtWtFqt3Lp1SwCImZmZxMfHizx4IFK/vnI9c/NM15QNG0QsLZX9DRuKJCfn6mW89NJL6j0bP368svH8eZHLl3N3H/IoL5/fnGCAiIqt9Im1Fy9eRLt27YxYm/w5ffq03nBnNQ8inSdPniA4OBgAMGbMGEyZMiXXScW681lbWyMsLAzTpk3Dd999VwA1LwRxccDLL2P4vn0IA2DbsyeuXLmC0VeuwO6TT/D8c88BX34JfPABsGgRkq5exS/PPINYc/NsT2tjY4N+/frle+6yn3/+GYmJiWjUqBHi4+Nx9epVzJgxA998802Wxyxbtgxnz56Fo6Mj1q5di379+uHnn3/G66+/jjZt2mR9schI4NVX056vXw+k5tUAwPLlywEo0wJUqlQJAKDRaPD1okVo2LAhUnbvRpVWrVB11izEOjnh0ZdfYsOGDdBoNFi4cCE0Gg18fHzg6uqKsLAwXLhwAc2aNQP8/ZXuqc2bgUGDcO7HH3EyLAz19+9Hq02b0upz7hyOjhiBK506AQDq1KmDtm3b6r+GsDBEHzuGjRs3qptOnz4NREUBffsCERHAnj1Aq1bZ3/jCVCjhVTHGlhuikuHx48ei0WgyfzMsYV5//XUBIC4uLgJAWrRokanMX3/9JQDEx8dH/P391W6OpBxaLqKjo9WunbVr16pdPf7+/oX1cvImOlrkzz9FHj4UCQkRadVKBJBEQF4G5NChQzJ58uTMrR5btohYW0u4hYVUTtdVl92jdu3aEhQUJBIaKpKH16/VaqVOnToCQBYvXix79+5VW2MuXrxo8JiwsDBxdXUVADJ//nwRERk9erQAkMaNG0tydi0fr7+utJDouuLc3NQWqqSkJClXrpwAkE2bNinlDx9W90+cONHga7cDZH6fPnotXdt8fOSFjF2VMTEizZqJAHIFEBtAuqS+H18BMj61NekRIC6p5zYxMZF///1XZP58kb//Frl3T6RqVUk0N5em6X6vra2tldf9/fcideqIFEILIrulssHghqhk+P333/X+gHfu3NnYVcqz6Ohosbe3FwCyZMkSASCWlpaSmJioV063r2fPnpKcnCwODg7Z5ufo6HIevLy8RERk8ODBAkDat28vWgO5HEXuyJG07hczMxFAtM7O0jk1aL1//74cOnRIAIibm5teUHDkm2+kNSDm5ubSv39/GTBgQJYPXZeem5ub3Hv+eSXHZObMXFVRdw9tbGzUrqiBAwcKAOnSpYvB+/jGG28IAKlbt676Xj548EAcHR3V99ogrVZkzhylS2n/fhFvb5GePUVSc3W2bdsmAMTd3V0SEhJEzp1TcpAaNBCJjJTIyEh59dVX9V77mG7dJMjdPS1QevVVJZcHkGRApg4Zol+He/ckytVV3gbE08NDBgwYIGO7dJEBAwbIoH795LqDg8Samsq01q2lXr16AkBerldPtCYmyn29cEGkd28RQO4Bsvijj8TW1lYAyKVLl5RrJCTk6t7nFYObbDC4ISoZPvjgAwEgDRo0UPMgSpqVK1cKAKlataokJyergc758+f1yr355psCQN555x0REenVq5cAkAXz54v8849IZKTB8y9cuFAASN++fUVE5NatW2JtbS0AZN26dYX74gz57z+Rl15SWmxERHbuFPH1TQtwqlWTwF271JYprVYriYmJalDw999/i4hIfHy8VK1aVQDIe++9p5zr77/VICCje/fuSdOmTaUiIPG6a+3dq3zIvvuuklSbBV3eyEsvvaRuu379ulhZWYklIPu++kov6dff319tLTt48KBIXJzI6dMiWq0sWLAgd7lPISHKv3Fxepv79OkjAOTdd99Vrtmpk/JaBg/O+lzXryutQboAJ93jI0CaNWuW6ZCh/fsLAJkxY0bm8124oN7ne/fuiYutrVzQnXPYMBEROXv4sJxP3ZZUr558Ua2aOAPy008/ZV3PAsDgJhsMbkqeW7duyeLFi3Nsoi8qWq1WfvjhBzl16pSxq1KqtW3bVgCoI2sASGhoqLGrlSdt2rQRADJnzhwREenQoYMAkJUrV4qEhytN/f/9p46O0Y04+fTTTwWA/F69uvKhUr68yJ49mc4/atQoASDTpk1Tt82aNUsASPny5SUqKipP9d2+fbvs3r07fy82LEykRg2lvq+8or8vOlrk4kWRxETZvn272n2jM2TIEAEgH330kYiIfPbZZ2qLVGRkpNICZGMjUqtWWmAgGS8RLQd9fEQA+QOQqR9/LNrXXlPq06iRiIF7ER4ero4yOnbsmN6+aVOnyr7UD/CT9evLh++8I++9954abD///PMit28rCbiAyAcfSFJSktra0bVrV3nvvffUx/vvvqv3POPjnXfeUYOmq1evKoEhoCT6piYWZyspSWkNeuUVEU9PifLzExNALCws9FoK4+Pjxc7OTgAo3U05ONa1qwggoRqNRAQFiYiS6F8JkHBdEjIgAYC8P3ZszvV8CgxussHgpuTR/eH7/vvvjV0VEUlrxq5Vq5axq1JqxcXFqaNXrl27po4gOnTokLGrlmsXL15Uczd0Q4R1ORNvvvmmyIIFygeDtbV8aGcnGkBOnjwpIiKHDx8WANLD1VX/2/irr+q14ug+aLdt26Zui4uLU+9Xlt0jBuh+rx1MTOTBunVK4DVmjNIFsWRJ9l0NiYkiXboodaxYMcsARETkq6++EgAyJF13yU8//SSAMnQ547BmEREJChJJDVykbl0lhyej8+dFmzrkuVlqMDy+b1/Rengoxw0YIJKSonfI4sWL1XydjN1Pcb/+qnfvDwLinHpeGxsbCf7tN5Fy5fTfnz175M8//8yUE9MBkHOANIfhfKEKgFTXle3QQalngwbKOSdPzvG9M0Sr1aotYulzsPbv3y8ApFy5cpKS4X5k8uWX6msbDMikSZMkOjpa7TY9OX++OsrqS13dCxGDm2wwuCl5atWqJQDkueeeM3ZVRERk+fLl6h+lnIaLUv4cOXJEAIinp6dotVrp27evAJBFixYZu2q59vbbbwsAGThwoLrt559/FkAZsitarUi7duqHxwFAoi9eFImN1Qvubhw9KvLWW2kfoJUrixw4IHGxseoQ5Nu3b+tdW9fyo+uuykn6Yc3NM3Rt6F13+XIlkEkvKkoJgpCaJJuhyy2jV199VQDIxx9/rG57+PChmjyua8XSDWtWXbum5KgASgtOxnykPn2Ufc89J8uXLxczMzMBIK/UrStaXQvDCy8oLWapmjRpIgBk3rx5mSs6cKCSeOvrK3EWFiKAPHRyks9GjZI/9u9PG7Zev77SXQMoXUP378uaNWtk0qRJMmnSJJk/dKh6/Im6ddXtusevqUGhf/Xq8v7778t///0n8vPPyvkcHZ8qMbdz584CQJYvX65umzBhggCQl19+OfuDN21S3/vg1FZUMzMzef/99wVQpixISUkR+ecfuTtzppgAYm9vn3PA9BQY3GSDwU3JkpSUJObm5moTdXFIkvzoo4/U4Ca3E61R3ui6JZ599lkREfUP6htvvGHkmuVOfHy8Oprm999/V7dfunRJ/eafnJwskpIi/02cKNG6AMLOTsTTU+TECbVLa9WqVcrBBw+KVKqkfuDcfPdd0SWfZvx/oRtxZW1tLbGxsTnWV5fQDEBsAQk0MxPtgAEiH30k8sknSp10dVy7Nu1AXU4IUiffS/das9KpUycBMudntG7dWq2DRqORM2fOZD44ICAtwDEzU5KGExPTEpdNTZUyIvLnn3+Ks7Oz0lLm5qa26kiFCiL79snp06cFULptDHZ3JiaKLF6sBBcXLigtUoDSmiEicuuWyMiRIhERSu5MgwZKwu3q1WnnOHlSxMFBOa5TJ2W0UkanTyv7rayUc8XHK4EkIPLZZznez+y88847AkDGjRsnIkprji6XacuWLdkfHB8v0qSJ0mIWHCz9U/N0dI9PP/1ULZqUlCRWVlYCQAJS739hYHCTDQY3JUtQUJDef6jAwEBjV0mGDh2q1ufrr782dnWK3L1792TcuHFyuZAm6hIR6d27twBpw2x13RaF3exdUNavX690N1SooDcCKDk5WT4wNxdvQL1/ixYtkqqAXHR2TgsUnntO3nvvPQEgo0ePTjtxRISSPGppKWtnzhQA4ufnp+TjLF+uJr5qtVqpUKGCAJCdO3dmW9ewR49kho2NlAfkiy++UAOCPelzfGJiRL7+WqRNG/2J9fr1EwHkiY2NXEpNhs5J+fLlBYD8888/ets/+eQT9f/V66+/nvUJHj4UefbZtHu1Zo3Ir78qI4Vee02vaEBAgFSrVk0ASDdra7lvaysCSIiNjTSpW1cydo9lKyRECTay+oJ15YrIH3+kPT99WsTJSalj+/YGc35ERDmfLlfp55+V5Od27US8vAwHQ3mgm+G7devWIiJy9epVAZQRaJFZJKnrSU5WW+qCgoLE0tJSdF2t9+7d0yvasmVLASDr169/qjpnh8FNNhjclCy7d+/WC27Ub7FG1KpVK7U+L7zwgrGrU+SmTZuWu2btfEpOThZHR0epDsjd118XadZMHj77rADKKJQibb27dUukbVuRL77I02G6GWOnTJmivyM1jyMUkA0//igiacOK3588WWkpmD1bJCFBfvvtNwEgNWvWzHyBqCgZM2aMAJAPP/xQ7UKRXr1E7t4VEZHXXnstV61dm9q2VboezMwk6ckTdeSWrtVMT8Z7f+OGzJsyRQBlPpRvc1g2ISoqSv2/k3E00cWLF0Wj0YiLi4s8evQo2/OIViuybp0yikjXDRIZabAL59GjR9KxY0elxQyQbwHpnO5vSqbW13Pnnn4os7+/iIuL8p60aZPlaDdV6tBt6d077fVlM8Irt3TBjLW1tSQlJcnXX38tAKR79+75Ot/UqVMFUGaWzkj3ezw5nzlCucHgJhsMbkoW3dBK3UPvW6yReHh4qPUpi0nFzz//vPJNuFu3Qjn/uXPnlJYDU1O9nI+Rqfc8JJtk1QKVmJiWV2FtrbSa5FL16tUzt36EhandO59CSc4UEfWDd3X67gxRJorT/Z49NJBA27RpUwEgmzdvFpk3L236fCcnkZ9/lt927BAAUrFixSwDwqBvv5WU1Pt7LTUIOn/+vJpf8eDBgxxfa/ruJF0XSFYjG8+ePasGqYYcOXJEyTkpYAkJCbJhwwZZtGiR3uP3338X+eEHkUmTRGJjlffYzU2kalWRp2kl3rlT6SJr2TJ3vzdXrqR1teUU2OVBSkqKOjLq4sWLag6OrkU0r5KTk2Xbtm0Gh7kvW7ZMdDlThYXBTTYY3JQs48aNE0CZLCvLb7FFKDo6Wu8PuUajyfNw25KuUaNGAigjTApU6gewbuj3nHr1lAnOhg8XASTcxETKAXLgwIGCvW5WPvxQP6E2XVJmdnR/YwDIk+3bRd54Q3mktpA88fISS0A6duwoIiJubm4CGJ6wT/d7nzE/IiEhQU04vn79urLx0iV19lkBJGnAAHFJ7Ua4cOFCpnNrz52TmNQAcn+VKnqtMrouhrlz52b7WtMnAr/77rvqzz179jT4N3bjxo163SRG9+hR2kzBtWqlJQbXrJn/da20WqXbrG9fkSdPcn9chQrKtVOnDSgo7du3FwCycOFCNcm6MLr3z5w5IwDEycmp0FpXGdxkg8FNydKjRw8BIF9++WW232KLyoULF9T/wLrcgZK6oGNycrKMGDFCJk2apCyulwtarVadjdTe3j7LciEhIdK9e3dp1KiR3qNv375Z/2GdOFFkwgR58bnnBIDM1M0wm5Qk0rSpCCBbAPl2wYK8vlR916+L5DRH0b59aSspP/ecyLRpynG5oBvG7ePjI7JokX6ApNHItdT8IQcHBwkODlaD5BgD+RW6pRsmTpyotz3LD5LERKVbK3U24A2pyaOff/65/om1WgmtVk0EkD9NTOR26vwlOkuXLhUAUqNGjWw/qHS5UA0bNhQRkS1btqiTCNarVy9TXocur2bkyJE538ii8vvvSn5L+vdp8+air8dXXynXbtmyQE+rGx1VqVKlQv2CmD7gDsrw+1RQGNxkg8FNyVK5cmU1gMjqW2xR2pHa1N+kSRPp16+fAJAFT/thmx/nzz/12i269Yx0ibqPHj1SvnUGBGSaD0Tn3r17ei1XWf0/+v777/XKpX+4urrKkYyrEu/YoX6wPJ86yujgwYNp+8+dk2QTExFAFvfokbcXGhYm8s03ymtKSVGSO83MlDyHjMOaRZSRL7oPu+wSWw0JCJBdL7wgAGTAgAHKaJnp09Mev/8uiYmJamLmDz/8IIAyg7EhmzZtEgDi6Oio10WUYxfA5s0igDzy8hITQNq1a6e3O3bdOhFAogH5RjcDcDpRUVFqd8bhw4ezfLm6Lkrd5HsiIqdOnRJPT08BkCkHZ8SIEQLoj7QpFsLClGHiSE3+NcaozMREZT6hDIm6T2v16tV6///eyWXid37oukrVdbEKGIObbDC4KTni4uLUZu4HDx6o32J1uQrGMH/+fAEggwcPlunTpxvnW+jUqWn98716KUmq+aAbbq17VKtWTR7p5ivp39/g6I6ME5RlNWJKN3R70KBBsnfvXtm7d6/s2rVLmjVrJoAy/FadoO32bTX5Mvzll0WX75GxJePcM8/IfEC656VLQ6tVXgtSJ8CLilJaYnTf0Js0UYbunjqlPzLlzz9F/PyUPIzcSjcPy/OAzJo1K8uizZs3F6QGHQCkf//+BsslJyerc7GkT+DWJW++++67Wb/udevkZkCAAEqyb/o8if9Sh5R/5+QkcRmWANDRJSy/+OKLBvcbWjZBR7csRP369fVafnTJ+L/88ovhehtbQMBTj1AqbnSTSeoeel8aCpheknshYHCTDQY3JYfuP6WDg4NotVpZs2aNAJDmzZsbrU66Jt53331XbcWpV69e0VVg5szMk6vlc7Vs3XDr119/XSpXrizlAIlLf95GjTKN2Pjxxx/1/lDu37/f4Ll1w+W/1M0JkiomJkaeeeYZ9fhpU6aItn175XpNm8raFSsEgLQ00DTvn5qM6uzsnPs+fd0swBYWIunnTdmwIW00S7ouI0m/gnJ6iYkiW7cqLTmGrn39ujqD7n+WluIG/fltMtIF6rpHdh8GupmDAciJEydEJG/DbnXLAaxNnZ8mICBA7M3M5G1Admaz/tSJEycEgFhZWcnjx48z7dcFuhkXvBRRVnTXzXuiq7OIqCtIF5tVy8uApKQktavQwcEh06KtBUk343OPvLau5hKDm2wwuCk5tm7dKkDawm+3bt0SQJljIVq3MF8R03VFLV68WO7evat+KzaUL1HgPv007YP466+VERYzZ4qkXx8mPl4kMVHCwsKkVatWaXkrGaSkpKjfuk+dOiUPHjyQ9aldMRcASdDNueLlpQxrTTV58mS9D+WVK1caPL9uXShD39BTUlLURTFnpr6eCEAa2tqqffaGms7j4uLUtXfu3byZeYbadFauXCl9vb0lOTX3RAwMUb566JD8aG8vR0xNJTQ1v2agtbXUqVMnU0uEREWJpM6RIhnWIJL799XJ9VJq1JBy6Va8zkrGIHHNmjVZlhURGT58uBrYJyQk5GnCtCnvvSfdoUxboNVq1UU5e/bsmW2QqNVq1eUdvvnmm0z7db8Lw4cPN3j8iy++KADkldR1ph49eqS+XmP9/y2rdC1mhT3L+8mTJ9WAtzCSihncZIPBTcnxxRdfCKA/l4yPj48ARThiJgNd3s+ePXtEq9Wqw8IzTkhW4P77T00SlYzJoTqhocrkX6+9JitSl4jIaoZa3XBrOzs7ddhu4sKFEmppKd0BmfLCC8oaPt7eeq03ullKddP+f/LJJwarUqVCBfED5PzPP2eZv/DP0KFqsPa/dB/0JiYmmRYx1KlRo4bYAfKocWNlIcUMs9impKTI+++/L3aA/Jd6bu2AAQbroFt0UvdwB8Qq9WdLS8vMrSIjRyr1TT9RXGysSPPmyvbq1eVk6qKQXl5eBuuvc+rUKb1rnz17Ntvy9+/fV1cU1y3rkKup7uPiJCY18Opqby9/fPutmECZxC03gZFu5mI3N7dMrTe1a9cWALJx40aDx+oSq21tbSUyMlKOHz8ugLKgJxWtr7/+WkxMTGTfvn2Fep24uLgslwQpCAxussHgpuQYPXq0AJAZM2ao23TdHem3FRWtVqsu6Kebi0P3LbhIFvXcti376dh371ZH+KxL7bYADM9Qu2jRIgEyT+a1ee3atK6/iAhleLFORIRscnQUP0DatmihdmlllHTmjJxJ391Ts6Yy2ujSJf0g4/x50ZqYyJNXXpHAwED1kd0EboMGDRJTQG7UqqWcu3x5NQFT1+XlBci/qde+BcgJA6tcR0REqO/lli1b1GsHBAToTTM/c+bMtG+gBw6kzSMTF6ckJ+vyd1xdRQID1VyTPn36ZP0+ibI8g25ZERMTkyzzXtJLP2IQyP1szSmpQ+mPAfIYkPOAfJrLROnExESpU6eOAJDx6bo/dTOHm5qaypMshjtrtVqpUaOGAJClS5eqia2dO3fO1bWp4Gi12lz9jhUEXWvf1q1bC/zcDG6yweCm5NDNz7A23Vo2ulE4BTWBXEpKSpYTjmUUEhIigDJsVzd0WrfOVGFOLpicnJz7xehSV/FNBqRn6ofg2LFjMxXTzaCbsdsqMDBQbbnI2Defsnq1GrBEW1vLD4B81KpVpvlAnrz/vjKfC5C2YKHuMXSofkVu3szd60r18ccfCwB5c8QIkTp11Fyd+4GB6kiNpmZmkmhpKRGWltIUkFGjRmU6j65FwtBq0MnJyTJp0iQ1iBg2bJjywZCSkrYy9S+/KM8/+EDE3FwkdUSRrjVo6tSpOb4W3UKVNWrUyNVrT0hIkJo1a6r1yjg8PEs3b0pi6kgzAeSymZlE5mH+lQMHDqhBmC5X5ttvvxUA0qlTp2yPnTt3rgCQFi1aqP9XXsuwRAKVLnn5P5BXDG6yweCm5ChXrpwAkH/T5ZToZk+1tbXNdVCSleTkZOncubN4enrKrVu3ciyva1b38fFRt/36668CQBo3bvxUdTEoIUFSRo2SES1aiLe3t8FZQTPRaiU2dUhrBCBNkHmGWq1Wq87Rc/DgQWUulnXrRJKTRavVqrk4GZM+7+zcKYsAeZA+WAFEPDxExo5VlioQkSMHD8pngLSoWFFp/fn5Z2VCM3NzpWstODjft0S3ZlPr1q1FgoKU2WQB+c3aWjSp3Sd//fWXyL598m/qhHE2NjaZ/r/rAqHs1gb74Ycf1Cb2AQMGKPdQN7Ff+tW2083bk5dvrbqWyUGDBuX69e/du1cNbjIuPJmdK716qe/Xn/kYbTh48GABIO3btxetVqs3/1R2Hjx4oLZQ6bp0y+J6bGVJblsv84PBTTYY3JQM6Wd5DQ8PV7enpKSIk5NTpqAnP3RzjOQ20W5tapeN2h2g1UroV1/JPCg5DPHx8UpLxPvvG54/Ja9SR0bdBsQcuV9Xa8uGDXIwXfBxApAL6Uas3Lx5U4DU4dZ37ojY2ytld+0SEVGnaF+xYoXeeXXrfDWsW1euLV4sPwASppvoDlCGdIuoo9oydT88efLU6+XoJlHUjaD7a84ciU+9/mpnZ73Jw7RarZoXsnjxYnW7bgI8c3Nzw6tBp7N//371w3n79u1p0+SbmIhkWJogr/kGx44dkwoVKsiOHTvydA/GjRsnNWrUyNNkllE3bkiAlZUc8/ERbW5bAdO5deuWOuLmxx9/VBO/r1y5kuOxusBI98jr66WSRTe6rzBmoWZwkw0GNyWDLuHS09Mz074+ffpIViM4cissLExcUyeL0z1ySlLWza760ksvKa0PuvlTAOkEyKl//hFp2FDZ1r173qZez+jCBdGam4sAMiQPAZiIyMSJE8UZkHOVKkkyIP8AMifdlO7nBwyQNwAZW7u2yOTJSn0bNFDzYd555x0BlDWC0tN1RQwaNEgePnyoBEiAJO7YIZIusVg3f05hzP+TkJCgTiGvm+5/eOp7kOLmpiRVp6NbKLBp06bqNt0cMc8//3yurqmbs6dKlSpK91TLlsoyBxkCtcIeKWJss2bNUoNi3f3Izevcs2eP3v+zq1evFkFtyVgSExMNTh1QEPLy+W0ComLov//+AwBUr14907527doBAP766698n3/atGkICwtD3bp18eqrrwIA3nrrLSQlJWV5zPXr1wEALWxtgfr1gR07AHNzLKtWDX8BOHP+PDBrFmBjA+zfD7RpA6Qek6UnT4DgYP1tycnAyy9Dk5SEbQAOurkBAPbu3Ztt/XSOHj2KJwAuzZmDn7/4Aq8C+P3335WdcXGou307FgH4/soV4KuvlO1TpwIaDQCgadOmAIAzZ87onVf3ntSoUQNubm6wsLBAMoB79esDH32klrt9+zYAoGLFijnWNa8sLCzU34kvv/wSIgKb115D8qZNMFm8GHB11Ss/YsQIWFhY4PTp0zh79ixiY2Oxdu1aAMCYMWNydc2PP/4Y3t7euH79Or766itg9Wpg2DDAxUWvnO5+NWnSBJrUe1mavPvuu/D19UVycjIAoE+fPrl6nd27d0elSpUAAKampvD19S3UepJxmZubw9nZ2djVAFtuqFiaOXOmAPqzsuocPXpUAGUYc37WdfL391fnSzl48KBeK052q+V27txZ3ACJSs3zkPr1Rc6dU7/ZqyOHzpxRRvEAIlWqZL0AX3S0Ui5ji8zcuWpCrldqHd3d3dNyZLIRFRWl1zWimxvIxMREGYX05In86OYm2wGJTl2hWtq00Vtu4erVqwIow8jTT86my7NYnrqApK+vrwCQo0eP6tVBNzng0qVLs61rfum6OTQajcybNy/H1oMhQ4YIoCRWr1q1SgCIr69v7pO0Ja1L0traOsv8rMKenbU42LZtm9oCs3fv3lwfp2v1yWqZCaLcYLdUNhjclAzDhg0TwMCCf6LMuNm6dWsBMkzjnwtarVYdhZW+m0c3eibjGj7pVa9YUf7S5ZdUqaJ2gehWOtabOfnuXWV4MCDy22+GK7NypbI//ZDeEyfUrq6XABkyZIiIiIwcOVKAnJee2L9/vwDKInk69evXF0CZKC79RGoPHz5UhjRnCA5SUlLUdYUuXryobk+/zpdI2mi2DRs26B2vmxE3Lx9+eXHgwAFp06ZNrnM3dPfEwcFBXf4hq/l5spLV7016hb2uTnGg1Wpl0qRJ8r///S9PM90+evRI+vTpI8uWLSvE2lFpx+AmGwxuSgbd2jtZLZIZHR2tN43/xx9/nKtv4uvWrTP4DTyrNXx0EhMTxU+jkWRAUhwcRNKtqXTt2jUBDAyffvttJVAZMMBwZdq2VfanX0RwxgwRQPYAYmNtLXdS8zp0CyjmtKKvbr2rYcOGqdt0swEPHTpUXTKiVq1a2Z5H90GuG5ETHx+vrvMVEhIiImnDydOPftFqtepkc7lJNi0KKSkpaisToMzNci8fixOmb/HLmJ9VFCsiE5V1zLmhEk1Ess25AQBbW1ts2rQJ77//PgDgk08+wdChQxEfH5/leaOjozF58mQAwJQpU/RyQkxNTbFw4UIAwIoVK3Dy5Em9Y2/fvo29InjW3ByaTZuA2rXVfVWrVoWjoyMSEhJw5cqVtIN0OR3h4UoeTXqXLwPHjkFMTfGrgwPWrFmDNWvWYLOTE153csJgAB99/DEqVKgAAOjRowfMzMwQEBCAwMDALF+jLg9Jl5cEAH379gUA7N69G4cOHcq035AmTZoASMsjCQoKgojAwcEBHh4eAIDy5csDAO7evaseFxERgaioKACFk3OTHyYmJnjllVfU53369IG3t3eez9OwYUO8/vrrADLnZ126dAmJiYlwcnJiTglRcVDooVYxw5ab4k83Ekej0RhcOiCjFStWqCM4sptDQzf/gjrqxQDdGj5du3bV275v3z4BlEnfDNENn+7Vq5dERkam7bh+3XBlUlt1tqUbRZL+UbVqVXWiQJ0uXbpkmxeUmJiozrqbvjspOTlZXbDQwcFBAMjq1asN1yuVbjbZ9u3bi0jmdb5ERL755ptMo450yzq4ublle/6idu/ePTUX6besuglzIX1+1oABA9Q1kpYtWyYApEuXLgVVZSLKgC03VKLpWm0qVqwIa2vrHMuPGjUKM2fOBAD8888/WZa78+ef2A3g4w4dYGVlZbDM7NmzodFocODAAQQFBQH37wO9e+Px8eMAgCpVqhg8bsqUKbCyssLu3bvRrl07dcQQDHyLT4mNRfTixQCApQCaN2+OHj16qI++fftizZo1sLS01DtO1wKjjnzKQDcayNnZGbXTtSyZmpqiV69eAIDIyEgAuW+5OXv2LLRaLa5duwZAvyXNUMtNYY6Uehre3t5YsmQJpkyZot6L/HBxccGKFStgaWmJ7du3o0OHDrh3757eSCkiKgaKINgqVthyU/ytWLFCgMzrHmXn0htvSDQgb6SbPTijLe7uaRPOZcPPz08Z9fLBB+pcNtcrVBBAf32djE6cOCGenp4CKPPznEg3cZ6EhorcvCmRkZHyeZMmIoDcAeSTGTNyPSdKQECAAMrkc3qtQ6l0c7r0TT97bipdrhEA8fb2zvGaSUlJ6qRtAQEB8sorrwgAmT59ulrm77//zpS8rFuzauDAgbl6TSXVsWPH1BFs3t7eUqVKFQGQebFNIiowbLmhEi2nfJtM/vkHtX/8EbYAnrt7F4mJiZmKaLVaTIqJSduQPjcmA938J48XL1bnsllUpw6ArFtuAKBFixY4efIk6tevjwcPHqBjx4547733sL1/fySXK4cLffuiffv2WHbmDL4zNcWjF1/ER9On53pOlBo1aqB69epISkrC/v37M+0/evQoAKB9+/aZ9vn5+cHERPnv3q5duxyvaWZmhoYNGwIATp8+rTfHjY6u5ebevXvQarUAim/LTUFr06YNTpw4gTp16uD+/fvqHEhsuSEqHhjcUNGJiQHeeANITWrNiqEP0iyFhwNDh0KTmrDbUgRBp05lKnb79m3cjI3F77oP9Z9+yvKU/fr1Qy1XV8yOiFA2fPwxDj9+DAA5JotWrFgRx44dQ58+fRAfH48vv/wSM3/7DWYpKahx8SLunjuHSA8PND92DI1+/jnn15dBnz59AGTumhIRNbgx1OXk4uKiBj0dO3bM1bXSJxUbek+8vLyg0WiQnJyM0NBQAMCtW7cAQJ20rTTz9fXF33//jR49egAAnJycUK1aNSPXiogABjdUlKZPBxYvBjp3zraYLr8jx+BGBHjlFeDmTcDXFx9XqwZvABfS5YDoXDl7FgBwMHX0EdasAVJSDJ7WwsIC693d4Q7ghr098MEH6jfz7FpudOzt7bF9+3YsXLgQY8eORauxY3HbzQ2WAFZ16YJ///0XLVu2zPE8hujybnbt2qW2lgBKQBgaGgpLS0t1huGMfvzxR3zxxRd6I4eyozvP4cOHERISAkC/Nc3c3Byenp4AlNYboOy03Og4Ojpi586dWLBgATZs2KC2jhGRkRV+L1nxwpwbI2rdOi3nJYs5aVJSUsTKykoASGC61ZYNWrdOOZeZmcjJk+oqy9OmTdMvl5wskQ4Osh+QN/v0EXFyUo774w/D5926VQSQJECaaTRy6dIlNV/FUK5Lrnz/fdprP3gw08R5uZWQkKDOI3Py5El1+9KlSwVIt6hnATh79qzeCC4PD49MZXST4m3fvl1ERCqk5ibp5RsRERWAvHx+mxkloqKy6f595d99+4DUb7hHjhzB8uXL1VaIhIQExMfHw8zMLOeujWeeAd56C6hUCWjeHPWOHQOgzDkCrVa9Bo4ehX1kJJoAONm8OeDjAyxZoqwR1LVr5vMuWwYA2ODjg1N37mDatGkAADc3N9jb2+fvtb/wgtIlBwBdugAJCYCFRZ5PY2FhgR49euDXX3/F+PHj1datU6ldcTmNgsqLOnXqwMLCQs1hMtSSVr58eZw6dQr37t1DUlIS7qe+x2Wl5YaIiicGN1Q07t8Hbt1SFmdM1yUzbtw4XLx4MVPxevXqwczMwK+nCPDoEeDuDlhaAgsWqLvq1q2LAQA+3rkT+PxzYMoUZcfmzQCA7QBqN2gA+PkBR44AzZsbruu2bcCPP8LEygoYPRq//vorgJzzbbLl6Ai89BKwahUweXK+AhudgQMH4tdff8XJkyczTTbY1VCwlk8WFhZo0KCBGjgZCm50kwzevXsX9+/fh1arhYWFhTrRHxGRMTC4oaKROk8M6tcHHBwAEcTExuLy5csAgE8//VSde0aj0aB3796Zz3HwIDBtGhAWBly8CJia6u2uW7cuXAA0io+Hdu1amEyZAmi1kF9/hQbAZgDf1K0LVK+uHJ/ViCEzM+CNNzAoLg5O77yD8PBwALnLt8nWwoVAt27A4MFPdZqhQ4dCq9Xi0aNHetvLly+PzjnkM+VVkyZN1ODG0Oi19COmdPk2Pj4+zD0hIqNicENF4++/lX/t7YF27QARnP/qK2i1WpQrVw5TdK0shly8CLz5ZtooK0tLwN8fyJA46+XlhQMODkiIjITl5cvAhQtAVBQ0wcGIAPCXhQWqVq1qOKg5d05JMv70U7VVxdraGi+++CK+++47AE/ZcgMAdnbAsGFPdw4ok/KNGDHiqc+TG+mHNufUclOWRkoRUfHG4IaKxsyZQN++QFwc0KcPYGoK/9TWnKxG9wBQgo4uXYDHj5Wg49VXgQ8/BAysDaTRaOBTvz52HzuGgQCwYYNyPQA7AFSpXRum6Vt7YmKULqhmzYDnnwdShzvjyy/VImPGjFGDm6duuSmB0r83WeXcAPotN8y3ISJjY3BDRcPOThkCLgJ4egIPHiDs4EEA2Ux8dvGi0o3z+LGSp7Npk5IMnI26detivS64Wb9eXbDy19R9ep57Dti9G/DwAB4+BMqXB1IX4tRp0KABOnXqhMOHD6NFixZ5f90lXL169eDl5QURMTiHS/olGBjcEFFxwY5xKloajdJSAsDU3x9ANsHNu+8qycPNmwN79+YY2ABKAPMbgDhTU+DGDaBHD5wpXx57kUVwAyiBjYmJEgy5uWU657Zt23DhwgV1xt6yxMrKCv/++y/+/fdfg+tx6YKb6OhoNTGcwQ0RGRuDGyp869YBb7+dlneTGtyUTx02nGW31Lp1wOjRSmDj6JirS9WrVw9xAPbb2Cgb7Oww2t0d8an79Dz7LKBbmHPGDMDAsgWAMlFbpsCoDClfvryaW5ORnZ0dHFPfG13iMYMbIjI2BjdU+DZtAubPzxTcNBGBm5tblh+ccHZW5pxxds71pXRByJKoKKT07YuUzp1xJXUdqUwBioMDsHEj8MUXacPGKc90719CQgIAJhQTkfEx54YKl0haUNOmjfJvaktNbQBtGjTQX8RxzRrgyRPgxRfzFNToeHh4wNXVFbvDwnB+1izY2toiISEB1tbWhkc79eunPCjfypcvr0ycmMonF92HRESFiS03VLiuX1dyWiwsAF1ujZcXbrq7YyeA1rVrp5UVAWbNUmYd3rIlX5fTaDRqC82lS5fUD93atWtz7pVCkr7lzd3dHda6rj4iIiPhX3sqXLpWm6ZNgXQJqYMrVsQAANU6dUore+QIcO2aMrJqyJB8X9JQcFOWc2YKmy6pGGC+DREVD+yWosKVsUsKQGJiIi5cuAAgw0ippUuVf4cOVQKcfEof3Nja2upto4KXvuWGwQ0RFQcMbqhwGQhuLl++jMTERDg5OsJX14Xx5Im6BhTGjHmqSzK4KVpsuSGi4obBDRWehARlAj5AL7g5ffo0bAAExsVB4+0NREQoicQJCUDDhupoqvzSBTI3btxQF9/MNAycCkz6lhuOlCKi4oA5N5Qv0dHRiIyMzL6QpSVw+7ayGni5curmM2fOIBaARpeDc/p0WpfUK69kvaBlLrm7u8PDwwMigqSkJNja2rJFoRCx5YaIihsGN5RnWq0WjRo1Qs2aNRGXunZTljQaIMMH3pkzZwAA0bqRUocOAVWrFtjCkoB+N1SdOnU4UqoQubq6wtLSEgCHgRNR8cC/+JRnYWFhCAoKQkhICC5fvpynY5OTk3Hu3DkAgHW7dsrGq1eBrVuB+/fzNbeNIemDG+bbFC6NRoOJEyfCz88PjRs3NnZ1iIgY3FDeBQcHqz+nn7xNz8OHQKVKwMCBSrJwqqtXryIuLg729vZw9fNTNqZO2w97+wKrI4ObovX5559jz549MDc3N3ZViIgY3FDepQ9udIslZrJokZJvExwMODmpm3VdUo0bN4aJLnH4+vW0xOMCwuCGiKjsYnBDeZZjy01srBLcAMrK3ukShE+fPg0gdX6b9F1Qy5cXaB3TBzQcKUVEVLZwKDjlWY7BzerVQFgY4OsLDBqkt0vXcqNO3rdtG/D778DrrxdoHV1cXDB79mzExsYyyZWIqIxhcEN5dv/+ffXnW7duITo6Gna6GYVTUoB585SfJ00CTE3VslqtFmfPngUANE1dPBMDBiiPQvDxxx8XynmJiKh4Y7cU5Vn6lhsA+iOmduwAAgOVLqdRo/TK/ffff4iJiYG1tTVq1qxZFFUlIqIyyOjBzaJFi1C5cmVYWVmhZcuWOHnyZLbl58+fj5o1a8La2ho+Pj54++23ER8fX0S1JSAtuNGk5tLodU19953y7xtvAKlLH+icSh0V1ahRI5ima9EhIiIqSEYNbjZu3IhJkyZh+vTpOHPmDBo2bAg/Pz88fPjQYPl169bhgw8+wPTp03HlyhUsX74cGzduxJQpU4q45mWbLrjR5c3oBTe//ALMmgWMH5/puKNHjwIA2qRbioGIiKigGTW4mTdvHsaMGYNRo0ahTp06WLJkCWxsbLBixQqD5f/++2+0bdsWL7zwAipXrowePXpg6NChObb2UMERETW46datG4AMwY2rKzB1qt5yCzq64KadbvI+IiKiQmC04CYxMRGnT59WPyABwMTEBN26dcPx48cNHtOmTRucPn1aDWauX7+OXbt2oXfv3lleJyEhAZGRkXoPyr+IiAi1G1AvuElMzPa4sLAwNQhq27Zt4VaSiIjKNKMFN48ePUJKSgo8PT31tnt6eiIkJMTgMS+88AJmzZqFdu3awdzcHFWrVkWnTp2y7ZaaM2cOHB0d1QeHBT8dXauNk5OTOuLpzp07iJ89G2jfHkhtncno77//BgDUqlUL7u7uRVNZIiIqk4yeUJwXhw4dwmeffYbvv/8eZ86cwZYtW7Bz507Mnj07y2M+/PBDREREqI87d+4UYY1LH90wcC8vLzg7O8Pb2xsAEHP0qBLYnDhh8Dh2SRERUVEx2jw3bm5uMDU1xYMHD/S2P3jwAOUM5GsAwNSpUzF8+HC88sorAID69esjJiYGr776Kj766CODKz9bWlqqKxbT09O13Hh5eQFQZgK+f/8+zHXLMGSxcOJff/0FAGjfvn3hV5KIiMo0o7XcWFhYoGnTpjhw4IC6TavV4sCBA2jdurXBY2JjYzMFMLohxSJSeJUllaHgxhGAw6NHSoFGjTIdExcXpw4DZ8sNEREVNqPOUDxp0iSMHDkSzZo1Q4sWLTB//nzExMRgVOrkbyNGjED58uUxZ84cAEC/fv0wb948NG7cGC1btkRgYCCmTp2Kfv36cd6UImIouGmo21mxIuDikumYkydPIikpCV5eXvD19S2imhIRUVll1OBmyJAhCA0NxbRp0xASEoJGjRphz549apLx7du39VpqPv74Y2g0Gnz88ce4d+8e3N3d0a9fP3z66afGeglljqHgRu2IyqJLSpdv0759e3XiPyIiosJi9LWlxo8fj/EGJnwDlATi9MzMzDB9+nRMnz69CGpGhuiCG10icZ06ddTgJq52bVgbOIbJxEREVJRK1GgpMr70o6UAwNHREea2tngC4KaTU6byKSkp6jBwJhMTEVFRYHBDeZKxWwoAfm7fHi4Ajjo6Zip/4cIFREZGwt7eHvXr1y+qahIRURnG4IZyLTo6GtHR0QD0g5u6desCAC5euZLpGN0Q8DZt2jDpm4iIigSDG8o1XauNra0t7O3tlY1abVpwo5vrJp30ycRERERFwegJxVRyGOqSwltvYejWrTgEYG/6BTShzD3EZGIiIipqbLmhXMs4UgoAcPYsrO7fRxKU2aXDwsLUXTdu3FBmLzY3R4sWLYq4tkREVFYxuKFcy9Ryk5ICnDsHAHiYuu1SutYbXatNs2bNYG1taJA4ERFRwWO3FOVaxmHgCAoCYmIAa2vYNG4MBAfj0KFD6iSMe/bsAcAuKSIiKlpsuaFcy9Ryc/as8m/9+qhdrx4AYPr06ahVqxZq1aqF9evXA2AyMRERFS223FCuZQpu/P2Vfxs3xpAhQ7Bp0yaEh4frHVOzZk106dKl6CpJRERlHoMbyrUsW24aN0aTJk1w/fp1I9WMiIgoDYMbyrVMwU2jRsDjx0DTpsarFBERUQYMbihX4uPj8eTJEwDphoJ//rkRa0RERGQYE4opV3StNpaWlnAysEAmERFRccHghnIlfZeURqMBHj4EEhKMXCsiIqLMGNxQrmTKt3njDcDODli50oi1IiIiyozBDeWKwZFSyclAxYpGrBUREVFmDG4oV/SCm/BwQDfsu1Ejo9WJiIjIEAY3lCt6i2aeOaNs9PUFXF2NWCsiIqLMGNxQrui13Jw6pWxs1syINSIiIjKMwQ3lit6imQxuiIioGGNwQ7mi13Lz77/KRgY3RERUDHGGYspRUlISQkNDAQBenp7AuHHA6dNAkyZGrhkREVFmDG4oRw8ePAAAmJmZwc3DA5g82cg1IiIiyhq7pShHui4pT09PmJjwV4aIiIo3flJRjvSGgR85Avz3H6DVGrlWREREhjG4oRzpjZQaNgyoWRM4etTItSIiIjKMwQ3lSBfc1HR0BO7eBTQaoHFjI9eKiIjIMAY3lK3Y2FisXr0aANDO2lrZWKsWYG9vxFoRERFljcENZWvu3Lm4ffs2KlasiJ7u7spGzm9DRETFGIMbytLNmzfxxRdfAAC+/vprWJw7p+xgcENERMUYgxvK0qRJkxAfH48uXbrg2Wee4bILRERUIjC4IYP279+PrVu3wtTUFN9++y00wcFASAhgYgI0amTs6hEREWWJMxRTJomJiXjrrbcAAOPHj0fdunWBmBhg61bg1i3AxsbINSQiIsoagxvKZOHChbh69Src3d0xY8YMZaOtLTBwoDGrRURElCvsliI9MTExmDlzJgDg8zlz4MRWGiIiKmEY3JCe0O+/x5yoKAyyt8eokyeB554DEhOBL74Adu9WfiYiIirG2C1Fesz37ME4ANVMTKD56ScgPh7w8wMOHQLMzICoKGNXkYiIKFtsuSE9dpcuAQD+qlYN2LYNsLBQAhsAaNAAsLIyWt2IiIhyg8ENpXnyBI4PHgAAImrUUFpstm4FzM2V/U2bGrFyREREucPghtKkTtIXCMDB11fZ1rs3sH070LUr8MYbxqsbERFRLjHnhtKcPAkA+BeAl5dX2vZevZQHERFRCcCWG0qTGtycRIbghoiIqARhcENpUvNtGNwQEVFJxm4pUsnx46hkZYWQxER4e3sbuzpERET5wuCGVOHh4biTOklfuXLljFwbIiKi/GG3FKnu378PAHB2doYV57MhIqISii03pBg8GJ63bqExgATm2xARUQnG4IaAlBRg3z64RUUhCUwmJiKiko3dUgQEBABRUUi0sMBlMLghIqKSjcENqfPb3HZ3hxYMboiIqGRjcENqcHPFzg4AOAyciIhKNAY3lLbsgkYDgC03RERUsjG4Kevi44Fz5wAAh+PiADC4ISKiko2jpcq6R4+ADh2A27dxOnWeGwY3RERUkjG4KesqVAAOHEBUZCRiHB0BMLghIqKSjd1SZVVcHLB+PSACAAgOCQEA2NnZwS41sZiIiKgkYnBT1sTHAwsXAlWrAi+8ABw8CAAIDg4GwJFSRERU8rFbqix5/Bho3Rr47z/leaVKSrCDtOCGXVJERFTSseWmLFm/Xgls3N2BxYuVn/v0AZC2aCaDGyIiKunYclOWbN6s/Pv++8Drr+vtYssNERGVFmy5KSsePACOHFF+fvbZTLsZ3BARUWnBlpuyIiQEaN4c0GqBypUz7WZwQ0REpUWeW24qV66MWbNm4fbt24VRHyosDRsC//wDHD5scDeDGyIiKi3yHNxMnDgRW7ZsQZUqVdC9e3ds2LABCQkJhVE3KgzW1gY3cyg4ERGVFvkKbvz9/XHy5EnUrl0bb775Jry8vDB+/HicOXOmMOpITyswEHjyBAAQEhKCmJgYvd1xcXEIDw8HwJYbIiIq+fKdUNykSRN8++23uH//PqZPn45ly5ahefPmaNSoEVasWAFJnfk2J4sWLULlypVhZWWFli1b4mTqCtVZCQ8Px7hx4+Dl5QVLS0vUqFEDu3btyu/LKBsmTAA8PHB37lxUqVIFAwcO1Nuta7WxsrKCY+oSDERERCVVvhOKk5KSsHXrVqxcuRL79+9Hq1atMHr0aNy9exdTpkzBH3/8gXXr1mV7jo0bN2LSpElYsmQJWrZsifnz58PPzw8BAQHw8PDIVD4xMRHdu3eHh4cHNm/ejPLly+PWrVtwcnLK78so/cLDgf37geRkrDx3DnFxcfjjjz9w7949lC9fHoB+vo1GozFiZYmIiJ5enoObM2fOYOXKlVi/fj1MTEwwYsQIfPPNN6hVq5ZaZtCgQWjevHmO55o3bx7GjBmDUaNGAQCWLFmCnTt3YsWKFfjggw8ylV+xYgUeP36Mv//+G+bm5gCUBGfKxm+/AUlJ0Napg6937lQ379q1C2PGjAHAZGIiIipd8twt1bx5c1y7dg2LFy/GvXv38NVXX+kFNgDg6+uL//3vf9meJzExEadPn0a3bt3SKmNigm7duuH48eMGj9mxYwdat26NcePGwdPTE/Xq1cNnn32GlJSUvL6MsiN14r4LNWogIiJC3bwzXaDD4IaIiEqTPLfcXL9+HZUqVcq2jK2tLVauXJltmUePHiElJQWenp562z09PXH16tUsr33w4EEMGzYMu3btQmBgIN544w0kJSVh+vTpBo9JSEjQG80VGRmZbb1KlchIYO9eAMC81KH7zzzzDLZs2YL9+/cjPj4eVlZWHClFRESlSp5bbh4+fIgTJ05k2n7ixAmcOnWqQCqVFa1WCw8PD/z4449o2rQphgwZgo8++ghLlizJ8pg5c+bA0dFRffj4+BRqHYuVnTuBhAQk+PripzNnYGJiggULFqB8+fKIjY3F4dQ5b9hyQ0REpUmeg5tx48bhzp07mbbfu3cP48aNy/V53NzcYGpqigcPHuhtf/DgAcqVK2fwGC8vL9SoUQOmpqbqttq1ayMkJASJiYkGj/nwww8RERGhPgzVvdT69VcAwF+prWO9e/dGhQoV0Cd1sczff/8dABfNJCKi0iXPwc3ly5fRpEmTTNsbN26My5cv5/o8FhYWaNq0KQ4cOKBu02q1OHDgAFq3bm3wmLZt2yIwMBBarVbd9t9//8HLywsWFhYGj7G0tISDg4Peo8yYMQPJy5djyn//AQBeeeUVAEDfvn0BKHk3IsKWGyIiKlXyHNxYWlpmam0BlK4NM7O8pfBMmjQJS5cuxerVq3HlyhWMHTsWMTEx6uipESNG4MMPP1TLjx07Fo8fP8aECRPw33//YefOnfjss8/y1GJUptSrh20ODvj38WN4eXmpLTZdunSBpaUlbty4gStXrjC4ISKiUiXPCcU9evTAhx9+iO3bt6sTvoWHh2PKlCno3r17ns41ZMgQhIaGYtq0aQgJCUGjRo2wZ88eNcn49u3bMDFJi798fHywd+9evP3222jQoAHKly+PCRMm4P3338/ryygzli5dCgAYNWqUGnza2tqic+fO2LNnD7Zu3YpHjx4BYHBDRESlg0ZyO5Vwqnv37qFDhw4ICwtD48aNAQD+/v7w9PTE/v37i33CbmRkJBwdHREREVG6u6gOH0bYn3+iy8yZOA8gMDAQVatWVXcvWrQI48ePR5UqVXD9+nWYmZkhISFBL5gkIiIqLvLy+Z3n4AYAYmJisHbtWpw7dw7W1tZo0KABhg4dqk6sV5yVmeBm/Hhg0SJ8AWB/1674448/9HbfvHkTvr6+6nMfHx+u9E5ERMVWXj6/87X8gq2tLV599dV8VY6KyIULyj+AmsOUXuXKlVG3bl1cunQJALukiIio9Mj32lKXL1/G7du3Mw3B7t+//1NXip6SCOTiRWgAXAQwK4vRZ3369GFwQ0REpU6+ZigeNGgQLly4AI1Go67+rVtwkUshFAPBwdA8foxkALetrbNcf6tv376YO3cuAAY3RERUeuQ5e3TChAnw9fXFw4cPYWNjg0uXLuHIkSNo1qwZDh06VAhVpDy7eBEAcA1AlTp1skwSbt26NZydnQEwuCEiotIjz8HN8ePHMWvWLLi5ucHExAQmJiZo164d5syZg7feeqsw6kh5lRrcXARQr169LIuZmZlhyJAhAIBmzZoVRc2IiIgKXZ6Dm5SUFNjb2wNQllDQTd1fqVIlBAQEFGztKH9Sk4kvAqhbt262Rb/55hucPXsWvXr1KoKKERERFb48Bzf16tXDuXPnAAAtW7bE3LlzcezYMcyaNQtVqlQp8ApSFrZtA2bPBtItRaGaPx8vVqmCn5FzcGNlZYVGjRqpOVNEREQlXZ4Tij/++GPExMQAAGbNmoW+ffuiffv2cHV1xcaNGwu8gmSACDBokPJzlSrAsGF6u5NsbPDLnTtIQs7BDRERUWmT5+DGz89P/blatWq4evUqHj9+DGdnZ377Lyp376b9vGtXpuDm2rVrSEpKgp2dHSpWrFjElSMiIjKuPHVLJSUlwczMDBdTE1Z1XFxcGNgUpfPn037evRtISkp7fuwYzN59F/0B1KlTh+8LERGVOXkKbszNzVGxYkXOZWNs1aoBU6cqPz95Avz5Z9q+gwdRY9cuDAK7pIiIqGzKc7fURx99hClTpuDnn3+Gi4tLYdSJclKzJjBrFhAXp3RRpX8f0g0DZ3BDRERlUZ6Dm++++w6BgYHw9vZGpUqVYGtrq7f/zJkzBVY5ysGXX2beli646cbghoiIyqA8BzcDBw4shGpQriUmAnv3Ag0aABUrAulzahISIAEB0EBZMDO7CfyIiIhKK43oFocqI/KyZHqxdO4c0KgR4OQEPH6sbLt8Gbh5E/DxARo2xBMAle3tER4RwYRiIiIqFfLy+Z3nSfzIyHQjpRo0UFptDh0C6tUDRo9WAh+k5tvUq8fAhoiIyqQ8d0uZmJhk+6HJkVSFLH1wAwBt2yqtOA8eAKtWAVC6pJhMTEREZVWeg5utW7fqPU9KSsLZs2exevVqzJw5s8AqRlnQBTf16yv/WlgAAwYAq1cDdetilJUV9u3ahXcZ3BARURmV5+BmwIABmbYNHjwYdevWxcaNGzF69OgCqRhlIXVRTLXlBgAGD1aCmy1bcNzGBvfBlhsiIiq7CiznplWrVjhw4EBBnY4MCQ0FgoOVn9OPhOreHbC3B+7dg2tgIAAGN0REVHYVSHATFxeHb7/9FuXLly+I01FWdK02VasCdnZp2y0tgQoVAACHReDk5AQvLy8jVJCIiMj48twtlXGBTBFBVFQUbGxssGbNmgKtHGVQrx6wZg2QnJx535dfAn37Yj2UVhuOlCIiorIqz8HNN998o/fBaWJiAnd3d7Rs2RLOzs4FWjnKwMNDXQH8/PnzCAwMxKBBg5T3o08fzB8zBlOXLsUwTt5HRERlWJ6Dm5deeqkQqkF5NWTIEFy9ehUvvPACli9fDisrKxx6+BDRYL4NERGVbXnOuVm5ciU2bdqUafumTZuwevXqAqkUGZCSAixYABw8CKSk4ObNmwCAdevWoWvXrggNDcXF1HWlGNwQEVFZlufgZs6cOXBzc8u03cPDA5999lmBVIoMCAwEJk4E+vVDbGws4uPjAQD29vb4+++/0aJFC1y/fh0AgxsiIirb8hzc3L59G76+vpm2V6pUCbdv3y6QSpEBusn76tVDWHg4AMDMzAwnTpxAlSpVcPPmTYgIXF1d4eHhYbx6EhERGVmegxsPDw+c133QpnPu3Dm4uroWSKXIgHTLLjxOXTDT1dUVtWvXxokTJ9CuXTsAQNOmTTlSioiIyrQ8JxQPHToUb731Fuzt7dGhQwcAwOHDhzFhwgT873//K/AKUirdHDf16yMsLAwA1GDSzc0Nf/zxB9avX6++J0RERGVVnoOb2bNn4+bNm+jatSvMzJTDtVotRowYwZybwpSu5SYsNBQA9FrKLC0tOZKNiIgI+QhuLCwssHHjRnzyySfw9/eHtbU16tevj0qVKhVG/QgAIiOBGzeUn+vXR1jqaDV2AxIREWWW5+BGp3r16qhevXpB1oWykjrEG+XLA66umbqliIiIKE2eE4qfffZZfPHFF5m2z507F88991yBVIoyaNIE+OcfYOlSAFCDGxcXF2PWioiIqFjKc3Bz5MgR9O7dO9P2Xr164ciRIwVSKcrAygpo2RLo1QsA2HJDRESUjTwHN9HR0bCwsMi03dzcHJGRkQVSKcoegxsiIqKs5Tm4qV+/PjZu3Jhp+4YNG1CnTp0CqRRl8P77wJIlQHQ0AOjNc0NERET68pxQPHXqVDzzzDMICgpCly5dAAAHDhzAunXrsHnz5gKvYJn36BEwd67y84svAmDLDRERUXbyHNz069cP27Ztw2effYbNmzfD2toaDRs2xMGDB5ngWhguXVL+rVwZsLMDwOCGiIgoO/kaCt6nTx/06dMHABAZGYn169dj8uTJOH36NFJSUgq0gmWeLrhJXQxTq9XiyZMnABjcEBERGZLnnBudI0eOYOTIkfD29sbXX3+NLl264J9//inIuhGQFtzUqwcACA8Ph1arBcCh4ERERIbkqeUmJCQEq1atwvLlyxEZGYnnn38eCQkJ2LZtG5OJC4tuAr/Ulhtdl5SdnZ3BUWtERERlXa5bbvr164eaNWvi/PnzmD9/Pu7fv4+FCxcWZt1IJFO3FPNtiIiIspfrlpvdu3fjrbfewtixY7nsQlEJDQXCwgCNBqhVCwCDGyIiopzkuuXm6NGjiIqKQtOmTdGyZUt89913ePToUWHWjTw8lADn778BGxsADG6IiIhykuvgplWrVli6dCmCg4Px2muvYcOGDfD29oZWq8X+/fsRFRVVmPUsu9zcgFat1KecwI+IiCh7eR4tZWtri5dffhlHjx7FhQsX8M477+Dzzz+Hh4cH+vfvXxh1pHTYckNERJS9fA8FB4CaNWti7ty5uHv3LtavX19QdSKdd98FJk8Grl9XN3FFcCIiouzlaxK/jExNTTFw4EAMHDiwIE5HgDJSatkyIDwcGD5c3cyWGyIiouw9VcsNFaLgYCWwMTEBatZUNzO4ISIiyh6Dm+JKN79NtWqAlZW6mcENERFR9hjcFFcZJu/TYXBDRESUPQY3xVWGNaV0GNwQERFlj8FNcWWg5SY+Ph6xsbEAGNwQERFlhcFNMbR9+3bEXLumPEkX3Ogm8DM1NYWjo6MxqkZERFTsFchQcCo4ycnJ+N///oeE+Hg8OHsW7qlrSgFpXVLOzs7QaDTGqiIREVGxxpabYubRo0eIj4+HALgcEQGYpcWfzLchIiLKGYObYiY0NFT9+b///tPbx+CGiIgoZwxuipmHDx9iNoAtAFL++ENvH4MbIiKinDG4KWZCQ0PRHcAgAJHp1pQCGNwQERHlBoObYuZRcDDqpP78V+roKB0GN0RERDljcFPMOP7zD+wBPABw4M4dpKSkqPt0Q8EZ3BAREWWNwU0xU/vUKQDAWgBxSUm4ffu2uk/XcuPi4mKMqhEREZUIDG6KkydP0PDmTQDAT6mb0o+YYrcUERFRzhjcFCe//AJzrRbnAFw2NwfA4IaIiCivGNwUJxUq4KS1NVYDaNq0KQDgmm4ZBjC4ISIiyg0GN8VJnz7ws7TENwDatWsHIK3lRkSYUExERJQLxSK4WbRoESpXrgwrKyu0bNkSJ0+ezNVxGzZsgEajwcCBAwu3gkUkMTER4eHhAIC2bdsCSAtuIiIi1JFTDG6IiIiyZvTgZuPGjZg0aRKmT5+OM2fOoGHDhvDz88PDhw+zPe7mzZuYPHky2rdvX0Q1LURaLfDDDwi7ehWAsup3ixYtAAC3bt1CQkKC2iVlY2MDKysro1WViIiouDN6cDNv3jyMGTMGo0aNQp06dbBkyRLY2NhgxYoVWR6TkpKCYcOGYebMmahSpUoR1raQHDkCvP463Dt2hBkANzc3eHl5wd7eHlqtFtevX1e7pDgMnIiIKHtGDW4SExNx+vRpdOvWTd1mYmKCbt264fjx41keN2vWLHh4eGD06NE5XiMhIQGRkZF6j2LnJ2Xgd0irVkgG4OHhAY1Ggxo1agBQuqaYTExERJQ7Rg1uHj16hJSUFHh6eupt9/T0REhIiMFjjh49iuXLl2Pp0qW5usacOXPg6OioPnx8fJ663gUqJgbYtAkAcLlZMwBKcAMA1atXB8DghoiIKC+M3i2VF1FRURg+fDiWLl0KNze3XB3z4YcfIiIiQn3cuXOnkGuZR9u2AdHRgK8vLjk7AwDc3d0BQG25uXbtGoMbIiKiXDIz5sXd3NxgamqKBw8e6G1/8OABypUrl6l8UFAQbt68iX79+qnbtFotAMDMzAwBAQGoWrWq3jGWlpawtLQshNoXkD//VP4dMgShjx4BSGu5Sd8t5eXlBYDBDRERUU6M2nJjYWGBpk2b4sCBA+o2rVaLAwcOoHXr1pnK16pVCxcuXIC/v7/66N+/Pzp37gx/f//i1+WUG6kjpNCwoTpCLGPLDbuliIiIcs+oLTcAMGnSJIwcORLNmjVDixYtMH/+fMTExGDUqFEAgBEjRqB8+fKYM2cOrKysUK9ePb3jnZycACDT9hJDF9zUqoWH69YByJxzExwcjFu3bgFgcENERJQTowc3Q4YMQWhoKKZNm4aQkBA0atQIe/bsUZOMb9++DROTEpUalDcXLgABAUCtWggNDQWQFtw4OTnB3d0doaGhOHHiBAAOBSciIsqJ0YMbABg/fjzGjx9vcN+hQ4eyPXbVqlUFX6Gi5OWlPIBM3VKA0jUVGhqqBj5suSEiIspeKW4SKXkyttwAaXk3OgxuiIiIslcsWm7KrJ9+Ai5fBgYORHyjRoiKigKg33Kjy7vRYXBDRESUPQY3xrR5M/Dbb0DFiggtXx4AYG5uDkdHR7UIW26IiIjyht1SxpR+pFS6fBuNRqMWSR/caDQadXQYERERGcbgxlgSE4Hr15Wfa9ZUg5v0+TYAUK1aNfVnZ2dnmJqaFlkViYiISiIGN8YSFASkpAB2doC3t8FkYgCwtrZWJydklxQREVHOGNwYS7ouKWg0BoeB6+i6pjjHDRERUc4Y3BhL+uAGhoeB6+iCG7bcEBER5YzBjbEEBSn/pgY32bXcNG7cGABQpUqVoqkbERFRCcah4Mby44/A9OlA6orl2bXcjBw5Em5ubujUqVNR1pCIiKhEYnBjLCYmQLpVzLNrubGwsMCgQYOKrGpEREQlGbuliomshoITERFR3jC4MYZTp4DnngMWLFA3ZdctRURERLnH4MYYTp9Wll7Ytw8AEBMTg9jYWACGu6WIiIgo9xjcGEMWw8CtrKxgZ2dnrFoRERGVCgxujEEX3NSsCQBZritFREREecfgxhjyMIEfERER5Q2Dm6IWFwfcuqX8nIsJ/IiIiChvGNwUtWvXABHAyQlIDWY4DJyIiKjgMLgpavfuARYW6oKZALuliIiIChJnKC5qvXoBsbHAkyfqJnZLERERFRy23BiDqSng5qY+ZcsNERFRwWFwUwyw5YaIiKjgMLgpSiJA27bACy8Ajx+rm9lyQ0REVHAY3BSlkBDg77+BjRuB1JmIRYQtN0RERAWIwU1RCglR/nV3V0ZMAYiOjkZCQkLqZgY3RERET4vBTVFK7X5CuiBG12pja2sLW1tbY9SKiIioVGFwU5SyCW7YakNERFQwGNwUJQPBDZOJiYiIChaDm6LElhsiIqJCx+CmKCUmAubmbLkhIiIqRFx+oSh9+SUwdy6QnKxuunHjBgDA09PTWLUiIiIqVdhyU9Q0GqX1BsocN3v27AEAtG/f3pi1IiIiKjUY3BjRhQsXcOfOHVhbW6Nz587Grg4REVGpwOCmKPXuDQwaBAQHAwB27twJAOjatSusra2NWTMiIqJSg8FNUUlOBnbvBrZtA8yUVKfff/8dANC3b18jVoyIiKh0YXBTVMLClH81GsDFBY8ePcI///wDAOjdu7cRK0ZERFS6MLgpKro5blxdAVNT7NmzB1qtFg0aNICPj49x60ZERFSKMLgpKrrgxs0NQFq+DbukiIiIChaDm6KSbnbi5ORkdQh4nz59jFgpIiKi0ofBTVFJF9z8/fffCA8Ph6urK1q2bGncehEREZUyDG6KSnw8YGEBuLuro6R69eoFU1NTI1eMiIiodOHyC0XlnXeASZOA5GTsbNQIAPNtiIiICgNbboqSRoMbd+/i8uXLMDU1hZ+fn7FrREREVOowuCliulFSbdu2hZOTk3ErQ0REVAqxW6qoDB8OREXB/9EjAOySIiIiKiwMborKvn3Aw4fwT10RnEPAiYiICge7pYqCVqsuv3A/KQmOjo6oXbu2kStFRERUOjG4KQpPngApKQCARwC8vLyg0WiMWyciIqJSisFNUUidwC/RxgZJADw9PY1bHyIiolKMwU1RSA1uYm1sADC4ISIiKkwMbopCanATYWkJgMENERFRYWJwUxRiYwFLSzw2UW53uXLljFwhIiKi0ovBTVF48UUgLg4z6tYFwJYbIiKiwsTgpqhoNLiX2j3F4IaIiKjwMLgpQiEhIQAY3BARERUmzlBcFCZOhNy4Ad+QENwDgxsiIqLCxOCmKBw6BM25c7BLfcrghoiIqPCwW6oopObahAJwcnKCZeqQcCIiIip4DG4Km4hecMNWGyIiosLF4KawRUYCSUkAGNwQEREVBQY3hS211SbJwgJx4AR+REREhY3BTWFLDW6ira0BsOWGiIiosDG4KWxRUYCVFcLNzQEwuCEiIipsDG4KW48eQGws3m7WDACDGyIiosLG4KYoaDS4m9o9xZwbIiKiwlUsgptFixahcuXKsLKyQsuWLXHy5Mksyy5duhTt27eHs7MznJ2d0a1bt2zLFxcPHjwAwJYbIiKiwmb04Gbjxo2YNGkSpk+fjjNnzqBhw4bw8/PDw4cPDZY/dOgQhg4dij///BPHjx+Hj48PevTogXv37hVxzXNp9mxI//5ozHWliIiIioRGRMSYFWjZsiWaN2+O7777DgCg1Wrh4+ODN998Ex988EGOx6ekpMDZ2RnfffcdRowYkWP5yMhIODo6IiIiAg4ODk9d/xz16gXs2YOXAawEEBcXBysrq8K/LhGVWSkpKUhKnV+LqCSxsLCAiYnhdpe8fH4bdW2pxMREnD59Gh9++KG6zcTEBN26dcPx48dzdY7Y2FgkJSXBxcXF4P6EhAQkJCSozyMjI5+u0nmVbnZiR0dHBjZEVGhEBCEhIQgPDzd2VYjyxcTEBL6+vrCwsHiq8xg1uHn06BFSUlIyddV4enri6tWruTrH+++/D29vb3Tr1s3g/jlz5mDmzJlPXdd8SxfcMJmYiAqTLrDx8PCAjY0NNBqNsatElGtarRb3799HcHAwKlas+FS/vyV6VfDPP/8cGzZswKFDh7JsEfnwww8xadIk9XlkZCR8fHyKqop6wU0F5tsQUSFJSUlRAxtXV1djV4coX9zd3XH//n0kJyfDPHV+uPwwanDj5uYGU1NTdSSRzoMHD3Js5fjqq6/w+eef448//kCDBg2yLGdpaWm8VbhjYoC4OABKcNOUwQ0RFRJdjo2NjY2Ra0KUf7ruqJSUlKcKbow6WsrCwgJNmzbFgQMH1G1arRYHDhxA69atszxu7ty5mD17Nvbs2YNmqZPjFUuprTbJpqaIAkdKEVHhY1cUlWQF9ftr9KHgkyZNwtKlS7F69WpcuXIFY8eORUxMDEaNGgUAGDFihF7C8RdffIGpU6dixYoVqFy5MkJCQhASEoLo6GhjvYSsRUQA1taITG05Ys4NEVHhq1y5MubPn2/sapARGT24GTJkCL766itMmzYNjRo1gr+/P/bs2aO2cty+fRvBwcFq+cWLFyMxMRGDBw+Gl5eX+vjqq6+M9RKy1rAhEBuL0R06AGDLDRFRehqNJtvHjBkz8nXef//9F6+++upT1e3GjRt44YUX4O3tDSsrK1SoUAEDBgzI9WAXAHjppZcwcODAXJc/fvw4TE1N0adPn3zUmNIrFgnF48ePx/jx4w3uO3TokN7zmzdvFn6FCtjdR48AMLghIkov/RfXjRs3Ytq0aQgICFC32dnZqT+LCFJSUmBmlvPHlru7+1PVKykpCd27d0fNmjWxZcsWeHl54e7du9i9e3ehDrNfvnw53nzzTSxfvhz379+Ht7d3oV0rJ4mJiU89HNuYjN5yUxZw6QUioszKlSunPhwdHaHRaNTnV69ehb29PXbv3o2mTZvC0tISR48eRVBQEAYMGABPT0/Y2dmhefPm+OOPP/TOm7FbSqPRYNmyZRg0aBBsbGxQvXp17NixI8t6Xbp0CUFBQfj+++/RqlUrVKpUCW3btsUnn3yCVq1aqeXu3LmD559/Hk5OTnBxccGAAQPUL+AzZszA6tWrsX37drUlKuOX9fSio6OxceNGjB07Fn369MGqVasylfntt9/QvHlzWFlZwc3NDYMGDVL3JSQk4P3334ePjw8sLS1RrVo1LF++HACwatUqODk56Z1r27ZtevktM2bMQKNGjbBs2TL4+vqqI5D37NmDdu3awcnJCa6urujbty+CgoL0znX37l0MHToULi4usLW1RbNmzXDixAncvHkTJiYmOHXqlF75+fPno1KlStBqtVnej6fF4KYwLV4M6dcPHe/fB8CcGyIqOiKCmJgYozwKcuL7Dz74AJ9//jmuXLmCBg0aIDo6Gr1798aBAwdw9uxZ9OzZE/369cPt27ezPc/MmTPx/PPP4/z58+jduzeGDRuGx48fGyzr7u4OExMTbN68GSkpKQbLJCUlwc/PD/b29vjrr79w7Ngx2NnZoWfPnkhMTMTkyZPx/PPPo2fPnggODkZwcDDatGmTZf1++eUX1KpVCzVr1sSLL76IFStW6N3HnTt3YtCgQejduzfOnj2LAwcOoEWLFur+ESNGYP369fj2229x5coV/PDDD3otX7kRGBiIX3/9FVu2bIG/vz8AICYmBpMmTcKpU6dw4MABmJiYYNCgQWpgEh0djY4dO+LevXvYsWMHzp07h/feew9arRaVK1dGt27dsHLlSr3rrFy5Ei+99FKWMxEXCCljIiIiBIBEREQU/sVeflkEkCmAAJC4uLjCvyYRlUlxcXFy+fJl9e9MdHS0IPVvT1E/oqOj81z/lStXiqOjo/r8zz//FACybdu2HI+tW7euLFy4UH1eqVIl+eabb9TnAOTjjz9Wn+vuze7du7M853fffSc2NjZib28vnTt3llmzZklQUJC6/+eff5aaNWuKVqtVtyUkJIi1tbXs3btXRERGjhwpAwYMyLH+IiJt2rSR+fPni4hIUlKSuLm5yZ9//qnub926tQwbNszgsQEBAQJA9u/fb3B/xnsrIrJ161ZJHwJMnz5dzM3N5eHDh9nWMzQ0VADIhQsXRETkhx9+EHt7ewkLCzNYfuPGjeLs7Czx8fEiInL69GnRaDRy48YNg+Uz/h6nl5fPb7bcFKYLFwAAt8GlF4iI8iPjdB/R0dGYPHkyateuDScnJ9jZ2eHKlSs5ttyknw/N1tYWDg4OWS7QDADjxo1DSEgI1q5di9atW2PTpk2oW7cu9u/fDwA4d+4cAgMDYW9vDzs7O9jZ2cHFxQXx8fGZum1yEhAQgJMnT2Lo0KEAADMzMwwZMkTtVgIAf39/dO3a1eDx/v7+MDU1RceOHfN03YwqVaqUKV/p2rVrGDp0KKpUqQIHBwdUrlwZANT77e/vj8aNG2e5BNLAgQNhamqKrVu3AlC6yDp37qyep7AUi4TiUik0FEjtZ/wDzLchoqJlY2NjtCkyCnIiQVtbW73nkydPxv79+/HVV1+hWrVqsLa2xuDBg5GYmJjteTJOCKfRaHLM+bC3t0e/fv3Qr18/fPLJJ/Dz88Mnn3yC7t27Izo6Gk2bNsXatWszHZfXhObly5cjOTlZL4FYRGBpaYnvvvsOjo6OsLa2zvL47PYBynpNkqGr0NDCqhnvNQD069cPlSpVwtKlS+Ht7Q2tVot69eqp9zuna1tYWGDEiBFYuXIlnnnmGaxbtw4LFizI9piCwOCmsOzfD4jgSaVKCLl1C9UZ3BBREdJoNAY/rEq6Y8eO4aWXXlKTaaOjo4tkFK1Go0GtWrXw999/AwCaNGmCjRs3wsPDI8sVqi0sLLLM2dFJTk7GTz/9hK+//ho9evTQ2zdw4ECsX78er7/+Oho0aIADBw6oc8ClV79+fWi1Whw+fNjgOovu7u6IiopCTEyM+juhy6nJTlhYGAICArB06VK0b98eAHD06FG9Mg0aNMCyZcvw+PHjLFtvXnnlFdSrVw/ff/89kpOT8cwzz+R47afFbqnCsmcPACCoWjUATCYmIioI1atXVxNez507hxdeeKHAR934+/tjwIAB2Lx5My5fvozAwEAsX74cK1aswIABAwAAw4YNg5ubGwYMGIC//voLN27cwKFDh/DWW2/h7t27AJRRW+fPn0dAQAAePXpksLXk999/x5MnTzB69GjUq1dP7/Hss8+qXVPTp0/H+vXrMX36dFy5cgUXLlzAF198oV5n5MiRePnll7Ft2za1Lr/88gsAoGXLlrCxscGUKVMQFBSEdevWGRyNlZGzszNcXV3x448/IjAwEAcPHtRbqxEAhg4dinLlymHgwIE4duwYrl+/jl9//RXHjx9Xy9SuXRutWrXC+++/j6FDh+bY2lMQGNwUBq0W2LsXAOCf2mLDbikioqc3b948ODs7o02bNujXrx/8/PzQpEmTAr1GhQoVULlyZcycORMtW7ZEkyZNsGDBAsycORMfffQRAKXr7ciRI6hYsSKeeeYZ1K5dG6NHj0Z8fLzakjNmzBjUrFkTzZo1g7u7O44dO5bpWsuXL0e3bt3g6OiYad+zzz6LU6dO4fz58+jUqRM2bdqEHTt2oFGjRujSpQtOnjypll28eDEGDx6MN954A7Vq1cKYMWMQExMDAHBxccGaNWuwa9cu1K9fH+vXr8/VBIkmJibYsGEDTp8+jXr16uHtt9/Gl19+qVfGwsIC+/btg4eHB3r37o369evj888/h6mpqV650aNHIzExES+//HKO1y0IGsnYEVfKRUZGwtHREREREVk2JT61x4+B4cOBU6fwWu/e+HHVKsyePRsff/xx4VyPiMq8+Ph43LhxQ2+OEqLiYvbs2di0aRPOnz+fbbnsfo/z8vnNlpvC4OIC7NwJ3L+P+5ydmIiIyqjo6GhcvHgR3333Hd58880iuy6Dm8JkaqrOTsycGyIiKmvGjx+Ppk2bolOnTkXWJQUwuCl4sbHAnTvq05CQEABsuSEiorJn1apVSEhIwMaNGzPl4RQmBjcFbe9eoGJFoF8/iAjXlSIiIipiDG4KWuoQcFSpgoiICHWiIwY3RERERYPBTUESSQtuevZUW2249AIREVHRYXBTkK5eBW7fBiwtgY4d2SVFRERkBAxuCpKu1aZjR8DGhsnERERERsDgpiClzkqMnj0BgC03RERERsDgpqDExQGHDys/M7ghIiIyGgY3BUWjAX76CXj7baBWLQBpc9xwAj8iosw0Gk22j9ysf5Tdubdt25ZjucOHD6NLly5wcXGBjY0NqlevjpEjR6ojXXOjcuXKmD9/fq7Lz5kzB6amppnWaaKCw+CmoFhZAc89B8ybB2g0CA0Nxa+//goAqJUa7BARUZrg4GD1MX/+fDg4OOhtmzx5cqFe//Lly+jZsyeaNWuGI0eO4MKFC1i4cCEsLCyQkpJSaNddsWIF3nvvPaxYsaLQrpFbeQniShIGN4Xko48+Qnh4OBo3boxBgwYZuzpERMVOuXLl1IejoyM0Go3etg0bNqB27dqwsrJCrVq18P3336vHJiYmYvz48fDy8oKVlRUqVaqEOXPmAFBaUgBg0KBB0Gg06vOM9u3bh3LlymHu3LmoV68eqlatip49e2Lp0qWwtrZWyx09ehTt27eHtbU1fHx88NZbb6krbnfq1Am3bt3C22+/rbY4Zefw4cOIi4vDrFmzEBkZib///ltvv1arxdy5c1GtWjVYWlqiYsWK+PTTT9X9d+/exdChQ+Hi4gJbW1s0a9YMJ06cAAC89NJLGDhwoN75Jk6ciE6dOqnPO3XqhPHjx2PixIlwc3ODn58fAGW19fr168PW1hY+Pj544403EB0drXeuY8eOoVOnTrCxsYGzszP8/Pzw5MkT/PTTT3B1dUVCQoJe+YEDB2L48OHZ3o/CwuCmEJw6dQrLli0DACxcuLBIp5wmItITE5P1Iz4+92Xj4nJXtoCsXbsW06ZNw6effoorV67gs88+w9SpU7F69WoAwLfffosdO3bgl19+QUBAANauXasGMf/++y8AYOXKlQgODlafZ1SuXDkEBwfjyJEjWdYjKCgIPXv2xLPPPovz589j48aNOHr0KMaPHw8A2LJlCypUqIBZs2apLU7ZWb58OYYOHQpzc3MMHToUy5cv19v/4Ycf4vPPP8fUqVNx+fJlrFu3Ts3bjI6ORseOHXHv3j3s2LED586dw3vvvQetVpvzDU1n9erVsLCwwLFjx7BkyRIAgImJCb799ltcunQJq1evxsGDB/Hee++px/j7+6Nr166oU6cOjh8/jqNHj6Jfv35ISUnBc889h5SUFOzYsUMt//DhQ+zcubNI15PSI2VMRESEAJCIiIhCOX9KSoq0atVKAMiLL75YKNcgIsooLi5OLl++LHFxcfo7lOlFDT9699Yva2OTddmOHfXLurkZLpdPK1euFEdHR/V51apVZd26dXplZs+eLa1btxYRkTfffFO6dOkiWq3W4PkAyNatW7O9ZnJysrz00ksCQMqVKycDBw6UhQsX6n0+jB49Wl599VW94/766y8xMTFR73WlSpXkm2++yfE1RkREiLW1tfj7+4uIyNmzZ8XOzk6ioqJERCQyMlIsLS1l6dKlBo//4YcfxN7eXsLCwgzuHzlypAwYMEBv24QJE6RjuveuY8eO0rhx4xzrumnTJnF1dVWfDx06VNq2bZtl+bFjx0qvXr3U519//bVUqVIly/cnK1n+HkvePr/ZclPAfv75Z/zzzz+ws7PDF198YezqEBGVODExMQgKCsLo0aNhZ2enPj755BMEBQUBULpg/P39UbNmTbz11lvYt29fnq9jamqKlStX4u7du5g7dy7Kly+Pzz77DHXr1lVbYM6dO4dVq1bp1cPPzw9arRY3btzI0/XWr1+PqlWromHDhgCARo0aoVKlSti4cSMA4MqVK0hISEDXrl0NHu/v74/GjRvDxcUlz681vaZNm2ba9scff6Br164oX7487O3tMXz4cISFhSE2Nla9dlb1AoAxY8Zg3759uHfvHgBlwcyXXnopx266wmJmlKuWUhEREXj//fcBAFOnToW3t7eRa0REZV6GvAk9GbvMHz7MuqxJhu/CN2/mu0o50eV6LF26FC1bttTbp+vmb9KkCW7cuIHdu3fjjz/+wPPPP49u3bph8+bNeb5e+fLlMXz4cAwfPhyzZ89GjRo1sGTJEsycORPR0dF47bXX8NZbb2U6rmLFinm6zvLly3Hp0iWYmaV99Gq1WqxYsQKjR4/Wy/MxJKf9JiYmEBG9bUlJSZnK2dra6j2/efMm+vbti7Fjx+LTTz+Fi4sLjh49itGjRyMxMRE2NjY5Xrtx48Zo2LAhfvrpJ/To0QOXLl3Czp07sz2mMDG4KUCzZs3CgwcPUKNGDUycONHY1SEiAjJ8kBmlbB55enrC29sb169fx7Bhw7Is5+DggCFDhmDIkCEYPHgwevbsicePH8PFxQXm5ub5GvHk7OwMLy8vNWG4SZMmuHz5MqpVq5blMbkZXXXhwgWcOnUKhw4d0mt5efz4MTp16oSrV6+ievXqsLa2xoEDB/DKK69kOkeDBg2wbNky9TVm5O7ujosXL+pt8/f3h7m5ebZ1O336NLRaLb7++muYpAaxv/zyS6ZrHzhwADNnzszyPK+88grmz5+Pe/fuoVu3bvDx8cn2uoUqT51hpUBh5dxcvnxZzMzMBIDs3r27QM9NRJST7HIVSoKMOTdLly4Va2trWbBggQQEBMj58+dlxYoV8vXXX4uIktOxbt06uXLligQEBMjo0aOlXLlykpKSIiIi1atXl7Fjx0pwcLA8fvzY4DWXLFkir7/+uuzdu1cCAwPl4sWL8t5774mJiYkcOnRIRETOnTsn1tbWMm7cODl79qz8999/sm3bNhk3bpx6nu7du0v//v3l7t27EhoaavBaEyZMkJYtWxrc16JFC5k8ebKIiMyYMUOcnZ1l9erVEhgYKMePH5dly5aJiEhCQoLUqFFD2rdvL0ePHpWgoCDZvHmz/P333yIismfPHtFoNLJ69Wr577//ZNq0aeLg4JAp52bChAl61/f39xcAMn/+fAkKCpKffvpJypcvLwDkyZMnIiISEBAgFhYWMnbsWDl37pxcuXJFvv/+e73XGx4eLjY2NmJhYSEbNmww+FpzUlA5NwxuCsiBAwfE29tb+vfvX6DnJSLKjdIW3IiIrF27Vho1aiQWFhbi7OwsHTp0kC1btoiIyI8//iiNGjUSW1tbcXBwkK5du8qZM2fUY3fs2CHVqlUTMzMzqVSpksFrnjlzRl588UXx9fUVS0tLcXV1lQ4dOsiOHTv0yp08eVK6d+8udnZ2YmtrKw0aNJBPP/1U3X/8+HFp0KCBWFpaiqE2g4SEBHF1dZW5c+carMcXX3whHh4ekpiYKCkpKfLJJ59IpUqVxNzcXCpWrCifffaZWvbmzZvy7LPPioODg9jY2EizZs3kxIkT6v5p06aJp6enODo6yttvvy3jx4/PMbgREZk3b554eXmJtbW1+Pn5yU8//aQX3IiIHDp0SNq0aSOWlpbi5OQkfn5+evtFRIYPHy4uLi4SHx9v8LXmpKCCG41Ihg66Ui4yMhKOjo6IiIiAg4NDgZ47Ojoa0dHRnJGYiIpcfHw8bty4AV9fX1hZWRm7OlRGde3aFXXr1sW3336br+Oz+z3Oy+c3c24KkC6TnoiIqCx58uQJDh06hEOHDulNtmgsDG6IiIjoqTRu3BhPnjzBF198gZo1axq7OgxuiIiI6OncLMSpAfKDk/gRERFRqcLghoiIiEoVBjdERKVIGRsAS6VMQf3+MrghIioFdLPQ6tYCIiqJEhMTAaQts5FfTCgmIioFTE1N4eTkhIep60PZ2NgYbdFCovzQarUIDQ2FjY2N3vpb+cHghoiolNBNIPowuwUwiYoxExMTVKxY8akDcwY3RESlhEajgZeXFzw8PAyuBk1U3FlYWKiLdz4NBjdERKWMqanpU+csEJVkTCgmIiKiUoXBDREREZUqDG6IiIioVClzOTe6CYIiIyONXBMiIiLKLd3ndm4m+itzwU1UVBQAwMfHx8g1ISIioryKioqCo6NjtmU0Usbm6tZqtbh//z7s7e0LfIKryMhI+Pj44M6dO3BwcCjQc5M+3uuiw3tddHiviw7vddEpqHstIoiKioK3t3eOw8XLXMuNiYkJKlSoUKjXcHBw4H+WIsJ7XXR4r4sO73XR4b0uOgVxr3NqsdFhQjERERGVKgxuiIiIqFRhcFOALC0tMX36dFhaWhq7KqUe73XR4b0uOrzXRYf3uugY416XuYRiIiIiKt3YckNERESlCoMbIiIiKlUY3BAREVGpwuCGiIiIShUGNwVk0aJFqFy5MqysrNCyZUucPHnS2FUqcY4cOYJ+/frB29sbGo0G27Zt09svIpg2bRq8vLxgbW2Nbt264dq1a3plHj9+jGHDhsHBwQFOTk4YPXo0oqOji/BVlAxz5sxB8+bNYW9vDw8PDwwcOBABAQF6ZeLj4zFu3Di4urrCzs4Ozz77LB48eKBX5vbt2+jTpw9sbGzg4eGBd999F8nJyUX5Uoq9xYsXo0GDBuoEZq1bt8bu3bvV/bzPhefzzz+HRqPBxIkT1W283wVjxowZ0Gg0eo9atWqp+41+n4We2oYNG8TCwkJWrFghly5dkjFjxoiTk5M8ePDA2FUrUXbt2iUfffSRbNmyRQDI1q1b9fZ//vnn4ujoKNu2bZNz585J//79xdfXV+Li4tQyPXv2lIYNG8o///wjf/31l1SrVk2GDh1axK+k+PPz85OVK1fKxYsXxd/fX3r37i0VK1aU6Ohotczrr78uPj4+cuDAATl16pS0atVK2rRpo+5PTk6WevXqSbdu3eTs2bOya9cucXNzkw8//NAYL6nY2rFjh+zcuVP+++8/CQgIkClTpoi5ublcvHhRRHifC8vJkyelcuXK0qBBA5kwYYK6nfe7YEyfPl3q1q0rwcHB6iM0NFTdb+z7zOCmALRo0ULGjRunPk9JSRFvb2+ZM2eOEWtVsmUMbrRarZQrV06+/PJLdVt4eLhYWlrK+vXrRUTk8uXLAkD+/fdftczu3btFo9HIvXv3iqzuJdHDhw8FgBw+fFhElHtrbm4umzZtUstcuXJFAMjx48dFRAlGTUxMJCQkRC2zePFicXBwkISEhKJ9ASWMs7OzLFu2jPe5kERFRUn16tVl//790rFjRzW44f0uONOnT5eGDRsa3Fcc7jO7pZ5SYmIiTp8+jW7duqnbTExM0K1bNxw/ftyINStdbty4gZCQEL377OjoiJYtW6r3+fjx43ByckKzZs3UMt26dYOJiQlOnDhR5HUuSSIiIgAALi4uAIDTp08jKSlJ737XqlULFStW1Lvf9evXh6enp1rGz88PkZGRuHTpUhHWvuRISUnBhg0bEBMTg9atW/M+F5Jx48ahT58+evcV4O91Qbt27Rq8vb1RpUoVDBs2DLdv3wZQPO5zmVs4s6A9evQIKSkpem8QAHh6euLq1atGqlXpExISAgAG77NuX0hICDw8PPT2m5mZwcXFRS1DmWm1WkycOBFt27ZFvXr1ACj30sLCAk5OTnplM95vQ++Hbh+luXDhAlq3bo34+HjY2dlh69atqFOnDvz9/XmfC9iGDRtw5swZ/Pvvv5n28fe64LRs2RKrVq1CzZo1ERwcjJkzZ6J9+/a4ePFisbjPDG6Iyrhx48bh4sWLOHr0qLGrUmrVrFkT/v7+iIiIwObNmzFy5EgcPnzY2NUqde7cuYMJEyZg//79sLKyMnZ1SrVevXqpPzdo0AAtW7ZEpUqV8Msvv8Da2tqINVOwW+opubm5wdTUNFMW+IMHD1CuXDkj1ar00d3L7O5zuXLl8PDhQ739ycnJePz4Md+LLIwfPx6///47/vzzT1SoUEHdXq5cOSQmJiI8PFyvfMb7bej90O2jNBYWFqhWrRqaNm2KOXPmoGHDhliwYAHvcwE7ffo0Hj58iCZNmsDMzAxmZmY4fPgwvv32W5iZmcHT05P3u5A4OTmhRo0aCAwMLBa/1wxunpKFhQWaNm2KAwcOqNu0Wi0OHDiA1q1bG7FmpYuvry/KlSund58jIyNx4sQJ9T63bt0a4eHhOH36tFrm4MGD0Gq1aNmyZZHXuTgTEYwfPx5bt27FwYMH4evrq7e/adOmMDc317vfAQEBuH37tt79vnDhgl5AuX//fjg4OKBOnTpF80JKKK1Wi4SEBN7nAta1a1dcuHAB/v7+6qNZs2YYNmyY+jPvd+GIjo5GUFAQvLy8isfv9VOnJJNs2LBBLC0tZdWqVXL58mV59dVXxcnJSS8LnHIWFRUlZ8+elbNnzwoAmTdvnpw9e1Zu3bolIspQcCcnJ9m+fbucP39eBgwYYHAoeOPGjeXEiRNy9OhRqV69OoeCGzB27FhxdHSUQ4cO6Q3ljI2NVcu8/vrrUrFiRTl48KCcOnVKWrduLa1bt1b364Zy9ujRQ/z9/WXPnj3i7u7OIbMZfPDBB3L48GG5ceOGnD9/Xj744APRaDSyb98+EeF9LmzpR0uJ8H4XlHfeeUcOHTokN27ckGPHjkm3bt3Ezc1NHj58KCLGv88MbgrIwoULpWLFimJhYSEtWrSQf/75x9hVKnH+/PNPAZDpMXLkSBFRhoNPnTpVPD09xdLSUrp27SoBAQF65wgLC5OhQ4eKnZ2dODg4yKhRoyQqKsoIr6Z4M3SfAcjKlSvVMnFxcfLGG2+Is7Oz2NjYyKBBgyQ4OFjvPDdv3pRevXqJtbW1uLm5yTvvvCNJSUlF/GqKt5dfflkqVaokFhYW4u7uLl27dlUDGxHe58KWMbjh/S4YQ4YMES8vL7GwsJDy5cvLkCFDJDAwUN1v7PusERF5+vYfIiIiouKBOTdERERUqjC4ISIiolKFwQ0RERGVKgxuiIiIqFRhcENERESlCoMbIiIiKlUY3BAREVGpwuCGiMiAVatWZVrVmIhKBgY3RPRUQkJCMGHCBFSrVg1WVlbw9PRE27ZtsXjxYsTGxhq7ev9v7+5Dmmr7OIB/TyzHtpzCFBODptPCRSunEbkoKYUYLB3kMgzK3gQjZDR7j5aBk5BKit4IhPw3lpRr0AtptYheEHvBiiYl0cIoyka+kLvuP26ePc+59ek2Hx+7793fz19e1/U717kuYePLOUfPmOj1ehw7dkzWt3r1arx8+fLXLIiI/ieKX70AIvr76u7uhsViQWJiIurq6jB37lwolUo8efIEZ8+eRVpaGlauXPlL1iaEwPDwMBSK8X3NqVQqqFSqCV4VEU0GXrkhonGrqqqCQqHAw4cP4XA4kJ2djYyMDBQXF8Pn88FmswEAPn/+jE2bNiE5ORlarRbLli1DZ2dndB6324358+ejubkZer0eCQkJKCsrw9evX6M1kUgEHo8H6enpUKlUmDdvHi5cuBAdb2trgyRJ8Pv9yM3NhVKpxJ07dxAMBlFcXIyUlBRMmzYNCxYswPXr16PHFRQU4M2bN3A6nZAkCZIkARj9ttSpU6dgMBgQFxeH2bNno7m5WTYuSRLOnTsHu90OtVqNrKwsXLp0acJ+30Q0Ngw3RDQuHz9+xNWrV7F161ZoNJpRa/4VFEpLS9Hb2wu/349Hjx7BbDZj+fLl+PTpU7Q2GAyipaUFra2taG1tRXt7O+rr66PjHo8H58+fx+nTp/Hs2TM4nU6sXbsW7e3tsnPu2rUL9fX16OrqgslkQjgchtVqxY0bN9DR0YEVK1bAZrOhp6cHAOD1ejFjxgzU1tYiFAohFAqNupeLFy+iuroa27dvx9OnT1FZWYmKigrcvHlTVnfw4EE4HA48fvwYVqsV5eXlsn0S0SSYkNdvEtE/zr179wQA4fV6Zf06nU5oNBqh0WjEjh07xO3bt4VWqxUDAwOyOoPBIM6cOSOEEOLAgQNCrVaLvr6+6HhNTY1YuHChEEKIgYEBoVarxd27d2VzbNy4UaxZs0YI8e+3yre0tPzp2ufMmSOOHz8ebc+cOVMcPXpUVtPU1CQSEhKi7fz8fLF582ZZTWlpqbBardE2ALFv375oOxwOCwDC7/f/6ZqIaOLwmRsimlD3799HJBJBeXk5BgcH0dnZiXA4DJ1OJ6vr7+9HMBiMtvV6PeLj46Pt1NRU9Pb2AgBevXqFb9++oaioSDbH0NAQcnJyZH15eXmydjgchtvths/nQygUwvfv39Hf3x+9cjNWXV1d2LJli6zPYrGgsbFR1mcymaI/azQaaLXa6D6IaHIw3BDRuGRmZkKSJLx48ULWn5GRAQDRh3HD4TBSU1PR1tY2Yo7/fKZl6tSpsjFJkhCJRKJzAIDP50NaWpqsTqlUytp/vEXmcrlw7do1NDQ0IDMzEyqVCqtWrcLQ0NAYd/pzfrQPIpocDDdENC46nQ5FRUU4ceIEtm3b9l+fuzGbzXj//j0UCgX0ev24zmU0GqFUKtHT04OlS5f+1LGBQADr16+H3W4H8HtQev36tawmLi4Ow8PDP5wnOzsbgUAA69atk81tNBp/aj1E9P/HcENE43by5ElYLBbk5eXB7XbDZDJhypQpePDgAZ4/f47c3FwUFhZi0aJFKCkpweHDhzFr1iy8e/cOPp8Pdrt9xG2k0cTHx8PlcsHpdCISiWDx4sX48uULAoEAtFqtLHD8UVZWFrxeL2w2GyRJwv79+0dcSdHr9bh16xbKysqgVCqRlJQ0Yp6amho4HA7k5OSgsLAQly9fhtfrlf3lFRH9NTDcENG4GQwGdHR0oK6uDrt378bbt2+hVCphNBrhcrlQVVUFSZJw5coV7N27FxUVFfjw4QOmT5+OJUuWICUlZcznOnToEJKTk+HxeNDd3Y3ExESYzWbs2bPnh8cdOXIEGzZsQH5+PpKSkrBz50709fXJampra1FZWQmDwYDBwUEIIUbMU1JSgsbGRjQ0NKC6uhrp6eloampCQUHBmPdARJNDEqN9iomIiIj+pvh/boiIiCimMNwQERFRTGG4ISIiopjCcENEREQxheGGiIiIYgrDDREREcUUhhsiIiKKKQw3REREFFMYboiIiCimMNwQERFRTGG4ISIiopjCcENEREQx5Tfiz/WesQjr4gAAAABJRU5ErkJggg==", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -341,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -398,9 +628,40038 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.0183 - sparse_categorical_accuracy: 0.2357 - val_loss: 1.7497 - val_sparse_categorical_accuracy: 0.3435\n", + "Epoch 2/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.7483 - sparse_categorical_accuracy: 0.3592 - val_loss: 1.7239 - val_sparse_categorical_accuracy: 0.3617\n", + "Epoch 3/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.6681 - sparse_categorical_accuracy: 0.3905 - val_loss: 1.6222 - val_sparse_categorical_accuracy: 0.4131\n", + "Epoch 4/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.6086 - sparse_categorical_accuracy: 0.4203 - val_loss: 1.6065 - val_sparse_categorical_accuracy: 0.4314\n", + "Epoch 5/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.5827 - sparse_categorical_accuracy: 0.4349 - val_loss: 1.5625 - val_sparse_categorical_accuracy: 0.4425\n", + "Epoch 6/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.5774 - sparse_categorical_accuracy: 0.4396 - val_loss: 1.5571 - val_sparse_categorical_accuracy: 0.4412\n", + "Epoch 7/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.5441 - sparse_categorical_accuracy: 0.4534 - val_loss: 1.6152 - val_sparse_categorical_accuracy: 0.4275\n", + "Epoch 8/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.5458 - sparse_categorical_accuracy: 0.4547 - val_loss: 1.5552 - val_sparse_categorical_accuracy: 0.4608\n", + "Epoch 9/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.5108 - sparse_categorical_accuracy: 0.4698 - val_loss: 1.5329 - val_sparse_categorical_accuracy: 0.4570\n", + "Epoch 10/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4833 - sparse_categorical_accuracy: 0.4784 - val_loss: 1.5404 - val_sparse_categorical_accuracy: 0.4670\n", + "Epoch 11/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4902 - sparse_categorical_accuracy: 0.4786 - val_loss: 1.5396 - val_sparse_categorical_accuracy: 0.4539\n", + "Epoch 12/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4626 - sparse_categorical_accuracy: 0.4895 - val_loss: 1.6269 - val_sparse_categorical_accuracy: 0.4349\n", + "Epoch 13/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4669 - sparse_categorical_accuracy: 0.4918 - val_loss: 1.4837 - val_sparse_categorical_accuracy: 0.4904\n", + "Epoch 14/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4613 - sparse_categorical_accuracy: 0.4959 - val_loss: 1.5325 - val_sparse_categorical_accuracy: 0.4853\n", + "Epoch 15/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4501 - sparse_categorical_accuracy: 0.5004 - val_loss: 1.5449 - val_sparse_categorical_accuracy: 0.4600\n", + "Epoch 16/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4634 - sparse_categorical_accuracy: 0.4941 - val_loss: 1.5874 - val_sparse_categorical_accuracy: 0.4659\n", + "Epoch 17/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4436 - sparse_categorical_accuracy: 0.5040 - val_loss: 1.6198 - val_sparse_categorical_accuracy: 0.4492\n", + "Epoch 18/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4326 - sparse_categorical_accuracy: 0.5101 - val_loss: 1.4996 - val_sparse_categorical_accuracy: 0.4915\n", + "Epoch 19/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4360 - sparse_categorical_accuracy: 0.5090 - val_loss: 1.5260 - val_sparse_categorical_accuracy: 0.4847\n", + "Epoch 20/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4265 - sparse_categorical_accuracy: 0.5128 - val_loss: 1.5542 - val_sparse_categorical_accuracy: 0.4831\n", + "Epoch 21/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4229 - sparse_categorical_accuracy: 0.5149 - val_loss: 1.5607 - val_sparse_categorical_accuracy: 0.4721\n", + "Epoch 22/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4130 - sparse_categorical_accuracy: 0.5164 - val_loss: 1.5588 - val_sparse_categorical_accuracy: 0.4801\n", + "Epoch 23/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4316 - sparse_categorical_accuracy: 0.5103 - val_loss: 1.5295 - val_sparse_categorical_accuracy: 0.4928\n", + "Epoch 24/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.3949 - sparse_categorical_accuracy: 0.5282 - val_loss: 1.5168 - val_sparse_categorical_accuracy: 0.4967\n", + "Epoch 25/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4056 - sparse_categorical_accuracy: 0.5242 - val_loss: 1.5187 - val_sparse_categorical_accuracy: 0.4885\n", + "Epoch 26/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.3994 - sparse_categorical_accuracy: 0.5224 - val_loss: 1.4979 - val_sparse_categorical_accuracy: 0.5075\n", + "Epoch 27/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4214 - sparse_categorical_accuracy: 0.5170 - val_loss: 1.5071 - val_sparse_categorical_accuracy: 0.4926\n", + "Epoch 28/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4282 - sparse_categorical_accuracy: 0.5148 - val_loss: 1.5476 - val_sparse_categorical_accuracy: 0.4993\n", + "Epoch 29/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.3998 - sparse_categorical_accuracy: 0.5273 - val_loss: 1.5179 - val_sparse_categorical_accuracy: 0.5076\n", + "Epoch 30/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4073 - sparse_categorical_accuracy: 0.5282 - val_loss: 1.5806 - val_sparse_categorical_accuracy: 0.5061\n", + "Epoch 31/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4058 - sparse_categorical_accuracy: 0.5272 - val_loss: 1.5850 - val_sparse_categorical_accuracy: 0.4753\n", + "Epoch 32/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4711 - sparse_categorical_accuracy: 0.5056 - val_loss: 1.5687 - val_sparse_categorical_accuracy: 0.4881\n", + "Epoch 33/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 1.4364 - 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sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 155/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 156/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 157/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 158/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 159/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 160/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 161/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 162/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 163/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 164/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 167/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - 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sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 179/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 182/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 184/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 186/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 194/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 195/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 196/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 198/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 199/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 200/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 201/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 202/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 203/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 204/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 205/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 206/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 207/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 208/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 209/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 211/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 213/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - 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sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 259/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 278/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 281/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 284/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 285/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 288/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 289/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 292/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 297/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 298/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 299/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 300/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 301/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 302/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 303/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 304/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 306/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 308/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 366/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 367/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 368/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.1028 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 374/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 377/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 399/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 400/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 403/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 477/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 478/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - 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sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.1026 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 645/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - 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sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 884/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 885/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 886/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 887/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - 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sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 904/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 905/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 906/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 907/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 908/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 909/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 910/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 911/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 912/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 913/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 914/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 915/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 916/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 917/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 918/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 919/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 920/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 921/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 923/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 925/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 928/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 929/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 930/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 931/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 935/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 937/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 938/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 940/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 941/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 942/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 943/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 945/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 946/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 947/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 948/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 949/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 950/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 951/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 952/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 953/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 954/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 955/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 956/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 957/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 958/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 959/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 960/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 961/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 962/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 963/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 964/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 965/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 966/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 967/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 973/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 974/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 975/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 976/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 977/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 979/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.1036 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1005/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1007/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1016/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1017/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1023/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1024/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1025/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1026/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1027/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1028/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1029/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1030/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1031/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1032/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1033/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1034/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1035/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1036/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1037/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1038/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1039/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1040/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1041/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1042/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1043/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1044/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1045/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1046/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1047/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1049/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1050/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1052/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1053/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1054/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1055/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1056/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1061/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1063/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1124/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1125/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1126/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1127/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1128/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - 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sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1149/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1150/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1151/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1152/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1153/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1154/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1155/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1156/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1157/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1158/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1159/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1160/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1161/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1162/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1163/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1164/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1167/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1174/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1175/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1178/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1179/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1182/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1184/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1186/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1194/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1195/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1196/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1198/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1199/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1200/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1201/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1202/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1203/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1204/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1205/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1206/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1207/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1208/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1209/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1211/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1213/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1214/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1215/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1216/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1217/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1218/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1219/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1220/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1221/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1222/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1223/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1228/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1238/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1239/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1240/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1244/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1245/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1247/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1248/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1249/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1250/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1251/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1252/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1253/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1258/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1259/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1278/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1281/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1284/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1285/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1288/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1289/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1292/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1297/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1298/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1299/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1300/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1301/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1302/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1303/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1304/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1306/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1308/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1309/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1310/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1311/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1312/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1313/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1314/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1315/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1316/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1317/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1318/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1319/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1320/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1321/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1322/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1323/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1324/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1325/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1327/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1334/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1339/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1341/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1344/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1345/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1347/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1348/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1349/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1351/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1352/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1353/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1354/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1355/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1357/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1358/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1359/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1360/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1361/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1362/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1363/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3081 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1366/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1367/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1368/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1370/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1371/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1372/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1373/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1374/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1377/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1379/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1380/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1381/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1382/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1383/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1389/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1390/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1391/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1392/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1393/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1394/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1395/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1396/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1397/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1398/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1399/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1400/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1403/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1404/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1405/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1406/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1407/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1408/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1409/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1411/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1412/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1413/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1414/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1415/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1416/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1417/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1418/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1420/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1421/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1422/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1423/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1424/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1425/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1429/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1430/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1431/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1432/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1433/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1434/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1436/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1437/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1440/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1441/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1442/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1443/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1444/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1445/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1446/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1449/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1450/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1451/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1452/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1453/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1454/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1455/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1456/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1457/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1458/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1459/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1460/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1461/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1462/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1463/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1464/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1465/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1466/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1467/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1472/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1475/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1476/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1477/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1478/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1483/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1484/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1485/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1505/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1506/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1511/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1512/20000\n", + "391/391 [==============================] - 2s 5ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1535/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1537/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1540/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1541/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1544/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1545/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1547/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1548/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1549/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1551/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1553/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1554/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1555/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1556/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1558/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1559/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1562/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1563/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1564/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1565/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1566/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1567/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1569/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1572/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1574/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1581/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1583/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1586/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1600/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1601/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1604/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1611/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1614/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1615/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1617/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1618/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1619/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1624/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1625/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1626/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1627/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1628/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1629/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1630/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1631/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1632/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1633/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1634/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1636/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1637/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1638/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1639/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1640/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1641/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1642/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1644/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1645/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1646/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1650/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1651/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1653/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1654/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1656/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1657/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1658/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1659/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1660/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1661/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1662/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1663/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1664/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1665/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1666/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1671/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1672/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1675/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1676/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1677/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1678/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1680/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1681/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1683/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1684/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1685/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1686/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1691/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1698/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1699/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1700/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1702/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1703/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1709/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1711/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1713/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1714/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1715/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1716/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1717/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1723/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1724/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1726/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1727/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1730/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1733/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1734/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1735/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1736/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1738/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1740/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1744/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1748/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1751/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1755/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1756/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1759/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1763/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0952 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1765/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1767/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1768/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1769/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1770/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1771/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1772/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1773/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1775/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1776/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1780/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1781/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1783/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1784/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1785/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1786/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1789/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1792/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1794/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1796/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1797/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1804/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1807/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1808/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1811/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1812/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1813/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1814/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1815/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1816/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1819/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1820/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1821/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1822/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1823/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1824/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1825/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1826/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1827/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 1828/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1829/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 1830/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1831/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1832/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1833/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1834/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1835/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1836/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 1838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0999 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2034/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2035/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2036/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2037/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2038/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2039/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2040/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2041/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2042/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2043/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2044/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2045/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2046/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2047/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2049/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2050/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2052/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2053/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2054/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2055/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2056/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2061/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2069/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2070/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2072/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2073/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2076/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2078/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2079/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2080/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2083/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2084/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2085/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2086/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2087/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2088/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2089/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2091/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2092/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2095/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2096/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2100/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2101/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2102/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2103/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2105/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2107/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2109/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2110/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2111/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2112/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2113/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2114/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2115/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2116/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2117/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2118/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2119/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2120/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2121/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2122/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2123/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2124/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2125/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2126/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2127/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2128/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2129/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2130/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2131/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2132/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2133/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2135/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2136/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2137/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2138/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2140/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2141/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2142/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2143/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2144/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2145/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2146/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2147/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2148/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2149/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2150/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2151/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2152/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2153/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2154/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2155/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2156/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2157/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2158/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2159/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2160/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2161/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2162/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2163/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2164/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2167/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2174/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2175/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2178/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2179/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2182/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2184/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2186/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2194/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2195/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2196/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2198/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2199/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2200/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2201/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2202/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2203/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2204/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2205/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2206/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2207/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2208/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2209/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2211/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2213/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2214/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2215/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2216/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2217/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2218/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2219/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2220/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2221/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2222/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2223/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2228/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2238/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2239/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2240/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2244/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2245/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2247/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2248/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2249/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2250/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2251/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2252/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2253/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2258/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2259/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2278/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2281/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2284/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2285/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2288/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2292/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2303/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2304/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2306/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2309/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2310/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2311/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2312/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2315/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2316/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2317/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2318/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2320/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2322/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2323/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2324/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2327/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2334/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2339/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2341/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2344/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2345/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2347/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2348/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2349/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2351/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2352/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2353/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2354/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2355/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2357/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2358/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2359/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2360/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2362/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2363/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2366/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2367/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2368/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2370/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2371/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2372/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2373/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2374/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2377/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2379/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2380/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2381/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2382/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2383/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2389/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2390/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2391/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2392/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2393/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2394/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2395/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2396/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2397/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2398/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2399/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2400/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2403/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2404/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2405/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2406/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2407/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2408/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2409/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2411/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2412/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2413/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2414/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2415/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2416/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2417/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2418/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2420/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2421/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2422/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2423/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2424/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2425/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2429/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2430/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2431/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2432/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2433/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2434/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2436/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2437/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2440/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2441/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2442/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2443/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2444/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2445/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2446/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2449/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2450/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2451/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2452/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2453/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2454/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2455/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2456/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2457/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2458/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2459/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2460/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2461/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2462/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2463/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2464/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2465/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2467/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2469/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2472/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2473/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2477/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2478/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2479/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2480/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2481/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2483/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2484/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2485/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2486/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2487/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2488/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2489/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2490/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2491/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2493/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2494/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2495/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2498/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2499/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0952 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2500/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2501/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2502/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2506/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2508/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2509/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2510/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2511/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2513/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2514/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2515/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2516/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2517/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2519/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2520/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2521/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2522/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2524/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2525/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2526/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2527/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2528/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2529/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2531/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2532/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2533/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2535/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2536/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2537/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2538/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2539/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2540/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2541/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2542/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2543/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2544/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2545/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2546/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2547/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2548/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2549/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2550/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2551/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2552/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2553/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2556/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2557/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2558/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2559/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2560/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2564/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2571/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2572/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2574/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2575/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2576/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2577/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2578/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2579/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2580/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2581/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2582/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2583/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2584/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2585/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2586/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2587/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2588/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2589/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2590/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2591/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2592/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2593/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2594/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2595/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2596/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2597/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2598/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2599/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2600/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2601/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2602/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2603/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2604/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2605/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2606/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2607/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2608/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2609/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2610/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2611/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2612/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2613/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2614/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2615/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2616/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2617/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2618/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2619/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2620/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2621/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2622/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2623/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2624/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2625/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2626/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2627/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2628/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2629/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2630/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2631/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2632/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2633/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2634/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2635/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2636/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2637/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2638/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2639/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2640/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2641/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2642/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2643/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2644/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2645/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2646/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2649/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2650/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2654/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2655/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2657/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2662/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2664/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2665/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2666/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2667/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2672/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2674/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2676/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2678/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2679/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2680/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2681/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2682/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2683/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2684/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2685/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2686/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2688/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2689/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2690/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2691/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2692/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2693/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2694/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2695/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2696/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2697/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2698/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2699/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2700/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2701/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2702/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2703/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2704/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2705/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2706/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2707/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2708/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2709/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2710/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2711/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2712/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2713/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2714/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2715/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2716/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2717/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2718/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2719/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2720/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2721/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2722/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2723/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2726/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2729/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2730/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2732/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2733/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2734/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2735/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2736/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2737/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2738/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2740/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2741/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2742/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2743/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2744/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2746/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2748/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2749/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2750/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2751/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2752/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2754/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2756/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2757/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2758/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2759/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2760/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2761/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2764/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2765/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2766/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2767/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2768/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2769/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2770/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2771/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2772/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2773/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2774/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2775/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2776/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2777/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2778/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2779/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2780/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2781/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2782/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2783/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2784/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2785/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2786/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2787/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2788/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2789/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2790/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2791/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2792/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2793/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2794/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2795/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2796/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2797/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2798/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2799/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2800/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2801/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2802/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2803/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2804/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2805/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2806/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2807/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2808/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2809/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2810/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2811/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2812/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2813/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2814/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2815/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2816/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2817/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2819/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2820/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2821/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2822/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2823/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2824/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2825/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2826/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2827/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2828/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2829/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2830/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2831/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2832/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2833/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2834/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2835/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2836/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2839/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2840/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2841/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2842/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2843/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2844/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2845/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2846/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2847/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2848/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2849/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2850/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2851/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2852/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2854/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2855/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2856/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2857/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2858/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2859/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2860/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2861/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2862/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2863/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2864/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2865/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2866/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2867/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2868/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2869/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2870/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2871/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2872/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2873/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2874/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2875/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2876/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2877/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2878/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2879/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2880/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2881/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2882/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2883/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2884/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2885/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2886/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2887/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2888/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2889/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2890/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2891/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2892/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2893/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2894/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2895/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2896/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2897/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2898/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2899/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2900/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2901/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2902/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2903/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2904/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2905/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2906/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2907/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2908/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2909/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2910/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2911/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2912/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2913/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2914/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2915/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2916/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2917/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2918/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2919/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2920/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2921/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2923/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2924/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2925/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2928/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2929/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2930/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2931/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2932/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2933/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2934/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2935/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2936/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2937/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2938/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2939/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2940/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2941/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2942/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2943/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2944/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2945/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2946/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2947/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2948/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2949/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2950/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2951/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2952/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2953/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2954/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2955/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2956/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2957/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2958/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2959/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2960/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2961/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2962/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2963/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2964/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2965/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2966/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2967/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2973/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2974/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2975/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2976/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 2977/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2979/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2984/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2985/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2987/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2988/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2990/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2991/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2992/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2993/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 2994/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2995/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2996/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2997/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2998/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 2999/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3000/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3001/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3005/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3007/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3016/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3017/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3023/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3024/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3025/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3026/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3027/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3028/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3029/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3030/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3031/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3032/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3033/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3034/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3035/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3036/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3037/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3038/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3039/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3040/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3041/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3042/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3043/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3044/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3045/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3046/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3047/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3049/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3050/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3052/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3053/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3054/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3055/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3056/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3061/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3063/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3069/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3070/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3072/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3073/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3076/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3078/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3079/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3083/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3084/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3085/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3086/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3087/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3088/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3089/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3091/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3092/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3095/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3096/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3097/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3100/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3101/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3102/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3103/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3104/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3105/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3106/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3107/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3108/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3109/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3110/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3111/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3112/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3113/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3114/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3115/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3116/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3117/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3118/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3119/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3120/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3121/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3122/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3123/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3124/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3125/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3126/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3127/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3128/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3129/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3130/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3131/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3132/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3133/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3135/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3136/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3137/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3138/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3140/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3141/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3142/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3143/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3144/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3145/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3146/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3147/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3148/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3149/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3150/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3151/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3152/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3153/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3154/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3155/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3156/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3157/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3158/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3159/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3160/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3161/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3162/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3163/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3164/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3167/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3174/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3175/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3179/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3182/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3184/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3194/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3195/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3196/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3198/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3199/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3200/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3201/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3202/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3203/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3204/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3205/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3206/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3207/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3208/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3209/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3211/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3213/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3214/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3215/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3216/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3217/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3218/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3219/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3220/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3221/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3222/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3223/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3228/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3238/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3239/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3240/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3244/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3245/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3247/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3248/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3249/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3250/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3251/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3252/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3253/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3258/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3259/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3278/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3281/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3285/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3288/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3289/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3292/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3300/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3301/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3302/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3309/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3312/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3313/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3314/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3315/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3316/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3317/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3318/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3320/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3321/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3322/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3323/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3324/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3325/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3327/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3334/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3339/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3341/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3344/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3345/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3347/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3348/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3349/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3351/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3352/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3353/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3354/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3355/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3357/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3358/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3359/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3360/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3361/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3362/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3363/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3366/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3367/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3368/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3370/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3371/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3372/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3373/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3374/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3377/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3379/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3380/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3381/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3382/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3383/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3389/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3390/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3391/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3392/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3393/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3394/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3395/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3396/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3397/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3398/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3399/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3400/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3403/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3404/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3405/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3406/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3407/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3408/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3409/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3411/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3412/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3413/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3414/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3415/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3416/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3417/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3418/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3420/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3421/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3423/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3424/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3425/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3429/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3430/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3431/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3432/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3433/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3434/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3436/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3437/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3440/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3441/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3442/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3443/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3444/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3445/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3446/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3449/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3450/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3451/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3452/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3453/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3454/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3455/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3456/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3457/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3458/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3459/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3460/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3461/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3462/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3463/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3464/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3465/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3091 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3466/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3467/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3469/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3472/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3473/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3477/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3478/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3479/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3480/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3481/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3483/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3484/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3485/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3486/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3487/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3488/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3489/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3490/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3491/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3493/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3494/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3495/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3498/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3499/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3500/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3501/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3502/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3506/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3508/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3509/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3510/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3511/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3513/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3514/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3515/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3516/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3517/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3519/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3520/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3521/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3522/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3524/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3525/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3526/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3527/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3528/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3529/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3531/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3532/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3533/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3535/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3536/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3537/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3538/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3539/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3540/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3541/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3542/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3543/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3544/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3545/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3546/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3547/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3548/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0967 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3556/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3558/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3560/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3564/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3571/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3572/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3574/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3575/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3576/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3577/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3578/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3579/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3580/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3581/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3582/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3583/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3584/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3585/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3586/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3587/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3588/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3589/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3590/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3591/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3592/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3593/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3594/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3595/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3596/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3597/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3598/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3599/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3600/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3601/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3602/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3603/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3604/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3605/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3606/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3607/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3608/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3609/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3610/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3611/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3612/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3613/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3614/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3615/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3616/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3617/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3618/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3619/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3620/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3621/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3622/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3623/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3624/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3625/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3626/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3627/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3628/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3629/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3630/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3631/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3632/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3633/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3634/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3635/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3636/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3637/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3638/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3639/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3640/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3641/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3642/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3643/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3644/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3645/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3646/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3649/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3650/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3654/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3655/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3657/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3662/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3664/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3665/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3666/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3667/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3676/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3678/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3679/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3680/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3681/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3682/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3683/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3684/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3685/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3686/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3688/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3689/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3690/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3691/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3692/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3693/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3694/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3695/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3696/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3697/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3698/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3699/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3700/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3701/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3703/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3704/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3705/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3706/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3707/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3708/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3709/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3710/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3711/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3712/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3713/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3714/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3715/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3716/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3717/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3718/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3719/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3720/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3721/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3722/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3723/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3726/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3729/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3730/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3732/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3733/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3734/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3735/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3736/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3737/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3738/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3740/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3741/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3742/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3743/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3744/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3746/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3748/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3749/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3750/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3751/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3752/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3754/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3756/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3757/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3758/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3759/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3760/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3761/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3764/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3765/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3766/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3767/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3768/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3769/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3770/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3771/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3772/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3773/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3774/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3775/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3776/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3777/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3778/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3779/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3780/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3781/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3782/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3783/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3784/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3785/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3786/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3787/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3788/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3789/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3790/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3791/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3792/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3793/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3794/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3795/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3796/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3797/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3798/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3799/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3800/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3801/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3802/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3803/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3804/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3805/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3806/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3807/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3808/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3835/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3836/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3839/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3840/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3841/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3842/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3846/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3852/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3854/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3855/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3856/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3857/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3858/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3859/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3860/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3861/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3862/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3863/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3864/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3865/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3866/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3867/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3868/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3869/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3873/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3874/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3875/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3881/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3882/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3883/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3884/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3885/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3886/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3887/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3888/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3892/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3894/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3895/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3896/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3898/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3899/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3900/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3905/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3906/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3907/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3908/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3909/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3910/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3920/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3921/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3923/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3937/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3939/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3940/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3941/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3942/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3943/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3944/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3945/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3946/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3947/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3948/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3949/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3950/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3951/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3952/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3953/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3954/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3955/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3956/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3957/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3958/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3959/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3960/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3961/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3962/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3963/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3964/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3965/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3966/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3967/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3973/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3974/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3975/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3976/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3977/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3979/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3984/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 3985/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3987/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3988/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3990/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3991/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3992/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 3993/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3994/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3995/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3996/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3997/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3998/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 3999/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4000/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4001/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4005/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4007/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4016/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4017/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4023/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4024/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4025/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4026/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4027/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4029/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4030/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4031/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4034/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4035/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4036/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4037/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4043/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4049/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4052/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4053/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3077 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4055/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4056/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4061/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4063/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4069/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4070/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4072/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4073/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4076/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4078/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4079/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4080/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4083/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4084/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4085/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4086/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4087/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4089/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4091/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4092/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4095/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4096/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4097/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4100/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4101/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4102/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4103/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4104/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4105/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4106/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4107/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4108/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4109/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4110/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4111/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4112/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4113/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4114/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4115/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4116/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4117/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4118/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4135/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4136/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4137/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4141/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4142/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4153/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4154/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4157/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4161/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4178/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4182/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4184/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4186/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4194/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4195/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4196/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4198/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4199/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4200/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4201/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4203/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4204/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4206/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4207/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4208/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4211/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4213/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - 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sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4219/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4220/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4221/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4222/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4223/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4238/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4239/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4240/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4244/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4245/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4247/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4248/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4249/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4250/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4251/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4252/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4253/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4258/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4259/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4278/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4281/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4284/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4297/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4298/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4300/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4301/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4302/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4303/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4304/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4308/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4309/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4310/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4312/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4314/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4315/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4319/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4320/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4321/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4322/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4323/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4325/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4327/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4334/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4341/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4344/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4345/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4347/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4348/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4349/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4351/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4352/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4353/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4354/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4355/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4357/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4358/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4359/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4360/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4361/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4362/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4363/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4366/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4367/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4368/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4370/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4371/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4372/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4373/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4374/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4377/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4379/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4380/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4381/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4382/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4383/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4389/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4390/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4391/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4392/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4393/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4394/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4395/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4398/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4408/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4413/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4414/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4415/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4422/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4423/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4424/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4431/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4432/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4434/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4440/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4444/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4445/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4446/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4449/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4451/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4452/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4453/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4454/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4455/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4456/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4457/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4458/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4459/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4460/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4461/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4462/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4463/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4464/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4465/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4466/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4467/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4469/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4472/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4473/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4477/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4479/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4480/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4481/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4483/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4484/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4485/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4486/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4488/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4489/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4490/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4491/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4493/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4494/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4495/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4498/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4506/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4508/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4509/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4510/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4511/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4513/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4514/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4515/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4516/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4517/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4519/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4520/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4521/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4522/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4524/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4525/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4526/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4527/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4528/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4529/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4531/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4532/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4533/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4535/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4536/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4537/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4538/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4539/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4540/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4541/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4542/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4543/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4544/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4545/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4546/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4547/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4548/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4549/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4550/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4551/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4552/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4559/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4560/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3082 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4571/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4572/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4574/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4575/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4576/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4577/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4578/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4579/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4580/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4581/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4582/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4583/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4584/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4585/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4586/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4587/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4588/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4589/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4590/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4591/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4592/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4593/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4594/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4595/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4596/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4597/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4598/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4599/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4600/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4601/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4602/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4604/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4605/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4606/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4607/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4608/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4609/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4611/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4613/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4616/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4617/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4618/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4619/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4621/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4622/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4623/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4624/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4625/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4626/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4627/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4628/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4629/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4630/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4631/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4632/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4633/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4634/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4635/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4636/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4637/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4638/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4639/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4640/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4641/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4642/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4643/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4644/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4645/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4646/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4649/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4650/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4654/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4655/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4657/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4662/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4664/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4665/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4666/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4667/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4672/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4674/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4676/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4678/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4679/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4680/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4681/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4682/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4683/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4684/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4685/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4686/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4688/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4689/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4690/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4691/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4692/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4693/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4694/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4695/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4696/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4697/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4698/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4699/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4700/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4701/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4702/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4703/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4704/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4705/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4706/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4707/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4708/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4709/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4710/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4711/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4712/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4713/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4714/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4715/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4716/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4717/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4718/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4719/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4720/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4721/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4722/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4723/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4726/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4729/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4730/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4732/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4733/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4735/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4741/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4744/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4748/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4749/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4751/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4752/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4756/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4757/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4758/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4759/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4760/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4761/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4764/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4765/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4766/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4767/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4768/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4769/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4770/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4771/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4772/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4773/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4774/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4775/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4776/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4777/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4778/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4779/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4780/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4781/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4782/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4783/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4784/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4785/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4786/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0949 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4787/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4789/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4790/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4791/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4792/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4793/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4794/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4795/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4796/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4797/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4798/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4799/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4800/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4801/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4802/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4803/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4804/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4805/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4806/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4807/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4808/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4809/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4810/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4811/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4812/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4813/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4814/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4815/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4816/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4817/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4819/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4820/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4821/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4822/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4823/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4824/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4825/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4827/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4829/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4831/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4832/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4833/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4834/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4835/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4836/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4839/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4840/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4841/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4842/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4843/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4844/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4845/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4846/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4847/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4848/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4849/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4850/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4851/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4852/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4854/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4855/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4856/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4857/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4858/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4859/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4860/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4861/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4862/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4863/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4864/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4865/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4866/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4867/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4868/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4869/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4870/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4871/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4872/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4873/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4874/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4875/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4876/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4877/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4878/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4879/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4880/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4881/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4882/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4883/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4884/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4885/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4886/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4887/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4888/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4889/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4890/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4891/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4892/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4893/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4894/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4895/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4896/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4897/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4898/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4899/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4900/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4901/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4902/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4903/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4904/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4905/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4906/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4907/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4908/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4909/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4910/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4911/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4912/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4913/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4914/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4915/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4916/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4917/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4918/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4919/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4920/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4921/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4923/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4924/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4925/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4928/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4929/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4930/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4931/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4932/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4933/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4936/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4938/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4939/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4940/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4941/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4946/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4947/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4948/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4949/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4954/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4955/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4956/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3081 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4957/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4960/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4961/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4962/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4963/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4964/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4965/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4966/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4967/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4973/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4979/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4984/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4985/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4987/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4988/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4990/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4991/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4992/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 4993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4994/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4995/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4996/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 4997/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4998/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 4999/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5000/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5001/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5007/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5029/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5032/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5033/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5034/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5035/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5036/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5037/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5039/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5040/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5041/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5042/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5043/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5044/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5045/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5046/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5047/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5049/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5050/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5052/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5053/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5054/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5055/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5056/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5061/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5063/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5078/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5079/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5080/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5083/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5086/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5087/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5088/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5089/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5091/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5092/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5096/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5097/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5100/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5101/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5102/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5103/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5104/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5105/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5106/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5107/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5108/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5109/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5110/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5111/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5112/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5113/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5114/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5115/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5116/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5117/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5118/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5119/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5120/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5121/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5122/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5123/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5124/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5125/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5126/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5127/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5128/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5129/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5130/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5131/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5132/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5133/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5136/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5137/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5140/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5141/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5142/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5144/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5145/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5146/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5148/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5149/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5150/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5151/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5152/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5153/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5155/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5156/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5157/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5158/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5159/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5160/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5161/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5162/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5163/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5164/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5167/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5174/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5175/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5178/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5179/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5182/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5184/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5186/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5194/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5195/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5196/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5198/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5201/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5205/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5206/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5207/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5208/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5209/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5211/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5214/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5215/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5216/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5217/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5218/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5219/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5220/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5221/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5222/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5223/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5228/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5237/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5245/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5251/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5253/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5278/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5281/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5284/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5285/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5288/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5289/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5292/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5297/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5298/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5299/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5300/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5301/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5302/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5303/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5304/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5306/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5308/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5309/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5310/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5311/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5313/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5316/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5317/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5318/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5319/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5320/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5321/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5322/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5324/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5325/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5334/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5339/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5341/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5344/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5345/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5347/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5348/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5349/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5351/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5352/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5353/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5354/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5355/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5357/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5358/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - 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sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5370/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5371/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5372/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5373/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5374/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5377/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5379/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5380/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5381/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5382/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5383/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5389/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5391/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5409/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5411/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5416/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5417/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5418/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5420/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5421/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5422/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5423/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5424/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5425/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5429/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5430/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5433/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5436/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5437/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5440/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5441/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5443/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5444/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5446/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5449/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5451/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5452/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5453/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5454/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5455/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5456/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5457/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5458/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5459/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5460/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5461/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5462/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5463/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5464/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5465/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5466/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5467/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5469/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5473/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5478/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5479/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5481/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5485/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5486/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5488/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5489/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5498/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5499/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5500/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5501/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5502/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5510/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5511/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5513/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5514/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5515/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5516/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5517/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5519/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5520/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5521/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5522/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5524/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5525/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5526/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5527/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5528/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5529/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5531/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5532/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5533/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5535/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5536/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5537/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5538/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5539/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5540/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5541/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5542/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5543/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5556/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5557/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5558/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5559/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5560/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5564/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5571/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5572/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5574/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5575/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5576/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5577/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5578/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5579/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5580/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5581/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5582/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5583/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5584/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5585/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5587/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5588/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5589/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5592/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5593/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5594/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5595/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5596/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5597/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5598/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5599/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5600/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5602/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5604/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5605/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5606/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5608/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5609/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5610/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5612/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5613/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5614/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5615/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5616/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5617/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5618/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5619/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5620/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5621/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5622/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5623/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5625/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5626/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5627/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5628/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5629/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5630/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5631/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5632/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5633/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5634/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5635/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5636/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5637/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5638/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5644/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0948 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5645/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5646/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5649/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5650/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5654/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5655/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5657/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5665/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5666/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5667/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5672/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5674/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5676/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5678/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5679/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5680/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5682/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5683/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5684/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5685/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5686/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5688/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5689/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5690/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5691/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5692/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5693/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5694/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5695/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5696/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5697/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5699/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5700/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5701/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5703/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5704/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5705/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5706/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5707/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5709/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5710/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5711/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5713/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5716/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5717/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5718/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5719/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5720/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5721/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5722/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5729/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5732/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5733/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5734/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5735/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5736/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5737/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5738/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5740/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5741/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5742/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5743/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5744/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5746/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5748/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5749/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5750/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5751/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5752/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5754/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5756/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5757/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5758/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5759/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5760/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5764/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5771/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5780/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5784/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5785/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5788/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5789/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5790/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5791/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5792/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5793/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5794/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5795/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5796/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5797/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5798/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5799/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5800/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5801/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5802/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5803/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5804/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5805/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5806/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5807/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5808/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5809/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5810/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5811/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5812/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5813/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5814/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5815/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5816/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5817/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5819/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5820/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5821/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5822/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5823/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5824/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5825/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5826/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5827/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5828/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5834/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5835/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5844/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5847/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5848/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5849/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5850/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5851/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5852/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5854/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5855/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5856/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5857/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5858/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5859/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5860/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5861/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5862/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5863/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5864/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5865/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5866/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5867/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5868/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5869/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5870/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5871/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5876/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5877/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5878/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5879/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5880/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5884/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5885/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5886/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5887/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5888/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5889/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5890/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5891/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5892/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5893/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1042 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5895/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5896/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5898/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5899/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5900/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5902/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5903/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5904/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5905/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5906/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5907/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5908/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5909/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5912/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5914/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5918/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5921/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5930/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 5940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5960/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5965/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5967/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5974/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5975/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5976/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5979/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5984/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5985/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5987/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5988/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5992/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5993/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5994/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5995/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5996/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 5997/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5998/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 5999/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6001/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6005/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6016/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6017/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6023/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6024/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6025/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6026/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6027/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6028/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6076/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6078/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6079/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6080/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6091/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6092/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6095/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6096/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6097/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6100/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6101/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6102/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6103/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6105/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6106/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6107/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6108/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6115/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6116/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6117/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6118/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6119/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6121/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6122/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6123/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6124/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6125/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6126/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6127/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6128/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6129/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6130/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6131/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6132/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6133/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6135/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6136/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6137/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6138/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6140/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6141/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6142/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6143/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6144/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6147/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6149/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6150/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6151/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6152/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6153/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6165/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6174/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6175/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6217/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6221/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6222/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6239/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6240/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6244/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6245/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6247/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6248/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6249/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6250/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6251/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6252/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6258/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3079 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6308/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6316/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6317/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6318/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6319/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6322/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6325/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6327/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6334/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6339/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6345/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6349/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6352/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6354/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6358/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6359/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6360/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6361/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6362/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6363/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6366/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6367/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6368/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6376/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6379/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6380/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6381/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6382/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6383/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6385/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6389/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6390/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6391/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6392/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6393/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6394/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6395/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6396/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6397/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6398/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6399/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6400/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6403/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6417/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6429/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6430/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6431/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6432/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6433/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6434/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6436/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6437/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6444/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6445/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6469/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6472/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6473/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6477/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6479/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6480/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6483/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6489/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6494/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6495/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6508/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6509/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6511/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6525/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6542/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6547/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1042 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6552/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6553/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6556/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6557/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6558/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6559/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6575/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6579/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6580/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6584/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6586/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6592/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6594/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6596/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6597/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6598/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6599/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6600/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6601/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6602/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6603/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6604/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6605/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6606/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6607/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6608/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6609/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6610/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6611/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6612/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6613/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6616/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6617/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6618/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6619/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6629/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6649/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6650/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6654/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6655/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6657/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6662/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6664/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6665/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6666/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6672/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6704/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6713/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6714/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6715/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6718/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6719/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6720/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6721/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6723/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6726/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6730/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6732/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6733/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6734/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6749/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6750/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6755/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6759/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6761/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6765/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6767/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6768/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6769/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6770/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6771/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6773/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6774/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6777/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6778/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6781/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6815/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6816/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6817/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6819/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6820/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6821/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6822/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6824/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6825/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6826/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6829/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6830/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6832/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6834/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6835/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6836/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6839/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6840/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6841/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6842/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6843/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6844/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6849/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6851/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6852/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6854/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6857/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6858/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6859/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6860/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6861/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6865/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6866/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6867/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6868/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6870/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6876/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6884/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6885/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6886/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6887/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6892/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6894/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6904/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6905/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6908/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6909/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6911/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6912/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6913/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6914/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6915/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6916/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6918/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6919/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6920/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6921/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6923/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 6924/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6925/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6929/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6931/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6938/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6958/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6974/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6975/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6976/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6977/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 6987/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6988/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6991/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6992/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6995/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6996/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6997/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6998/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 6999/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7000/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7001/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7005/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7007/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7016/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7017/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7023/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7025/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 7026/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7027/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7028/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7029/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7030/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7031/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7032/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7033/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7034/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7039/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7040/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0984 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7089/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7099/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7103/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7111/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7112/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7121/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7125/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7126/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7128/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7129/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7130/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7132/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7133/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7136/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7141/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7146/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7147/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.0999 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7204/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7205/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 7216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7224/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7226/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7227/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7228/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7238/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7239/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7240/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7249/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - 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sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7284/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7285/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7286/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7287/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7288/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7289/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7292/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7297/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7298/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - 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sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - 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sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7369/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7373/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7375/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3079 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7411/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7418/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7456/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7458/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7459/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7461/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7466/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 7468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7469/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7490/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7493/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7519/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7524/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7533/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7535/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7537/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7556/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7564/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7571/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 7572/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7578/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7579/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7580/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7584/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7585/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7601/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 7616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7623/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7624/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7625/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7628/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7629/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7630/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7631/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7632/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7633/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7644/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7645/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7646/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7647/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7648/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7649/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7650/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7666/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7667/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7676/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7689/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7690/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7691/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7693/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7696/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7697/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7698/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7699/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7700/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7702/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7704/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7707/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7710/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7722/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7723/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7726/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7729/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7738/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7740/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7741/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7742/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7743/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7744/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7746/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7748/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7749/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7750/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7751/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0948 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7752/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7754/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7756/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7758/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7761/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7766/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7769/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7770/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7771/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7777/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7783/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7785/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7786/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7787/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7789/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7790/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7791/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7792/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7793/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7796/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7800/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7802/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7803/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7804/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7805/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7806/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7807/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7808/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7809/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7810/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7815/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7816/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7817/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7819/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7823/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - 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sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7844/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7845/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7846/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7847/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7848/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7851/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7855/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 7858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7861/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7867/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7869/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7872/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7880/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7881/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7882/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7883/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7888/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7894/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7899/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7901/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7902/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3052 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7940/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7941/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7942/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7943/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7945/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7946/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7948/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7950/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7952/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7953/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7962/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7984/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 7998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 7999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8024/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8026/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8047/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8052/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8060/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8061/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8063/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8069/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8070/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8072/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8073/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8076/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - 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sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8179/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8180/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8181/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8187/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8193/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8229/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8233/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - 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sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8273/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8275/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8276/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8277/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8279/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8280/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8282/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8283/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8291/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8293/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8294/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8296/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8297/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8300/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8301/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8302/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8304/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8305/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8309/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8319/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8321/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8323/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8324/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8325/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8327/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8335/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8378/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8384/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8386/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8387/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8388/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8392/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8430/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8431/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8437/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8442/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8443/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8445/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8446/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8447/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8449/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8450/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8451/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8462/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8467/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8471/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8473/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8474/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8475/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8477/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8478/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8479/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8480/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8481/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8483/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8484/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8490/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8493/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8494/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8498/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8499/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8500/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8501/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8502/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3087 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8506/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8509/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8511/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8529/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8531/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8532/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8533/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8540/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8544/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8546/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8547/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8557/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8559/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8560/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8564/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8576/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8577/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8581/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8582/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8588/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8591/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8593/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8595/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8605/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8606/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8607/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8608/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8609/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8610/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8612/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8614/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8615/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8616/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8617/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8618/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8619/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8627/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8628/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8651/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8655/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8672/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8674/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8678/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8685/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8688/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8711/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8712/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8723/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8726/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8727/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8729/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8730/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8734/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8735/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8737/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8738/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8740/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8741/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8742/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8743/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8744/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8747/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8748/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8751/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8752/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8753/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8757/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8758/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8759/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8760/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8761/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8763/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8764/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8765/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8767/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8768/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8769/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8773/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8774/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8775/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8778/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8779/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8780/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8782/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8821/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8822/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8825/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8827/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8828/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8840/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8841/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8842/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8843/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8844/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8845/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8846/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8847/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8848/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8849/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8850/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8851/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8852/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8853/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8854/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8855/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8856/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8857/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8858/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8859/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8860/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8863/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8866/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.0969 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8889/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8890/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8892/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8893/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8895/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8897/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8900/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8901/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8903/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8904/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8940/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8941/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8942/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8943/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8946/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8947/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8948/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8950/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8954/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8955/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8956/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8957/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8958/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 8959/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8960/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8961/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8962/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8963/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8971/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8980/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8981/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8982/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8983/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8984/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8985/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8987/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8988/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8990/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8991/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8992/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8993/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8994/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8995/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8996/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 8997/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8998/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 8999/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9000/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9001/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9004/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9005/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9006/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9007/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9008/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9010/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9011/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9040/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9044/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9045/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9046/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9047/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9049/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9059/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9068/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9072/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9080/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9084/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9085/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9088/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9089/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9099/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9103/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9104/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9106/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9119/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9131/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9133/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9136/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9137/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9138/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9139/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9142/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9144/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9145/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9146/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9160/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9163/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9183/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9185/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9188/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9189/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9190/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9191/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9192/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9197/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9210/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9212/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9213/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9214/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9215/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9216/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9218/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9219/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9225/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9228/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9230/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9231/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9232/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9234/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9235/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9236/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9237/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9243/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9246/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9247/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9253/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9254/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9255/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9256/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9257/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9258/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9259/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9260/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9262/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9264/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9265/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9266/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9267/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9268/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9269/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9270/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9271/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9272/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9290/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9295/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9307/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9313/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9314/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9315/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9316/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9317/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9318/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9321/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9324/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9326/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9328/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9331/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9336/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9337/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9338/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9339/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9340/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9341/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9342/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9343/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9344/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9346/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9350/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9356/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9357/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9364/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9365/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9394/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9399/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9401/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9403/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9404/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9405/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9406/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9407/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9408/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9409/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9410/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9421/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9423/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9427/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9428/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9432/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9435/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9438/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9439/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9450/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9454/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9465/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9470/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9476/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9482/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9484/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9485/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9486/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9487/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9488/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9489/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9490/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9491/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9492/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9493/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9494/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9495/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9496/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9498/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9499/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9500/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9501/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9502/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9514/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9515/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9516/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9517/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9520/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9521/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9523/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9524/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9525/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9526/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9527/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9528/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9529/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9530/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9531/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9532/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9534/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9535/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9539/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9546/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9548/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9551/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9553/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9554/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9557/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9558/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9563/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9570/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9571/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9572/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9573/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9574/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9576/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9577/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9581/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9586/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9588/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9593/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9594/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9596/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9597/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9598/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9604/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9630/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9636/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9637/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9652/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9654/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9656/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9658/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9659/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9662/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9665/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9667/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9668/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9669/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9670/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9671/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9673/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9674/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9675/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9676/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9686/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9687/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9688/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9700/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9701/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9707/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9708/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9710/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9711/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9715/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9718/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9722/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9724/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9725/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9728/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9730/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9731/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9732/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9734/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9737/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9738/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9739/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9740/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9742/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9745/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9755/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9757/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9760/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9762/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9768/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9775/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9777/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9779/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9780/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9782/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9783/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9784/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9785/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9786/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9787/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9788/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9789/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9790/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9792/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9797/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9799/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9815/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9817/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9818/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - 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sparse_categorical_accuracy: 0.1002 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9837/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9868/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9870/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9872/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9873/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9878/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9880/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9881/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9882/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 9883/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9884/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9885/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9886/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9887/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9891/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9899/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9900/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9920/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9922/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9925/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9927/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9931/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9932/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9933/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9934/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9935/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9942/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9943/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9945/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9947/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9948/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9950/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9954/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9955/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9965/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9968/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9969/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9970/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9972/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9973/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9974/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9976/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9977/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9978/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9985/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9986/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 9992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 9999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10009/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10012/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10013/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10014/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10015/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10016/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10017/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10018/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10022/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10023/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10024/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10025/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10027/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10036/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10037/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10039/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10041/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10042/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10043/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10044/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10045/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10046/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10048/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10050/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10051/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10053/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10055/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10056/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10057/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10058/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10062/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10064/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10065/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10066/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10067/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10069/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10072/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10073/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10074/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10075/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10077/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10078/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10079/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10080/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10081/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10082/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10083/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10085/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10086/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 10087/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10088/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10091/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10094/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10097/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10101/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 10107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10111/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10133/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10134/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10135/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10136/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10138/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10166/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10167/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10168/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10169/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10176/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10178/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - 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10240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10241/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10242/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10263/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - 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0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10274/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - 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2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10329/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10330/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10332/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10333/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - 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2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10373/20000\n", + 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2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10398/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10400/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10402/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - 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sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10415/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10416/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10417/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10419/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10420/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10422/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10425/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10426/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10440/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10448/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10453/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10468/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 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loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10497/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10499/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10502/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10503/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 10504/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10505/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10507/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10509/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10510/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10512/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10513/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10514/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10515/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10516/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10517/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10518/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10520/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10522/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10547/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10551/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10555/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10556/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10557/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10558/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10559/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10560/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10561/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10562/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10564/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10565/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10566/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10567/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10568/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10569/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10570/20000\n", + "391/391 [==============================] 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10585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10622/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10627/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10631/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10633/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10634/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10635/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10653/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 10659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10660/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10661/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10663/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 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2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10674/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10677/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0978 - 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loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10710/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 10744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - 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"Epoch 10797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10808/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10809/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10810/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 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sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10827/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10828/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10829/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10830/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10833/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10834/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10836/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10838/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10840/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10841/20000\n", + "391/391 [==============================] - 1s 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loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10923/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10924/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10925/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10926/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10933/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + 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loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10979/20000\n", + "391/391 [==============================] - 1s 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- 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10989/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 10997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 10999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11002/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11003/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11019/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11020/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11021/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 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11068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11071/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11073/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11084/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11085/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11086/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11090/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11093/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11098/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11099/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11103/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11108/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11132/20000\n", + 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11137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11151/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11152/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11170/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11171/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11172/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11173/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11174/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11177/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 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sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11261/20000\n", + "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 11287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11355/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11358/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11361/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 11376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 11459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - 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2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 11561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11605/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11755/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0953 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 11863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0950 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 11996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 11999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 12008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12099/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12103/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12133/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12134/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12136/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12237/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3091 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3026 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 12418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 12466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12640/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 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"Epoch 12729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12755/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - 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sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12854/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12857/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12858/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12860/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 12996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 12999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + 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0.1000\n", + "Epoch 13010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 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loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13118/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13119/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13120/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13123/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13133/20000\n", + 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13138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13192/20000\n", + "391/391 [==============================] 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"Epoch 13212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0946 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13237/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13396/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13397/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3091 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13399/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13456/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13462/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 13482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 13550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13654/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13673/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13718/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13732/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13749/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13754/20000\n", + 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"Epoch 13764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13951/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13986/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 13998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 13999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1050 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14099/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14103/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - 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- 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 14178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0953 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1038 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 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loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14222/20000\n", + "391/391 [==============================] - 1s 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"Epoch 14247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - 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sparse_categorical_accuracy: 0.1019 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14278/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14284/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14306/20000\n", + 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0.1000\n", + "Epoch 14321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 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sparse_categorical_accuracy: 0.1015 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14441/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 14442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14486/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14503/20000\n", + "391/391 [==============================] 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14518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14520/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14521/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14525/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14526/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14529/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14534/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 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2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - 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sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14638/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 14654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - 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0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - 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loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", + "Epoch 14705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14710/20000\n", + "391/391 [==============================] 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"Epoch 14730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14755/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14784/20000\n", + "391/391 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"Epoch 14799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - 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sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + 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0.1000\n", + "Epoch 14873/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - 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sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 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sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 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loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 14982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 14986/20000\n", + "391/391 [==============================] 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15001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15055/20000\n", + "391/391 [==============================] 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0.1001\n", + "Epoch 15080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - 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2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - 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2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15133/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15134/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15136/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 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val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 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loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15188/20000\n", + "391/391 [==============================] - 1s 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- 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15237/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - 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2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15252/20000\n", + "391/391 [==============================] - 1s 4ms/step - 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15277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15336/20000\n", + "391/391 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"391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 15351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15364/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15365/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15366/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15367/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15368/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15369/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15370/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - 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sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - 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loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15464/20000\n", + "391/391 [==============================] - 1s 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[==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15479/20000\n", + 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15484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0952 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 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loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15533/20000\n", + "391/391 [==============================] - 1s 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"Epoch 15558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - 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2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15617/20000\n", + 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0.1000\n", + "Epoch 15632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - 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2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - 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2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 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loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15740/20000\n", + "391/391 [==============================] - 1s 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"391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15814/20000\n", + "391/391 [==============================] 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"Epoch 15834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - 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2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3078 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 15903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - 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sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 15996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 15999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - 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loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16026/20000\n", + "391/391 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"391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16095/20000\n", + "391/391 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"Epoch 16110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - 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2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - 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sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16163/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + 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0.1000\n", + "Epoch 16184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - 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sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16237/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 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sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 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loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16376/20000\n", + 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"Epoch 16386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16549/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16573/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16627/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16634/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16636/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16638/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16645/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16647/20000\n", + "391/391 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"391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 16662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 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sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16738/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16739/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - 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sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3087 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16844/20000\n", + "391/391 [==============================] - 1s 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"Epoch 16869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + 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0.1000\n", + "Epoch 16943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 16997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 16999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 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loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17056/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17066/20000\n", + 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17071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17099/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17103/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17125/20000\n", + "391/391 [==============================] 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0.1001\n", + "Epoch 17150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - 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0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17199/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 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"Epoch 17214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 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val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - 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sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17290/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17294/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17358/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17359/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17360/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17361/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17377/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17379/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17380/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17381/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3026 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17679/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17755/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17842/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17844/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17850/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17851/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17936/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0950 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 17997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 17999/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18001/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18002/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18003/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18004/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18005/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18006/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18007/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18028/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18029/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18030/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18031/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18032/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18033/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18034/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18035/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18036/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18037/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18038/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18039/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18040/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18041/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18042/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18071/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18082/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18083/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18084/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18085/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18086/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18087/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18091/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18092/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18093/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18094/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18095/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18096/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18097/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18098/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18099/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18100/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18101/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18102/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18103/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18104/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18105/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18106/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18107/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18108/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18109/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18110/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18111/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18124/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18133/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18134/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18136/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18146/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18157/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18160/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18168/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18178/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18184/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18189/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18195/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18205/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18211/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18216/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18227/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18229/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18231/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18237/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18241/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18242/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18244/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18373/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18374/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18375/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18376/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18377/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18378/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18379/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18380/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18381/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18382/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18383/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18384/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18385/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18386/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18387/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18388/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18389/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18390/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18391/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18392/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18436/20000\n", + "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18457/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18458/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18459/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18460/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18461/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18466/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18467/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18468/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18469/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18470/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18471/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18472/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18473/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18474/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18475/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18476/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18477/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18478/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18479/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18480/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18481/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18482/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18483/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18484/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18485/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18486/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18487/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18488/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18489/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18490/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3036 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18550/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18551/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18552/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18553/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18554/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18555/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18556/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18557/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18558/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18559/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18560/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18561/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18562/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18563/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18564/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18565/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18566/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18567/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18568/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18569/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18570/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18571/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18572/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18573/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18574/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18580/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18581/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18582/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18583/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18584/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18585/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18586/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18587/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18588/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18589/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18590/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18591/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18592/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18593/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18594/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18625/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18644/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18645/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18646/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18647/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18648/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18649/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18650/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18651/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18652/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18653/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18654/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18655/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18656/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18657/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18658/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18659/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18660/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18661/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18662/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18663/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18664/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18665/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18666/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18667/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18668/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18669/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18670/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18671/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3088 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18672/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18673/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18674/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18675/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18676/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18677/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18678/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18679/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18680/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18681/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18682/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18683/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18709/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18713/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18714/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18715/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18717/20000\n", + "391/391 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"391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18747/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18748/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18749/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18750/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18751/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18752/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18753/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18754/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18755/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18756/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18757/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18758/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18759/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18760/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18761/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18762/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18763/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18764/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18765/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18766/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18800/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18801/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18802/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18843/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18872/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18878/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18894/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18915/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18917/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18918/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18919/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18920/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18926/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18927/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18928/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18929/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18930/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18931/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18932/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18933/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18934/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18935/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18936/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18937/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18938/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18948/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18951/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18959/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18973/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 18996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 18998/20000\n", + 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"Epoch 19008/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19009/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19010/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19011/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19012/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19013/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19014/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19015/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19016/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19017/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19018/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19019/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19020/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19021/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19022/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19023/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19024/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19025/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19026/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19027/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - 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2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19043/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19044/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19045/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19046/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19047/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19048/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19049/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19050/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19051/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19052/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19053/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19054/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19055/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19056/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19057/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19058/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19059/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19060/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19061/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19062/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19063/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19064/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19065/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19066/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19067/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19068/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19069/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19070/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19071/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19072/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19073/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19074/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19075/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19076/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19077/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19078/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19079/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19080/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19081/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 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val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19087/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19088/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19089/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19090/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19091/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 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2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19112/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19113/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19114/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19115/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19116/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19117/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19118/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19119/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19120/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19121/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19122/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19123/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19124/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19125/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19126/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19127/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19128/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19129/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19130/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19131/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19132/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19133/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19134/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19135/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19136/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19137/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19138/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19139/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19140/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19141/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19142/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19143/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19144/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19145/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19146/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19147/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19148/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19149/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19150/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19151/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19152/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19153/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19154/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19155/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19156/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19157/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19158/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19159/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19160/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19161/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19162/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19163/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19164/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19165/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19166/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19167/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19168/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19169/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19170/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19171/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19172/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19173/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19174/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19175/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19176/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19177/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19178/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19179/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19180/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19181/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19182/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19183/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19184/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19185/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19186/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19187/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19188/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19189/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19190/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19191/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19192/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19193/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19194/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19195/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19196/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19197/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19198/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19199/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19200/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19201/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19202/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19203/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19204/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19205/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19206/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19207/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19208/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19209/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19210/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19211/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19212/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19213/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19214/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19215/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19216/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19217/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19218/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19219/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19220/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19221/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19222/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19223/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19224/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3078 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19225/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19226/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19227/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19228/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19229/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19230/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19231/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19232/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19233/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19234/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19235/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19236/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19237/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19238/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19239/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19240/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19241/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19242/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19243/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19244/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19245/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19246/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19247/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19248/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19249/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19250/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19251/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19252/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19253/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19254/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19255/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19256/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19257/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19258/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19259/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19260/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19261/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19262/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19263/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19264/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19265/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19266/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19267/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19268/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19269/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19270/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19271/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19272/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19273/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19274/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19275/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19276/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19277/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19278/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19279/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19280/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19281/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19282/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19283/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19284/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19285/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19286/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19287/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19288/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19289/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19290/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19291/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19292/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19293/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19294/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19295/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19296/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19297/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19298/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19299/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19300/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19301/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19302/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19303/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19304/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19305/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19306/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19307/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19308/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19309/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19310/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19311/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19312/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19313/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19314/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19315/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19316/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19317/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19318/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19319/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19320/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19321/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19322/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19323/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19324/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19325/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19326/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19327/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19328/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19329/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19330/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19331/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19332/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19333/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19334/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19335/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19336/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19337/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19338/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19339/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19340/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19341/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19342/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19343/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19344/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19345/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19346/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19347/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19348/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19349/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19350/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19351/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19352/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19353/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19354/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19355/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19356/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19357/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19358/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19359/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19360/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19361/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19362/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19363/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19364/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19365/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19366/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19367/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19368/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19369/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19370/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19371/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19372/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - 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loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19393/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19394/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19395/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19396/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19397/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19398/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19399/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19400/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19401/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19402/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19403/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19404/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19405/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19406/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19407/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19408/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19409/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19410/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19411/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19412/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19413/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19414/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19415/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19416/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19417/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19418/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19419/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19420/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19421/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19422/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19423/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19424/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19425/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19426/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19427/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19428/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19429/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19430/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19431/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19432/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19433/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19434/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19435/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19436/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19437/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19438/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19439/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19440/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19441/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19442/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19443/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19444/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19445/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19446/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19447/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19448/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19449/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19450/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19451/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19452/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19453/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19454/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19455/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19456/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 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loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19462/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19463/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19464/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19465/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19466/20000\n", + "391/391 [==============================] - 1s 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"Epoch 19491/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19492/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19493/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19494/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19495/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19496/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19497/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19498/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19499/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19500/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19501/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19502/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19503/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19504/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19505/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19506/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19507/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19508/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19509/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19510/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19511/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19512/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19513/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19514/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19515/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19516/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19517/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19518/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19519/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19520/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19521/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19522/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19523/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19524/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19525/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19526/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19527/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19528/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19529/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19530/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19531/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19532/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19533/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19534/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19535/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19536/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19537/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19538/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19539/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19540/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19541/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19542/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19543/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19544/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19545/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19546/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19547/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19548/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19549/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19550/20000\n", + 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2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19575/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19576/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19577/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19578/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19579/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - 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2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19595/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19596/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19597/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19598/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19599/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19600/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19601/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19602/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19603/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19604/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19605/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19606/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19607/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19608/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19609/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19610/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19611/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19612/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19613/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19614/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19615/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19616/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19617/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19618/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19619/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19620/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19621/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19622/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19623/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19624/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19625/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19626/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19627/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19628/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19629/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19630/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19631/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19632/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19633/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19634/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19635/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19636/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19637/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19638/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19639/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19640/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19641/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19642/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19643/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 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[==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19684/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19685/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19686/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19687/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19688/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19689/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19690/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19691/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19692/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19693/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19694/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19695/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19696/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19697/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19698/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19699/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19700/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19701/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19702/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19703/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19704/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19705/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19706/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19707/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19708/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19709/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19710/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19711/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19712/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19713/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19714/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19715/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19716/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19717/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19718/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19719/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19720/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19721/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19722/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19723/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19724/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19725/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19726/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19727/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19728/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19729/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19730/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19731/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19732/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19733/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19734/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19735/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19736/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19737/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19738/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19739/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19740/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19741/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19742/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19743/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19744/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19745/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19746/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19747/20000\n", + "391/391 [==============================] 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"Epoch 19767/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19768/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19769/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19770/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19771/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19772/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19773/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19774/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19775/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19776/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19777/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19778/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19779/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19780/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19781/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19782/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19783/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19784/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19785/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19786/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19787/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19788/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19789/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19790/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19791/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19792/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19793/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19794/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19795/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19796/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19797/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19798/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19799/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19800/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19801/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19802/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19803/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19804/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19805/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19806/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19807/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19808/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19809/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19810/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19811/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19812/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19813/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19814/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19815/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19816/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19817/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19818/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19819/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19820/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19821/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19822/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19823/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19824/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19825/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19826/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19827/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19828/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19829/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19830/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19831/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19832/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19833/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19834/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19835/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19836/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19837/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19838/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19839/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19840/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19841/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19842/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19843/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19844/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19845/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19846/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19847/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19848/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19849/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19850/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19851/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19852/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19853/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19854/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19855/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19856/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19857/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19858/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19859/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19860/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19861/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19862/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19863/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19864/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19865/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19866/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19867/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19868/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19869/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19870/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19871/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19872/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19873/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19874/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19875/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19876/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19877/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19878/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19879/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19880/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19881/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19882/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19883/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19884/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19885/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19886/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19887/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19888/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19889/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19890/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19891/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19892/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19893/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19894/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19895/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19896/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19897/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19898/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19899/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19900/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19901/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19902/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19903/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19904/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19905/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19906/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19907/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19908/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19909/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19910/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19911/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19912/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19913/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19914/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19915/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19916/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19917/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19918/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19919/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19920/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19921/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19922/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19923/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19924/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19925/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19926/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19927/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19928/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19929/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19930/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19931/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19932/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19933/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19934/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19935/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19936/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19937/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19938/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19939/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19940/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19941/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19942/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19943/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19944/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19945/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19946/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19947/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19948/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19949/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19950/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19951/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19952/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19953/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19954/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19955/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19956/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19957/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19958/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19959/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19960/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19961/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19962/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19963/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19964/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19965/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19966/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19967/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19968/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19969/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19970/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19971/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19972/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19973/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19974/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19975/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19976/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19977/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19978/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19979/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19980/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19981/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19982/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19983/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19984/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19985/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19986/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19987/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19988/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19989/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19990/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19991/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19992/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19993/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19994/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19995/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19996/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19997/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 19998/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", + "Epoch 19999/20000\n", + "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", + "Epoch 20000/20000\n", + "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", + "Test Loss: 2.3040647506713867\n", + "Test Accuracy: 0.10010000318288803\n" + ] + }, + { + "data": { + "image/png": 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", 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iJEk9evRQpUqVFB0dLUl688031aRJE9WoUUMpKSl65513dOTIEfXu3dvKYeRSGL97BwCA24Hl4aZLly46efKkRo0apcTERNWrV0+LFi1y3GR89OhRpynWZ8+eVZ8+fZSYmKiyZcuqYcOGWrNmjWrXrm3VEPLEPTcAAFjDZm6zP4KUlpamgIAApaamuvwSVWBgoFJTUyVJq1ev1oMPPujS7QMAcLsqyPG7yM2WKio4cwMAgDUINy70x0Bzm50QAwCg0CDcuNAfAw1nbgAAsAbhxoX+GG44cwMAgDUINy7EmRsAAKxHuHEhztwAAGA9wo0L8VfBAQCwHuHGhThzAwCA9Qg3LsQ9NwAAWI9w40KcuQEAwHqEGxfizA0AANYj3LgQZ24AALAe4caFmC0FAID1CDduwpkbAACsQbhxkSvDDOEGAABrEG5chDADAEDhQLhxEc7cAABQOBBuXOTKG4gJNwAAWINw4yKcuQEAoHAg3LgIYQYAgMKBcOMinLkBAKBwINy4COEGAIDCgXDjIoQZAAAKB8KNizBbCgCAwoFw4yJclgIAoHAg3LgIYQYAgMKBcOMinLkBAKBwINy4COEGAIDCgXDjIpcuXXJ6TLgBAMAahBsXuXK2FAAAsAbhxk04cwMAgDUIN25CuAEAwBqEGwAA4FEINy7CbCkAAAoHwo2bEG4AALAG4cZNCDcAAFiDcAMAADwK4cZNOHMDAIA1CDcuwg3FAAAUDoQbAADgUQg3bsKZGwAArEG4cRPCDQAA1iDcAAAAj0K4cRFuKAYAoHAg3LgJ4QYAAGsQbtyEcAMAgDUINwAAwKMQbtyEMzcAAFiDcOMi3FAMAEDhQLgBAAAehXDjJpy5AQDAGoQbNyHcAABgDcKNixBmAAAoHAg3bkLYAQDAGoQbNyHcAABgDcKNmxBuAACwBuEGAAB4FMKNi/AlfgAAFA6EGzch3AAAYA3CDQAA8CiEGzfhzA0AANYg3LgJ4QYAAGsQblyEG4oBACgcCDcAAMCjEG7chDM3AABYo1CEm0mTJik8PFy+vr6KiIjQhg0b8rXerFmzZLPZ1KFDB/cWeAMINwAAWMPycDN79mwNHTpUo0eP1ubNm1W3bl21atVKycnJ11zv8OHDGjZsmJo3b36LKgUAAEWB5eHm3XffVZ8+fRQVFaXatWvr448/VsmSJTV16tSrrpOTk6Pu3btr7Nixqlat2i2s9uq4oRgAgMLB0nCTlZWlTZs2KTIy0tHm5eWlyMhIrV279qrrvfnmm6pQoYJ69ep1K8q8IYQbAACsUdzKFz916pRycnIUEhLi1B4SEqK9e/fmuc6qVav02WefaevWrfl6jczMTGVmZjoep6Wl3XC9AACg8LP8slRBpKen69lnn9Wnn36qoKCgfK0THR2tgIAAx1K5cmU3V/k7ztwAAGANS8/cBAUFqVixYkpKSnJqT0pKUmhoaK7+8fHxOnz4sNq1a+dos9vtkqTixYtr3759ql69utM6I0eO1NChQx2P09LS3BJwuOcGAIDCwdJw4+3trYYNGyouLs4xndtutysuLk6DBg3K1b9WrVrasWOHU9vrr7+u9PR0vf/++3mGFh8fH/n4+Lil/msh3AAAYA1Lw40kDR06VD179lSjRo3UuHFjTZgwQefPn1dUVJQkqUePHqpUqZKio6Pl6+ur++67z2n9wMBAScrVDgAAbk+Wh5suXbro5MmTGjVqlBITE1WvXj0tWrTIcZPx0aNH5eVVpG4NksSZGwAArGJ5uJGkQYMG5XkZSpKWL19+zXWnT5/u+oJcgHADAIA1it4pkUKKMAMAQOFAuHETwg4AANYg3LgJ4QYAAGsQbgAAgEch3LgJZ24AALAG4cZF+IZiAAAKB8KNmxBuAACwBuEGAAB4FMKNm3DmBgAAaxBu3IRwAwCANQg3LkKYAQCgcCDcuAlhBwAAaxBu3IRwAwCANQg3bkK4AQDAGoQbFyHMAABQOBBu3ISwAwCANQg3bkK4AQDAGoQbAADgUQg3bsKZGwAArEG4cRH+KjgAAIUD4QYAAHgUwo2bcOYGAABrEG7chHADAIA1CDduQrgBAMAahBsXIcwAAFA4EG7chLADAIA1CDduQrgBAMAahBsAAOBRCDcAAMCjFDjchIeH680339TRo0fdUU+RxWUoAAAKhwKHm7///e+aN2+eqlWrpscee0yzZs1SZmamO2oDAAAosBsKN1u3btWGDRt0zz33aPDgwapYsaIGDRqkzZs3u6NGAACAfLvhe24aNGigDz74QAkJCRo9erT+85//6IEHHlC9evU0derU2/4yze0+fgAArFL8RlfMzs7W/PnzNW3aNC1evFhNmjRRr169dPz4cb366qtasmSJvvzyS1fWWqgRZgAAKBwKHG42b96sadOmaebMmfLy8lKPHj303nvvqVatWo4+HTt21AMPPODSQgEAAPKjwOHmgQce0GOPPabJkyerQ4cOKlGiRK4+VatWVdeuXV1SIAAAQEEUONwcPHhQVapUuWafUqVKadq0aTdcFAAAwI0q8A3FycnJWr9+fa729evXa+PGjS4pCgAA4EYVONwMHDhQx44dy9X+22+/aeDAgS4pqii68oZibjAGAMAaBQ43u3fvVoMGDXK1169fX7t373ZJUQAAADeqwOHGx8dHSUlJudpPnDih4sVveGY5AACASxQ43Dz++OMaOXKkUlNTHW0pKSl69dVX9dhjj7m0OAAAgIIq8KmW8ePHq0WLFqpSpYrq168vSdq6datCQkL0xRdfuLxAAACAgihwuKlUqZK2b9+u2NhYbdu2TX5+foqKilK3bt3y/M6b2wU3FAMAUDjc0E0ypUqVUt++fV1dCwAAwE274TuAd+/eraNHjyorK8up/cknn7zpogAAAG7UDX1DcceOHbVjxw7ZbDbH5RebzSZJysnJcW2FAAAABVDg2VJDhgxR1apVlZycrJIlS2rXrl1asWKFGjVqpOXLl7uhRAAAgPwr8JmbtWvXaunSpQoKCpKXl5e8vLz00EMPKTo6Wi+88IK2bNnijjoLPW4gBgCgcCjwmZucnByVKVNGkhQUFKSEhARJUpUqVbRv3z7XVleEEXYAALBGgc/c3Hfffdq2bZuqVq2qiIgIvf322/L29tYnn3yiatWquaNGAACAfCtwuHn99dd1/vx5SdKbb76pP//5z2revLnKly+v2bNnu7xAAACAgihwuGnVqpXj3zVq1NDevXt15swZlS1b1jFj6nbEZSgAAAqHAt1zk52dreLFi2vnzp1O7eXKlbutgw0AACg8ChRuSpQooTvvvJPvsskHzuQAAGCNAs+Weu211/Tqq6/qzJkz7qgHAADgphT4npuJEyfqwIEDCgsLU5UqVVSqVCmn5zdv3uyy4gAAAAqqwOGmQ4cObiij6OMyFAAAhUOBw83o0aPdUQcAAIBLFPieGwAAgMKswGduvLy8rjntm5lUv+MyFQAA1ihwuJk/f77T4+zsbG3ZskWff/65xo4d67LCAAAAbkSBw0379u1ztT3zzDO69957NXv2bPXq1cslhRU1nKkBAKBwcNk9N02aNFFcXJyrNgcAAHBDXBJuLly4oA8++ECVKlVyxeYAAABuWIHDTdmyZVWuXDnHUrZsWZUpU0ZTp07VO++8c0NFTJo0SeHh4fL19VVERIQ2bNhw1b7z5s1To0aNFBgYqFKlSqlevXr64osvbuh1AQCA5ynwPTfvvfee02wpLy8vBQcHKyIiQmXLli1wAbNnz9bQoUP18ccfKyIiQhMmTFCrVq20b98+VahQIVf/cuXK6bXXXlOtWrXk7e2t7777TlFRUapQoYLTXyy3GvfgAABgDZux+CgcERGhBx54QBMnTpQk2e12Va5cWYMHD9Yrr7ySr200aNBAbdu21VtvvXXdvmlpaQoICFBqaqr8/f1vqvY/2r59u+rWret4PGDAAE2aNMll2wcA4HZWkON3gS9LTZs2TXPmzMnVPmfOHH3++ecF2lZWVpY2bdqkyMjI/yvIy0uRkZFau3btddc3xiguLk779u1TixYtCvTaAADAMxU43ERHRysoKChXe4UKFTRu3LgCbevUqVPKyclRSEiIU3tISIgSExOvul5qaqpKly4tb29vtW3bVh9++KEee+yxPPtmZmYqLS3NaQEAAJ6rwPfcHD16VFWrVs3VXqVKFR09etQlRV1PmTJltHXrVp07d05xcXEaOnSoqlWrpocffjhX3+joaL5cEACA20iBz9xUqFBB27dvz9W+bds2lS9fvkDbCgoKUrFixZSUlOTUnpSUpNDQ0Kuu5+XlpRo1aqhevXp66aWX9Mwzzyg6OjrPviNHjlRqaqpjOXbsWIFqvFHcUAwAgDUKHG66deumF154QcuWLVNOTo5ycnK0dOlSDRkyRF27di3Qtry9vdWwYUOnL/+z2+2Ki4tT06ZN870du92uzMzMPJ/z8fGRv7+/0+IOhBkAAAqHAl+Weuutt3T48GE9+uijKl7899Xtdrt69OhR4HtuJGno0KHq2bOnGjVqpMaNG2vChAk6f/68oqKiJEk9evRQpUqVHGdmoqOj1ahRI1WvXl2ZmZlauHChvvjiC02ePLnArw0AADxPgcONt7e3Zs+erX/84x/aunWr/Pz8VKdOHVWpUuWGCujSpYtOnjypUaNGKTExUfXq1dOiRYscNxkfPXpUXl7/d4Lp/PnzGjBggI4fPy4/Pz/VqlVLM2bMUJcuXW7o9QEAgGex/HtubjV3fc/Ntm3bVK9ePcfj/v3766OPPnLZ9gEAuJ259Xtunn76af3rX//K1f7222+rU6dOBd2cx7jNMiIAAIVWgcPNihUr9MQTT+Rqb9OmjVasWOGSojwBYQcAAGsUONycO3dO3t7eudpLlCjBF+QBAADLFTjc1KlTR7Nnz87VPmvWLNWuXdslRQEAANyoAs+WeuONN/TUU08pPj5ejzzyiCQpLi5OX375pebOnevyAgEAAAqiwOGmXbt2WrBggcaNG6e5c+fKz89PdevW1dKlS1WuXDl31FgkcI8NAACFQ4HDjSS1bdtWbdu2lfT71KyZM2dq2LBh2rRpk3JyclxaIAAAQEEU+J6by1asWKGePXsqLCxM//73v/XII49o3bp1rqytSONMDgAA1ijQmZvExERNnz5dn332mdLS0tS5c2dlZmZqwYIF3EwMAAAKhXyfuWnXrp1q1qyp7du3a8KECUpISNCHH37oztoAAAAKLN9nbn744Qe98MIL6t+/v+666y531lQkcRkKAIDCId9nblatWqX09HQ1bNhQERERmjhxok6dOuXO2gAAAAos3+GmSZMm+vTTT3XixAn169dPs2bNUlhYmOx2uxYvXqz09HR31lnkcCYHAABrFHi2VKlSpfT8889r1apV2rFjh1566SXFxMSoQoUKevLJJ91RIwAAQL7d8FRwSapZs6befvttHT9+XDNnznRVTQAAADfspsLNZcWKFVOHDh30v//9zxWbK5K4DAUAQOHgknADAABQWBBuAACARyHcuAmXqQAAsAbhxkUIMwAAFA6EGwAA4FEINwAAwKMQbgAAgEch3AAAAI9CuHGRK28o5gZjAACsQbgBAAAehXADAAA8CuEGAAB4FMINAADwKIQbF+GGYgAACgfCDQAA8CiEGwAA4FEINwAAwKMQbgAAgEch3LgINxADAFA4EG7chLADAIA1CDcAAMCjEG4AAIBHIdy4CJehAAAoHAg3AADAoxBu3IQzOQAAWINwAwAAPArhBgAAeBTCjYsEBQWpR48eVpcBAMBtj3DjIjVq1NDnn3+umJgYq0sBAOC2RrgBAAAehXDjJsyWAgDAGoQbF7PZbFaXAADAbY1wAwAAPArhBgAAeBTCDQAA8CiEGwAA4FEIN27CbCkAAKxBuHExZksBAGAtwg0AAPAohBsAAOBRCDcAAMCjEG7chBuKAQCwBuHGxbihGAAAaxFuAACARyHcAAAAj0K4AQAAHoVwAwAAPArhxk2YLQUAgDUINy7GbCkAAKxVKMLNpEmTFB4eLl9fX0VERGjDhg1X7fvpp5+qefPmKlu2rMqWLavIyMhr9gcAALcXy8PN7NmzNXToUI0ePVqbN29W3bp11apVKyUnJ+fZf/ny5erWrZuWLVumtWvXqnLlynr88cf122+/3eLKAQBAYWR5uHn33XfVp08fRUVFqXbt2vr4449VsmRJTZ06Nc/+sbGxGjBggOrVq6datWrpP//5j+x2u+Li4m5x5QAAoDCyNNxkZWVp06ZNioyMdLR5eXkpMjJSa9euzdc2MjIylJ2drXLlyrmrTAAAUIQUt/LFT506pZycHIWEhDi1h4SEaO/evfnaxogRIxQWFuYUkP4oMzNTmZmZjsdpaWk3XnABMFsKAABrWH5Z6mbExMRo1qxZmj9/vnx9ffPsEx0drYCAAMdSuXJlt9bEbCkAAKxlabgJCgpSsWLFlJSU5NSelJSk0NDQa647fvx4xcTE6KefftL9999/1X4jR45UamqqYzl27JhLagcAAIWTpeHG29tbDRs2dLoZ+PLNwU2bNr3qem+//bbeeustLVq0SI0aNbrma/j4+Mjf399pAQAAnsvSe24kaejQoerZs6caNWqkxo0ba8KECTp//ryioqIkST169FClSpUUHR0tSfrXv/6lUaNG6csvv1R4eLgSExMlSaVLl1bp0qUtGwcAACgcLA83Xbp00cmTJzVq1CglJiaqXr16WrRokeMm46NHj8rL6/9OME2ePFlZWVl65plnnLYzevRojRkz5laWfk3cUAwAgDUsDzeSNGjQIA0aNCjP55YvX+70+PDhw+4v6CZwQzEAANYq0rOlAAAArkS4AQAAHoVwAwAAPArhBgAAeBTCjZswWwoAAGsQblyM2VIAAFiLcAMAADwK4QYAAHgUwg0AAPAohBsAAOBRCDduwmwpAACsQbhxMWZLAQBgLcINAADwKIQbAADgUQg3AADAoxBu3IQbigEAsAbhxsW4oRgAAGsRbgAAgEch3AAAAI9CuAEAAB6FcAMAADwK4cZNmC0FAIA1CDcuxmwpAACsRbgBAAAehXADAAA8CuEGAAB4FMKNm3BDMQAA1iDcuBg3FAMAYC3CDQAA8CiEGwAA4FEINwAAwKMQbgAAgEch3LgJs6UAALAG4cbFmC0FAIC1CDcAAMCjEG4AAIBHIdwAAACPQrgBAAAehXDjJsyWAgDAGoQbF2O2FAAA1iLcAAAAj0K4AQAAHoVwAwAAPArhxk24oRgAAGsQblyMG4oBALAW4QYAAHgUwg0AAPAohBsAAOBRCDcAAMCjEG7chNlSAABYg3DjYsyWAgDAWoQbAADgUQg3AADAoxBuAACARyHcAAAAj0K4cRNmSwEAYA3CjYsxWwoAAGsRbgAAgEch3AAAAI9CuAEAAB6FcOMm3FAMAIA1CDcuxg3FAABYi3ADAAA8CuEGAAB4FMINAOCGcX8hCiPCjQfyxA+bs2fPFnhcxhilpKS4p6B8vv7PP/+ss2fPFnjdjIwMLVy4UBcvXnRDZbfGuXPnZLfbrS5DknT+/Plb+nthjMn36yUmJhbZ39kpU6aoYsWK2rZtm4wxysnJsbqkPOXk5BTZ9xg3xvJwM2nSJIWHh8vX11cRERHasGHDVfvu2rVLTz/9tMLDw2Wz2TRhwoRbV2gB/fEXKTs7W4cPH9bevXu1c+dO/fbbb5J+//CvUaOGunfvLmOMtm/froyMDBljlJWVpWPHjunvf/+7unXrpgYNGmjnzp2O9WbOnKnQ0FBt375dknTo0CHZ7XatWrVKXl5estls2rJli9544w1HH0n69ddfFRcX5/R6knTx4kWlpqbKbrdrwoQJGj9+vNN4FixYIJvNppiYGG3ZskXGGE2YMEFLliyRJCUkJGjLli0aPHiwvv32W82fP1/nz59X165dNWXKFB08eFADBgzQ66+/7hiHMUZz587Vl19+KUk6fPiwoqOjtW3bNkVGRuqTTz5RamqqHnjgAZUrV069e/fWuXPnJEnx8fHKyclRQkKCMjMzdeLEiVz7oFOnTipbtqzGjRunUaNG6ciRI7pw4YIyMzNls9lks9m0d+9e/fTTT1q9enWuA/H+/ft1+vTpXNs9efKkRo0apfT0dEnSvn379M9//lOpqamOPhs2bFC3bt308MMPq3r16kpISNCwYcM0dOhQGWO0ceNG7d27VwkJCbl+Xk6cOKG2bduqbdu26tq1q6MWb29v2Ww2/fzzz7p06ZKys7OVnJyshQsXqly5coqNjdXevXslSQsXLtQ999yjBQsWaNGiRY7X2LZtmzIzM7Vv3z5FRUXp8OHDucZ3PZcuXVL//v319NNPa+DAgQoMDNRnn33meI2XX35Z/fv3V5kyZVSrVi1J0pdffqlp06Y5tpGVlaUDBw44fv7OnTvn9B689NJLGjZsmIwxys7O1qRJkzR37lxlZGQoKSnJqR673a6lS5fq9OnTstvtjp+Ry/Vs375dpUuXVs+ePR1tX375pYYNG6aDBw86bevgwYP67rvvtH37dscBW5KWLVum1atXa9u2bTp//rwkKS0tTZMmTdLmzZudtmGMUYMGDeTl5ZXn+7tr1y7Hz9+///1vVaxYUY888ogyMjI0ffp0JSQkyG63a9GiRY5QtmvXLuXk5Gjjxo3q0KGD1q1bpy5dushms2n16tW5XsMYo8TERF28eFGDBw9Ws2bNtGvXLl24cEE5OTlas2aN5s6dq1OnTik7O1u//vqrxo4dqzNnzmj8+PFau3btNX4C5Nhnf/vb35SUlKTevXvrqaeeUo0aNXThwgUdPHhQffr00fbt23Xq1CnHOmlpacrMzJTdbncEocTERK1fvz7XPr3cf/ny5crJyVFycrKaNWumqVOn6tdff9U///lPpaWlXbW+zMxMffvttzp79qzuvvtuNW/eXIcPH9bGjRt1+PBh/frrrzp+/LguXbqkU6dOafLkyXrttddks9nUp08fvfzyy47PldTUVE2ePFmbN2/O8z8cGRkZOn78uPbv3y/p9zA9f/58xcTEKDY2VgcOHNAPP/ygZcuWKTY2VnFxcRo3bpxCQkI0fvx4ff3113rrrbd0+vRpxcbG6n//+5+MMTp37py++uorp88WY4z69u2rZs2aqXnz5tq4caPTc+np6fLx8VGfPn0cbWlpadq1a5fOnj2ruXPnOh0TJGnHjh0aP368fvrpJ7Vp00abNm3KM6AfPnxYo0eP1smTJ5WWlia73a6jR486np89e7YaNWqk+Pj4q+6XW8ZYaNasWcbb29tMnTrV7Nq1y/Tp08cEBgaapKSkPPtv2LDBDBs2zMycOdOEhoaa9957r8CvmZqaaiSZ1NTUm6w+b1OmTDGSWDxsefjhh02NGjUsr8PKpXjx4mbixIk3tY3q1avnauvatavlY7vW8uCDD1peAwvLtZYnn3zyqs8FBARYUtO5c+dcfnwtyPHb0nDTuHFjM3DgQMfjnJwcExYWZqKjo6+7bpUqVQpluKlUqZLlP+gsLCwsLCxWL65WkOO3ZZelsrKytGnTJkVGRjravLy8FBkZma9TovmVmZmptLQ0p8WdLl9yAgAA1rAs3Jw6dUo5OTkKCQlxag8JCVFiYqLLXic6OloBAQGOpXLlyi7bNgAAKHwsv6HY3UaOHKnU1FTHcuzYMatLAgAAbmRZuAkKClKxYsVyzXxISkpSaGioy17Hx8dH/v7+Tos7rVy50q3bBwCgsFuwYIGlr29ZuPH29lbDhg0VFxfnaLPb7YqLi1PTpk2tKuumPfTQQ8rMzHRMyXvuued01113Sfp9OmxCQoK++uorLVu2TPXr11e3bt303XffOaYQSlKTJk0kSc2bN3fa9oABA9S8eXMtWbJEn3zyiSZNmqRly5blWq9r1676xz/+oTNnzuSaylyyZEkdO3ZMgwcP1uzZsyVJNWvWdDxfokQJxcTEaOXKlYqPj9c777yj2NjYXNOkU1NTlZ6ervXr1+uDDz5wtD/xxBNO91FdNmfOHO3bt08DBw5UqVKl9Msvv2jt2rXavn27Tp48qX/9618aMmSIo3/dunXVrl07vfDCC5o5c6bS0tJ05MgRNWnSRD/++KM+/PBDvfzyy2rfvr2GDx+ulJQUtW3bVjNmzNCmTZtkt9t14cIFzZs3T8nJyYqLi5Ovr6/effddjR8/Xm3atMlVY1hYmJYvX+7UduTIEe3YscPxeNSoUfroo4+0aNEiffrpp7m2cXnqc7FixdS9e3d98803+u9//6vw8HAFBwdr1KhR2rRpky5evChjjL7//nt9/fXXubbz1Vdfafr06Tp48KCOHTum1NRUde/eXX379tWYMWM0atQozZs3T8YYHT16VKtXr1blypX1008/yW63KzU1VadPn1ZGRoY+/fRTvfjii5Kk8uXLy8fHR5I0ePBgxcbGOr3uxo0bNXr0aGVlZWnz5s2On91hw4YpKipKM2fO1Mcff6wxY8ZoxowZWrBggT755BN1795dd999txYtWqROnTpp0qRJjm1WqFBBBw8eVFpamsaMGSNJatmypQYNGqS//vWvkqSIiAhHf2OM7Ha7MjMztWHDBm3ZskXR0dGSpDfeeEN2u11RUVEqU6aMypUrp44dO+rSpUuaMWOG9uzZo+PHj6tixYqSpI8//liS1KJFC2VmZmrq1KkaNWqU2rdvr/Llyys9PV0///xzrvd/8eLF+v777/XCCy/owoULSklJUf/+/bVy5UrNnz/f0e/999/PNV22X79+mj9/vlq3bq1nnnlGklS2bNlcr3FZw4YNNW7cOPXq1UtTp07V3LlztWHDBi1atEjbtm1Tenq69uzZo99++03du3eXJD355JPas2ePFi9e7LStMmXKOMZbvHhxR/uQIUOUmpqqkSNHOtq8vLx04sQJPffcc6pQoYKj3cfHx7Gut7e32rZtK0m6++67VadOHc2fP18XL17Url279Pnnn2vXrl0aMWKE03gua9GihT788EPZ7XZNmTJFkvTqq6/qm2++0YABAxxf5xEUFKT9+/crPT3dsf9bt24tHx8fhYeH67777tOSJUs0b968q76Pn332mX788UetWbPG0bZr1y6tXLlSTz31lGbPnu34+bv33ntlt9t15MgRjR8/Xo8++qguXLig//znPxo3bpzq1q0rSerdu7d69eqlOnXqaO7cuerVq5ek3z/n/3icuvxz/MEHH+j8+fP6xz/+IUk6cOCA0z2kiYmJWrBggXx9fdWiRQvNmjVL06ZNU0xMjOLi4jR9+nSlpaVp6dKlVx2n9Pt/ords2eL4vbn8n/hTp05p3bp1mjVrlkqVKiVJWr16tfz9/eXn5ydJmjhxog4dOqThw4fr0UcflSRVrlxZ8+bNc5rW3a1bN8XExCgtLU2pqam5vqZl1qxZMsbo4sWL6tKli6N92LBhGj58uNq3b3/NMbidy29nLoBZs2YZHx8fM336dLN7927Tt29fExgYaBITE40xxjz77LPmlVdecfTPzMw0W7ZsMVu2bDEVK1Y0w4YNM1u2bDH79+/P92u6e7bUzdi2bZtZvHixU1tOTo5LtrtixQoTExNjjhw5kmefQ4cOmbNnz15zO+vWrTMzZsy47utlZ2eb48ePmyNHjpjXX3/dJCQk5LtWd0wfzEtKSor5y1/+Yr777rtcz6WlpZk9e/Y4tW3bts3ExMSYixcvOrX/+uuv5sSJE+b11183ffv2NXa73WzevLnA48jOzjbp6enGGGMuXLhQwNEUjN1ud3q8c+dO06JFC7N8+fJcfXNycm54n2RkZJiMjIwbWjc/bvZ348r1T58+7fjsuZ6vv/7a7N692/H4yJEjZv369ddd7+zZsyYlJcX06tXLdO/e3Zw/f75gRechISHB7Nmzx2RlZRljrv2+ZGVlmSZNmpjevXtf9efsyp+P/MjMzDSxsbGO3/W0tLQCb+NqLl265DSmixcvmrS0NLNixQqzYcMGExMTk+/9drlWV3yuJicnmzFjxpjDhw9ft29GRoZJTk4u0Pa//vprM2jQIJOenm7Onz9voqOjzbp168ylS5fy7H+t/Xa1dQoqIyPDnD59Otdr2e12s2/fvhv62SmIghy/bcZY+7WNEydO1DvvvKPExETVq1dPH3zwgSONPvzwwwoPD9f06dMl/f4FQlWrVs21jZYtW+b6H/fVpKWlKSAgQKmpqW6/RAUAAFyjIMdvy8PNrUa4AQCg6CnI8dvjZ0sBAIDbC+EGAAB4FMINAADwKIQbAADgUQg3AADAoxBuAACARyHcAAAAj0K4AQAAHoVwAwAAPArhBgAAeBTCDQAA8CiEGwAA4FEINwAAwKMUt7qAW+3yH0FPS0uzuBIAAJBfl4/bl4/j13LbhZv09HRJUuXKlS2uBAAAFFR6eroCAgKu2cdm8hOBPIjdbldCQoLKlCkjm83m0m2npaWpcuXKOnbsmPz9/V267cLA08cnef4YGV/R5+ljZHxFn7vGaIxRenq6wsLC5OV17btqbrszN15eXrrjjjvc+hr+/v4e+0Mref74JM8fI+Mr+jx9jIyv6HPHGK93xuYybigGAAAehXADAAA8CuHGhXx8fDR69Gj5+PhYXYpbePr4JM8fI+Mr+jx9jIyv6CsMY7ztbigGAACejTM3AADAoxBuAACARyHcAAAAj0K4AQAAHoVw4yKTJk1SeHi4fH19FRERoQ0bNlhdUp6io6P1wAMPqEyZMqpQoYI6dOigffv2OfV5+OGHZbPZnJa//e1vTn2OHj2qtm3bqmTJkqpQoYKGDx+uS5cuOfVZvny5GjRoIB8fH9WoUUPTp0939/A0ZsyYXLXXqlXL8fzFixc1cOBAlS9fXqVLl9bTTz+tpKSkIjG2y8LDw3ON0WazaeDAgZKK3v5bsWKF2rVrp7CwMNlsNi1YsMDpeWOMRo0apYoVK8rPz0+RkZHav3+/U58zZ86oe/fu8vf3V2BgoHr16qVz58459dm+fbuaN28uX19fVa5cWW+//XauWubMmaNatWrJ19dXderU0cKFC906vuzsbI0YMUJ16tRRqVKlFBYWph49eighIcFpG3nt85iYmEIxvuuNUZKee+65XPW3bt3aqU9R3YeS8vx9tNlseueddxx9CvM+zM9x4VZ+drrkeGpw02bNmmW8vb3N1KlTza5du0yfPn1MYGCgSUpKsrq0XFq1amWmTZtmdu7cabZu3WqeeOIJc+edd5pz5845+rRs2dL06dPHnDhxwrGkpqY6nr906ZK57777TGRkpNmyZYtZuHChCQoKMiNHjnT0OXjwoClZsqQZOnSo2b17t/nwww9NsWLFzKJFi9w6vtGjR5t7773XqfaTJ086nv/b3/5mKleubOLi4szGjRtNkyZNzIMPPlgkxnZZcnKy0/gWL15sJJlly5YZY4re/lu4cKF57bXXzLx584wkM3/+fKfnY2JiTEBAgFmwYIHZtm2befLJJ03VqlXNhQsXHH1at25t6tata9atW2dWrlxpatSoYbp16+Z4PjU11YSEhJju3bubnTt3mpkzZxo/Pz8zZcoUR5/Vq1ebYsWKmbffftvs3r3bvP7666ZEiRJmx44dbhtfSkqKiYyMNLNnzzZ79+41a9euNY0bNzYNGzZ02kaVKlXMm2++6bRP//g7a+X4rjdGY4zp2bOnad26tVP9Z86ccepTVPehMcZpXCdOnDBTp041NpvNxMfHO/oU5n2Yn+PCrfrsdNXxlHDjAo0bNzYDBw50PM7JyTFhYWEmOjrawqryJzk52UgyP//8s6OtZcuWZsiQIVddZ+HChcbLy8skJiY62iZPnmz8/f1NZmamMcaYl19+2dx7771O63Xp0sW0atXKtQO4wujRo03dunXzfC4lJcWUKFHCzJkzx9G2Z88eI8msXbvWGFO4x3Y1Q4YMMdWrVzd2u90YU7T335UHDrvdbkJDQ80777zjaEtJSTE+Pj5m5syZxhhjdu/ebSSZX375xdHnhx9+MDabzfz222/GGGM++ugjU7ZsWcf4jDFmxIgRpmbNmo7HnTt3Nm3btnWqJyIiwvTr189t48vLhg0bjCRz5MgRR1uVKlXMe++9d9V1Csv4jMl7jD179jTt27e/6jqetg/bt29vHnnkEae2orQPrzwu3MrPTlcdT7ksdZOysrK0adMmRUZGOtq8vLwUGRmptWvXWlhZ/qSmpkqSypUr59QeGxuroKAg3XfffRo5cqQyMjIcz61du1Z16tRRSEiIo61Vq1ZKS0vTrl27HH3++J5c7nMr3pP9+/crLCxM1apVU/fu3XX06FFJ0qZNm5Sdne1UV61atXTnnXc66irsY7tSVlaWZsyYoeeff97pD8EW5f33R4cOHVJiYqJTLQEBAYqIiHDaZ4GBgWrUqJGjT2RkpLy8vLR+/XpHnxYtWsjb29vRp1WrVtq3b5/Onj3r6FMYxpyamiqbzabAwECn9piYGJUvX17169fXO++843S6vyiMb/ny5apQoYJq1qyp/v376/Tp0071e8o+TEpK0vfff69evXrleq6o7MMrjwu36rPTlcfT2+4PZ7raqVOnlJOT47RDJSkkJER79+61qKr8sdvt+vvf/65mzZrpvvvuc7T/5S9/UZUqVRQWFqbt27drxIgR2rdvn+bNmydJSkxMzHO8l5+7Vp+0tDRduHBBfn5+bhlTRESEpk+frpo1a+rEiRMaO3asmjdvrp07dyoxMVHe3t65DhohISHXrbswjC0vCxYsUEpKip577jlHW1Hef1e6XE9etfyx1goVKjg9X7x4cZUrV86pT9WqVXNt4/JzZcuWveqYL2/jVrh48aJGjBihbt26Of3BwRdeeEENGjRQuXLltGbNGo0cOVInTpzQu+++6xhDYR5f69at9dRTT6lq1aqKj4/Xq6++qjZt2mjt2rUqVqyYR+3Dzz//XGXKlNFTTz3l1F5U9mFex4Vb9dl59uxZlx1PCTe3sYEDB2rnzp1atWqVU3vfvn0d/65Tp44qVqyoRx99VPHx8apevfqtLrNA2rRp4/j3/fffr4iICFWpUkVfffXVLQ0dt8pnn32mNm3aKCwszNFWlPff7Sw7O1udO3eWMUaTJ092em7o0KGOf99///3y9vZWv379FB0dXSS+xr9r166Of9epU0f333+/qlevruXLl+vRRx+1sDLXmzp1qrp37y5fX1+n9qKyD692XChquCx1k4KCglSsWLFcd40nJSUpNDTUoqqub9CgQfruu++0bNky3XHHHdfsGxERIUk6cOCAJCk0NDTP8V5+7lp9/P39b2nICAwM1N13360DBw4oNDRUWVlZSklJyVXX9eq+/Ny1+tzqsR05ckRLlixR7969r9mvKO+/y/Vc6/crNDRUycnJTs9funRJZ86cccl+vRW/x5eDzZEjR7R48WKnszZ5iYiI0KVLl3T48GFJhX98V6pWrZqCgoKcfiaL+j6UpJUrV2rfvn3X/Z2UCuc+vNpx4VZ9drryeEq4uUne3t5q2LCh4uLiHG12u11xcXFq2rSphZXlzRijQYMGaf78+Vq6dGmu06B52bp1qySpYsWKkqSmTZtqx44dTh9Glz+Qa9eu7ejzx/fkcp9b/Z6cO3dO8fHxqlixoho2bKgSJUo41bVv3z4dPXrUUVdRGtu0adNUoUIFtW3b9pr9ivL+q1q1qkJDQ51qSUtL0/r16532WUpKijZt2uTos3TpUtntdkewa9q0qVasWKHs7GxHn8WLF6tmzZoqW7aso48VY74cbPbv368lS5aofPny111n69at8vLyclzKKczjy8vx48d1+vRpp5/JorwPL/vss8/UsGFD1a1b97p9C9M+vN5x4VZ9drr0eFqg24+Rp1mzZhkfHx8zffp0s3v3btO3b18TGBjodNd4YdG/f38TEBBgli9f7jQlMSMjwxhjzIEDB8ybb75pNm7caA4dOmS++eYbU61aNdOiRQvHNi5P+Xv88cfN1q1bzaJFi0xwcHCeU/6GDx9u9uzZYyZNmnRLpku/9NJLZvny5ebQoUNm9erVJjIy0gQFBZnk5GRjzO/TGe+8806zdOlSs3HjRtO0aVPTtGnTIjG2P8rJyTF33nmnGTFihFN7Udx/6enpZsuWLWbLli1Gknn33XfNli1bHLOFYmJiTGBgoPnmm2/M9u3bTfv27fOcCl6/fn2zfv16s2rVKnPXXXc5TSNOSUkxISEh5tlnnzU7d+40s2bNMiVLlsw1zbZ48eJm/PjxZs+ePWb06NEumWZ7rfFlZWWZJ5980txxxx1m69atTr+Tl2eYrFmzxrz33ntm69atJj4+3syYMcMEBwebHj16FIrxXW+M6enpZtiwYWbt2rXm0KFDZsmSJaZBgwbmrrvuMhcvXnRso6juw8tSU1NNyZIlzeTJk3OtX9j34fWOC8bcus9OVx1PCTcu8uGHH5o777zTeHt7m8aNG5t169ZZXVKeJOW5TJs2zRhjzNGjR02LFi1MuXLljI+Pj6lRo4YZPny40/ekGGPM4cOHTZs2bYyfn58JCgoyL730ksnOznbqs2zZMlOvXj3j7e1tqlWr5ngNd+rSpYupWLGi8fb2NpUqVTJdunQxBw4ccDx/4cIFM2DAAFO2bFlTsmRJ07FjR3PixIkiMbY/+vHHH40ks2/fPqf2orj/li1blufPZM+ePY0xv08Hf+ONN0xISIjx8fExjz76aK5xnz592nTr1s2ULl3a+Pv7m6ioKJOenu7UZ9u2beahhx4yPj4+plKlSiYmJiZXLV999ZW5++67jbe3t7n33nvN999/79bxHTp06Kq/k5e/t2jTpk0mIiLCBAQEGF9fX3PPPfeYcePGOQUDK8d3vTFmZGSYxx9/3AQHB5sSJUqYKlWqmD59+uQ6WBXVfXjZlClTjJ+fn0lJScm1fmHfh9c7Lhhzaz87XXE8tf3/gQEAAHgE7rkBAAAehXADAAA8CuEGAAB4FMINAADwKIQbAADgUQg3AADAoxBuAACARyHcAEAepk+fnuuvIAMoGgg3AG5KYmKihgwZoho1asjX11chISFq1qyZJk+erIyMDKvLy5fw8HBNmDDBqa1Lly769ddfrSkIwE0pbnUBAIqugwcPqlmzZgoMDNS4ceNUp04d+fj4aMeOHfrkk09UqVIlPfnkk5bUZoxRTk6Oihe/sY85Pz+/W/oX0AG4DmduANywAQMGqHjx4tq4caM6d+6se+65R9WqVVP79u31/fffq127dpKklJQU9e7dW8HBwfL399cjjzyibdu2ObYzZswY1atXT1988YXCw8MVEBCgrl27Kj093dHHbrcrOjpaVatWlZ+fn+rWrau5c+c6nl++fLlsNpt++OEHNWzYUD4+Plq1apXi4+PVvn17hYSEqHTp0nrggQe0ZMkSx3oPP/ywjhw5ohdffFE2m002m01S3pelJk+erOrVq8vb21s1a9bUF1984fS8zWbTf/7zH3Xs2FElS5bUXXfdpf/9738ue78B5A/hBsANOX36tH766ScNHDhQpUqVyrPP5aDQqVMnJScn64cfftCmTZvUoEEDPfroozpz5oyjb3x8vBYsWKDvvvtO3333nX7++WfFxMQ4no+OjtZ///tfffzxx9q1a5defPFF/fWvf9XPP//s9JqvvPKKYmJitGfPHt1///06d+6cnnjiCcXFxWnLli1q3bq12rVrp6NHj0qS5s2bpzvuuENvvvmmTpw4oRMnTuQ5lvnz52vIkCF66aWXtHPnTvXr109RUVFatmyZU7+xY8eqc+fO2r59u5544gl1797daZwAboEC/6lNADDGrFu3zkgy8+bNc2ovX768KVWqlClVqpR5+eWXzcqVK42/v3+uv4BcvXp1M2XKFGOMMaNHjzYlS5Y0aWlpjueHDx9uIiIijDHGXLx40ZQsWdKsWbPGaRu9evUy3bp1M8b8319uXrBgwXVrv/fee82HH37oeFylShXz3nvvOfWZNm2aCQgIcDx+8MEHTZ8+fZz6dOrUyTzxxBOOx5LM66+/7nh87tw5I8n88MMP160JgOtwzw0Al9qwYYPsdru6d++uzMxMbdu2TefOnVP58uWd+l24cEHx8fGOx+Hh4SpTpozjccWKFZWcnCxJOnDggDIyMvTYY485bSMrK0v169d3amvUqJHT43PnzmnMmDH6/vvvdeLECV26dEkXLlxwnLnJrz179qhv375Obc2aNdP777/v1Hb//fc7/l2qVCn5+/s7xgHg1iDcALghNWrUkM1m0759+5zaq1WrJkmOm3HPnTunihUravny5bm28cd7WkqUKOH0nM1mk91ud2xDkr7//ntVqlTJqZ+Pj4/T4ysvkQ0bNkyLFy/W+PHjVaNGDfn5+emZZ55RVlZWPkdaMNcaB4Bbg3AD4IaUL19ejz32mCZOnKjBgwdf9b6bBg0aKDExUcWLF1d4ePgNvVbt2rXl4+Ojo0ePqmXLlgVad/Xq1XruuefUsWNHSb8HpcOHDzv18fb2Vk5OzjW3c88992j16tXq2bOn07Zr165doHoAuB/hBsAN++ijj9SsWTM1atRIY8aM0f333y8vLy/98ssv2rt3rxo2bKjIyEg1bdpUHTp00Ntvv627775bCQkJ+v7779WxY8dcl5HyUqZMGQ0bNkwvvvii7Ha7HnroIaWmpmr16tXy9/d3ChxXuuuuuzRv3jy1a9dONptNb7zxRq4zKeHh4VqxYoW6du0qHx8fBQUF5drO8OHD1blzZ9WvX1+RkZH69ttvNW/ePKeZVwAKB8INgBtWvXp1bdmyRePGjdPIkSN1/Phx+fj4qHbt2ho2bJgGDBggm82mhQsX6rXXXlNUVJROnjyp0NBQtWjRQiEhIfl+rbfeekvBwcGKjo7WwYMHFRgYqAYNGujVV1+95nrvvvuunn/+eT344IMKCgrSiBEjlJaW5tTnzTffVL9+/VS9enVlZmbKGJNrOx06dND777+v8ePHa8iQIapataqmTZumhx9+ON9jAHBr2Exev8UAAABFFN9zAwAAPArhBgAAeBTCDQAA8CiEGwAA4FEINwAAwKMQbgAAgEch3AAAAI9CuAEAAB6FcAMAADwK4QYAAHgUwg0AAPAohBsAAOBR/h+lGpglO1wPkQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Convert labels to integers\n", "train_labels = train_labels.flatten()\n", @@ -499,9 +40758,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CIFAR-10 file not found. Downloading CIFAR data (Size = 163MB)\n", + "This may take a few minutes, please wait.\n", + "Saving images from file: data_batch_1\n", + "Saving images from file: data_batch_2\n", + "Saving images from file: data_batch_3\n", + "Saving images from file: data_batch_4\n", + "Saving images from file: data_batch_5\n", + "Saving images from file: test_batch\n", + "Writing labels file, temp\\cifar10_labels.txt\n" + ] + } + ], "source": [ "# In this script, we download the CIFAR-10 images and\n", "# transform/save them in the Inception Retraining Format\n", From bf6efdc40c0993980b6fed423fdb16709b612f69 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sat, 2 Dec 2023 22:31:37 +0800 Subject: [PATCH 20/28] Delete output of fit --- .../deep-learning/cnn.ipynb | 40044 +--------------- 1 file changed, 13 insertions(+), 40031 deletions(-) diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb index 5539b21a9c..e4cb1231f0 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb @@ -628,40038 +628,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.0183 - sparse_categorical_accuracy: 0.2357 - val_loss: 1.7497 - val_sparse_categorical_accuracy: 0.3435\n", - "Epoch 2/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.7483 - sparse_categorical_accuracy: 0.3592 - val_loss: 1.7239 - val_sparse_categorical_accuracy: 0.3617\n", - "Epoch 3/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.6681 - sparse_categorical_accuracy: 0.3905 - val_loss: 1.6222 - val_sparse_categorical_accuracy: 0.4131\n", - "Epoch 4/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.6086 - sparse_categorical_accuracy: 0.4203 - val_loss: 1.6065 - val_sparse_categorical_accuracy: 0.4314\n", - "Epoch 5/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.5827 - sparse_categorical_accuracy: 0.4349 - val_loss: 1.5625 - val_sparse_categorical_accuracy: 0.4425\n", - "Epoch 6/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.5774 - sparse_categorical_accuracy: 0.4396 - val_loss: 1.5571 - val_sparse_categorical_accuracy: 0.4412\n", - "Epoch 7/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.5441 - sparse_categorical_accuracy: 0.4534 - val_loss: 1.6152 - val_sparse_categorical_accuracy: 0.4275\n", - "Epoch 8/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.5458 - sparse_categorical_accuracy: 0.4547 - val_loss: 1.5552 - val_sparse_categorical_accuracy: 0.4608\n", - "Epoch 9/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.5108 - sparse_categorical_accuracy: 0.4698 - val_loss: 1.5329 - val_sparse_categorical_accuracy: 0.4570\n", - "Epoch 10/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4833 - sparse_categorical_accuracy: 0.4784 - val_loss: 1.5404 - val_sparse_categorical_accuracy: 0.4670\n", - "Epoch 11/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4902 - sparse_categorical_accuracy: 0.4786 - val_loss: 1.5396 - val_sparse_categorical_accuracy: 0.4539\n", - "Epoch 12/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4626 - sparse_categorical_accuracy: 0.4895 - val_loss: 1.6269 - val_sparse_categorical_accuracy: 0.4349\n", - "Epoch 13/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4669 - sparse_categorical_accuracy: 0.4918 - val_loss: 1.4837 - val_sparse_categorical_accuracy: 0.4904\n", - "Epoch 14/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4613 - sparse_categorical_accuracy: 0.4959 - val_loss: 1.5325 - val_sparse_categorical_accuracy: 0.4853\n", - "Epoch 15/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4501 - sparse_categorical_accuracy: 0.5004 - val_loss: 1.5449 - val_sparse_categorical_accuracy: 0.4600\n", - "Epoch 16/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4634 - sparse_categorical_accuracy: 0.4941 - val_loss: 1.5874 - val_sparse_categorical_accuracy: 0.4659\n", - "Epoch 17/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4436 - sparse_categorical_accuracy: 0.5040 - val_loss: 1.6198 - val_sparse_categorical_accuracy: 0.4492\n", - "Epoch 18/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 1.4326 - sparse_categorical_accuracy: 0.5101 - val_loss: 1.4996 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 94/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 95/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 96/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 97/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 98/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 99/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 104/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 114/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 115/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 117/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 118/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 120/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 126/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 127/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 128/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 129/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 130/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 132/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 135/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 138/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 140/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 145/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 148/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 149/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 155/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 156/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 158/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 159/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 160/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 161/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 162/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 163/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 164/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 199/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 200/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 201/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 202/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 203/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 204/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 205/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 206/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 207/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 209/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 210/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 212/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 215/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 224/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 248/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 250/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 252/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 258/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 294/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 295/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 297/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 298/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 299/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 300/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 301/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 302/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 303/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 304/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 306/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 307/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 308/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 309/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 310/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 311/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 312/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 313/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 314/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 315/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 316/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 319/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 320/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 321/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 322/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 323/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 324/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 325/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 347/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 348/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 351/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 352/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 353/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 355/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 358/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 393/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 395/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 396/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 397/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 399/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 400/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 401/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 402/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 403/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 404/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 405/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 406/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 407/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 408/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 409/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 411/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3088 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 412/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 413/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 414/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 415/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 416/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 417/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 418/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 420/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 421/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 422/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 423/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 424/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 425/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 429/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 433/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 434/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 436/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 441/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 442/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 443/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 444/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 445/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 446/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 447/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 449/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 450/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 451/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 452/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 453/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3063 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 485/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3081 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 612/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 645/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 719/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 720/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0953 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 724/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 729/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 730/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 735/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 884/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 885/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 886/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 887/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 890/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 891/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 892/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 893/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 896/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 897/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 898/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 901/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 902/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 903/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 904/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 905/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 906/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 907/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 908/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 909/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 910/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 911/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 912/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 913/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 914/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 915/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 916/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 917/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 918/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 919/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 920/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 921/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 923/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 925/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 928/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 929/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 930/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 931/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 935/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 937/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 938/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 940/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 941/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 942/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 943/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 949/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 950/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 951/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 952/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 953/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 954/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 955/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 956/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 957/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 958/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 959/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 960/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 961/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 962/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 963/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 964/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 966/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 967/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 968/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 969/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 972/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 973/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 979/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 990/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 991/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 993/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1025/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1026/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1027/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1028/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1029/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1030/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1031/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1032/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1033/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1034/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1035/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1036/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1037/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1038/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3056 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1044/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1046/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1047/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1054/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1055/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1056/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1057/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1058/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1059/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1060/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1061/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1062/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1063/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1064/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1066/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1068/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1069/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1070/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1076/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1078/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1080/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1083/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1087/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1092/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1095/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1096/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1097/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1104/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1109/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1113/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1140/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1143/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1144/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1145/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1146/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1147/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1148/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1149/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1150/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1154/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1155/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1156/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1158/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1159/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1160/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1161/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1162/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1163/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1164/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1169/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1173/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1175/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1178/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1179/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1180/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1181/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1182/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1183/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1184/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1185/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1186/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1187/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1188/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1194/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1195/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1196/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1197/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1198/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1199/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1200/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1201/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1202/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1203/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1204/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1205/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1206/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1207/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1209/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1210/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1212/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1215/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1224/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1248/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1250/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1252/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1258/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1259/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1260/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1261/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1262/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1263/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1264/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1265/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1266/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1267/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1268/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1269/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1270/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1271/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1272/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1273/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1275/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1276/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1278/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1280/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1281/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1282/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1284/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1285/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1288/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1289/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1290/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1291/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1292/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1294/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1295/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1297/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1298/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1299/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1300/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1301/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1302/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1303/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1304/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1306/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1307/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1308/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1309/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1310/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1311/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1312/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1313/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1314/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1315/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1316/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1319/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1320/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1321/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1322/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1323/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1324/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1325/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1347/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1348/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1351/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1352/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1353/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1355/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1358/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1359/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1360/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1361/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1362/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1363/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1364/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1365/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3081 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1366/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1367/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1368/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1369/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1370/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1371/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1372/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1373/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1374/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1375/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1376/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1377/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1378/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1385/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1393/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1395/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1396/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1397/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1399/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1400/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1401/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1402/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1403/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1404/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1405/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1406/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1407/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1408/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1409/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1411/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1412/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1413/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1414/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1415/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1416/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1417/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1418/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1424/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1425/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1429/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1433/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1434/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1436/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1441/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1442/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1443/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1444/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1445/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1446/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1447/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1449/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1450/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1451/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1452/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1453/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1454/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1455/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1456/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1457/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1458/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1459/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1460/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1461/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1462/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1463/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1464/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1465/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1466/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1467/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1472/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1475/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1476/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1477/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1478/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1483/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1484/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1485/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1505/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1506/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1511/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1512/20000\n", - "391/391 [==============================] - 2s 5ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1535/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1537/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1540/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1541/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1544/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1545/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1547/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1548/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1549/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1551/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1553/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1554/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1555/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1556/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1558/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1559/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1562/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1563/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1564/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1565/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1566/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1567/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1569/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1572/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1574/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1581/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1583/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1586/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1600/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1601/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1604/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1614/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1615/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1617/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1618/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1619/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1624/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1625/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1626/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1627/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1628/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1629/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1630/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1631/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1632/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1633/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1634/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1638/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1639/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1640/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1641/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1642/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1644/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1645/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1646/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1647/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1648/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1650/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1651/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1653/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1654/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1656/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1657/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1658/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1659/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1660/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1661/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1662/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1663/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1664/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1665/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1666/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1671/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1672/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1675/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1676/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1677/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1678/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1680/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1681/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1683/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1684/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1685/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1686/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1691/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1698/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1699/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1700/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1702/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1703/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1709/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1711/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1713/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1714/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1715/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1716/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1717/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1723/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1724/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1726/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1727/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1730/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1733/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1734/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1735/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1736/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1738/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1740/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1744/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1748/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1751/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1755/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1756/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1759/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1763/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0952 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1765/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1767/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1768/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1769/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1770/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1771/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1772/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1773/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1775/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1776/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1780/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1781/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1783/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1784/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1785/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1786/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1789/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1792/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1794/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1796/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1797/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1804/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1807/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1808/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1811/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1812/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1813/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1814/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1815/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1816/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1821/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1822/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1823/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1824/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1825/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1826/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1827/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1828/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1829/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1830/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1831/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1832/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1833/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1834/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1835/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1836/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1837/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1838/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1839/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1840/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1841/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1842/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1843/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1844/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1845/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1846/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1847/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1848/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1849/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1850/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1856/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1862/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1863/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1864/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1865/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1867/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1869/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1870/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1871/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1872/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1873/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1874/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1875/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1876/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1877/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1878/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1879/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1880/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1881/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1882/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1883/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1888/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1896/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1897/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 1898/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 1996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 1999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2025/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2026/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2027/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2028/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2029/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2030/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2031/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2032/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2033/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2034/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2035/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2036/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2037/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2038/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2039/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2040/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2041/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2042/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2043/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2044/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2046/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2047/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2049/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2050/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2051/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2052/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2053/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2054/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2055/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2056/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2057/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2058/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2059/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2060/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2061/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2062/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2064/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2066/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2068/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2069/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2070/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2076/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2078/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2080/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2083/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2087/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2092/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2095/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2096/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2109/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2113/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2114/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2115/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2116/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2117/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2118/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2120/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2121/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2123/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2124/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2125/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2126/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2127/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2128/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2129/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2130/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2132/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2135/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2138/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2140/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2143/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2144/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2145/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2146/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2147/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2148/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2149/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2150/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2154/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2155/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2156/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2158/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2159/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2160/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2161/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2162/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2163/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2164/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2169/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2173/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2175/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2178/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2184/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2185/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2186/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2187/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2188/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2194/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2195/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2196/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2197/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2198/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2199/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2200/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2201/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2202/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2203/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2204/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2205/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2206/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2207/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2209/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2210/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2212/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2215/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2224/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2248/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2250/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2252/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2258/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2259/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2260/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2261/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2262/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2263/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2264/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2265/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2266/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2267/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2268/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2269/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2270/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2271/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2272/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2273/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2275/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2276/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2278/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2280/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2281/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2282/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2284/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2285/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2288/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2292/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2303/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2304/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2306/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2307/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2309/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2310/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2311/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2312/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2315/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2316/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2320/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2322/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2323/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2324/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2347/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2348/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2351/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2352/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2353/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2355/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2358/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2359/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2360/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2362/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2363/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2364/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2365/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2366/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2367/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2368/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2374/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2375/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2376/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2377/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2378/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2385/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2393/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2395/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2396/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2397/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2399/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2400/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2401/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2402/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2403/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2404/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2405/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2406/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2407/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2408/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2409/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2411/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2412/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2413/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2414/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2415/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2416/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2417/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2418/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2420/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2421/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2422/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2423/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2424/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2425/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2429/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2433/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2434/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2436/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2441/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2442/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2443/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2444/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2445/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2446/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2447/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2449/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2450/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2451/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2452/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2453/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2454/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2455/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2456/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2457/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2458/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2459/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2460/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2461/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2462/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2463/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2464/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2465/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2467/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2469/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2472/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2473/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2474/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2476/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2477/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2478/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2479/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2480/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2481/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2482/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2483/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2484/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2485/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2486/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2487/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2488/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2489/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2490/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2491/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2492/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2493/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2494/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2495/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2496/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2497/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2498/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2499/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0952 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2500/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2501/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2502/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2506/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2507/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2508/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2509/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2510/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2511/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2513/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2514/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2515/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2516/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2517/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2519/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2520/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2521/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2522/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2525/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2526/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2527/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2528/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2529/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2531/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2532/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2533/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2535/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2536/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2537/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2538/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2539/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2540/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2541/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2542/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2543/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2544/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2545/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2546/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2548/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2549/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2550/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2551/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2552/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2553/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2556/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2557/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2558/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2559/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2560/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2564/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2572/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2574/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2575/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2577/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2578/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2579/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2580/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2581/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2582/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2583/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2584/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2585/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2586/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2587/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2588/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2589/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2590/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2591/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2592/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2593/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2594/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2595/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2596/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2597/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2598/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2599/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2600/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2601/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2602/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2603/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2604/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2605/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2606/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2607/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2608/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2609/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2610/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2611/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2612/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2613/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2614/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2615/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2616/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2617/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2618/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2619/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2620/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2621/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2622/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2623/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2624/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2625/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2626/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2627/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2628/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2629/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2630/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2631/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2632/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2633/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2634/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2635/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2638/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2639/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2640/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2641/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2642/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2643/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2644/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2645/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2646/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2647/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2648/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2649/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2650/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2651/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2652/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2653/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2654/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2655/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2657/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2659/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2660/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2662/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2664/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2665/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2666/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2672/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2674/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2676/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2678/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2679/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2680/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2681/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2682/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2683/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2684/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2685/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2686/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2687/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2688/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2689/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2690/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2691/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2692/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2693/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2694/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2695/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2696/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2697/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2698/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2699/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2700/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2701/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2702/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2703/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2704/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2705/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2706/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2707/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2708/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2709/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2710/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2711/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2712/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2713/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2714/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2715/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2716/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2717/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2718/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2719/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2720/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2721/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2722/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2723/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2726/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2727/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2728/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2729/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2730/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2731/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2732/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2733/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2734/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2735/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2736/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2737/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2738/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2739/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2740/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2741/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2742/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2743/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2744/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2745/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2746/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2747/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2748/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2749/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2750/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2751/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2753/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2754/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2756/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2757/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2758/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2759/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2760/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2761/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2764/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2765/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2766/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2767/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2768/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2770/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2772/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2773/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2774/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2775/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2776/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2778/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2779/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2780/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2781/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2782/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2783/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2784/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2785/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2786/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2787/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2788/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2789/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2790/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2791/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2792/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2793/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2794/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2795/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2796/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2797/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2798/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2799/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2800/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2801/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2802/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2803/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2804/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2805/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2806/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2807/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2808/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2809/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2810/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2811/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2812/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2813/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2814/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2815/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2816/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2817/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2821/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2822/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2823/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2824/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2825/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2826/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2827/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2828/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2829/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2830/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2831/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2832/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2833/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2834/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2835/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2836/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2837/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2838/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2849/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2850/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2856/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2862/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2863/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2864/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2865/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2867/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2869/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2870/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2871/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2872/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2873/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2874/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2875/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2876/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2877/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2878/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2879/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2880/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2881/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2882/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2883/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2888/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2889/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2890/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2891/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2892/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2893/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2896/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2897/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2898/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2901/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2902/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2903/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2904/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2905/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2906/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2907/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2908/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2909/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2910/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2911/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2912/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2913/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2914/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2915/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2916/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2917/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2918/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2919/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2920/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2921/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2923/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2924/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2925/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2928/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2929/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2930/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2931/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2932/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2933/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2934/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2935/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2936/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2937/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2938/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2944/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2945/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2946/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2947/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2948/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2949/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2950/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2951/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2952/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2953/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2954/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2955/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2956/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2957/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2958/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2959/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2960/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2961/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2962/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2963/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2964/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2966/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2967/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2968/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2969/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2972/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2973/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 2977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2979/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2990/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2991/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2993/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 2994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 2999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3025/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3026/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3027/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3028/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3029/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3030/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3031/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3032/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3033/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3034/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3035/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3036/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3037/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3038/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3039/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3040/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3041/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3042/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3043/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3044/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3046/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3047/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3049/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3050/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3051/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3052/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3053/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3054/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3055/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3056/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3057/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3058/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3059/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3060/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3061/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3062/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3063/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3064/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3066/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3068/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3069/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3070/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3076/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3078/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3083/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3087/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3092/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3095/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3096/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3097/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3104/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3109/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3113/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3114/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3115/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3116/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3117/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3118/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3120/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3121/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3123/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3124/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3125/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3126/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3127/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3128/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3129/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3130/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3132/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3135/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3138/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3140/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3143/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3144/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3145/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3146/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3147/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3148/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3149/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3150/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3154/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3155/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3156/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3158/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3159/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3160/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3161/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3162/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3163/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3164/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3169/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3173/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3175/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3179/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3180/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3181/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3182/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3183/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3184/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3185/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3187/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3188/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3194/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3195/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3196/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3197/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3198/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3199/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3200/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3201/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3202/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3203/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3204/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3205/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3206/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3207/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3209/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3210/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3212/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3215/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3248/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3250/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3252/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3258/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3259/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3260/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3261/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3262/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3263/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3264/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3265/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3266/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3267/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3268/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3269/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3270/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3271/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3272/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3273/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3275/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3276/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3278/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3280/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3281/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3282/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3285/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3288/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3289/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3290/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3291/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3292/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3294/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3295/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3300/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3301/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3302/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3309/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3312/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3313/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3314/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3315/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3316/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3324/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3325/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3347/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3348/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3351/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3352/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3353/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3355/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3358/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3359/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3360/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3361/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3362/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3363/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3364/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3365/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3366/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3367/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3368/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3369/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3370/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3371/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3372/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3373/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3374/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3375/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3376/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3377/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3378/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3385/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3393/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3395/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3396/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3397/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3399/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3400/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3401/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3402/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3403/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3404/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3405/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3406/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3407/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3408/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3409/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3411/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3412/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3413/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3414/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3415/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3416/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3417/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3418/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3420/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3421/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3423/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3424/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3425/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3429/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3433/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3434/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3436/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3441/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3442/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3443/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3444/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3445/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3446/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3447/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3449/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3450/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3451/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3452/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3453/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3454/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3455/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3456/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3457/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3458/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3459/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3460/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3461/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3462/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3463/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3464/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3465/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3091 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3466/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3467/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3469/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3472/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3473/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3474/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3476/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3477/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3478/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3479/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3480/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3481/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3482/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3483/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3484/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3485/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3486/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3487/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3488/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3489/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3490/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3491/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3492/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3493/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3494/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3495/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3496/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3497/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3498/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3499/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3500/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3501/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3502/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3506/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3507/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3508/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3509/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3510/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3511/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3513/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3514/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3515/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3516/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3517/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3519/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3520/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3521/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3522/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3525/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3526/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3527/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3528/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3529/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3531/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3532/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3533/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3535/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3536/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3537/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3538/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3539/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3540/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3541/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3542/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3543/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3544/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3545/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3546/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3548/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3549/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3550/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3551/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3552/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3553/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3556/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3558/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3560/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3564/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3572/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3574/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3575/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3577/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3578/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3579/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3580/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3581/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3582/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3583/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3584/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3585/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3586/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3587/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3588/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3589/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3590/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3591/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3592/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3593/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3594/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3595/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3596/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3597/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3598/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3599/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3600/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3601/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3602/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3603/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3609/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3610/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3611/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3612/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3613/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3614/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3615/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3616/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3617/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3618/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3619/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3620/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3621/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3622/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3623/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3624/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3625/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3626/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3627/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3628/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3629/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3630/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3631/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3632/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3633/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3634/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3635/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3638/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3639/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3640/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3641/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3642/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3643/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3644/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3645/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3646/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3647/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3648/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3649/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3650/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3651/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3652/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3653/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3654/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3655/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3657/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3659/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3660/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3662/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3664/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3665/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3666/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3676/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3678/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3679/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3680/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3681/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3682/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3683/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3684/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3685/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3686/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3687/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3688/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3689/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3690/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3691/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3692/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3693/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3694/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3695/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3696/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3697/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3698/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3052 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3704/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3705/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3706/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3707/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3708/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3709/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3710/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3711/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3712/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3713/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3714/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3715/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3716/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3717/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3718/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3719/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3720/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3721/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3722/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3723/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3726/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3727/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3728/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3729/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3730/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3731/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3732/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3733/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3734/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3735/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3736/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3737/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3738/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3739/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3740/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3741/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3742/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3743/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3744/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3745/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3746/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3747/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3748/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3749/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3750/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3751/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3753/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3754/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3756/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3757/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3758/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3759/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3760/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3761/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3764/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3765/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3766/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3767/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3768/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3770/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3772/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3773/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3774/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3775/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3776/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3778/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3779/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3780/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3781/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3782/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3783/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3784/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3785/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3786/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3787/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3788/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3794/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3795/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3796/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3797/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3798/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3799/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3800/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3801/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3802/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3803/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3804/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3805/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3806/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3807/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3808/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3835/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3836/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3837/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3838/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3839/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3840/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3841/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3842/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3846/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3856/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3862/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3863/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3864/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3865/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3867/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3869/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3873/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3874/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3875/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3881/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3882/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3883/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3888/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3896/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3898/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3905/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3906/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3907/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3908/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3909/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3910/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3920/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3921/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3923/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3937/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3939/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3940/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3941/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3942/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3943/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3944/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3945/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3946/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3947/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3948/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3949/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3950/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3951/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3952/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3953/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3954/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3955/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3956/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3957/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3958/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3959/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3960/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3961/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3962/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3963/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3964/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3966/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3967/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3968/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3969/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3972/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3973/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3979/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 3985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3990/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3991/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 3993/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 3999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4025/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4026/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4027/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4029/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4030/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4031/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4034/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4035/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4036/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4037/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4043/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4049/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4051/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4052/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4053/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3077 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4055/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4056/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4057/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4059/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4060/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4061/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4062/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4063/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4064/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4066/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4068/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4069/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4070/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4076/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4078/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4087/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4092/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4095/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4096/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4097/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4104/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4109/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4113/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4114/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4115/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4116/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4117/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4118/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4120/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4121/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4123/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4124/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4125/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4126/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4127/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4128/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4129/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4130/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4132/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4135/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4154/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4161/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4184/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4185/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4186/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4187/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4188/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4194/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4195/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4196/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4197/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4198/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4199/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4200/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4201/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4203/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4204/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4206/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4207/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4210/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4212/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4215/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4224/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4248/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4250/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4252/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4275/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4276/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4278/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4280/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4281/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4282/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4284/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4290/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4291/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4294/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4295/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4297/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4298/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4300/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4301/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4302/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4303/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4304/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4307/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4308/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4309/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4310/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4312/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4314/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4315/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4319/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4320/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4321/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4322/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4323/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4325/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4347/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4348/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4351/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4352/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4353/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4355/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4358/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4369/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4370/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4371/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4372/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4373/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4374/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4375/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4376/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4377/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4378/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4385/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4393/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4395/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4408/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4413/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4414/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4415/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4422/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4423/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4424/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4434/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4474/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4476/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4477/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4479/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4480/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4481/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4482/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4483/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4484/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4485/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4486/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4488/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4489/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4490/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4491/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4492/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4493/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4494/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4495/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4496/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4497/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4498/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4499/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4500/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4501/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4502/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4506/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4507/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4508/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4509/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4510/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4511/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4513/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4514/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4515/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4516/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4517/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4519/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4520/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4521/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4522/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4525/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4526/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4527/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4528/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4529/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4531/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4532/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4533/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4535/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4536/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4537/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4538/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4539/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4540/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4541/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4542/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4543/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4544/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4545/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4546/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4548/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4549/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4550/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4551/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4552/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4559/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4560/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3082 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4572/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4574/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4575/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4577/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4578/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4579/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4580/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4581/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4582/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4583/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4584/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4585/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4586/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4587/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4588/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4589/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4590/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4591/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4592/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4593/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4594/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4595/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4596/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4597/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4598/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4599/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4600/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4601/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4602/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4604/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4605/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4606/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4607/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4608/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4609/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4611/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4613/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4616/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4617/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4618/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4619/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4621/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4622/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4623/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4624/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4625/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4626/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4627/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4628/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4629/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4630/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4631/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4632/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4633/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4634/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4635/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4638/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4654/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4655/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4657/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4664/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4665/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4666/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4672/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4674/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4676/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4678/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4679/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4680/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4681/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4682/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4683/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4684/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4685/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4686/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4687/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4688/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4689/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4690/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4691/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4692/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4693/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4694/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4695/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4696/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4697/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4698/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4699/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4700/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4701/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4702/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4703/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4704/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4705/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4706/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4707/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4708/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4709/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4710/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4711/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4712/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4713/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4714/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4715/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4716/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4717/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4718/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4719/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4720/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4721/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4722/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4723/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4726/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4727/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4728/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4749/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4751/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4753/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4756/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4757/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4758/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4759/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4760/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4761/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4764/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4765/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4766/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4767/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4768/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4770/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4772/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4773/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4774/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4775/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4776/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4778/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4779/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4780/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4781/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4782/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4783/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4784/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4785/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4786/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0949 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4787/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4789/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4790/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4791/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4792/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4793/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4794/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4795/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4796/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4797/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4798/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4799/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4800/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4801/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4802/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4803/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4804/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4805/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4806/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4807/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4808/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4809/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4810/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4811/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4812/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4813/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4814/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4815/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4816/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4817/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4821/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4822/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4823/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4829/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4831/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4832/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4833/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4834/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4835/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4836/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4837/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4838/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3049 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4844/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4845/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4846/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4847/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4848/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4849/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4850/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4856/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4862/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4863/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4864/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4865/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4867/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4869/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4870/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4871/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4872/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4873/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4874/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4875/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4876/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4877/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4878/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4879/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4880/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4881/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4882/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4883/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4888/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4889/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4890/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4891/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4892/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4893/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4896/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4897/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4898/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4901/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4902/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4903/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4904/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4905/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4906/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4907/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4908/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4909/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4910/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4911/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4912/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4913/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4914/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4915/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4916/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4917/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4918/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4919/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4920/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4921/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4923/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4924/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4925/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4928/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4939/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4940/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4941/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4946/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4947/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4948/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4949/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4954/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4955/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4956/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3081 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4957/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4960/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4961/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4962/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4963/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4964/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4966/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4967/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4968/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4969/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4972/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4973/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4979/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4990/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4991/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 4993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 4997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 4999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5034/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5035/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5036/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5037/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5039/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5040/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5041/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5042/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5043/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5044/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5046/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5047/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5049/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5050/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5051/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5052/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5053/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5054/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5055/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5056/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5057/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5058/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5059/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5060/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5061/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5062/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5063/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5064/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5066/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5068/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5069/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5070/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5078/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5080/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5083/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5087/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5092/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5096/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5097/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5104/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5109/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5113/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5114/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5115/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5116/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5117/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5118/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5120/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5121/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5123/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5129/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5130/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5132/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5140/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5144/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5145/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5146/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5148/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5149/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5150/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5155/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5156/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5158/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5159/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5160/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5161/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5162/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5163/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5164/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5169/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5173/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5175/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5178/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5179/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5180/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5181/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5182/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5183/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5184/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5185/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5186/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5187/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5188/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5194/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5195/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5196/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5197/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5198/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5201/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5205/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5206/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5207/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5209/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5210/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5212/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5224/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5260/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5261/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5262/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5263/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5264/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5265/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5266/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5267/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5268/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5269/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5270/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5271/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5272/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5273/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5275/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5276/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5278/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5280/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5281/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5282/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5284/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5285/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5288/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5289/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5290/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5291/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5292/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5294/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5295/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5297/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5298/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5299/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5300/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5301/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5302/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5303/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5304/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5306/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5307/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5308/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5319/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5320/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5321/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5322/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5347/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5348/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5351/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5352/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5353/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5355/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5358/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5359/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5360/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5361/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5362/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5363/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5364/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5365/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5366/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5367/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5368/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5369/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5370/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5371/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5372/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5373/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5374/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5375/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5376/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5377/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5378/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5385/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5416/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5417/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5418/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5420/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5421/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5422/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5423/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5424/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5425/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5429/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5433/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5436/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5441/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5443/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5444/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5446/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5447/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5449/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5451/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5452/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5453/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5454/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5455/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5456/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5457/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5458/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5459/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5460/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5461/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5462/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5463/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5464/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5465/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5466/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5467/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5469/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5473/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5474/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5478/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5479/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5481/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5482/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5485/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5486/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5488/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5489/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5492/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5497/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5498/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5510/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5511/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5513/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5514/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5515/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5516/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5517/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5519/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5520/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5521/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5522/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5525/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5526/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5527/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5528/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5529/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5531/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5532/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5533/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5535/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5536/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5537/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5538/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5539/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5540/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5541/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5542/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5543/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5544/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5545/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5546/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5548/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5549/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5550/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5551/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5552/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5553/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5556/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5557/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5558/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5559/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5560/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5564/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5572/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5574/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5575/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5577/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5578/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5579/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5580/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5581/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5582/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5583/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5584/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5585/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5587/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5588/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5589/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5592/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5593/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5594/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5595/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5596/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5597/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5598/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5599/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5600/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5602/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5604/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5605/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5606/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5608/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5609/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5610/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5612/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5613/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5614/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5615/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5616/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5617/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5618/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5619/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5620/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5621/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5622/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5623/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5625/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5626/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5627/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5628/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5629/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5630/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5631/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5632/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5633/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5634/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5635/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5638/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5639/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5640/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5641/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5642/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5643/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5644/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0948 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5645/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5646/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5647/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5648/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5649/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5650/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5651/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5652/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5653/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5654/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5655/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5657/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5659/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5660/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5665/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5666/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5672/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5674/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5676/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5678/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5679/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5680/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5682/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5683/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5699/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5700/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5701/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5703/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3063 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5709/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5710/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5711/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5713/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5716/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5717/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5718/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5719/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5720/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5721/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5722/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5727/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5728/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5729/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5731/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5732/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5733/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5734/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5735/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5736/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5737/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5738/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5739/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5740/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5741/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5742/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5743/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5744/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5745/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5746/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5747/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5748/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5749/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5750/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5751/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5753/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5754/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5756/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5757/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5758/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5759/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5760/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5764/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5780/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5784/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5785/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5788/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5794/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5795/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5796/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5797/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5798/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5799/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5800/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5801/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5802/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5803/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5804/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5805/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5806/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5807/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5808/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5809/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5810/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5811/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5812/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5813/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5814/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5815/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5816/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5817/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5821/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5822/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5823/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5824/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5825/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5826/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5827/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5828/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5829/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5830/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5832/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5833/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5834/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5835/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5837/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5838/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5839/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5841/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5842/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5843/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5844/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5847/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5848/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5849/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5850/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5856/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5862/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5863/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5864/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5865/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5867/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5869/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5870/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5871/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5876/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5877/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5878/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5879/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5880/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5889/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5890/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5891/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5892/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5893/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1042 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5896/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5898/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5902/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5903/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5904/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5905/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5906/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5907/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5908/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5909/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5912/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5914/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5918/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5921/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5930/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 5940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5960/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5967/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5969/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5972/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5979/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5993/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 5997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 5999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6025/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6026/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6027/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6028/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6080/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 6085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6092/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6095/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6096/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6097/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6100/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6102/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6105/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6107/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6115/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6116/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6117/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6118/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6121/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6123/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6124/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6125/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6126/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6127/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6128/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6129/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6130/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6132/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6135/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6138/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6140/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6143/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6144/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6147/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6149/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6150/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6175/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3058 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6224/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6248/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6250/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6251/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6252/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6258/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6272/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6273/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3079 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6307/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6308/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6316/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6319/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6322/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6325/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6334/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6364/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6365/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6366/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6367/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6368/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6369/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6376/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6379/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6380/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6381/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6382/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6383/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6385/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6393/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6395/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6396/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6397/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6399/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6400/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6401/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6402/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6403/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6417/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 6429/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6433/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6434/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6436/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6444/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6445/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6447/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3058 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6469/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6472/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6473/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6476/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6477/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6479/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6480/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6482/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6483/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6489/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6492/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6494/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6495/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6496/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6507/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6508/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6509/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6511/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6525/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6542/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6556/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6557/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6558/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6559/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6575/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6579/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6580/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6584/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6586/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6592/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6594/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6596/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6597/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6598/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6599/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6600/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6601/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6602/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 6603/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6604/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6605/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6606/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6607/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6608/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6609/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6610/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6611/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6612/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6613/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6616/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6617/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6618/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6619/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6629/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6649/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6650/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6651/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6652/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6653/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6659/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6660/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6662/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6664/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6665/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6666/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6672/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6704/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6713/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6714/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6715/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6718/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6719/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6720/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6721/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6723/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6726/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6727/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6728/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6730/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6731/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6732/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6733/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6747/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6759/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 6761/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6765/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6767/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6768/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6770/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6773/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6774/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6778/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6781/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6815/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6816/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6817/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6821/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6822/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6824/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6825/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6826/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6839/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6840/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6841/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6842/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6843/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3066 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6849/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6865/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6867/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6870/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6876/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6892/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6904/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6905/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6908/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6909/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6911/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6912/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6913/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6914/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6915/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6916/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6918/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6919/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6920/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6921/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6923/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 6924/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6925/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3067 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6938/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6958/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6968/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6972/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 6987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6991/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 6999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7010/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7011/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7029/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7030/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7031/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7032/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7033/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7034/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7039/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7040/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7083/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7112/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7125/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7126/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7128/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7141/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7146/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7147/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7150/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7153/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7157/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7165/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7169/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7173/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7178/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7179/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7180/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7182/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7183/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7239/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7240/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7244/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7245/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7249/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7284/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7285/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7288/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7289/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7291/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7292/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7349/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7354/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7369/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7373/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7375/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7411/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7418/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7456/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7458/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7459/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7461/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7466/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7469/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7474/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7490/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7493/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7496/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7519/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7533/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7535/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7537/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7548/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7553/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7556/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7564/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7572/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7578/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7584/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7585/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7601/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7623/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7624/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7625/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7628/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7629/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7630/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7631/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7632/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7633/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7634/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7635/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7638/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7639/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7640/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7641/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7642/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7643/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7644/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7645/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7646/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7647/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7648/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7649/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7650/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7651/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7652/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7653/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7659/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7660/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7666/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3060 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7696/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7697/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7698/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7699/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7700/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7702/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7704/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7707/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7710/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7722/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7723/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7726/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7727/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7729/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7731/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7738/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7739/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7740/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7741/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7742/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7743/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7744/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7745/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7746/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7747/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7748/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7749/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7750/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7751/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0948 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7753/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7754/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7756/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7758/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7761/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7766/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7770/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7789/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7790/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7791/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7792/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7793/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7796/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7800/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7802/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7803/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7804/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7805/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7806/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7807/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7808/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7809/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7810/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7815/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7816/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7817/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7823/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7844/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7845/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7846/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7847/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7848/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 7858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7861/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7901/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7902/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3052 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7940/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7941/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7942/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7943/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7945/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7946/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7948/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7950/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7952/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7953/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7962/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7969/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 7998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 7999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8026/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8047/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8051/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8052/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8076/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8109/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8110/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8122/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8175/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8179/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8180/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8181/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8183/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8187/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8188/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8193/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8194/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8199/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8208/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8209/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8211/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8217/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8233/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8275/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8276/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8279/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8280/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8282/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8283/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8290/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8291/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8294/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8296/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8297/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8300/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8301/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8302/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8304/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8305/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8309/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8319/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8321/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8323/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8324/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8325/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8378/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8384/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8430/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8431/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8437/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8442/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8443/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8467/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8471/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8473/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8474/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8475/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8476/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8477/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8478/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8479/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8480/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8481/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8482/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8483/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8484/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8490/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8492/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8493/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8494/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8496/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8497/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8498/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8499/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8500/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8501/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8502/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3087 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8506/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8507/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8509/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8511/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8529/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8531/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8532/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8533/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8557/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8559/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8560/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8564/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8577/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8581/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8582/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8588/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8591/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8593/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8595/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8605/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8606/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8607/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8608/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8609/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8610/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8612/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8614/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8615/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8616/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8617/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8618/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8619/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8627/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8628/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8651/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8655/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8672/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8674/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8678/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8679/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8680/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8681/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8685/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8687/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8688/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8711/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8712/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8723/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8739/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8740/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8741/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8742/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8743/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8751/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8753/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8757/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8758/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8759/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8760/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8761/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8763/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8764/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8765/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8767/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8768/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8773/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8774/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8775/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8778/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8779/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8780/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8782/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8844/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8845/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8846/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8847/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8848/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8849/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8850/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8851/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8852/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8853/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8854/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8855/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8856/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8857/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8858/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8859/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8860/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8863/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8866/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8872/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8877/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8878/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8879/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8883/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8888/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8889/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8890/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8892/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8893/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8895/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8897/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8901/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8903/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8940/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8941/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8942/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8943/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8946/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8947/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8948/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8950/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8954/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8955/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8956/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8957/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8958/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 8959/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8960/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8961/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8962/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8963/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8970/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8971/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8975/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8980/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8982/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8984/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8987/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8988/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8990/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8991/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8992/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8993/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8994/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8995/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8996/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 8997/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8998/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 8999/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9000/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9001/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9004/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9005/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9006/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9007/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9008/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9040/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9044/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9046/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9047/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9049/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9058/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9059/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9068/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9080/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9089/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9103/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9104/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9106/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9119/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9131/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9133/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9136/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9137/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9138/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9139/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9142/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9144/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9145/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9146/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9160/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9163/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9189/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9190/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9191/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9192/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9214/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9215/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9216/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9218/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9219/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9225/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9237/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9243/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9246/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9247/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9253/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9254/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9255/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9256/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9257/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9258/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9259/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9260/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9261/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9262/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9263/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9264/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9265/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9266/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9267/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9268/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9269/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9270/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9271/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9272/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9313/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9314/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9315/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9316/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9321/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9324/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9326/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9328/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9331/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9346/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9350/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9356/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9357/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9364/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9365/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9394/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3044 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9404/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9405/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9406/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9407/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9408/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9421/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9423/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9427/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9428/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9432/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9435/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9438/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9439/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9450/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9454/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9465/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9470/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9476/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9499/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9500/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9501/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9502/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9514/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9515/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9516/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9517/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9520/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9521/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9523/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9524/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9525/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9526/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9527/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9528/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9529/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9530/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9531/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9532/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9534/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9535/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9539/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9546/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9548/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9551/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9553/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9554/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9557/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9558/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9563/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9570/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9572/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9573/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9574/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9577/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9581/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9586/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9588/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9594/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9596/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9597/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9598/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9604/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9630/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9636/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9637/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9652/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9654/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9658/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9659/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9662/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9665/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9674/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9675/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9676/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - 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val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9700/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9701/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9707/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9708/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9710/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9711/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9715/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9718/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9722/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9724/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9725/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9728/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9730/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9731/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9732/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9734/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9737/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9738/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9739/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9740/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9742/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9745/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9755/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9757/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9760/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9762/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9768/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9775/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9779/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9780/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9782/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9783/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9784/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9785/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9786/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9787/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9788/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9789/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9790/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9792/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9797/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9799/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9815/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9817/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9818/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9819/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9824/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9826/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9827/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9830/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9837/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9868/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9870/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9872/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9873/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9880/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9881/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9882/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 9883/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9885/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9886/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9887/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9891/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9899/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9900/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9920/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9922/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9925/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9927/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9931/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9932/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9933/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9934/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9935/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9942/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9943/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9945/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9947/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9948/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9950/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9954/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9955/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9965/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9968/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - 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val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9974/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9976/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9977/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9978/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9985/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9986/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 9992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 9999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10009/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10012/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10013/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10014/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10015/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10016/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10017/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10018/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10022/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10023/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10024/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10025/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10027/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10036/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10037/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10039/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10041/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10042/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10043/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10044/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10046/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10048/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10050/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10051/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10053/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10055/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10056/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10057/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10058/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10062/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10064/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10066/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10069/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10072/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10074/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10075/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10077/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10078/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10079/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10080/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10081/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10082/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10083/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 10087/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10088/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10091/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10094/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10097/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10101/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10111/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10134/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10135/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10138/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10166/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10167/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10168/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10169/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10176/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10178/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10213/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10220/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10221/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10222/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10223/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10226/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10227/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10228/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10229/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10230/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10231/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10232/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10234/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10235/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10236/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10238/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 10239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10241/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10242/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10263/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10268/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10274/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10277/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10278/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10286/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10287/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10292/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10293/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10295/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10311/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10317/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10318/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10327/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10329/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10330/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10332/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10333/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10335/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10336/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10337/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10338/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10339/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10340/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10341/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10342/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10343/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10344/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10345/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10386/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10387/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10388/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10389/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10390/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10391/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10392/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10396/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10397/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10398/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10400/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10402/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10403/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10410/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10411/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10412/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10415/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10416/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10417/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10419/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10420/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10422/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10425/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10426/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10440/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10448/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10452/20000\n", - "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10468/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10485/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10497/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10499/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10502/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10503/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 10504/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10505/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10507/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10509/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10510/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10512/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10513/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10514/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10515/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10516/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10517/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10518/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10520/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10522/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10547/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10551/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10555/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10556/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10557/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10558/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10559/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10560/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10561/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10562/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10564/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10565/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10566/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10567/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10568/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10569/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10571/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10574/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10575/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10576/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10578/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10579/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10580/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10622/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10627/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10631/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10633/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10634/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10635/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10653/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10654/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10655/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10656/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10660/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10661/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10663/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10664/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10667/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10668/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10669/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10670/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10671/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10672/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10673/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10674/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10677/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10686/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10690/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10710/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 10744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10749/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10752/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10765/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10769/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10770/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10771/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10773/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10777/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10779/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10785/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3085 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10791/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10808/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10809/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10810/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10820/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10821/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10822/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10825/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10827/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10828/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10829/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10830/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10833/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10834/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10836/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10838/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10840/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10873/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10875/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10876/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10879/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10881/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10882/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10884/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10894/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10897/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10923/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10924/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10925/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10926/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10933/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10935/20000\n", - "391/391 [==============================] 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[==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 10942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10979/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10981/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10983/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10989/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 10997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 10999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11002/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11003/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11019/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11020/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11021/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11045/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11052/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11053/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11061/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11065/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11067/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11071/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11073/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11084/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11085/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11086/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11090/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11093/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11098/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11099/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11108/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11151/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11152/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11170/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11171/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11172/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11173/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11174/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11177/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11261/20000\n", - "391/391 [==============================] - 1s 3ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 11287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11355/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11358/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11361/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 11376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 11459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 11561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11605/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11606/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11607/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0953 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 11863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0950 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3080 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1039 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 11996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 11999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 12008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3091 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3026 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 12418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 12466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12640/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0955 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12798/20000\n", - "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12821/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12854/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12857/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12858/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12860/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 12996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 12999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13110/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13113/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13118/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13119/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13120/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0946 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13396/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13397/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3091 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13399/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13456/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13462/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 13482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 13550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13654/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13673/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13718/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13732/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13749/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13986/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 13998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 13999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1037 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1050 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14109/20000\n", - "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 14132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0953 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1038 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14227/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14229/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14278/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14284/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14345/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14441/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 14442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14486/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14520/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14521/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14525/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14526/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14529/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14534/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14638/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 14654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.0999\n", - "Epoch 14705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14785/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14786/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14791/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0960 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14820/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14873/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 14998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 14999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15144/20000\n", - "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15364/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15365/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15366/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15367/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15368/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15369/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15370/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15371/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15372/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15373/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15374/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15375/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15376/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15377/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15378/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15420/20000\n", - "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0954 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0952 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15589/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3078 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1038 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 15996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 15999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16163/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16283/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1033 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1034 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16549/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16627/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16634/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16636/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16638/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16645/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16738/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16739/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3087 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3037 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3073 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 16997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 16999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17199/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17290/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17294/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17358/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17359/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17360/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17361/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3076 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3026 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3072 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17842/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17844/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17850/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17851/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17936/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0950 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 17997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 17999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1025 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18071/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18087/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0951 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18091/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18098/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18100/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18101/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18124/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0957 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18146/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18157/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18168/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18178/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18184/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18189/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18195/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18205/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18211/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18216/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18227/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18229/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1028 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18231/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18237/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18241/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18242/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18244/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1035 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18353/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18354/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18355/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18356/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18357/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18358/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18377/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18379/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18380/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18381/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18479/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18487/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3036 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18556/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18582/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18625/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3088 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18675/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18679/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18709/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18713/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18714/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18715/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0958 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3063 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18800/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18801/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18802/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18843/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18872/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18878/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1029 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18894/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18915/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18917/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18918/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18919/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18920/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18926/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18927/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18928/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18929/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18930/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18931/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18932/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18933/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18934/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18935/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18936/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18937/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18938/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18948/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18951/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3074 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18959/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0964 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18973/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 18996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 18999/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19001/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3038 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19002/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19003/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19004/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19005/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19006/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19007/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19008/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19009/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19010/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19011/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19012/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19013/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19014/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19015/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19016/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19017/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19018/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19019/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19020/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19021/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19022/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19023/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19024/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19025/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19026/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19027/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19028/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19029/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19030/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19031/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19032/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19033/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19034/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19035/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19036/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19037/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19038/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19039/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19040/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19041/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19042/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19043/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19044/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19045/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19046/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19047/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19048/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19049/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19050/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19051/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19052/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19053/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19054/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19055/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19056/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19057/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19058/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19059/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19060/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19061/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19062/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19063/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19064/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19065/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19066/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19067/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19068/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19069/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19070/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19071/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19072/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19073/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19074/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19075/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19076/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19077/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19078/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19079/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19080/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19081/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19082/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19083/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19084/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19085/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19086/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19087/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19088/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19089/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19090/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19091/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19092/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19093/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19094/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19095/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19096/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19097/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19098/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19099/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19100/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19101/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19102/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19103/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19104/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19105/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19106/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19107/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19108/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19109/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19110/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19111/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19112/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19113/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19114/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19115/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19116/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19117/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19118/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19119/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19120/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19121/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19122/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19123/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19124/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19125/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19126/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19127/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19128/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19129/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19130/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19131/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19132/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19133/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19134/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19135/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19136/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19137/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19138/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1031 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19139/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19140/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19141/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19142/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19143/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19144/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19145/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19146/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19147/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19148/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19149/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19150/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19151/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19152/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19153/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19154/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19155/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19156/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19157/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19158/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19159/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19160/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19161/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19162/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19163/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19164/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19165/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19166/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19167/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19168/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19169/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19170/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19171/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19172/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19173/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19174/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19175/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19176/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19177/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19178/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19179/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19180/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19181/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19182/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19183/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19184/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19185/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19186/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19187/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19188/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19189/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19190/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19191/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19192/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19193/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19194/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19195/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19196/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19197/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19198/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19199/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19200/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19201/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19202/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19203/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19204/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19205/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19206/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19207/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19208/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19209/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19210/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19211/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19212/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19213/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19214/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19215/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19216/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19217/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19218/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19219/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19220/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19221/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19222/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19223/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19224/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3078 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19225/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19226/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19227/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19228/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19229/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19230/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19231/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19232/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19233/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19234/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19235/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19236/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19237/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19238/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19239/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19240/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19241/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1030 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19242/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19243/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19244/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19245/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19246/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19247/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19248/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19249/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0961 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19250/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19251/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19252/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19253/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19254/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19255/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19256/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19257/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19258/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19259/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19260/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19261/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19262/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19263/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19264/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19265/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19266/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19267/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19268/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19269/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19270/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19271/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19272/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19273/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19274/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19275/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19276/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19277/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19278/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19279/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19280/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19281/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19282/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19283/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19284/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19285/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19286/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19287/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19288/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19289/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19290/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19291/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19292/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19293/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19294/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19295/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19296/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19297/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19298/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19299/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19300/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19301/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19302/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19303/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19304/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19305/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19306/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19307/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19308/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19309/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19310/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19311/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19312/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19313/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19314/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0965 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19315/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19316/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19317/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19318/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19319/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19320/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19321/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19322/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19323/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19324/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19325/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19326/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19327/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19328/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19329/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19330/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19331/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19332/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19333/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19334/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19335/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19336/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19337/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19338/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19339/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19340/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19341/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19342/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19343/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19344/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3070 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19345/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19346/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19347/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19348/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19349/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19350/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19351/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19352/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19353/20000\n", - "391/391 [==============================] 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[==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19359/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19360/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19361/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19362/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19363/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19364/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19365/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19366/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0966 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19367/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19368/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19369/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19370/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19371/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19372/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19373/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19374/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19375/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19376/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19377/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19378/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19379/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19380/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3075 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19381/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19382/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19383/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19384/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19385/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19386/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19387/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3071 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19388/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19389/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19390/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19391/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19392/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19393/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19394/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19395/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19396/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19397/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19398/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19399/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19400/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19401/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19402/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19403/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19404/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19405/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19406/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19407/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19408/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19409/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19410/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19411/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19412/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19413/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19414/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19415/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19416/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19417/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19418/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19419/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19420/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19421/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19422/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19423/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19424/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19425/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19426/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19427/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19428/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19429/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19430/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19431/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19432/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19433/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19434/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19435/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19436/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19437/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19438/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19439/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19440/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19441/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19442/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19443/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19444/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19445/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19446/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19447/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19448/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19449/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19450/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19451/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19452/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19453/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19454/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19455/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19456/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19457/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19458/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19459/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19460/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19461/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19462/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19463/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19464/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19465/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19466/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19467/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19468/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19469/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19470/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19471/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19472/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19473/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19474/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19475/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19476/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19477/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19478/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19479/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19480/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19481/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19482/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19483/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19484/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19485/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19486/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19487/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19488/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19489/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19490/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19491/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19492/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19493/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19494/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19495/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19496/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19497/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19498/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19499/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19500/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19501/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19502/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19503/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19504/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3068 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19505/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19506/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19507/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19508/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19509/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19510/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19511/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19512/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19513/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19514/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19515/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19516/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19517/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19518/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19519/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19520/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19521/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19522/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19523/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0967 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19524/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19525/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19526/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19527/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19528/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19529/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19530/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19531/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19532/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19533/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19534/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19535/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19536/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19537/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19538/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19539/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19540/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19541/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19542/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19543/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19544/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19545/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19546/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19547/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19548/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19549/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19550/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19551/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19552/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19553/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19554/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19555/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19556/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19557/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19558/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19559/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19560/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19561/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19562/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19563/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19564/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19565/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19566/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19567/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19568/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19569/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19570/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19571/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19572/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19573/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19574/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19575/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19576/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19577/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19578/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19579/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19580/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19581/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19582/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19583/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3027 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19584/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19585/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19586/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19587/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19588/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1032 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19589/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19590/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19591/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19592/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19593/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19594/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19595/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19596/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19597/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19598/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19599/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0972 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19600/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19601/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19602/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19603/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19604/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19605/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19606/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19607/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19608/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19609/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19610/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19611/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19612/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19613/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19614/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19615/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19616/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19617/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19618/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19619/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19620/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19621/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19622/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19623/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19624/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19625/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19626/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19627/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19628/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19629/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19630/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19631/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19632/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19633/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19634/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19635/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0959 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19636/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19637/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19638/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19639/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19640/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19641/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19642/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19643/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19644/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19645/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19646/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19647/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19648/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19649/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1018 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19650/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19651/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19652/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19653/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3065 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19654/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19655/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19656/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19657/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19658/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19659/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19660/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19661/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19662/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19663/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19664/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19665/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19666/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19667/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19668/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19669/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19670/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19671/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19672/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19673/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19674/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19675/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19676/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19677/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19678/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1026 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19679/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19680/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19681/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19682/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19683/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3059 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19684/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19685/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19686/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3050 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19687/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19688/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19689/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19690/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3067 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19691/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19692/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19693/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19694/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19695/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19696/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19697/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19698/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19699/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19700/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19701/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19702/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19703/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19704/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19705/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19706/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19707/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19708/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19709/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19710/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19711/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19712/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19713/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19714/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19715/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19716/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19717/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19718/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19719/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19720/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19721/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19722/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19723/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19724/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19725/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19726/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19727/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19728/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19729/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19730/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19731/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19732/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19733/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19734/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19735/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0979 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19736/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19737/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19738/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19739/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19740/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19741/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19742/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19743/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1021 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19744/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19745/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19746/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19747/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19748/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19749/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19750/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19751/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3066 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19752/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19753/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19754/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19755/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19756/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19757/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19758/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19759/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19760/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19761/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19762/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1027 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19763/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3051 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19764/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19765/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19766/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19767/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19768/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19769/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19770/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3061 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19771/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19772/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19773/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19774/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3030 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19775/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19776/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19777/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19778/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19779/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19780/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19781/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19782/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19783/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19784/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19785/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19786/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19787/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1023 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19788/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19789/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19790/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19791/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19792/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19793/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19794/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19795/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19796/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19797/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19798/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19799/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19800/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19801/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19802/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19803/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19804/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19805/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19806/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19807/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3057 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19808/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19809/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19810/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19811/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19812/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19813/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19814/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0974 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19815/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19816/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19817/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19818/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19819/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19820/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3039 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19821/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19822/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0970 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19823/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19824/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19825/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19826/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19827/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19828/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19829/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19830/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19831/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19832/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19833/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19834/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19835/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19836/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19837/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19838/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19839/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1022 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19840/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19841/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19842/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19843/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3055 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19844/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19845/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0973 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19846/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19847/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19848/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19849/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3062 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19850/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19851/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19852/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19853/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19854/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19855/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0977 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19856/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19857/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0956 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19858/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19859/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19860/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19861/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19862/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19863/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19864/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19865/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19866/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19867/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19868/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19869/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19870/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19871/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19872/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19873/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19874/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0969 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19875/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19876/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19877/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0968 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19878/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19879/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3049 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19880/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19881/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19882/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19883/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19884/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19885/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19886/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19887/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19888/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3050 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19889/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1024 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19890/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19891/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19892/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3048 - sparse_categorical_accuracy: 0.0963 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19893/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19894/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19895/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19896/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19897/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19898/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.1019 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19899/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19900/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3069 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19901/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19902/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19903/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19904/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19905/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1020 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19906/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19907/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3056 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19908/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19909/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0983 - val_loss: 2.3053 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19910/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19911/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19912/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19913/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0992 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19914/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19915/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19916/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3046 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19917/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19918/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19919/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19920/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19921/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19922/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0978 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19923/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3035 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19924/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19925/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1017 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19926/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0984 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19927/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19928/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1003 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19929/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19930/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19931/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19932/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19933/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19934/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1016 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19935/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19936/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19937/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0981 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19938/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19939/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1036 - val_loss: 2.3054 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19940/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19941/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19942/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19943/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19944/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0986 - val_loss: 2.3034 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19945/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3028 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19946/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19947/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19948/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19949/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19950/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3060 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19951/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19952/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1004 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19953/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0990 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19954/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1011 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19955/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19956/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3036 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19957/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.1002 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19958/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0998 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19959/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3033 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19960/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19961/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0980 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19962/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1014 - val_loss: 2.3052 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19963/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0976 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19964/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3040 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19965/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19966/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19967/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0988 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19968/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0962 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19969/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19970/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1013 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19971/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0997 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19972/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3058 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19973/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19974/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19975/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0991 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19976/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3064 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19977/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19978/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.1012 - val_loss: 2.3032 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19979/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0985 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19980/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1005 - val_loss: 2.3038 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19981/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0982 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19982/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3047 - sparse_categorical_accuracy: 0.0975 - val_loss: 2.3047 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19983/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3040 - sparse_categorical_accuracy: 0.0996 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19984/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0994 - val_loss: 2.3044 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19985/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3049 - sparse_categorical_accuracy: 0.0971 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19986/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1000 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19987/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3045 - sparse_categorical_accuracy: 0.0989 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19988/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3043 - sparse_categorical_accuracy: 0.1010 - val_loss: 2.3045 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19989/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1008 - val_loss: 2.3051 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19990/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19991/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1009 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19992/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.1001 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19993/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19994/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0999 - val_loss: 2.3037 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19995/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3046 - sparse_categorical_accuracy: 0.0987 - val_loss: 2.3029 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19996/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1015 - val_loss: 2.3039 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19997/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3041 - sparse_categorical_accuracy: 0.1006 - val_loss: 2.3031 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 19998/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.0995 - val_loss: 2.3042 - val_sparse_categorical_accuracy: 0.1001\n", - "Epoch 19999/20000\n", - "391/391 [==============================] - 2s 4ms/step - loss: 2.3042 - sparse_categorical_accuracy: 0.1007 - val_loss: 2.3043 - val_sparse_categorical_accuracy: 0.1000\n", - "Epoch 20000/20000\n", - "391/391 [==============================] - 1s 4ms/step - loss: 2.3044 - sparse_categorical_accuracy: 0.0993 - val_loss: 2.3041 - val_sparse_categorical_accuracy: 0.1001\n", - "Test Loss: 2.3040647506713867\n", - "Test Accuracy: 0.10010000318288803\n" - ] - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Convert labels to integers\n", "train_labels = train_labels.flatten()\n", @@ -40715,6 +686,17 @@ "plt.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, { "attachments": {}, "cell_type": "markdown", From f6244ef172906ee3c9497da827ff342edaa71b83 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 12:05:14 +0800 Subject: [PATCH 21/28] Modify cnn to sections and add tableofcontents --- open-machine-learning-jupyter-book/_toc.yml | 5 +++-- .../deep-learning/{ => cnn}/cnn-deepdream.ipynb | 0 .../deep-learning/{ => cnn}/cnn-vgg.ipynb | 0 .../deep-learning/{ => cnn}/cnn.ipynb | 10 ++++++++++ 4 files changed, 13 insertions(+), 2 deletions(-) rename open-machine-learning-jupyter-book/deep-learning/{ => cnn}/cnn-deepdream.ipynb (100%) rename open-machine-learning-jupyter-book/deep-learning/{ => cnn}/cnn-vgg.ipynb (100%) rename open-machine-learning-jupyter-book/deep-learning/{ => cnn}/cnn.ipynb (99%) diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index 3fc182ee10..0c2b95429f 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -90,8 +90,9 @@ parts: chapters: - file: deep-learning/dl-overview - file: deep-learning/cnn - - file: deep-learning/cnn-vgg - - file: deep-learning/cnn-deepdream + section: + - file: deep-learning/cnn-vgg + - file: deep-learning/cnn-deepdream - file: deep-learning/gan.ipynb - file: deep-learning/rnn.ipynb - file: deep-learning/autoencoder.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn-deepdream.ipynb similarity index 100% rename from open-machine-learning-jupyter-book/deep-learning/cnn-deepdream.ipynb rename to open-machine-learning-jupyter-book/deep-learning/cnn/cnn-deepdream.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn-vgg.ipynb similarity index 100% rename from open-machine-learning-jupyter-book/deep-learning/cnn-vgg.ipynb rename to open-machine-learning-jupyter-book/deep-learning/cnn/cnn-vgg.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb similarity index 99% rename from open-machine-learning-jupyter-book/deep-learning/cnn.ipynb rename to open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb index e4cb1231f0..2a7b6207fa 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb @@ -901,6 +901,16 @@ "\n", "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). It inspires the majority of the content in this chapter.\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "```{tableofcontents}\n", + "```" + ] } ], "metadata": { From 540957175ed8f72841df31be81e9c92f38cb0043 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 12:16:17 +0800 Subject: [PATCH 22/28] Modify cnn to sections and add tableofcontents --- open-machine-learning-jupyter-book/_toc.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index 0c2b95429f..b4ccdd75ad 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -90,7 +90,7 @@ parts: chapters: - file: deep-learning/dl-overview - file: deep-learning/cnn - section: + sections: - file: deep-learning/cnn-vgg - file: deep-learning/cnn-deepdream - file: deep-learning/gan.ipynb From b4906ea9090769999a520b0aaf8e81f122cc8fd6 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 13:29:58 +0800 Subject: [PATCH 23/28] Modify cnn to sections and add tableofcontents --- open-machine-learning-jupyter-book/_toc.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index b4ccdd75ad..3f62ea5c8d 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -89,10 +89,10 @@ parts: numbered: True chapters: - file: deep-learning/dl-overview - - file: deep-learning/cnn + - file: deep-learning/cnn/cnn sections: - - file: deep-learning/cnn-vgg - - file: deep-learning/cnn-deepdream + - file: deep-learning/cnn/cnn-vgg + - file: deep-learning/cnn/cnn-deepdream - file: deep-learning/gan.ipynb - file: deep-learning/rnn.ipynb - file: deep-learning/autoencoder.ipynb From ab0ea9a35efca3add7b755ae134337174afb338f Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 14:04:40 +0800 Subject: [PATCH 24/28] Modify cnn to sections and add tableofcontents --- open-machine-learning-jupyter-book/_toc.yml | 6 +++--- .../deep-learning/cnn/cnn.ipynb | 20 +++++++++---------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index 3f62ea5c8d..24fea356c9 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -90,9 +90,9 @@ parts: chapters: - file: deep-learning/dl-overview - file: deep-learning/cnn/cnn - sections: - - file: deep-learning/cnn/cnn-vgg - - file: deep-learning/cnn/cnn-deepdream + # sections: + # - file: deep-learning/cnn/cnn-vgg + # - file: deep-learning/cnn/cnn-deepdream - file: deep-learning/gan.ipynb - file: deep-learning/rnn.ipynb - file: deep-learning/autoencoder.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb index 2a7b6207fa..8f5ae21a66 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb @@ -855,6 +855,16 @@ " labels_file.write(\"{}\\n\".format(item))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "```{tableofcontents}\n", + "```" + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -901,16 +911,6 @@ "\n", "Thanks to [Nick](https://github.com/nfmcclure) for creating the open-source course [tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook). It inspires the majority of the content in this chapter.\n" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "```{tableofcontents}\n", - "```" - ] } ], "metadata": { From 8d99fa7f88800f78fca25ec96bb2d8509bb64b1a Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 15:51:11 +0800 Subject: [PATCH 25/28] Modify cnn to sections and add tableofcontents --- open-machine-learning-jupyter-book/_toc.yml | 8 ++++---- .../deep-learning/cnn/cnn.ipynb | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index fc52924778..88ede84073 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -95,10 +95,10 @@ parts: numbered: True chapters: - file: deep-learning/dl-overview - - file: deep-learning/cnn/cnn - # sections: - # - file: deep-learning/cnn/cnn-vgg - # - file: deep-learning/cnn/cnn-deepdream + - file: deep-learning/cnn/cnn.ipynb + sections: + - file: deep-learning/cnn/cnn-vgg.ipynb + - file: deep-learning/cnn/cnn-deepdream.ipynb - file: deep-learning/gan.ipynb - file: deep-learning/rnn.ipynb - file: deep-learning/autoencoder.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb index 8f5ae21a66..ff04796c50 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb @@ -929,7 +929,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.18" } }, "nbformat": 4, From 830cf82d92d22dd20098bd76b7e669bc703609ff Mon Sep 17 00:00:00 2001 From: fuqiongying <3047530642@qq.com> Date: Sun, 3 Dec 2023 16:15:25 +0800 Subject: [PATCH 26/28] uncomment --- .../pandas/advanced-pandas-techniques.ipynb | 178 ++++---- .../pandas/data-selection.ipynb | 178 ++++---- .../introduction-and-data-structures.ipynb | 400 +++++++++--------- 3 files changed, 371 insertions(+), 385 deletions(-) diff --git a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb index d88b627c00..e5a76e6776 100644 --- a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb +++ b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/advanced-pandas-techniques.ipynb @@ -81,7 +81,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 2, "id": "b08dcc94", "metadata": { "attributes": { @@ -102,7 +102,7 @@ "dtype: object" ] }, - "execution_count": 39, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -123,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 3, "id": "32049abb", "metadata": { "attributes": { @@ -144,14 +144,12 @@ "dtype: object" ] }, - "execution_count": 40, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Remove # and run to see the ValueError raised by verify_integrity=True\n", - "\n", "pd.concat([s1, s2], ignore_index=True)" ] }, @@ -165,7 +163,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "id": "d5b95507", "metadata": { "attributes": { @@ -186,7 +184,7 @@ "dtype: object" ] }, - "execution_count": 41, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -205,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 5, "id": "6d54830d", "metadata": { "attributes": { @@ -227,7 +225,7 @@ "dtype: object" ] }, - "execution_count": 42, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 6, "id": "fec72294", "metadata": { "attributes": { @@ -304,7 +302,7 @@ "1 b 2" ] }, - "execution_count": 43, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -317,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 7, "id": "80a1f5b0", "metadata": { "attributes": { @@ -374,7 +372,7 @@ "1 d 4" ] }, - "execution_count": 44, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -387,7 +385,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 8, "id": "4e9e65f6", "metadata": { "attributes": { @@ -456,7 +454,7 @@ "1 d 4" ] }, - "execution_count": 45, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -475,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 9, "id": "f50e8ede", "metadata": { "attributes": { @@ -535,7 +533,7 @@ "1 d 4 dog" ] }, - "execution_count": 46, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -548,7 +546,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 10, "id": "9def1cdd", "metadata": { "attributes": { @@ -622,7 +620,7 @@ "1 d 4 dog" ] }, - "execution_count": 47, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -641,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 11, "id": "ef69d51c", "metadata": { "attributes": { @@ -710,7 +708,7 @@ "1 d 4" ] }, - "execution_count": 48, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -729,7 +727,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 12, "id": "2159161d", "metadata": { "attributes": { @@ -792,7 +790,7 @@ "1 b 2 monkey george" ] }, - "execution_count": 49, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -813,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 13, "id": "45bea28a", "metadata": { "attributes": { @@ -862,7 +860,7 @@ "a 1" ] }, - "execution_count": 50, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -874,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 14, "id": "db871526", "metadata": { "attributes": { @@ -923,7 +921,7 @@ "a 2" ] }, - "execution_count": 51, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -935,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "1ab6b3b0", "metadata": { "attributes": { @@ -950,9 +948,7 @@ }, "outputs": [], "source": [ - "# Remove # and run to see the ValueError raised by verify_integrity=True\n", - "\n", - "# pd.concat([df5, df6], verify_integrity=True)" + "pd.concat([df5, df6], verify_integrity=True)" ] }, { @@ -965,7 +961,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "007c1ed6", "metadata": { "attributes": { @@ -1028,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "9dbaddff", "metadata": { "attributes": { @@ -1059,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "ad2d1313", "metadata": { "attributes": { @@ -1168,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "e223179b", "metadata": {}, "outputs": [], @@ -1189,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "e22da8fc", "metadata": {}, "outputs": [ @@ -1288,7 +1284,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "6147bab8-4644-4a23-ba71-205573a1c3f9", "metadata": { "tags": [ @@ -1355,7 +1351,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "3dea68f6", "metadata": { "attributes": { @@ -1370,9 +1366,7 @@ }, "outputs": [], "source": [ - "# Remove # and run to see the exception raised caused by overlapping columns in the DataFrames\n", - "\n", - "# df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))" + "df1.merge(df2, left_on='lkey', right_on='rkey', suffixes=(False, False))" ] }, { @@ -1385,7 +1379,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "1026fc27", "metadata": {}, "outputs": [], @@ -1396,7 +1390,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "b4379cb1", "metadata": {}, "outputs": [ @@ -1453,7 +1447,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "90916930-6a8e-40e3-871e-d0043aae93d8", "metadata": { "tags": [ @@ -1509,7 +1503,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "id": "2a8bb3d7", "metadata": {}, "outputs": [ @@ -1573,7 +1567,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "id": "467da7f9-a710-442e-9fcf-afb4990ea3b0", "metadata": { "tags": [ @@ -1627,7 +1621,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "8951b7b9", "metadata": {}, "outputs": [], @@ -1638,7 +1632,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "93051401", "metadata": {}, "outputs": [ @@ -1711,7 +1705,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "bc243059-83f7-485c-bcd0-453d611c3d1f", "metadata": { "tags": [ @@ -1788,7 +1782,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "5ad178d6", "metadata": { "attributes": { @@ -1806,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "ff1aa936", "metadata": { "attributes": { @@ -1832,7 +1826,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "a2517b83", "metadata": {}, "outputs": [ @@ -1931,7 +1925,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "81738ab5-bc94-4264-bb43-8c64c041c332", "metadata": { "tags": [ @@ -2002,7 +1996,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "91c6f0f0", "metadata": {}, "outputs": [ @@ -2093,7 +2087,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "f942120e-c151-473d-aa0a-3ed6b0679204", "metadata": { "tags": [ @@ -2164,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "d8fbb1f7", "metadata": { "attributes": { @@ -2271,7 +2265,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "b4d1eb0d", "metadata": { "attributes": { @@ -2365,7 +2359,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "7f6bc83d", "metadata": { "attributes": { @@ -2478,7 +2472,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "38adb2b7", "metadata": {}, "outputs": [ @@ -2545,7 +2539,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "917ba231-1ee4-4f2c-bcb9-4262d7eba119", "metadata": { "tags": [ @@ -2616,7 +2610,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "5e84fd8b", "metadata": {}, "outputs": [ @@ -2684,7 +2678,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "8d6ff678-1c1e-4629-9e06-1874511ecdf0", "metadata": { "tags": [ @@ -2742,7 +2736,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "5a7a2d6a", "metadata": {}, "outputs": [ @@ -2805,7 +2799,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "31f4c668-6a8b-4dba-a6db-29673e7fbdba", "metadata": { "tags": [ @@ -2872,7 +2866,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "f27b6536", "metadata": {}, "outputs": [ @@ -2941,7 +2935,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "47261c15-1d74-4a39-a7bb-073f6835cbf8", "metadata": { "tags": [ @@ -2997,7 +2991,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "815ba4c3", "metadata": {}, "outputs": [ @@ -3070,7 +3064,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "17c93213-8bcf-4ac8-a30d-09df48b9ca71", "metadata": { "tags": [ @@ -3124,7 +3118,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "id": "719dc004", "metadata": {}, "outputs": [ @@ -3193,7 +3187,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "id": "ba2d22de-ed75-4d52-a6d8-badf4791429f", "metadata": { "tags": [ @@ -3251,7 +3245,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "id": "cce87c6a", "metadata": {}, "outputs": [ @@ -3324,7 +3318,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "id": "70cc2217-577e-4b8c-8fc2-ce02f036622b", "metadata": { "tags": [ @@ -3389,7 +3383,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "id": "1fa5930a", "metadata": { "attributes": { @@ -3482,7 +3476,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "id": "67e4668e", "metadata": { "attributes": { @@ -3574,7 +3568,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "c8e1b317", "metadata": { "attributes": { @@ -3731,7 +3725,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "id": "7206f156", "metadata": { "attributes": { @@ -3831,7 +3825,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "6cfd03f9", "metadata": { "attributes": { @@ -3931,7 +3925,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "900dc876", "metadata": { "attributes": { @@ -4032,7 +4026,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "id": "36ccdfaf", "metadata": { "attributes": { @@ -4164,7 +4158,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "id": "db6fdd36", "metadata": { "attributes": { @@ -4251,7 +4245,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "id": "92e71f86", "metadata": { "attributes": { @@ -4292,7 +4286,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "id": "b6387047", "metadata": { "attributes": { @@ -4384,7 +4378,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "a5322c51", "metadata": { "attributes": { @@ -4478,7 +4472,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "13d2dffa", "metadata": { "attributes": { @@ -4579,7 +4573,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "id": "8ee5ceea", "metadata": { "attributes": { @@ -4694,7 +4688,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "id": "d99bb798", "metadata": { "attributes": { @@ -4791,7 +4785,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "id": "c228b08b", "metadata": { "attributes": { @@ -4863,7 +4857,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "id": "28a30c04", "metadata": { "attributes": { @@ -4935,7 +4929,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "id": "4d06bb30", "metadata": { "attributes": { @@ -5007,7 +5001,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "id": "f8dacc1f", "metadata": { "attributes": { @@ -5071,7 +5065,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "id": "6ec1ded1-6f8a-46ca-b304-25621fe08677", "metadata": { "tags": [ diff --git a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb index a12db150ef..0715047cf6 100644 --- a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb +++ b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/data-selection.ipynb @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 2, "id": "19faf0a0", "metadata": { "attributes": { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "5cd6165e", "metadata": { "attributes": { @@ -133,9 +133,7 @@ }, "outputs": [], "source": [ - "# Remove # and run to see the TypeError\n", - "\n", - "# dfl.loc[2:3]" + "dfl.loc[2:3]" ] }, { @@ -155,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "3f5fb2f0", "metadata": {}, "outputs": [ @@ -230,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "abe5968b-ffe5-4302-9918-81a1d97ed568", "metadata": { "tags": [ @@ -325,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "8a174f11", "metadata": {}, "outputs": [ @@ -352,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "b276bd82-797f-4eb6-8886-51153d771bb0", "metadata": { "tags": [ @@ -412,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "11e56acc", "metadata": {}, "outputs": [ @@ -433,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "74a7ae51-b334-4d5f-b9a2-e2080958663f", "metadata": { "tags": [ @@ -501,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "8fe78c41", "metadata": {}, "outputs": [ @@ -529,7 +527,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "e32f82e4-6b3e-48a7-ab56-c6ea820274e5", "metadata": { "tags": [ @@ -585,7 +583,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "cfb25d9f", "metadata": {}, "outputs": [ @@ -664,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "de1a7123-2c8e-4910-b435-cdd489baff5b", "metadata": { "tags": [ @@ -730,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "2934e9e8", "metadata": {}, "outputs": [ @@ -809,7 +807,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "ccbffe12", "metadata": {}, "outputs": [ @@ -834,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "id": "c9570d12-8020-4328-94e8-91266619e666", "metadata": { "tags": [ @@ -900,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "id": "e60fdddf", "metadata": {}, "outputs": [ @@ -925,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "id": "4a9f2648-9f92-4077-a7ec-00836c2f28fd", "metadata": { "tags": [ @@ -981,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "id": "d6226934", "metadata": {}, "outputs": [ @@ -1066,7 +1064,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "id": "f8ae65cd-dbea-4f40-a464-7b07554b9b11", "metadata": { "tags": [ @@ -1134,7 +1132,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "id": "0ca93c29", "metadata": { "attributes": { @@ -1165,7 +1163,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "fd577bd5", "metadata": {}, "outputs": [ @@ -1232,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "id": "4f1b5f67-5c56-4e47-8953-4d6383f283e1", "metadata": { "tags": [ @@ -1300,7 +1298,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "7e425a66", "metadata": {}, "outputs": [ @@ -1321,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "50e88f3d-07f0-443d-994c-d7fb36c4dc7a", "metadata": { "tags": [ @@ -1393,7 +1391,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "id": "2bd13eab", "metadata": {}, "outputs": [ @@ -1418,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "id": "63081450-8216-403c-8b53-04b2cc18e442", "metadata": { "tags": [ @@ -1490,7 +1488,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "id": "a08caf62", "metadata": {}, "outputs": [ @@ -1516,7 +1514,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "id": "7d665bb1-9bd1-4826-9a0f-f13496d64549", "metadata": { "tags": [ @@ -1578,7 +1576,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "a5f5d2ba", "metadata": {}, "outputs": [ @@ -1603,7 +1601,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "81114a6f-4511-4f2e-990b-c7edd5e4cf86", "metadata": { "tags": [ @@ -1673,7 +1671,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "id": "318b8e37", "metadata": {}, "outputs": [ @@ -1698,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "id": "537dd0b6-b4fc-468b-88a4-5d828eba5ed8", "metadata": { "tags": [ @@ -1804,7 +1802,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "id": "e7b93cb1", "metadata": {}, "outputs": [ @@ -1830,7 +1828,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "id": "24d4de8c-5c42-484b-89d7-e21ebb0ba7c3", "metadata": { "tags": [ @@ -1886,7 +1884,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "id": "fe63cdf3", "metadata": {}, "outputs": [ @@ -1907,7 +1905,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "id": "ed15834b-fd14-4000-bbdb-0eb86a214984", "metadata": { "tags": [ @@ -1971,7 +1969,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "id": "9c4e8129", "metadata": {}, "outputs": [ @@ -1998,7 +1996,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "id": "5b793d9f-5ddb-4121-8218-8a5eda713eab", "metadata": { "tags": [ @@ -2062,7 +2060,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "id": "3d55d682", "metadata": {}, "outputs": [ @@ -2141,7 +2139,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "172e44bf-8faf-42a1-b9a7-3adab79b97d1", "metadata": { "tags": [ @@ -2195,7 +2193,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "id": "b5427ec6", "metadata": {}, "outputs": [ @@ -2276,7 +2274,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "id": "d86dd6d1", "metadata": {}, "outputs": [ @@ -2343,7 +2341,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "id": "a5e2a6ba-671b-4aab-b63d-5ab4ee92501f", "metadata": { "tags": [ @@ -2397,7 +2395,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "id": "8528cc39", "metadata": {}, "outputs": [ @@ -2464,7 +2462,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "id": "178d6f69-464f-464e-ad45-fac857b9a370", "metadata": { "tags": [ @@ -2524,7 +2522,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": null, "id": "f9288433", "metadata": {}, "outputs": [ @@ -2609,7 +2607,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "id": "71859ce4-7ad5-4bea-9df2-f5929c0c2470", "metadata": { "tags": [ @@ -2667,7 +2665,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "id": "eb3f25f3", "metadata": {}, "outputs": [ @@ -2688,7 +2686,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "id": "5dad7d1a-0bf5-40d8-a4ef-2c3e573ae6fc", "metadata": { "tags": [ @@ -2755,7 +2753,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "id": "cc95030f", "metadata": {}, "outputs": [ @@ -2780,7 +2778,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "id": "bfa6df43-353d-4ba4-94a0-e65c9a659468", "metadata": { "tags": [ @@ -2846,7 +2844,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "id": "0c635e2f", "metadata": { "attributes": { @@ -2875,7 +2873,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": null, "id": "bae9b708", "metadata": { "attributes": { @@ -2903,7 +2901,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": null, "id": "ccb95b2c", "metadata": { "attributes": { @@ -2931,7 +2929,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": null, "id": "fcaaeb73", "metadata": { "attributes": { @@ -2966,7 +2964,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": null, "id": "19e7f165", "metadata": {}, "outputs": [ @@ -2989,7 +2987,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": null, "id": "3b612356-7774-472e-849e-0f3dc267b578", "metadata": { "tags": [ @@ -3045,7 +3043,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": null, "id": "2a25cc5c", "metadata": { "attributes": { @@ -3081,7 +3079,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": null, "id": "f9024d15", "metadata": { "attributes": { @@ -3099,7 +3097,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": null, "id": "5837f585", "metadata": {}, "outputs": [ @@ -3163,7 +3161,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": null, "id": "4b81ac82-5d47-4410-90b9-040f0dac662b", "metadata": { "tags": [ @@ -3217,7 +3215,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": null, "id": "d0e19553", "metadata": {}, "outputs": [ @@ -3290,7 +3288,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": null, "id": "39dab713-a3f6-4189-bad9-cba564f56951", "metadata": { "tags": [ @@ -3346,7 +3344,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": null, "id": "f91ab868", "metadata": {}, "outputs": [ @@ -3401,7 +3399,7 @@ }, { "cell_type": "code", - "execution_count": 137, + "execution_count": null, "id": "220aa5af-5003-45e9-87cf-c4f5d0ac6d93", "metadata": { "tags": [ @@ -3466,7 +3464,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": null, "id": "f3496be2", "metadata": { "attributes": { @@ -3481,14 +3479,12 @@ }, "outputs": [], "source": [ - "# Remove # and run to see the IndexError\n", - "\n", - "# dfl.iloc[[4, 5, 6]]" + "dfl.iloc[[4, 5, 6]]" ] }, { "cell_type": "code", - "execution_count": 139, + "execution_count": null, "id": "7b081f89", "metadata": { "attributes": { @@ -3503,9 +3499,7 @@ }, "outputs": [], "source": [ - "# Remove # and run to see the IndexError\n", - "\n", - "# dfl.iloc[:, 4]" + "dfl.iloc[:, 4]" ] }, { @@ -3520,7 +3514,7 @@ }, { "cell_type": "code", - "execution_count": 140, + "execution_count": null, "id": "72420538", "metadata": {}, "outputs": [ @@ -3599,7 +3593,7 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": null, "id": "7206088f-3aa5-4392-9982-cadec553e616", "metadata": { "tags": [ @@ -3653,7 +3647,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": null, "id": "ab18a18f", "metadata": {}, "outputs": [ @@ -3738,7 +3732,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": null, "id": "2166496e-975d-4539-a3b6-54cedd012e73", "metadata": { "tags": [ @@ -3798,7 +3792,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": null, "id": "aeb4a77e", "metadata": {}, "outputs": [ @@ -3883,7 +3877,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": null, "id": "e8fe3be5-15de-4036-ab8a-d6483abf265f", "metadata": { "tags": [ @@ -3941,7 +3935,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": null, "id": "ec331b54", "metadata": {}, "outputs": [ @@ -3968,7 +3962,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": null, "id": "31840764-a775-4e5f-8023-6c4762005ff6", "metadata": { "tags": [ @@ -4033,7 +4027,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": null, "id": "d4e60491", "metadata": {}, "outputs": [ @@ -4057,7 +4051,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": null, "id": "1d7a46f1-98ce-4d87-924a-288812c6b4ed", "metadata": { "tags": [ @@ -4124,7 +4118,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": null, "id": "978312bb", "metadata": {}, "outputs": [ @@ -4151,7 +4145,7 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": null, "id": "a8844d1c-fdc5-4c85-923c-092ac6367692", "metadata": { "tags": [ @@ -4216,7 +4210,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": null, "id": "2e7e25d2", "metadata": {}, "outputs": [ @@ -4239,7 +4233,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": null, "id": "48f7feb0-9334-441f-893a-42815523e739", "metadata": { "tags": [ @@ -4308,7 +4302,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": null, "id": "7c0b22e6", "metadata": {}, "outputs": [ @@ -4369,7 +4363,7 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": null, "id": "c0924629-67d8-43b6-a435-d91bb8bf6408", "metadata": { "tags": [ diff --git a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb index 540d02ee10..c1d9b22f30 100644 --- a/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb +++ b/open-machine-learning-jupyter-book/data-science/working-with-data/pandas/introduction-and-data-structures.ipynb @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "646c8580", "metadata": { "attributes": { @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "2d2455c1", "metadata": { "attributes": { @@ -148,15 +148,15 @@ { "data": { "text/plain": [ - "a 0.389808\n", - "b 1.166912\n", - "c 1.083422\n", - "d -0.751227\n", - "e -0.930881\n", + "a -1.093235\n", + "b -0.875870\n", + "c 0.548668\n", + "d -0.396314\n", + "e -0.462231\n", "dtype: float64" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -167,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "20f33329", "metadata": { "attributes": { @@ -184,7 +184,7 @@ "Index(['a', 'b', 'c', 'd', 'e'], dtype='object')" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -195,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "5376f720", "metadata": { "attributes": { @@ -209,15 +209,15 @@ { "data": { "text/plain": [ - "0 0.532715\n", - "1 0.890063\n", - "2 -1.069293\n", - "3 1.279518\n", - "4 0.599430\n", + "0 0.820731\n", + "1 -1.040583\n", + "2 -1.494295\n", + "3 0.214854\n", + "4 0.969364\n", "dtype: float64" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "e8095575", "metadata": { "attributes": { @@ -258,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "ba462934", "metadata": { "attributes": { @@ -278,7 +278,7 @@ "dtype: int64" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "03488418", "metadata": { "attributes": { @@ -314,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "c35e968c", "metadata": { "attributes": { @@ -334,7 +334,7 @@ "dtype: float64" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -345,7 +345,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "95eafc4d", "metadata": { "attributes": { @@ -366,7 +366,7 @@ "dtype: float64" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -391,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "6f744115", "metadata": { "attributes": { @@ -413,7 +413,7 @@ "dtype: float64" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -434,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "2ca453e9", "metadata": { "attributes": { @@ -448,10 +448,10 @@ { "data": { "text/plain": [ - "0.3898080889883227" + "-1.0932348256866344" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -462,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "4cf8e176", "metadata": { "attributes": { @@ -476,13 +476,13 @@ { "data": { "text/plain": [ - "a 0.389808\n", - "b 1.166912\n", - "c 1.083422\n", + "a -1.093235\n", + "b -0.875870\n", + "c 0.548668\n", "dtype: float64" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -493,7 +493,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "1bab7730", "metadata": { "attributes": { @@ -507,12 +507,12 @@ { "data": { "text/plain": [ - "b 1.166912\n", - "c 1.083422\n", + "c 0.548668\n", + "d -0.396314\n", "dtype: float64" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -523,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "id": "b5e98d89", "metadata": { "attributes": { @@ -537,13 +537,13 @@ { "data": { "text/plain": [ - "e -0.930881\n", - "d -0.751227\n", - "b 1.166912\n", + "e -0.462231\n", + "d -0.396314\n", + "b -0.875870\n", "dtype: float64" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -554,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "id": "c98a7190", "metadata": { "attributes": { @@ -568,15 +568,15 @@ { "data": { "text/plain": [ - "a 1.476697\n", - "b 3.212059\n", - "c 2.954773\n", - "d 0.471787\n", - "e 0.394206\n", + "a 0.335131\n", + "b 0.416500\n", + "c 1.730946\n", + "d 0.672796\n", + "e 0.629877\n", "dtype: float64" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -595,7 +595,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "id": "b0298996", "metadata": { "attributes": { @@ -612,7 +612,7 @@ "dtype('float64')" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -631,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "1989c3a9", "metadata": { "attributes": { @@ -646,12 +646,12 @@ "data": { "text/plain": [ "\n", - "[ 0.3898080889883227, 1.1669122983173188, 1.083421954866096,\n", - " -0.7512272141904102, -0.9308814331814397]\n", + "[ -1.0932348256866344, -0.8758697962853178, 0.5486679425929234,\n", + " -0.39631364702254346, -0.46223111162737424]\n", "Length: 5, dtype: float64" ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -670,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "1cc04172", "metadata": { "attributes": { @@ -684,10 +684,10 @@ { "data": { "text/plain": [ - "array([ 0.38980809, 1.1669123 , 1.08342195, -0.75122721, -0.93088143])" + "array([-1.09323483, -0.8758698 , 0.54866794, -0.39631365, -0.46223111])" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -710,7 +710,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "bcfe90c9", "metadata": { "attributes": { @@ -724,10 +724,10 @@ { "data": { "text/plain": [ - "0.3898080889883227" + "-1.0932348256866344" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -738,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "id": "00c68766", "metadata": { "attributes": { @@ -755,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "id": "74f58473", "metadata": { "attributes": { @@ -769,15 +769,15 @@ { "data": { "text/plain": [ - "a 0.389808\n", - "b 1.166912\n", - "c 1.083422\n", - "d -0.751227\n", + "a -1.093235\n", + "b -0.875870\n", + "c 0.548668\n", + "d -0.396314\n", "e 12.000000\n", "dtype: float64" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -788,7 +788,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "id": "2f822110", "metadata": { "attributes": { @@ -805,7 +805,7 @@ "True" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -816,7 +816,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "id": "164dcf61", "metadata": { "attributes": { @@ -833,7 +833,7 @@ "False" ] }, - "execution_count": 24, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -852,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "id": "40a23c62-9c88-4a6e-9316-60317abe7859", "metadata": { "attributes": { @@ -867,9 +867,7 @@ }, "outputs": [], "source": [ - "# Remove # and run to see the exception\n", - "\n", - "# s[\"f\"]" + "s[\"f\"]" ] }, { @@ -882,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "id": "ad2a67c6", "metadata": { "attributes": { @@ -899,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "id": "13c1c13b", "metadata": { "attributes": { @@ -916,7 +914,7 @@ "nan" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -939,7 +937,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "id": "35540134", "metadata": { "attributes": { @@ -953,15 +951,15 @@ { "data": { "text/plain": [ - "a 0.779616\n", - "b 2.333825\n", - "c 2.166844\n", - "d -1.502454\n", + "a -2.186470\n", + "b -1.751740\n", + "c 1.097336\n", + "d -0.792627\n", "e 24.000000\n", "dtype: float64" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -972,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "id": "aea7c1dc", "metadata": { "attributes": { @@ -986,15 +984,15 @@ { "data": { "text/plain": [ - "a 0.779616\n", - "b 2.333825\n", - "c 2.166844\n", - "d -1.502454\n", + "a -2.186470\n", + "b -1.751740\n", + "c 1.097336\n", + "d -0.792627\n", "e 24.000000\n", "dtype: float64" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1005,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "id": "4dcdc8c4", "metadata": { "attributes": { @@ -1019,15 +1017,15 @@ { "data": { "text/plain": [ - "a 1.476697\n", - "b 3.212059\n", - "c 2.954773\n", - "d 0.471787\n", + "a 0.335131\n", + "b 0.416500\n", + "c 1.730946\n", + "d 0.672796\n", "e 162754.791419\n", "dtype: float64" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1046,7 +1044,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "id": "563555a0", "metadata": { "attributes": { @@ -1061,14 +1059,14 @@ "data": { "text/plain": [ "a NaN\n", - "b 2.333825\n", - "c 2.166844\n", - "d -1.502454\n", + "b -1.751740\n", + "c 1.097336\n", + "d -0.792627\n", "e NaN\n", "dtype: float64" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1095,7 +1093,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "id": "3b39834b", "metadata": { "attributes": { @@ -1112,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "18210d7f", "metadata": { "attributes": { @@ -1145,7 +1143,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "06f09ce2", "metadata": { "attributes": { @@ -1183,7 +1181,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "bd079c61", "metadata": { "attributes": { @@ -1200,7 +1198,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "a1767258", "metadata": { "attributes": { @@ -1256,7 +1254,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "aa7ddc8a", "metadata": { "attributes": { @@ -1276,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "f526badc", "metadata": { "attributes": { @@ -1293,7 +1291,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "69ddc66c", "metadata": { "attributes": { @@ -1373,7 +1371,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "id": "1f5e8ccb", "metadata": { "attributes": { @@ -1447,7 +1445,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "9940fb65", "metadata": { "attributes": { @@ -1533,7 +1531,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "8a3ba6ae", "metadata": { "attributes": { @@ -1561,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "13684125", "metadata": { "attributes": { @@ -1599,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "c4789555", "metadata": { "attributes": { @@ -1616,7 +1614,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "29098be0", "metadata": { "attributes": { @@ -1696,7 +1694,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "5600834a", "metadata": { "attributes": { @@ -1786,7 +1784,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "0b3b5090", "metadata": { "attributes": { @@ -1803,7 +1801,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "543153a7", "metadata": { "attributes": { @@ -1820,7 +1818,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "id": "c5278e68", "metadata": { "attributes": { @@ -1891,7 +1889,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "fefbfc51", "metadata": { "attributes": { @@ -1962,7 +1960,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "f76d517a", "metadata": { "attributes": { @@ -2046,7 +2044,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "a2aa6cb3", "metadata": { "attributes": { @@ -2063,7 +2061,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "1e45ffbc", "metadata": { "attributes": { @@ -2134,7 +2132,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "8d6db924", "metadata": { "attributes": { @@ -2205,7 +2203,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "id": "258fa418", "metadata": { "attributes": { @@ -2283,7 +2281,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "id": "89af5166", "metadata": { "attributes": { @@ -2396,7 +2394,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "id": "77ff8552", "metadata": { "attributes": { @@ -2413,7 +2411,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "a86d1926", "metadata": { "attributes": { @@ -2493,7 +2491,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, "id": "67fd765e", "metadata": { "attributes": { @@ -2510,7 +2508,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": null, "id": "d4524af3", "metadata": { "attributes": { @@ -2527,7 +2525,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": null, "id": "02f0937c", "metadata": { "attributes": { @@ -2601,7 +2599,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": null, "id": "4c81da05", "metadata": { "attributes": { @@ -2618,7 +2616,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": null, "id": "6731aad6", "metadata": { "attributes": { @@ -2708,7 +2706,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": null, "id": "5fe92237", "metadata": { "attributes": { @@ -2725,7 +2723,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "id": "e13b27cf", "metadata": { "attributes": { @@ -2742,7 +2740,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": null, "id": "df6b2816", "metadata": { "attributes": { @@ -2826,7 +2824,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": null, "id": "a52d0734", "metadata": { "attributes": { @@ -2906,7 +2904,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": null, "id": "804405d6", "metadata": { "attributes": { @@ -2938,7 +2936,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": null, "id": "dfa00c9b", "metadata": { "attributes": { @@ -2955,7 +2953,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": null, "id": "0f98ffa9", "metadata": { "attributes": { @@ -2972,7 +2970,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "id": "1ef5e1a3", "metadata": { "attributes": { @@ -3070,7 +3068,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": null, "id": "b418f585", "metadata": { "attributes": { @@ -3087,7 +3085,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": null, "id": "209ebb78", "metadata": { "attributes": { @@ -3104,7 +3102,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": null, "id": "9aee9b49", "metadata": { "attributes": { @@ -3192,7 +3190,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": null, "id": "1bddfbc5", "metadata": { "attributes": { @@ -3209,7 +3207,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": null, "id": "e2613bd3", "metadata": { "attributes": { @@ -3302,7 +3300,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": null, "id": "c20564a5", "metadata": { "attributes": { @@ -3319,7 +3317,7 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": null, "id": "877b972d-49b8-4225-855e-ec77bd876d8b", "metadata": { "tags": [ @@ -3381,7 +3379,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": null, "id": "76026aba", "metadata": { "attributes": { @@ -3481,7 +3479,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": null, "id": "8dbfb773", "metadata": { "attributes": { @@ -3498,7 +3496,7 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": null, "id": "27dea852", "metadata": { "attributes": { @@ -3603,7 +3601,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": null, "id": "e9e4dead", "metadata": { "attributes": { @@ -3620,7 +3618,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": null, "id": "38eef1a4", "metadata": { "attributes": { @@ -3724,7 +3722,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": null, "id": "ed27d63b", "metadata": { "attributes": { @@ -3842,7 +3840,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": null, "id": "4f39885a", "metadata": { "attributes": { @@ -3962,7 +3960,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "id": "0508916b", "metadata": { "attributes": { @@ -4019,7 +4017,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": null, "id": "60b7e3c7", "metadata": { "attributes": { @@ -4036,7 +4034,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": null, "id": "4c821875", "metadata": { "attributes": { @@ -4140,7 +4138,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": null, "id": "82154750", "metadata": {}, "outputs": [ @@ -4166,7 +4164,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": null, "id": "743d6893-bbf3-4fbf-a158-a3aaae040b39", "metadata": { "tags": [ @@ -4226,7 +4224,7 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": null, "id": "2fae006c", "metadata": { "attributes": { @@ -4269,7 +4267,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": null, "id": "a3e29475", "metadata": { "attributes": { @@ -4286,7 +4284,7 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": null, "id": "c4634479", "metadata": { "attributes": { @@ -4303,7 +4301,7 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": null, "id": "09eb77aa", "metadata": { "attributes": { @@ -4449,7 +4447,7 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": null, "id": "c2a8adda", "metadata": { "attributes": { @@ -4595,7 +4593,7 @@ }, { "cell_type": "code", - "execution_count": 96, + "execution_count": null, "id": "d4cc4904", "metadata": { "attributes": { @@ -4733,7 +4731,7 @@ }, { "cell_type": "code", - "execution_count": 97, + "execution_count": null, "id": "131ec689", "metadata": { "attributes": { @@ -4871,7 +4869,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": null, "id": "a2d50c6f", "metadata": { "attributes": { @@ -5017,7 +5015,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": null, "id": "edbec52a", "metadata": { "attributes": { @@ -5034,7 +5032,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": null, "id": "727cd263", "metadata": { "attributes": { @@ -5051,7 +5049,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": null, "id": "523bbe29", "metadata": { "attributes": { @@ -5125,7 +5123,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": null, "id": "b1a355fc", "metadata": { "attributes": { @@ -5199,7 +5197,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": null, "id": "e89dc58b", "metadata": { "attributes": { @@ -5273,7 +5271,7 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": null, "id": "9b438ef3", "metadata": { "attributes": { @@ -5357,7 +5355,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": null, "id": "84f274b9", "metadata": { "attributes": { @@ -5530,7 +5528,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": null, "id": "12d39083", "metadata": { "attributes": { @@ -5667,7 +5665,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": null, "id": "1eee749c", "metadata": { "attributes": { @@ -5705,7 +5703,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": null, "id": "5a18bcbc", "metadata": { "attributes": { @@ -5827,7 +5825,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": null, "id": "be2e73fe", "metadata": { "attributes": { @@ -5975,7 +5973,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": null, "id": "4e8a2ee9", "metadata": { "attributes": { @@ -6079,7 +6077,7 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": null, "id": "cf8c39ef", "metadata": { "attributes": { @@ -6199,7 +6197,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": null, "id": "da9754c5", "metadata": { "attributes": { @@ -6314,7 +6312,7 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": null, "id": "86dec0c0", "metadata": { "attributes": { @@ -6332,7 +6330,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": null, "id": "69ea1e07", "metadata": { "attributes": { @@ -6360,7 +6358,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": null, "id": "ce9f7637", "metadata": { "attributes": { @@ -6396,7 +6394,7 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": null, "id": "10cead84", "metadata": { "attributes": { @@ -6428,7 +6426,7 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": null, "id": "6db24b96", "metadata": { "attributes": { @@ -6551,7 +6549,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": null, "id": "99790bfe", "metadata": { "attributes": { @@ -6705,7 +6703,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": null, "id": "29d1e1b0", "metadata": { "attributes": { @@ -6789,7 +6787,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": null, "id": "b55c8c4d", "metadata": { "attributes": { @@ -6816,7 +6814,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": null, "id": "e0a12bf3", "metadata": { "attributes": { @@ -6898,7 +6896,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": null, "id": "ab285a63", "metadata": { "attributes": { @@ -6934,7 +6932,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": null, "id": "73654be5", "metadata": { "attributes": { @@ -6967,7 +6965,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": null, "id": "bafda5a6", "metadata": { "attributes": { @@ -6999,7 +6997,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": null, "id": "e28c3dc5", "metadata": { "attributes": { @@ -7043,7 +7041,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": null, "id": "46dbb94c", "metadata": { "attributes": { @@ -7089,7 +7087,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": null, "id": "d2c3c1af", "metadata": { "attributes": { @@ -7159,7 +7157,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": null, "id": "c46fa1e7", "metadata": { "attributes": { From 1ce65325f4e7094a7269abd37b94293d26debe95 Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 16:55:12 +0800 Subject: [PATCH 27/28] Modify cnn to sections and add tableofcontents --- open-machine-learning-jupyter-book/_toc.yml | 7 ++++--- .../cnn/{cnn.ipynb => cnn-handwritten.ipynb} | 10 ---------- .../deep-learning/cnn/cnn.md | 6 ++++++ 3 files changed, 10 insertions(+), 13 deletions(-) rename open-machine-learning-jupyter-book/deep-learning/cnn/{cnn.ipynb => cnn-handwritten.ipynb} (99%) create mode 100644 open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index 88ede84073..a08051f51d 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -95,10 +95,11 @@ parts: numbered: True chapters: - file: deep-learning/dl-overview - - file: deep-learning/cnn/cnn.ipynb + - file: deep-learning/cnn/cnn sections: - - file: deep-learning/cnn/cnn-vgg.ipynb - - file: deep-learning/cnn/cnn-deepdream.ipynb + - file: deep-learning/cnn/cnn-handwritten + - file: deep-learning/cnn/cnn-vgg + - file: deep-learning/cnn/cnn-deepdream - file: deep-learning/gan.ipynb - file: deep-learning/rnn.ipynb - file: deep-learning/autoencoder.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn-handwritten.ipynb similarity index 99% rename from open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb rename to open-machine-learning-jupyter-book/deep-learning/cnn/cnn-handwritten.ipynb index ff04796c50..989023e6de 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn-handwritten.ipynb @@ -855,16 +855,6 @@ " labels_file.write(\"{}\\n\".format(item))" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---\n", - "\n", - "```{tableofcontents}\n", - "```" - ] - }, { "attachments": {}, "cell_type": "markdown", diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md new file mode 100644 index 0000000000..fa377f9025 --- /dev/null +++ b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md @@ -0,0 +1,6 @@ +# CNN + +-- + +```{tableofcontents} +``` From 61a5c9aeb7a334196ab0439bc367760a2c2411aa Mon Sep 17 00:00:00 2001 From: Xu Senbo <86239038+bestfw@users.noreply.github.com> Date: Sun, 3 Dec 2023 17:21:56 +0800 Subject: [PATCH 28/28] Add exclude folder --- open-machine-learning-jupyter-book/_config.yml | 1 + open-machine-learning-jupyter-book/_toc.yml | 1 - .../cnn/{cnn-handwritten.ipynb => cnn.ipynb} | 10 ++++++++++ .../deep-learning/cnn/cnn.md | 6 ------ 4 files changed, 11 insertions(+), 7 deletions(-) rename open-machine-learning-jupyter-book/deep-learning/cnn/{cnn-handwritten.ipynb => cnn.ipynb} (99%) delete mode 100644 open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md diff --git a/open-machine-learning-jupyter-book/_config.yml b/open-machine-learning-jupyter-book/_config.yml index afc1dc7082..850c8e0d95 100644 --- a/open-machine-learning-jupyter-book/_config.yml +++ b/open-machine-learning-jupyter-book/_config.yml @@ -14,6 +14,7 @@ execute: - 'assignments/*' - 'assignments/**/*' - 'deep-learning/*' + - 'deep-learning/*/*' - 'slides/*' - 'slides/**/*' - 'data-science/working-with-data/pandas/*' diff --git a/open-machine-learning-jupyter-book/_toc.yml b/open-machine-learning-jupyter-book/_toc.yml index a08051f51d..c0462654a2 100644 --- a/open-machine-learning-jupyter-book/_toc.yml +++ b/open-machine-learning-jupyter-book/_toc.yml @@ -97,7 +97,6 @@ parts: - file: deep-learning/dl-overview - file: deep-learning/cnn/cnn sections: - - file: deep-learning/cnn/cnn-handwritten - file: deep-learning/cnn/cnn-vgg - file: deep-learning/cnn/cnn-deepdream - file: deep-learning/gan.ipynb diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn-handwritten.ipynb b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb similarity index 99% rename from open-machine-learning-jupyter-book/deep-learning/cnn/cnn-handwritten.ipynb rename to open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb index 989023e6de..ff04796c50 100644 --- a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn-handwritten.ipynb +++ b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.ipynb @@ -855,6 +855,16 @@ " labels_file.write(\"{}\\n\".format(item))" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "```{tableofcontents}\n", + "```" + ] + }, { "attachments": {}, "cell_type": "markdown", diff --git a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md b/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md deleted file mode 100644 index fa377f9025..0000000000 --- a/open-machine-learning-jupyter-book/deep-learning/cnn/cnn.md +++ /dev/null @@ -1,6 +0,0 @@ -# CNN - --- - -```{tableofcontents} -```