HW2.ipynb

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      "name": "HW2.ipynb",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sommoMicc/DeepLearning-Homeworks/blob/master/HW2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUc9frYnVBI_",
        "colab_type": "text"
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      "source": [
        "#**HW 2: Optimize and train Deep Models**\n",
        "\n",
        "In this homework, we will explore how to develop a simple Deep Neural Network for image classification. We will explore two common libraries: TensorFlow and Keras.\n",
        "Then we will explore how to face a well know problem that is common to encounter during the training phase: the Overfitting.\n",
        "Finally, we will study how to perform a fair model selection.\n",
        "Hint: Before starting the exercise take a look at how Tensorflow and Keras are designed.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e-Nc7mFSVEiU",
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      "source": [
        "##Exercise 2.1: Image Classficiation with Tensorflow and Keras\n",
        "\n",
        "In this first exercise we will develop a 3 layer Neural Network to perfrom image classification.\n",
        "\n",
        "Let's start importing the libraries we will need and setting a couple of environmental variables.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWn9FzneVqJo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import sklearn\n",
        "import sys\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras  # tf.keras\n",
        "import time\n",
        "\n",
        "#Set the log level in order to hide the unuseful warnings\n",
        "import logging\n",
        "logging.disable(logging.WARNING)\n",
        "os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"3\" \n",
        "\n",
        "tf.random.set_seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LT2HMYDEXcx0",
        "colab_type": "text"
      },
      "source": [
        "### Load Data: Fashion MNIST dataset\n",
        "\n",
        "We will use the Fashion MNIST dataset, a dataset of Zalando's articles. Each sample is a 28x28 pixels grayscale image, associated with a label from 10 classes:\n",
        "\n",
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]\n",
        "\n",
        "Each pixel intensity is represented by a uint8 (byte) from 0 to 255.\n",
        "We will divide the dataset in training, testing and validation set. As you already know, the training set will be used to train the model, the validation set will be used to perform model selection and finally, the test set will be used to asses the performance of deep network.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JZv86gBLXj-D",
        "colab_type": "code",
        "outputId": "c6f367ee-f20e-41c7-93c9-52d7a7ff318a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "source": [
        "fashion_mnist = keras.datasets.fashion_mnist\n",
        "(X_train_full, y_train_full), (X_test, y_test) = (\n",
        "    fashion_mnist.load_data()) #The dataset is already divede in test and training...\n",
        "\n",
        "#We extract the first 5000 samples of the training set, to use them as the validation set\n",
        "X_valid, X_train = X_train_full[:5000], X_train_full[5000:] \n",
        "y_valid, y_train = y_train_full[:5000], y_train_full[5000:]\n",
        "\n",
        "class_names = [\"T-shirt/top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\",\n",
        "               \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t71N3VCLYzpK",
        "colab_type": "text"
      },
      "source": [
        "Let's take a look at a sample of the images in the dataset:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yw_1HUI6YyRq",
        "colab_type": "code",
        "outputId": "5c53719b-2e87-49d6-d9a3-b6805e8364e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "source": [
        "n_rows = 5\n",
        "n_cols = 10\n",
        "plt.figure(figsize=(n_cols*1.4, n_rows * 1.6))\n",
        "for row in range(n_rows):\n",
        "    for col in range(n_cols):\n",
        "        index = n_cols * row + col\n",
        "        plt.subplot(n_rows, n_cols, index + 1)\n",
        "        plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\")\n",
        "        plt.axis('off')\n",
        "        plt.title(class_names[y_train[index]])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1008x576 with 50 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jGUg8TexZMNf",
        "colab_type": "text"
      },
      "source": [
        "### Define the model:\n",
        "\n",
        "Let's build a Sequential model (keras.models.Sequential) and add four layers to it by calling its add() method:\n",
        "\n",
        "\n",
        "*  we start with a Flatten layer (keras.layers.Flatten) to convert each 28x28 image to a single row of 784 pixel values. Since it is the first layer in your model, you should specify the input_shape argument, leaving out the batch size: [28, 28];\n",
        "*  a Dense layer (keras.layers.Dense) with 300 neurons, and the \"relu\" activation function;\n",
        "* another Dense layer with 100 neurons, also with the \"relu\" activation function;\n",
        "* a final Dense layer with 10 output neurons (one per class), and with the \"softmax\" activation function to ensure that the sum of all the estimated class probabilities for each image is equal to 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaoEISyaZL56",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fYfxUOQVZljh",
        "colab_type": "text"
      },
      "source": [
        "Call the model's summary() method to check if the model has been built correctly. Also, try using keras.utils.plot_model() to save an image of your model's architecture."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IpGkQl5dZoWq",
        "colab_type": "code",
        "outputId": "cd21d321-f3a6-4924-914d-e56e68c0269c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 300)               235500    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               30100     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                1010      \n",
            "=================================================================\n",
            "Total params: 266,610\n",
            "Trainable params: 266,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1NbkrXRZwOA",
        "colab_type": "code",
        "outputId": "a63e9a48-e35a-4de0-b21a-d1b2c1640e35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        }
      },
      "source": [
        "keras.utils.plot_model(model, \"my_mnist_model.png\", show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mc3xzuA-33WH",
        "colab_type": "text"
      },
      "source": [
        "As you can see, the first dimension of each layer is not defined (?), indeed this dimension in each layer is variable and depends on the batch size."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2ySdJmzxaSuz",
        "colab_type": "text"
      },
      "source": [
        "### Optimize the model:\n",
        "\n",
        "After the model has been created, you must call its compile() method to specify the loss function and the optimizer to use.\n",
        "\n",
        "The conventional way is to have the target outputs to match the output shape. In this case that would mean to convert the targets to the one-hot encoded array to match with the output shape.\n",
        "However, with the help of the sparse_categorical_crossentropy loss function, we can skip that step and keep the integers as targets.\n",
        "\n",
        " In this case, we want to use the \"sparse_categorical_crossentropy\" loss, and the SGD optimizer (stochastic gradient descent). \n",
        "\n",
        "\n",
        "\n",
        "Moreover, you can optionally specify a list of additional metrics that should be measured during training. In this case we specify metrics=[\"accuracy\"]."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x3bbsY3uaeh5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
        "              metrics=[\"accuracy\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NB9SV0MpaeTg",
        "colab_type": "text"
      },
      "source": [
        "Now our model is ready to be trained. Call its fit() method, passing to it the input features (X_train) and the target classes (y_train). Set the number of epoch to 20. \n",
        "In order to validate our model we will also pass the validation data by setting validation_data=(X_valid, y_valid). Keras will compute the loss and the additional metrics (the accuracy in this case) on the validation set at the end of each epoch. If the loss on the training set is much lower than the one on the validation set, your model is probably overfitting the training set. Note: the fit() method will return a History object containing training stats."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5whx-LU3aoKn",
        "colab_type": "code",
        "outputId": "bc8d6332-0d7f-47f7-e9f9-2aabf6de0dde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        }
      },
      "source": [
        "history = model.fit(X_train, y_train, epochs=20,\n",
        "                    validation_data=(X_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 2.4612 - accuracy: 0.7195 - val_loss: 0.6746 - val_accuracy: 0.7664\n",
            "Epoch 2/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.5560 - accuracy: 0.7993 - val_loss: 0.5250 - val_accuracy: 0.8206\n",
            "Epoch 3/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4885 - accuracy: 0.8192 - val_loss: 0.5444 - val_accuracy: 0.8116\n",
            "Epoch 4/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4534 - accuracy: 0.8325 - val_loss: 0.4841 - val_accuracy: 0.8244\n",
            "Epoch 5/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4294 - accuracy: 0.8409 - val_loss: 0.4528 - val_accuracy: 0.8422\n",
            "Epoch 6/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4113 - accuracy: 0.8475 - val_loss: 0.4652 - val_accuracy: 0.8450\n",
            "Epoch 7/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3966 - accuracy: 0.8532 - val_loss: 0.4533 - val_accuracy: 0.8470\n",
            "Epoch 8/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3858 - accuracy: 0.8562 - val_loss: 0.4786 - val_accuracy: 0.8364\n",
            "Epoch 9/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3742 - accuracy: 0.8606 - val_loss: 0.4349 - val_accuracy: 0.8504\n",
            "Epoch 10/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3653 - accuracy: 0.8643 - val_loss: 0.4306 - val_accuracy: 0.8558\n",
            "Epoch 11/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3564 - accuracy: 0.8663 - val_loss: 0.4207 - val_accuracy: 0.8606\n",
            "Epoch 12/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3500 - accuracy: 0.8693 - val_loss: 0.4236 - val_accuracy: 0.8580\n",
            "Epoch 13/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3426 - accuracy: 0.8709 - val_loss: 0.4288 - val_accuracy: 0.8574\n",
            "Epoch 14/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3383 - accuracy: 0.8734 - val_loss: 0.4361 - val_accuracy: 0.8548\n",
            "Epoch 15/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3307 - accuracy: 0.8756 - val_loss: 0.4157 - val_accuracy: 0.8640\n",
            "Epoch 16/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3272 - accuracy: 0.8782 - val_loss: 0.4252 - val_accuracy: 0.8628\n",
            "Epoch 17/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3232 - accuracy: 0.8792 - val_loss: 0.4357 - val_accuracy: 0.8562\n",
            "Epoch 18/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3169 - accuracy: 0.8805 - val_loss: 0.4290 - val_accuracy: 0.8652\n",
            "Epoch 19/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3128 - accuracy: 0.8829 - val_loss: 0.4140 - val_accuracy: 0.8688\n",
            "Epoch 20/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3082 - accuracy: 0.8839 - val_loss: 0.4263 - val_accuracy: 0.8594\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPxiw8iCADr-",
        "colab_type": "text"
      },
      "source": [
        "Let's plot the loss and the accuracy trends on both training and validation sets. We define the \"plot_learning_curves\" because we will reuse it in the next steps."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AJg-F7mUa7st",
        "colab_type": "code",
        "outputId": "87bc940a-1c23-4838-bbf0-c0324ff782bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        }
      },
      "source": [
        "def plot_learning_curves(history):\n",
        "    pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
        "    plt.grid(True)\n",
        "    plt.gca().set_ylim(0, 1)\n",
        "    plt.show()\n",
        "plot_learning_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9O3-sLmxansJ",
        "colab_type": "text"
      },
      "source": [
        "### Evaluate the model:\n",
        "\n",
        "Now, our model has been optimized on the training set, and as you can see the performance on the validation set in quite similar (so it does not overfit the training data). Let's now evaluate the performance of our model using the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q-ZvJEcObdyB",
        "colab_type": "code",
        "outputId": "42575458-a600-4bb1-87df-70111970f8b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.4750 - accuracy: 0.8357\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.47502079606056213, 0.8356999754905701]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0Xbey3u6yss",
        "colab_type": "text"
      },
      "source": [
        "### Input normalization:\n",
        "\n",
        "When using Gradient Descent, it is usually a good idea to ensure that all the features have a similar scale. It's a common practice to scale your data inputs to have zero mean and unit variance. This is known as the standard scaler approach.\n",
        "Try to standardize the pixel values and see if this improves the performance of your neural network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNFVWb8iTigz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#To complete: define X_train_scaled, X_valid_scaled and X_test_scaled, the sets \n",
        "#that contain the normalized inputs.\n",
        "#Hint: For each feature (pixel intensity), you must subtract the mean() of that \n",
        "#feature (across all instances, so use axis=0) and divide by its standard \n",
        "#deviation (std(), again axis=0)\n",
        "\n",
        "# Matrices computed using numpy broadcasting\n",
        "\n",
        "X_train_scaled = (X_train - (X_train.mean(axis=0))) / X_train.std(axis=0)\n",
        "X_valid_scaled = (X_valid - (X_train.mean(axis=0))) / X_train.std(axis=0)\n",
        "X_test_scaled = (X_test - (X_train.mean(axis=0))) / X_train.std(axis=0)\n",
        "\n",
        "#Make sure you compute the means and standard deviations on the training set,\n",
        "#and use these statistics to scale the training set, the validation set and the\n",
        "# test set\n",
        "\n",
        "#Alternatively, you can use Scikit-Learn's StandardScaler..."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pqd4Xw9UTwy2",
        "colab_type": "text"
      },
      "source": [
        "Let's define the model in order to re-initialize the weights and then train it by using the normalize data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmhZXTqHTy-C",
        "colab_type": "code",
        "outputId": "55c9eaf7-b0c3-491e-ece7-5e1a920587e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "model.add(keras.layers.Dense(300, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(100, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr=1e-3),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "history = model.fit(X_train_scaled, y_train, epochs=20,\n",
        "                    validation_data=(X_valid_scaled, y_valid))\n",
        "print(\"Model Evaluation\")\n",
        "model.evaluate(X_test_scaled, y_test)\n",
        "plot_learning_curves(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.9391 - accuracy: 0.6949 - val_loss: 0.6307 - val_accuracy: 0.7850\n",
            "Epoch 2/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.5797 - accuracy: 0.7997 - val_loss: 0.5275 - val_accuracy: 0.8114\n",
            "Epoch 3/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.5077 - accuracy: 0.8214 - val_loss: 0.4838 - val_accuracy: 0.8300\n",
            "Epoch 4/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4691 - accuracy: 0.8342 - val_loss: 0.4545 - val_accuracy: 0.8412\n",
            "Epoch 5/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4438 - accuracy: 0.8440 - val_loss: 0.4367 - val_accuracy: 0.8470\n",
            "Epoch 6/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4248 - accuracy: 0.8503 - val_loss: 0.4231 - val_accuracy: 0.8520\n",
            "Epoch 7/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4097 - accuracy: 0.8559 - val_loss: 0.4114 - val_accuracy: 0.8552\n",
            "Epoch 8/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3973 - accuracy: 0.8599 - val_loss: 0.4039 - val_accuracy: 0.8596\n",
            "Epoch 9/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3866 - accuracy: 0.8640 - val_loss: 0.3950 - val_accuracy: 0.8622\n",
            "Epoch 10/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3773 - accuracy: 0.8675 - val_loss: 0.3896 - val_accuracy: 0.8618\n",
            "Epoch 11/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3693 - accuracy: 0.8703 - val_loss: 0.3835 - val_accuracy: 0.8656\n",
            "Epoch 12/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3619 - accuracy: 0.8732 - val_loss: 0.3788 - val_accuracy: 0.8660\n",
            "Epoch 13/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3552 - accuracy: 0.8757 - val_loss: 0.3733 - val_accuracy: 0.8690\n",
            "Epoch 14/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3492 - accuracy: 0.8773 - val_loss: 0.3704 - val_accuracy: 0.8688\n",
            "Epoch 15/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3434 - accuracy: 0.8793 - val_loss: 0.3672 - val_accuracy: 0.8690\n",
            "Epoch 16/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3382 - accuracy: 0.8813 - val_loss: 0.3626 - val_accuracy: 0.8724\n",
            "Epoch 17/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3332 - accuracy: 0.8821 - val_loss: 0.3603 - val_accuracy: 0.8722\n",
            "Epoch 18/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3284 - accuracy: 0.8846 - val_loss: 0.3566 - val_accuracy: 0.8732\n",
            "Epoch 19/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3240 - accuracy: 0.8852 - val_loss: 0.3557 - val_accuracy: 0.8754\n",
            "Epoch 20/20\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.3199 - accuracy: 0.8861 - val_loss: 0.3518 - val_accuracy: 0.8762\n",
            "Model Evaluation\n",
            "313/313 [==============================] - 0s 1ms/step - loss: 0.3812 - accuracy: 0.8636\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8l8V4fSRVc6D",
        "colab_type": "text"
      },
      "source": [
        "Note that the validation curve is smoother than in the previous test and it is also more simlar to the training curve."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aeGlzKe864Gq",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 2.2: Overfiting\n",
        "\n",
        "A common problem that occurs when you train a deep neural network is overfittig. Overfitting occurs when you achieve a good fit of your model on the training data, while it does not generalize well on new, unseen data. In other words, the model learned patterns specific to the training data, which are irrelevant in other data.\n",
        "As we have seen in the previous exercise, our model does not overfit the training data. In this exercise, we try to modify the training parameters in order to have a model that overfits.\n",
        "Overfitting can have many causes and usually is a combination of some of them, for instance: too many parameters/ layers, too few training samples, wrong learning rate (usualy too high), etc.."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T2B2DFEC8f88",
        "colab_type": "code",
        "outputId": "544a5096-2f61-4772-9616-c559bc4ca971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#To complete: modify the previuos model in order to obtain  a new mdel that overfit the training data\n",
        "#HINT: A model tends to overfit when too few training examples are available\n",
        "\n",
        "n_train_small = 50 # Number of example on the small training set: the smaller\n",
        "                  # the higher the probability of overfitting\n",
        "\n",
        "lr = 1e-3 # Learning rate.\n",
        "\n",
        "epochs = 500 # Number of epochs. High number of epochs means \n",
        "            # high probability of overfitting\n",
        "\n",
        "\n",
        "# Small training set generation\n",
        "X_train_small = X_train_scaled[0:n_train_small]\n",
        "y_train_small = y_train[0:n_train_small]\n",
        "\n",
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "\n",
        "#To complete: Add layers\n",
        "#HINT: A model tends to overfit  when the number of learnable parameters is very high compared to the number of training samples\n",
        "#HINT: Is it possible to use different activation functions than ReLu\n",
        "model.add(keras.layers.Dense(3000, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(1000, activation=\"relu\"))\n",
        "\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
        "\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr=lr),\n",
        "              metrics=[\"sparse_categorical_accuracy\"]) #To complete:learning rate\n",
        "\n",
        "#HINT: A model tends to overfit when the number of training epochs is high\n",
        "history = model.fit(X_train_small, y_train_small, epochs=epochs, #To complete: number of epochs\n",
        "                    validation_data=(X_valid_scaled, y_valid))\n",
        "print(\"Model Evaluation\")\n",
        "model.evaluate(X_test_scaled, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 2.6499 - sparse_categorical_accuracy: 0.0400 - val_loss: 2.6089 - val_sparse_categorical_accuracy: 0.0424\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 2.5433 - sparse_categorical_accuracy: 0.0600 - val_loss: 2.5619 - val_sparse_categorical_accuracy: 0.0472\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 2.4435 - sparse_categorical_accuracy: 0.1200 - val_loss: 2.5174 - val_sparse_categorical_accuracy: 0.0506\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 2.3504 - sparse_categorical_accuracy: 0.1400 - val_loss: 2.4778 - val_sparse_categorical_accuracy: 0.0638\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 2.2640 - sparse_categorical_accuracy: 0.2000 - val_loss: 2.4383 - val_sparse_categorical_accuracy: 0.0796\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 2.1774 - sparse_categorical_accuracy: 0.2400 - val_loss: 2.4025 - val_sparse_categorical_accuracy: 0.0962\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 2.0981 - sparse_categorical_accuracy: 0.3000 - val_loss: 2.3672 - val_sparse_categorical_accuracy: 0.1182\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 2.0228 - sparse_categorical_accuracy: 0.3600 - val_loss: 2.3340 - val_sparse_categorical_accuracy: 0.1504\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 1s 745ms/step - loss: 1.9507 - sparse_categorical_accuracy: 0.4600 - val_loss: 2.3048 - val_sparse_categorical_accuracy: 0.1790\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 1.8874 - sparse_categorical_accuracy: 0.5200 - val_loss: 2.2770 - val_sparse_categorical_accuracy: 0.2064\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 1.8263 - sparse_categorical_accuracy: 0.6000 - val_loss: 2.2519 - val_sparse_categorical_accuracy: 0.2250\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 1.7702 - sparse_categorical_accuracy: 0.6600 - val_loss: 2.2266 - val_sparse_categorical_accuracy: 0.2456\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 1.7147 - sparse_categorical_accuracy: 0.6600 - val_loss: 2.2031 - val_sparse_categorical_accuracy: 0.2640\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 1.6633 - sparse_categorical_accuracy: 0.6600 - val_loss: 2.1824 - val_sparse_categorical_accuracy: 0.2802\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 1.6163 - sparse_categorical_accuracy: 0.6800 - val_loss: 2.1604 - val_sparse_categorical_accuracy: 0.2916\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 1.5721 - sparse_categorical_accuracy: 0.6800 - val_loss: 2.1420 - val_sparse_categorical_accuracy: 0.3044\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 1.5305 - sparse_categorical_accuracy: 0.7600 - val_loss: 2.1247 - val_sparse_categorical_accuracy: 0.3142\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 1.4902 - sparse_categorical_accuracy: 0.7600 - val_loss: 2.1078 - val_sparse_categorical_accuracy: 0.3226\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 1.4533 - sparse_categorical_accuracy: 0.8000 - val_loss: 2.0917 - val_sparse_categorical_accuracy: 0.3304\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 1.4171 - sparse_categorical_accuracy: 0.8200 - val_loss: 2.0760 - val_sparse_categorical_accuracy: 0.3372\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 1.3833 - sparse_categorical_accuracy: 0.8200 - val_loss: 2.0608 - val_sparse_categorical_accuracy: 0.3440\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 1.3523 - sparse_categorical_accuracy: 0.8200 - val_loss: 2.0456 - val_sparse_categorical_accuracy: 0.3540\n",
            "Epoch 23/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 1.3203 - sparse_categorical_accuracy: 0.8200 - val_loss: 2.0315 - val_sparse_categorical_accuracy: 0.3586\n",
            "Epoch 24/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 1.2908 - sparse_categorical_accuracy: 0.8400 - val_loss: 2.0193 - val_sparse_categorical_accuracy: 0.3604\n",
            "Epoch 25/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 1.2637 - sparse_categorical_accuracy: 0.8400 - val_loss: 2.0053 - val_sparse_categorical_accuracy: 0.3636\n",
            "Epoch 26/500\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 1.2370 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9939 - val_sparse_categorical_accuracy: 0.3678\n",
            "Epoch 27/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 1.2119 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9813 - val_sparse_categorical_accuracy: 0.3726\n",
            "Epoch 28/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 1.1873 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9707 - val_sparse_categorical_accuracy: 0.3752\n",
            "Epoch 29/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 1.1640 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9609 - val_sparse_categorical_accuracy: 0.3796\n",
            "Epoch 30/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 1.1411 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9504 - val_sparse_categorical_accuracy: 0.3832\n",
            "Epoch 31/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 1.1192 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9401 - val_sparse_categorical_accuracy: 0.3850\n",
            "Epoch 32/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 1.0982 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9305 - val_sparse_categorical_accuracy: 0.3860\n",
            "Epoch 33/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 1.0777 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.9213 - val_sparse_categorical_accuracy: 0.3876\n",
            "Epoch 34/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 1.0581 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.9122 - val_sparse_categorical_accuracy: 0.3894\n",
            "Epoch 35/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 1.0391 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.9041 - val_sparse_categorical_accuracy: 0.3924\n",
            "Epoch 36/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 1.0213 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8959 - val_sparse_categorical_accuracy: 0.3942\n",
            "Epoch 37/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 1.0048 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8864 - val_sparse_categorical_accuracy: 0.3962\n",
            "Epoch 38/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.9865 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8780 - val_sparse_categorical_accuracy: 0.3976\n",
            "Epoch 39/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.9700 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8695 - val_sparse_categorical_accuracy: 0.3988\n",
            "Epoch 40/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.9541 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8610 - val_sparse_categorical_accuracy: 0.3996\n",
            "Epoch 41/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.9389 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8542 - val_sparse_categorical_accuracy: 0.4020\n",
            "Epoch 42/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.9244 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.8469 - val_sparse_categorical_accuracy: 0.4046\n",
            "Epoch 43/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.9101 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8393 - val_sparse_categorical_accuracy: 0.4064\n",
            "Epoch 44/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.8950 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8319 - val_sparse_categorical_accuracy: 0.4070\n",
            "Epoch 45/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.8813 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8266 - val_sparse_categorical_accuracy: 0.4072\n",
            "Epoch 46/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.8674 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8207 - val_sparse_categorical_accuracy: 0.4074\n",
            "Epoch 47/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.8544 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8138 - val_sparse_categorical_accuracy: 0.4084\n",
            "Epoch 48/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.8425 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8072 - val_sparse_categorical_accuracy: 0.4098\n",
            "Epoch 49/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.8297 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.8010 - val_sparse_categorical_accuracy: 0.4110\n",
            "Epoch 50/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.8177 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7938 - val_sparse_categorical_accuracy: 0.4114\n",
            "Epoch 51/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.8056 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7881 - val_sparse_categorical_accuracy: 0.4126\n",
            "Epoch 52/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.7941 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7807 - val_sparse_categorical_accuracy: 0.4124\n",
            "Epoch 53/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.7826 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7765 - val_sparse_categorical_accuracy: 0.4124\n",
            "Epoch 54/500\n",
            "2/2 [==============================] - 2s 750ms/step - loss: 0.7714 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7718 - val_sparse_categorical_accuracy: 0.4122\n",
            "Epoch 55/500\n",
            "2/2 [==============================] - 2s 752ms/step - loss: 0.7615 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7647 - val_sparse_categorical_accuracy: 0.4132\n",
            "Epoch 56/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.7503 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7597 - val_sparse_categorical_accuracy: 0.4142\n",
            "Epoch 57/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.7406 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7531 - val_sparse_categorical_accuracy: 0.4146\n",
            "Epoch 58/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.7294 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7485 - val_sparse_categorical_accuracy: 0.4146\n",
            "Epoch 59/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.7205 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7444 - val_sparse_categorical_accuracy: 0.4150\n",
            "Epoch 60/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.7107 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7388 - val_sparse_categorical_accuracy: 0.4162\n",
            "Epoch 61/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.7019 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7323 - val_sparse_categorical_accuracy: 0.4162\n",
            "Epoch 62/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.6922 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7279 - val_sparse_categorical_accuracy: 0.4186\n",
            "Epoch 63/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.6835 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7244 - val_sparse_categorical_accuracy: 0.4194\n",
            "Epoch 64/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.6748 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7199 - val_sparse_categorical_accuracy: 0.4206\n",
            "Epoch 65/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.6662 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7140 - val_sparse_categorical_accuracy: 0.4204\n",
            "Epoch 66/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.6576 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7099 - val_sparse_categorical_accuracy: 0.4202\n",
            "Epoch 67/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.6498 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7034 - val_sparse_categorical_accuracy: 0.4210\n",
            "Epoch 68/500\n",
            "2/2 [==============================] - 2s 787ms/step - loss: 0.6412 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6990 - val_sparse_categorical_accuracy: 0.4226\n",
            "Epoch 69/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.6340 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6958 - val_sparse_categorical_accuracy: 0.4240\n",
            "Epoch 70/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.6258 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6903 - val_sparse_categorical_accuracy: 0.4242\n",
            "Epoch 71/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.6182 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6854 - val_sparse_categorical_accuracy: 0.4246\n",
            "Epoch 72/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.6111 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6821 - val_sparse_categorical_accuracy: 0.4256\n",
            "Epoch 73/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.6033 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6783 - val_sparse_categorical_accuracy: 0.4266\n",
            "Epoch 74/500\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.5966 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6741 - val_sparse_categorical_accuracy: 0.4274\n",
            "Epoch 75/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.5895 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6699 - val_sparse_categorical_accuracy: 0.4278\n",
            "Epoch 76/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.5830 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6667 - val_sparse_categorical_accuracy: 0.4276\n",
            "Epoch 77/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.5766 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6623 - val_sparse_categorical_accuracy: 0.4278\n",
            "Epoch 78/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.5702 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6594 - val_sparse_categorical_accuracy: 0.4284\n",
            "Epoch 79/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.5633 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6542 - val_sparse_categorical_accuracy: 0.4300\n",
            "Epoch 80/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.5566 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6512 - val_sparse_categorical_accuracy: 0.4296\n",
            "Epoch 81/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.5510 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6479 - val_sparse_categorical_accuracy: 0.4298\n",
            "Epoch 82/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.5445 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6435 - val_sparse_categorical_accuracy: 0.4304\n",
            "Epoch 83/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.5381 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6403 - val_sparse_categorical_accuracy: 0.4306\n",
            "Epoch 84/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.5325 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6370 - val_sparse_categorical_accuracy: 0.4324\n",
            "Epoch 85/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.5271 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6343 - val_sparse_categorical_accuracy: 0.4334\n",
            "Epoch 86/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.5216 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6300 - val_sparse_categorical_accuracy: 0.4348\n",
            "Epoch 87/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.5155 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6261 - val_sparse_categorical_accuracy: 0.4354\n",
            "Epoch 88/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.5100 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6235 - val_sparse_categorical_accuracy: 0.4360\n",
            "Epoch 89/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.5048 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6202 - val_sparse_categorical_accuracy: 0.4370\n",
            "Epoch 90/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.4996 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6159 - val_sparse_categorical_accuracy: 0.4370\n",
            "Epoch 91/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.4943 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6127 - val_sparse_categorical_accuracy: 0.4380\n",
            "Epoch 92/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.4895 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6105 - val_sparse_categorical_accuracy: 0.4382\n",
            "Epoch 93/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.4844 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6075 - val_sparse_categorical_accuracy: 0.4386\n",
            "Epoch 94/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.4792 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6036 - val_sparse_categorical_accuracy: 0.4380\n",
            "Epoch 95/500\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 0.4745 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.6006 - val_sparse_categorical_accuracy: 0.4396\n",
            "Epoch 96/500\n",
            "2/2 [==============================] - 2s 787ms/step - loss: 0.4699 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5977 - val_sparse_categorical_accuracy: 0.4404\n",
            "Epoch 97/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.4649 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5951 - val_sparse_categorical_accuracy: 0.4410\n",
            "Epoch 98/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.4607 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5918 - val_sparse_categorical_accuracy: 0.4424\n",
            "Epoch 99/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.4560 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5893 - val_sparse_categorical_accuracy: 0.4430\n",
            "Epoch 100/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.4516 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5865 - val_sparse_categorical_accuracy: 0.4432\n",
            "Epoch 101/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.4469 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5840 - val_sparse_categorical_accuracy: 0.4438\n",
            "Epoch 102/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.4426 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5820 - val_sparse_categorical_accuracy: 0.4444\n",
            "Epoch 103/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.4381 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5795 - val_sparse_categorical_accuracy: 0.4448\n",
            "Epoch 104/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.4341 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5774 - val_sparse_categorical_accuracy: 0.4464\n",
            "Epoch 105/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.4302 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5755 - val_sparse_categorical_accuracy: 0.4464\n",
            "Epoch 106/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.4260 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5719 - val_sparse_categorical_accuracy: 0.4468\n",
            "Epoch 107/500\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.4220 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5699 - val_sparse_categorical_accuracy: 0.4474\n",
            "Epoch 108/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.4185 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5679 - val_sparse_categorical_accuracy: 0.4482\n",
            "Epoch 109/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.4148 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5662 - val_sparse_categorical_accuracy: 0.4486\n",
            "Epoch 110/500\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 0.4111 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5646 - val_sparse_categorical_accuracy: 0.4494\n",
            "Epoch 111/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.4071 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5605 - val_sparse_categorical_accuracy: 0.4502\n",
            "Epoch 112/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.4038 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5586 - val_sparse_categorical_accuracy: 0.4506\n",
            "Epoch 113/500\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.3995 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5557 - val_sparse_categorical_accuracy: 0.4516\n",
            "Epoch 114/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.3961 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5535 - val_sparse_categorical_accuracy: 0.4518\n",
            "Epoch 115/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.3925 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5509 - val_sparse_categorical_accuracy: 0.4526\n",
            "Epoch 116/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.3889 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5491 - val_sparse_categorical_accuracy: 0.4534\n",
            "Epoch 117/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.3857 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5478 - val_sparse_categorical_accuracy: 0.4534\n",
            "Epoch 118/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.3825 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5459 - val_sparse_categorical_accuracy: 0.4540\n",
            "Epoch 119/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.3790 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5438 - val_sparse_categorical_accuracy: 0.4538\n",
            "Epoch 120/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.3755 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5410 - val_sparse_categorical_accuracy: 0.4544\n",
            "Epoch 121/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.3721 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5381 - val_sparse_categorical_accuracy: 0.4554\n",
            "Epoch 122/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.3688 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5366 - val_sparse_categorical_accuracy: 0.4552\n",
            "Epoch 123/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.3656 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5347 - val_sparse_categorical_accuracy: 0.4552\n",
            "Epoch 124/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.3625 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5332 - val_sparse_categorical_accuracy: 0.4550\n",
            "Epoch 125/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.3595 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5315 - val_sparse_categorical_accuracy: 0.4558\n",
            "Epoch 126/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.3567 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5288 - val_sparse_categorical_accuracy: 0.4570\n",
            "Epoch 127/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.3535 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5279 - val_sparse_categorical_accuracy: 0.4574\n",
            "Epoch 128/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.3508 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5247 - val_sparse_categorical_accuracy: 0.4582\n",
            "Epoch 129/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.3477 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5231 - val_sparse_categorical_accuracy: 0.4580\n",
            "Epoch 130/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.3448 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5206 - val_sparse_categorical_accuracy: 0.4592\n",
            "Epoch 131/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.3420 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5183 - val_sparse_categorical_accuracy: 0.4604\n",
            "Epoch 132/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.3393 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5167 - val_sparse_categorical_accuracy: 0.4610\n",
            "Epoch 133/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.3362 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5139 - val_sparse_categorical_accuracy: 0.4622\n",
            "Epoch 134/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.3335 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5115 - val_sparse_categorical_accuracy: 0.4626\n",
            "Epoch 135/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.3309 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5104 - val_sparse_categorical_accuracy: 0.4638\n",
            "Epoch 136/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.3283 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5084 - val_sparse_categorical_accuracy: 0.4640\n",
            "Epoch 137/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.3256 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5068 - val_sparse_categorical_accuracy: 0.4640\n",
            "Epoch 138/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.3232 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5062 - val_sparse_categorical_accuracy: 0.4652\n",
            "Epoch 139/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.3206 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5038 - val_sparse_categorical_accuracy: 0.4660\n",
            "Epoch 140/500\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.3180 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5021 - val_sparse_categorical_accuracy: 0.4670\n",
            "Epoch 141/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.3154 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.5008 - val_sparse_categorical_accuracy: 0.4678\n",
            "Epoch 142/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.3130 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4990 - val_sparse_categorical_accuracy: 0.4682\n",
            "Epoch 143/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.3107 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4970 - val_sparse_categorical_accuracy: 0.4682\n",
            "Epoch 144/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.3081 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4952 - val_sparse_categorical_accuracy: 0.4682\n",
            "Epoch 145/500\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 0.3057 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4933 - val_sparse_categorical_accuracy: 0.4682\n",
            "Epoch 146/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.3036 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4918 - val_sparse_categorical_accuracy: 0.4694\n",
            "Epoch 147/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.3015 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4906 - val_sparse_categorical_accuracy: 0.4694\n",
            "Epoch 148/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.2993 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4894 - val_sparse_categorical_accuracy: 0.4700\n",
            "Epoch 149/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.2969 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4878 - val_sparse_categorical_accuracy: 0.4712\n",
            "Epoch 150/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.2948 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4864 - val_sparse_categorical_accuracy: 0.4714\n",
            "Epoch 151/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.2926 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4840 - val_sparse_categorical_accuracy: 0.4714\n",
            "Epoch 152/500\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.2905 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4837 - val_sparse_categorical_accuracy: 0.4716\n",
            "Epoch 153/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.2882 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4813 - val_sparse_categorical_accuracy: 0.4734\n",
            "Epoch 154/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.2862 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4792 - val_sparse_categorical_accuracy: 0.4752\n",
            "Epoch 155/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.2841 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4776 - val_sparse_categorical_accuracy: 0.4760\n",
            "Epoch 156/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.2819 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4767 - val_sparse_categorical_accuracy: 0.4760\n",
            "Epoch 157/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.2797 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4757 - val_sparse_categorical_accuracy: 0.4756\n",
            "Epoch 158/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.2777 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4740 - val_sparse_categorical_accuracy: 0.4780\n",
            "Epoch 159/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.2757 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4730 - val_sparse_categorical_accuracy: 0.4784\n",
            "Epoch 160/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.2739 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4724 - val_sparse_categorical_accuracy: 0.4790\n",
            "Epoch 161/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.2720 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4714 - val_sparse_categorical_accuracy: 0.4796\n",
            "Epoch 162/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.2700 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4709 - val_sparse_categorical_accuracy: 0.4798\n",
            "Epoch 163/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.2682 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4693 - val_sparse_categorical_accuracy: 0.4802\n",
            "Epoch 164/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.2663 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4683 - val_sparse_categorical_accuracy: 0.4814\n",
            "Epoch 165/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.2646 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4670 - val_sparse_categorical_accuracy: 0.4818\n",
            "Epoch 166/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.2627 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4657 - val_sparse_categorical_accuracy: 0.4822\n",
            "Epoch 167/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.2610 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4646 - val_sparse_categorical_accuracy: 0.4830\n",
            "Epoch 168/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.2592 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4639 - val_sparse_categorical_accuracy: 0.4830\n",
            "Epoch 169/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.2575 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4636 - val_sparse_categorical_accuracy: 0.4836\n",
            "Epoch 170/500\n",
            "2/2 [==============================] - 2s 866ms/step - loss: 0.2558 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4634 - val_sparse_categorical_accuracy: 0.4830\n",
            "Epoch 171/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.2540 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4621 - val_sparse_categorical_accuracy: 0.4836\n",
            "Epoch 172/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.2523 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4604 - val_sparse_categorical_accuracy: 0.4852\n",
            "Epoch 173/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.2507 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4598 - val_sparse_categorical_accuracy: 0.4848\n",
            "Epoch 174/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.2490 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4579 - val_sparse_categorical_accuracy: 0.4850\n",
            "Epoch 175/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.2472 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4564 - val_sparse_categorical_accuracy: 0.4860\n",
            "Epoch 176/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.2455 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4558 - val_sparse_categorical_accuracy: 0.4864\n",
            "Epoch 177/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.2440 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4547 - val_sparse_categorical_accuracy: 0.4866\n",
            "Epoch 178/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.2425 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4546 - val_sparse_categorical_accuracy: 0.4868\n",
            "Epoch 179/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.2407 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4531 - val_sparse_categorical_accuracy: 0.4880\n",
            "Epoch 180/500\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 0.2393 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4530 - val_sparse_categorical_accuracy: 0.4874\n",
            "Epoch 181/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.2378 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4527 - val_sparse_categorical_accuracy: 0.4872\n",
            "Epoch 182/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.2364 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4512 - val_sparse_categorical_accuracy: 0.4888\n",
            "Epoch 183/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.2348 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4494 - val_sparse_categorical_accuracy: 0.4890\n",
            "Epoch 184/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.2334 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4488 - val_sparse_categorical_accuracy: 0.4892\n",
            "Epoch 185/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.2319 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4483 - val_sparse_categorical_accuracy: 0.4894\n",
            "Epoch 186/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.2306 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4477 - val_sparse_categorical_accuracy: 0.4898\n",
            "Epoch 187/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.2291 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4463 - val_sparse_categorical_accuracy: 0.4916\n",
            "Epoch 188/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.2276 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4452 - val_sparse_categorical_accuracy: 0.4918\n",
            "Epoch 189/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.2263 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4445 - val_sparse_categorical_accuracy: 0.4922\n",
            "Epoch 190/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.2249 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4439 - val_sparse_categorical_accuracy: 0.4928\n",
            "Epoch 191/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.2235 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4429 - val_sparse_categorical_accuracy: 0.4934\n",
            "Epoch 192/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.2221 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4417 - val_sparse_categorical_accuracy: 0.4940\n",
            "Epoch 193/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.2207 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4412 - val_sparse_categorical_accuracy: 0.4942\n",
            "Epoch 194/500\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.2194 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4408 - val_sparse_categorical_accuracy: 0.4948\n",
            "Epoch 195/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.2181 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4401 - val_sparse_categorical_accuracy: 0.4946\n",
            "Epoch 196/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.2168 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4390 - val_sparse_categorical_accuracy: 0.4950\n",
            "Epoch 197/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.2153 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4380 - val_sparse_categorical_accuracy: 0.4952\n",
            "Epoch 198/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.2141 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4377 - val_sparse_categorical_accuracy: 0.4952\n",
            "Epoch 199/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.2126 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4368 - val_sparse_categorical_accuracy: 0.4958\n",
            "Epoch 200/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.2115 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4361 - val_sparse_categorical_accuracy: 0.4956\n",
            "Epoch 201/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.2103 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4350 - val_sparse_categorical_accuracy: 0.4968\n",
            "Epoch 202/500\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 0.2089 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4342 - val_sparse_categorical_accuracy: 0.4970\n",
            "Epoch 203/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.2076 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4335 - val_sparse_categorical_accuracy: 0.4972\n",
            "Epoch 204/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.2066 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4329 - val_sparse_categorical_accuracy: 0.4976\n",
            "Epoch 205/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.2054 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4315 - val_sparse_categorical_accuracy: 0.4976\n",
            "Epoch 206/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.2042 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4312 - val_sparse_categorical_accuracy: 0.4976\n",
            "Epoch 207/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.2031 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4300 - val_sparse_categorical_accuracy: 0.4986\n",
            "Epoch 208/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.2019 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4292 - val_sparse_categorical_accuracy: 0.4988\n",
            "Epoch 209/500\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 0.2006 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4288 - val_sparse_categorical_accuracy: 0.4990\n",
            "Epoch 210/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.1996 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4276 - val_sparse_categorical_accuracy: 0.4994\n",
            "Epoch 211/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1983 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4267 - val_sparse_categorical_accuracy: 0.5000\n",
            "Epoch 212/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.1973 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4260 - val_sparse_categorical_accuracy: 0.5002\n",
            "Epoch 213/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1962 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4254 - val_sparse_categorical_accuracy: 0.5004\n",
            "Epoch 214/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.1951 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4250 - val_sparse_categorical_accuracy: 0.5010\n",
            "Epoch 215/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.1940 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4245 - val_sparse_categorical_accuracy: 0.5010\n",
            "Epoch 216/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1929 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4240 - val_sparse_categorical_accuracy: 0.5018\n",
            "Epoch 217/500\n",
            "2/2 [==============================] - 2s 787ms/step - loss: 0.1919 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4228 - val_sparse_categorical_accuracy: 0.5020\n",
            "Epoch 218/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.1908 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4219 - val_sparse_categorical_accuracy: 0.5022\n",
            "Epoch 219/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.1898 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4217 - val_sparse_categorical_accuracy: 0.5026\n",
            "Epoch 220/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.1888 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4211 - val_sparse_categorical_accuracy: 0.5024\n",
            "Epoch 221/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.1878 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4204 - val_sparse_categorical_accuracy: 0.5022\n",
            "Epoch 222/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1868 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4190 - val_sparse_categorical_accuracy: 0.5026\n",
            "Epoch 223/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1857 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4178 - val_sparse_categorical_accuracy: 0.5038\n",
            "Epoch 224/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.1846 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4172 - val_sparse_categorical_accuracy: 0.5036\n",
            "Epoch 225/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1835 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4165 - val_sparse_categorical_accuracy: 0.5042\n",
            "Epoch 226/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.1827 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4161 - val_sparse_categorical_accuracy: 0.5046\n",
            "Epoch 227/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1817 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4159 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 228/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.1807 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4150 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 229/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.1797 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4142 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 230/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.1788 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4134 - val_sparse_categorical_accuracy: 0.5054\n",
            "Epoch 231/500\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 0.1778 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4124 - val_sparse_categorical_accuracy: 0.5058\n",
            "Epoch 232/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.1769 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4118 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 233/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.1759 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4115 - val_sparse_categorical_accuracy: 0.5054\n",
            "Epoch 234/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.1751 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4105 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 235/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1742 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4092 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 236/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.1732 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4089 - val_sparse_categorical_accuracy: 0.5062\n",
            "Epoch 237/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.1723 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4085 - val_sparse_categorical_accuracy: 0.5058\n",
            "Epoch 238/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1713 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4078 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 239/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.1706 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4073 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 240/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.1697 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4066 - val_sparse_categorical_accuracy: 0.5064\n",
            "Epoch 241/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.1689 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4060 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 242/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1678 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4053 - val_sparse_categorical_accuracy: 0.5070\n",
            "Epoch 243/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.1670 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4055 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 244/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1661 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4051 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 245/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.1653 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4046 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 246/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.1645 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4041 - val_sparse_categorical_accuracy: 0.5072\n",
            "Epoch 247/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.1636 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4038 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 248/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.1628 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4027 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 249/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.1620 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4023 - val_sparse_categorical_accuracy: 0.5074\n",
            "Epoch 250/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.1612 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4020 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 251/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.1605 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4012 - val_sparse_categorical_accuracy: 0.5074\n",
            "Epoch 252/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.1598 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4004 - val_sparse_categorical_accuracy: 0.5080\n",
            "Epoch 253/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.1589 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4002 - val_sparse_categorical_accuracy: 0.5080\n",
            "Epoch 254/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.1581 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3999 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 255/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.1574 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3993 - val_sparse_categorical_accuracy: 0.5084\n",
            "Epoch 256/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1566 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3991 - val_sparse_categorical_accuracy: 0.5084\n",
            "Epoch 257/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.1558 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3989 - val_sparse_categorical_accuracy: 0.5084\n",
            "Epoch 258/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1552 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3983 - val_sparse_categorical_accuracy: 0.5086\n",
            "Epoch 259/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.1545 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3978 - val_sparse_categorical_accuracy: 0.5088\n",
            "Epoch 260/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.1537 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3972 - val_sparse_categorical_accuracy: 0.5092\n",
            "Epoch 261/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.1529 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3968 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 262/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.1521 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3961 - val_sparse_categorical_accuracy: 0.5102\n",
            "Epoch 263/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.1515 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3959 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 264/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.1508 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3959 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 265/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.1501 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3948 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 266/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.1494 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3945 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 267/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1488 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3934 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 268/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.1481 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3933 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 269/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.1475 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3928 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 270/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1468 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3919 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 271/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1462 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3916 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 272/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1455 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3914 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 273/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.1447 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3910 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 274/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1442 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3909 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 275/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1435 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3906 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 276/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.1428 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3905 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 277/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.1424 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3906 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 278/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1417 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3905 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 279/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.1411 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3901 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 280/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1405 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3900 - val_sparse_categorical_accuracy: 0.5116\n",
            "Epoch 281/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.1399 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3900 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 282/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1393 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3894 - val_sparse_categorical_accuracy: 0.5118\n",
            "Epoch 283/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.1386 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3884 - val_sparse_categorical_accuracy: 0.5122\n",
            "Epoch 284/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.1380 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3878 - val_sparse_categorical_accuracy: 0.5128\n",
            "Epoch 285/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.1375 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3881 - val_sparse_categorical_accuracy: 0.5130\n",
            "Epoch 286/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.1369 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3874 - val_sparse_categorical_accuracy: 0.5130\n",
            "Epoch 287/500\n",
            "2/2 [==============================] - 2s 777ms/step - loss: 0.1363 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3873 - val_sparse_categorical_accuracy: 0.5132\n",
            "Epoch 288/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.1358 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3865 - val_sparse_categorical_accuracy: 0.5132\n",
            "Epoch 289/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.1352 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3862 - val_sparse_categorical_accuracy: 0.5132\n",
            "Epoch 290/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.1347 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3861 - val_sparse_categorical_accuracy: 0.5132\n",
            "Epoch 291/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.1340 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3857 - val_sparse_categorical_accuracy: 0.5134\n",
            "Epoch 292/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.1334 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3852 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 293/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.1329 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3850 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 294/500\n",
            "2/2 [==============================] - 2s 753ms/step - loss: 0.1324 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3850 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 295/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.1318 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3847 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 296/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.1312 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3845 - val_sparse_categorical_accuracy: 0.5134\n",
            "Epoch 297/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.1306 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3841 - val_sparse_categorical_accuracy: 0.5140\n",
            "Epoch 298/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.1300 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3839 - val_sparse_categorical_accuracy: 0.5144\n",
            "Epoch 299/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.1295 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3836 - val_sparse_categorical_accuracy: 0.5144\n",
            "Epoch 300/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1290 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3828 - val_sparse_categorical_accuracy: 0.5148\n",
            "Epoch 301/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1285 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3825 - val_sparse_categorical_accuracy: 0.5150\n",
            "Epoch 302/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.1279 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3824 - val_sparse_categorical_accuracy: 0.5152\n",
            "Epoch 303/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.1274 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3817 - val_sparse_categorical_accuracy: 0.5154\n",
            "Epoch 304/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1269 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3813 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 305/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.1264 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3812 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 306/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1259 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3810 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 307/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.1254 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3807 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 308/500\n",
            "2/2 [==============================] - 1s 735ms/step - loss: 0.1249 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3803 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 309/500\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 0.1244 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3800 - val_sparse_categorical_accuracy: 0.5158\n",
            "Epoch 310/500\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 0.1240 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3795 - val_sparse_categorical_accuracy: 0.5160\n",
            "Epoch 311/500\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 0.1234 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3790 - val_sparse_categorical_accuracy: 0.5162\n",
            "Epoch 312/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.1230 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3783 - val_sparse_categorical_accuracy: 0.5164\n",
            "Epoch 313/500\n",
            "2/2 [==============================] - 1s 743ms/step - loss: 0.1225 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3783 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 314/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.1220 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3783 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 315/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1215 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3777 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 316/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.1210 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3773 - val_sparse_categorical_accuracy: 0.5180\n",
            "Epoch 317/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.1206 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3767 - val_sparse_categorical_accuracy: 0.5182\n",
            "Epoch 318/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1202 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3766 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 319/500\n",
            "2/2 [==============================] - 2s 772ms/step - loss: 0.1197 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3763 - val_sparse_categorical_accuracy: 0.5186\n",
            "Epoch 320/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.1192 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3758 - val_sparse_categorical_accuracy: 0.5190\n",
            "Epoch 321/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.1188 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3758 - val_sparse_categorical_accuracy: 0.5198\n",
            "Epoch 322/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.1183 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3753 - val_sparse_categorical_accuracy: 0.5204\n",
            "Epoch 323/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1179 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3750 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 324/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.1175 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3748 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 325/500\n",
            "2/2 [==============================] - 2s 752ms/step - loss: 0.1170 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3745 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 326/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1165 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3740 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 327/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.1161 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3738 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 328/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.1157 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3732 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 329/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1153 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3735 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 330/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.1148 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3731 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 331/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.1144 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3730 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 332/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.1140 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3728 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 333/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.1136 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3724 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 334/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1131 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3717 - val_sparse_categorical_accuracy: 0.5224\n",
            "Epoch 335/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.1127 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3712 - val_sparse_categorical_accuracy: 0.5224\n",
            "Epoch 336/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.1123 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3710 - val_sparse_categorical_accuracy: 0.5226\n",
            "Epoch 337/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.1119 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3705 - val_sparse_categorical_accuracy: 0.5228\n",
            "Epoch 338/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.1115 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3705 - val_sparse_categorical_accuracy: 0.5224\n",
            "Epoch 339/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.1111 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3702 - val_sparse_categorical_accuracy: 0.5224\n",
            "Epoch 340/500\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 0.1107 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3697 - val_sparse_categorical_accuracy: 0.5228\n",
            "Epoch 341/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.1102 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3695 - val_sparse_categorical_accuracy: 0.5232\n",
            "Epoch 342/500\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 0.1099 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3695 - val_sparse_categorical_accuracy: 0.5234\n",
            "Epoch 343/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1095 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3695 - val_sparse_categorical_accuracy: 0.5236\n",
            "Epoch 344/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.1091 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3690 - val_sparse_categorical_accuracy: 0.5236\n",
            "Epoch 345/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.1087 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3686 - val_sparse_categorical_accuracy: 0.5242\n",
            "Epoch 346/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.1083 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3689 - val_sparse_categorical_accuracy: 0.5252\n",
            "Epoch 347/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1079 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3689 - val_sparse_categorical_accuracy: 0.5246\n",
            "Epoch 348/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.1075 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3688 - val_sparse_categorical_accuracy: 0.5248\n",
            "Epoch 349/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.1072 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3682 - val_sparse_categorical_accuracy: 0.5248\n",
            "Epoch 350/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.1068 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3677 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 351/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.1065 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3676 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 352/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.1061 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3674 - val_sparse_categorical_accuracy: 0.5248\n",
            "Epoch 353/500\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 0.1057 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3671 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 354/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.1053 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3665 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 355/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.1050 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3664 - val_sparse_categorical_accuracy: 0.5256\n",
            "Epoch 356/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.1046 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3663 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 357/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.1043 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3656 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 358/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.1039 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3654 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 359/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.1035 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3653 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 360/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.1032 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3652 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 361/500\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.1028 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3650 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 362/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.1026 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3649 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 363/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1021 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3649 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 364/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.1018 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3651 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 365/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.1015 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3650 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 366/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.1012 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3648 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 367/500\n",
            "2/2 [==============================] - 2s 765ms/step - loss: 0.1008 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3648 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 368/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.1004 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3644 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 369/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.1001 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3641 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 2s 753ms/step - loss: 0.0998 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3638 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.0995 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3636 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 1s 740ms/step - loss: 0.0991 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3639 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.0988 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3635 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.0985 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3631 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.0981 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3628 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.0978 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3629 - val_sparse_categorical_accuracy: 0.5268\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0974 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3624 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 2s 752ms/step - loss: 0.0971 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3621 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 0.0968 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3618 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.0966 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3615 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.0962 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3613 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.0960 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3608 - val_sparse_categorical_accuracy: 0.5284\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 2s 753ms/step - loss: 0.0957 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3606 - val_sparse_categorical_accuracy: 0.5290\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.0954 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3606 - val_sparse_categorical_accuracy: 0.5288\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.0950 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3602 - val_sparse_categorical_accuracy: 0.5288\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.0947 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3601 - val_sparse_categorical_accuracy: 0.5284\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 1s 739ms/step - loss: 0.0944 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3597 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 1s 741ms/step - loss: 0.0941 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3592 - val_sparse_categorical_accuracy: 0.5290\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 1s 734ms/step - loss: 0.0938 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3590 - val_sparse_categorical_accuracy: 0.5288\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0935 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3589 - val_sparse_categorical_accuracy: 0.5292\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 1s 740ms/step - loss: 0.0932 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3589 - val_sparse_categorical_accuracy: 0.5292\n",
            "Epoch 392/500\n",
            "2/2 [==============================] - 1s 729ms/step - loss: 0.0929 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3589 - val_sparse_categorical_accuracy: 0.5290\n",
            "Epoch 393/500\n",
            "2/2 [==============================] - 1s 726ms/step - loss: 0.0926 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3586 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 394/500\n",
            "2/2 [==============================] - 1s 734ms/step - loss: 0.0923 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3582 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 395/500\n",
            "2/2 [==============================] - 1s 742ms/step - loss: 0.0920 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3580 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0918 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3577 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 1s 732ms/step - loss: 0.0915 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3572 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 2s 752ms/step - loss: 0.0912 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3571 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 1s 740ms/step - loss: 0.0909 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3573 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 400/500\n",
            "2/2 [==============================] - 1s 739ms/step - loss: 0.0906 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3570 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 401/500\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 0.0903 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3569 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0900 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3570 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 1s 739ms/step - loss: 0.0898 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3571 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0896 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3572 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.0893 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3573 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 2s 750ms/step - loss: 0.0890 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3570 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 1s 741ms/step - loss: 0.0887 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3569 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.0884 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3571 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 1s 741ms/step - loss: 0.0882 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3568 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 410/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0879 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3566 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 411/500\n",
            "2/2 [==============================] - 2s 769ms/step - loss: 0.0876 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3565 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 412/500\n",
            "2/2 [==============================] - 1s 745ms/step - loss: 0.0873 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3565 - val_sparse_categorical_accuracy: 0.5308\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.0871 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3563 - val_sparse_categorical_accuracy: 0.5308\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 2s 764ms/step - loss: 0.0868 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3560 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0866 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3564 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.0863 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3563 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.0860 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3558 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.0858 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3559 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0855 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3558 - val_sparse_categorical_accuracy: 0.5312\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0852 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3558 - val_sparse_categorical_accuracy: 0.5312\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.0850 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3559 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 1s 740ms/step - loss: 0.0847 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3556 - val_sparse_categorical_accuracy: 0.5312\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.0845 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3554 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0842 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3550 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 2s 757ms/step - loss: 0.0840 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3549 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.0837 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3545 - val_sparse_categorical_accuracy: 0.5322\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0835 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3541 - val_sparse_categorical_accuracy: 0.5324\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 1s 737ms/step - loss: 0.0833 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3543 - val_sparse_categorical_accuracy: 0.5324\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 1s 738ms/step - loss: 0.0830 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3542 - val_sparse_categorical_accuracy: 0.5322\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0828 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3540 - val_sparse_categorical_accuracy: 0.5324\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 2s 761ms/step - loss: 0.0825 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3538 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0823 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3536 - val_sparse_categorical_accuracy: 0.5326\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 1s 736ms/step - loss: 0.0820 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3534 - val_sparse_categorical_accuracy: 0.5326\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 1s 748ms/step - loss: 0.0818 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3533 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 1s 743ms/step - loss: 0.0816 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3532 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 0.0813 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3533 - val_sparse_categorical_accuracy: 0.5326\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 0.0811 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3530 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 2s 768ms/step - loss: 0.0808 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3527 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 1s 750ms/step - loss: 0.0806 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3522 - val_sparse_categorical_accuracy: 0.5330\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0804 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3523 - val_sparse_categorical_accuracy: 0.5332\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 1s 738ms/step - loss: 0.0802 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3520 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 0.0799 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3519 - val_sparse_categorical_accuracy: 0.5330\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 2s 753ms/step - loss: 0.0798 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3514 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0795 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3514 - val_sparse_categorical_accuracy: 0.5332\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 2s 760ms/step - loss: 0.0793 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3515 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 2s 754ms/step - loss: 0.0791 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3516 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.0789 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3513 - val_sparse_categorical_accuracy: 0.5336\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0786 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3512 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 1s 743ms/step - loss: 0.0784 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3514 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 2s 753ms/step - loss: 0.0782 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3513 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0780 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3514 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 2s 771ms/step - loss: 0.0778 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3513 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 2s 767ms/step - loss: 0.0776 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3510 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 2s 758ms/step - loss: 0.0774 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3511 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0771 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3509 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 1s 737ms/step - loss: 0.0769 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3506 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.0768 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3507 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 2s 763ms/step - loss: 0.0765 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3503 - val_sparse_categorical_accuracy: 0.5342\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.0763 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3505 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.0761 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3507 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.0759 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3506 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.0757 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3506 - val_sparse_categorical_accuracy: 0.5346\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.0755 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3505 - val_sparse_categorical_accuracy: 0.5346\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.0753 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3506 - val_sparse_categorical_accuracy: 0.5344\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.0751 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3505 - val_sparse_categorical_accuracy: 0.5346\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.0749 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3504 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 1s 741ms/step - loss: 0.0747 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3505 - val_sparse_categorical_accuracy: 0.5344\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0745 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3504 - val_sparse_categorical_accuracy: 0.5344\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0743 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3500 - val_sparse_categorical_accuracy: 0.5348\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 0.0741 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3498 - val_sparse_categorical_accuracy: 0.5346\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.0739 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3495 - val_sparse_categorical_accuracy: 0.5350\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 1s 745ms/step - loss: 0.0738 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3496 - val_sparse_categorical_accuracy: 0.5350\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 1s 738ms/step - loss: 0.0736 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3491 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 1s 747ms/step - loss: 0.0733 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3491 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 1s 739ms/step - loss: 0.0732 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3488 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.0729 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3486 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 0.0728 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3481 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 2s 755ms/step - loss: 0.0726 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3483 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0724 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3480 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 1s 739ms/step - loss: 0.0722 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3479 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 1s 748ms/step - loss: 0.0720 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3478 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0718 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3476 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 0.0716 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3476 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 2s 751ms/step - loss: 0.0714 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3476 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.0713 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3476 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 1s 735ms/step - loss: 0.0711 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3474 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 1s 746ms/step - loss: 0.0709 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3473 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 2s 762ms/step - loss: 0.0707 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3472 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 2s 759ms/step - loss: 0.0705 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3468 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 1s 742ms/step - loss: 0.0703 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3466 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 2s 766ms/step - loss: 0.0702 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3466 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 1s 745ms/step - loss: 0.0700 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3466 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 2s 756ms/step - loss: 0.0698 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3464 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 1s 744ms/step - loss: 0.0697 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3459 - val_sparse_categorical_accuracy: 0.5372\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 1s 737ms/step - loss: 0.0695 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3455 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 1s 738ms/step - loss: 0.0693 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3457 - val_sparse_categorical_accuracy: 0.5372\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 1s 739ms/step - loss: 0.0691 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3454 - val_sparse_categorical_accuracy: 0.5380\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.0690 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3453 - val_sparse_categorical_accuracy: 0.5378\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 1s 743ms/step - loss: 0.0688 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3452 - val_sparse_categorical_accuracy: 0.5380\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 1s 749ms/step - loss: 0.0686 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3453 - val_sparse_categorical_accuracy: 0.5376\n",
            "Model Evaluation\n",
            "313/313 [==============================] - 3s 10ms/step - loss: 1.3779 - sparse_categorical_accuracy: 0.5150\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.3778554201126099, 0.5149999856948853]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MS0fujEqh_m3",
        "colab_type": "text"
      },
      "source": [
        "Define the plot_learning_loss function and plot the loss and val_loss trends"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q5EsklC0DZYH",
        "colab_type": "code",
        "outputId": "ad004147-521e-4261-e3e3-f64617453f2b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        }
      },
      "source": [
        "def plot_learning_loss(history):\n",
        "    metrics_to_plot = {\n",
        "      \"loss\": history.history[\"loss\"],\n",
        "      \"val_loss\": history.history[\"val_loss\"]\n",
        "    }\n",
        "    pd.DataFrame(metrics_to_plot).plot(figsize=(8, 5))\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n",
        "    metrics_to_plot = {\n",
        "        \"sparse_categorical_accuracy\": \n",
        "                                history.history[\"sparse_categorical_accuracy\"], \n",
        "        \"val_sparse_categorical_accuracy\": \n",
        "                            history.history[\"val_sparse_categorical_accuracy\"]\n",
        "    }\n",
        "    pd.DataFrame(metrics_to_plot).plot(figsize=(8, 5))\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "plot_learning_loss(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OW4Kf4fgDfOs",
        "colab_type": "text"
      },
      "source": [
        "### L1-norm\n",
        "\n",
        "One possible way to solve the overitting issue is by using regularization methods. The two most common regularization methods in Deep Learning are the L1-norm regularization and the L2-norm regularization. Both These techniques are based on limiting the capacity of models, by adding a parameter norm penalty to the objective function $\\mathcal{J}$:\n",
        "$$\n",
        "\\hat{\\mathcal{J}}(\\theta,\\mathbf{X},\\mathbf{y}) = \\mathcal{J}(\\theta,\\mathbf{X},\\mathbf{y}) + \\alpha \\Omega(\\theta)\n",
        "$$\n",
        "where $\\alpha$ is a hyperparameter that weighs the relative contribution of the norm penalty $\\Omega$.\n",
        "Lets start by considering the L1-norm regularization where the regularization term is defined as:\n",
        "$$\n",
        " \\Omega(\\theta)=||\\mathbf{W}||_1=\\sum_i |\\mathbf{w}|\n",
        "$$\n",
        "Let's find the values for the $\\alpha$ parameters that allow removing remove the overfitting effect."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QzD8520dDgw3",
        "colab_type": "code",
        "outputId": "9a7f3f7f-f2bb-4b7c-8269-a0552d98c302",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "\n",
        "#In Keras is neccesary to add the regularizer by using the attribute kernel_regularizer\n",
        "#to each layer whose weights will be considered in the Omega function.\n",
        "#is it also possibile to consider the bias by using the attribute bias_regularizer \n",
        "#tf.keras.regularizers.l1(alpha) perfrom the L1-norm regularization\n",
        "\n",
        "#To complete: insert the model that you previously define that overfit the \n",
        "#trainnig data, and add the L1-norm regularization. Use the proper value for the\n",
        "#alpha hyper-parameter, that prevents the model from overfitting. In general, a\n",
        "#layer that exploits L1-norm regularization is defined as follow:\n",
        "l1_alpha = 0.003\n",
        "\n",
        "model.add(keras.layers.Dense(3000, activation=\"relu\",kernel_regularizer=tf.keras.regularizers.l1(l1_alpha)))\n",
        "model.add(keras.layers.Dense(1000, activation=\"relu\",kernel_regularizer=tf.keras.regularizers.l1(l1_alpha)))\n",
        "\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr),\n",
        "              metrics=[\"sparse_categorical_accuracy\"]) #To complete: learning rate\n",
        "\n",
        "history = model.fit(X_train_small, y_train_small, epochs=epochs, #To complete: number of epochs\n",
        "                    validation_data=(X_valid_scaled, y_valid))\n",
        "print(\"Model Evaluation\")\n",
        "model.evaluate(X_test_scaled, y_test)\n",
        "plot_learning_loss(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 316.9406 - sparse_categorical_accuracy: 0.1200 - val_loss: 316.9510 - val_sparse_categorical_accuracy: 0.1166\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 316.7590 - sparse_categorical_accuracy: 0.1800 - val_loss: 316.8220 - val_sparse_categorical_accuracy: 0.1432\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 316.5868 - sparse_categorical_accuracy: 0.2600 - val_loss: 316.6934 - val_sparse_categorical_accuracy: 0.1646\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 316.4203 - sparse_categorical_accuracy: 0.3200 - val_loss: 316.5692 - val_sparse_categorical_accuracy: 0.1854\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 316.2598 - sparse_categorical_accuracy: 0.3400 - val_loss: 316.4452 - val_sparse_categorical_accuracy: 0.2046\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 316.0998 - sparse_categorical_accuracy: 0.4000 - val_loss: 316.3231 - val_sparse_categorical_accuracy: 0.2198\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.9457 - sparse_categorical_accuracy: 0.4400 - val_loss: 316.2011 - val_sparse_categorical_accuracy: 0.2342\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.7946 - sparse_categorical_accuracy: 0.4800 - val_loss: 316.0807 - val_sparse_categorical_accuracy: 0.2436\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.6451 - sparse_categorical_accuracy: 0.5000 - val_loss: 315.9626 - val_sparse_categorical_accuracy: 0.2544\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.5013 - sparse_categorical_accuracy: 0.5400 - val_loss: 315.8450 - val_sparse_categorical_accuracy: 0.2636\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.3581 - sparse_categorical_accuracy: 0.5800 - val_loss: 315.7300 - val_sparse_categorical_accuracy: 0.2676\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.2191 - sparse_categorical_accuracy: 0.6400 - val_loss: 315.6153 - val_sparse_categorical_accuracy: 0.2754\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 315.0804 - sparse_categorical_accuracy: 0.6600 - val_loss: 315.5008 - val_sparse_categorical_accuracy: 0.2798\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.9444 - sparse_categorical_accuracy: 0.6800 - val_loss: 315.3882 - val_sparse_categorical_accuracy: 0.2850\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.8107 - sparse_categorical_accuracy: 0.7000 - val_loss: 315.2744 - val_sparse_categorical_accuracy: 0.2904\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.6789 - sparse_categorical_accuracy: 0.7000 - val_loss: 315.1630 - val_sparse_categorical_accuracy: 0.2944\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.5484 - sparse_categorical_accuracy: 0.7200 - val_loss: 315.0526 - val_sparse_categorical_accuracy: 0.2982\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.4185 - sparse_categorical_accuracy: 0.7200 - val_loss: 314.9422 - val_sparse_categorical_accuracy: 0.3000\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.2917 - sparse_categorical_accuracy: 0.7200 - val_loss: 314.8325 - val_sparse_categorical_accuracy: 0.3066\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.1644 - sparse_categorical_accuracy: 0.7400 - val_loss: 314.7231 - val_sparse_categorical_accuracy: 0.3122\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 314.0391 - sparse_categorical_accuracy: 0.7400 - val_loss: 314.6135 - val_sparse_categorical_accuracy: 0.3190\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.9159 - sparse_categorical_accuracy: 0.7400 - val_loss: 314.5037 - val_sparse_categorical_accuracy: 0.3266\n",
            "Epoch 23/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.7915 - sparse_categorical_accuracy: 0.7800 - val_loss: 314.3946 - val_sparse_categorical_accuracy: 0.3322\n",
            "Epoch 24/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.6690 - sparse_categorical_accuracy: 0.8200 - val_loss: 314.2877 - val_sparse_categorical_accuracy: 0.3366\n",
            "Epoch 25/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.5489 - sparse_categorical_accuracy: 0.8200 - val_loss: 314.1792 - val_sparse_categorical_accuracy: 0.3382\n",
            "Epoch 26/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.4291 - sparse_categorical_accuracy: 0.8200 - val_loss: 314.0735 - val_sparse_categorical_accuracy: 0.3418\n",
            "Epoch 27/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.3107 - sparse_categorical_accuracy: 0.8400 - val_loss: 313.9662 - val_sparse_categorical_accuracy: 0.3438\n",
            "Epoch 28/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.1928 - sparse_categorical_accuracy: 0.8400 - val_loss: 313.8608 - val_sparse_categorical_accuracy: 0.3472\n",
            "Epoch 29/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 313.0755 - sparse_categorical_accuracy: 0.8400 - val_loss: 313.7557 - val_sparse_categorical_accuracy: 0.3522\n",
            "Epoch 30/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.9584 - sparse_categorical_accuracy: 0.8400 - val_loss: 313.6496 - val_sparse_categorical_accuracy: 0.3578\n",
            "Epoch 31/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.8421 - sparse_categorical_accuracy: 0.8600 - val_loss: 313.5437 - val_sparse_categorical_accuracy: 0.3624\n",
            "Epoch 32/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.7263 - sparse_categorical_accuracy: 0.8600 - val_loss: 313.4384 - val_sparse_categorical_accuracy: 0.3652\n",
            "Epoch 33/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.6116 - sparse_categorical_accuracy: 0.8800 - val_loss: 313.3343 - val_sparse_categorical_accuracy: 0.3682\n",
            "Epoch 34/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.4974 - sparse_categorical_accuracy: 0.8800 - val_loss: 313.2307 - val_sparse_categorical_accuracy: 0.3720\n",
            "Epoch 35/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.3842 - sparse_categorical_accuracy: 0.9000 - val_loss: 313.1270 - val_sparse_categorical_accuracy: 0.3746\n",
            "Epoch 36/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.2716 - sparse_categorical_accuracy: 0.9000 - val_loss: 313.0233 - val_sparse_categorical_accuracy: 0.3786\n",
            "Epoch 37/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.1603 - sparse_categorical_accuracy: 0.9000 - val_loss: 312.9190 - val_sparse_categorical_accuracy: 0.3822\n",
            "Epoch 38/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 312.0478 - sparse_categorical_accuracy: 0.9000 - val_loss: 312.8156 - val_sparse_categorical_accuracy: 0.3838\n",
            "Epoch 39/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.9365 - sparse_categorical_accuracy: 0.9200 - val_loss: 312.7124 - val_sparse_categorical_accuracy: 0.3856\n",
            "Epoch 40/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.8260 - sparse_categorical_accuracy: 0.9200 - val_loss: 312.6089 - val_sparse_categorical_accuracy: 0.3882\n",
            "Epoch 41/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.7161 - sparse_categorical_accuracy: 0.9200 - val_loss: 312.5071 - val_sparse_categorical_accuracy: 0.3920\n",
            "Epoch 42/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.6068 - sparse_categorical_accuracy: 0.9400 - val_loss: 312.4047 - val_sparse_categorical_accuracy: 0.3954\n",
            "Epoch 43/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.4979 - sparse_categorical_accuracy: 0.9400 - val_loss: 312.3023 - val_sparse_categorical_accuracy: 0.3980\n",
            "Epoch 44/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.3881 - sparse_categorical_accuracy: 0.9400 - val_loss: 312.2000 - val_sparse_categorical_accuracy: 0.4004\n",
            "Epoch 45/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.2797 - sparse_categorical_accuracy: 0.9400 - val_loss: 312.1000 - val_sparse_categorical_accuracy: 0.4004\n",
            "Epoch 46/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.1714 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.9992 - val_sparse_categorical_accuracy: 0.4038\n",
            "Epoch 47/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 311.0636 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.8978 - val_sparse_categorical_accuracy: 0.4050\n",
            "Epoch 48/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.9571 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.7964 - val_sparse_categorical_accuracy: 0.4074\n",
            "Epoch 49/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.8500 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.6957 - val_sparse_categorical_accuracy: 0.4098\n",
            "Epoch 50/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.7435 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.5937 - val_sparse_categorical_accuracy: 0.4112\n",
            "Epoch 51/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.6366 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.4935 - val_sparse_categorical_accuracy: 0.4128\n",
            "Epoch 52/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.5309 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.3916 - val_sparse_categorical_accuracy: 0.4146\n",
            "Epoch 53/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.4249 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.2928 - val_sparse_categorical_accuracy: 0.4164\n",
            "Epoch 54/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.3189 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.1927 - val_sparse_categorical_accuracy: 0.4170\n",
            "Epoch 55/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.2145 - sparse_categorical_accuracy: 0.9400 - val_loss: 311.0909 - val_sparse_categorical_accuracy: 0.4178\n",
            "Epoch 56/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.1086 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.9915 - val_sparse_categorical_accuracy: 0.4174\n",
            "Epoch 57/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 310.0043 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.8904 - val_sparse_categorical_accuracy: 0.4184\n",
            "Epoch 58/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.8986 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.7915 - val_sparse_categorical_accuracy: 0.4188\n",
            "Epoch 59/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.7950 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.6928 - val_sparse_categorical_accuracy: 0.4192\n",
            "Epoch 60/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.6906 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.5927 - val_sparse_categorical_accuracy: 0.4208\n",
            "Epoch 61/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.5872 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.4922 - val_sparse_categorical_accuracy: 0.4230\n",
            "Epoch 62/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.4832 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.3932 - val_sparse_categorical_accuracy: 0.4234\n",
            "Epoch 63/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.3800 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.2947 - val_sparse_categorical_accuracy: 0.4250\n",
            "Epoch 64/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.2768 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.1960 - val_sparse_categorical_accuracy: 0.4272\n",
            "Epoch 65/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.1737 - sparse_categorical_accuracy: 0.9400 - val_loss: 310.0955 - val_sparse_categorical_accuracy: 0.4294\n",
            "Epoch 66/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 309.0706 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.9968 - val_sparse_categorical_accuracy: 0.4306\n",
            "Epoch 67/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.9681 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.8956 - val_sparse_categorical_accuracy: 0.4336\n",
            "Epoch 68/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.8649 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.7969 - val_sparse_categorical_accuracy: 0.4342\n",
            "Epoch 69/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.7634 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.6992 - val_sparse_categorical_accuracy: 0.4344\n",
            "Epoch 70/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.6606 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.5995 - val_sparse_categorical_accuracy: 0.4368\n",
            "Epoch 71/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.5587 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.4999 - val_sparse_categorical_accuracy: 0.4386\n",
            "Epoch 72/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.4571 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.4024 - val_sparse_categorical_accuracy: 0.4394\n",
            "Epoch 73/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.3547 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.3039 - val_sparse_categorical_accuracy: 0.4396\n",
            "Epoch 74/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.2535 - sparse_categorical_accuracy: 0.9400 - val_loss: 309.2053 - val_sparse_categorical_accuracy: 0.4412\n",
            "Epoch 75/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.1519 - sparse_categorical_accuracy: 0.9600 - val_loss: 309.1070 - val_sparse_categorical_accuracy: 0.4418\n",
            "Epoch 76/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 308.0510 - sparse_categorical_accuracy: 0.9600 - val_loss: 309.0096 - val_sparse_categorical_accuracy: 0.4432\n",
            "Epoch 77/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.9500 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.9108 - val_sparse_categorical_accuracy: 0.4444\n",
            "Epoch 78/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.8492 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.8135 - val_sparse_categorical_accuracy: 0.4454\n",
            "Epoch 79/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.7479 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.7142 - val_sparse_categorical_accuracy: 0.4478\n",
            "Epoch 80/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.6469 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.6165 - val_sparse_categorical_accuracy: 0.4482\n",
            "Epoch 81/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.5468 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.5189 - val_sparse_categorical_accuracy: 0.4488\n",
            "Epoch 82/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.4460 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.4203 - val_sparse_categorical_accuracy: 0.4534\n",
            "Epoch 83/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.3452 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.3228 - val_sparse_categorical_accuracy: 0.4532\n",
            "Epoch 84/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.2454 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.2248 - val_sparse_categorical_accuracy: 0.4552\n",
            "Epoch 85/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.1456 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.1281 - val_sparse_categorical_accuracy: 0.4554\n",
            "Epoch 86/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 307.0457 - sparse_categorical_accuracy: 0.9600 - val_loss: 308.0295 - val_sparse_categorical_accuracy: 0.4574\n",
            "Epoch 87/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.9454 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.9313 - val_sparse_categorical_accuracy: 0.4586\n",
            "Epoch 88/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.8456 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.8344 - val_sparse_categorical_accuracy: 0.4578\n",
            "Epoch 89/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.7459 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.7368 - val_sparse_categorical_accuracy: 0.4586\n",
            "Epoch 90/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.6463 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.6383 - val_sparse_categorical_accuracy: 0.4608\n",
            "Epoch 91/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.5469 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.5411 - val_sparse_categorical_accuracy: 0.4622\n",
            "Epoch 92/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.4476 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.4445 - val_sparse_categorical_accuracy: 0.4636\n",
            "Epoch 93/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.3482 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.3470 - val_sparse_categorical_accuracy: 0.4644\n",
            "Epoch 94/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.2487 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.2490 - val_sparse_categorical_accuracy: 0.4664\n",
            "Epoch 95/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.1497 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.1516 - val_sparse_categorical_accuracy: 0.4670\n",
            "Epoch 96/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 306.0508 - sparse_categorical_accuracy: 0.9600 - val_loss: 307.0545 - val_sparse_categorical_accuracy: 0.4662\n",
            "Epoch 97/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.9515 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.9576 - val_sparse_categorical_accuracy: 0.4682\n",
            "Epoch 98/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.8529 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.8601 - val_sparse_categorical_accuracy: 0.4690\n",
            "Epoch 99/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.7540 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.7636 - val_sparse_categorical_accuracy: 0.4694\n",
            "Epoch 100/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.6555 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.6667 - val_sparse_categorical_accuracy: 0.4700\n",
            "Epoch 101/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.5566 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.5699 - val_sparse_categorical_accuracy: 0.4716\n",
            "Epoch 102/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.4581 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.4737 - val_sparse_categorical_accuracy: 0.4712\n",
            "Epoch 103/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.3595 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.3768 - val_sparse_categorical_accuracy: 0.4714\n",
            "Epoch 104/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.2611 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.2805 - val_sparse_categorical_accuracy: 0.4712\n",
            "Epoch 105/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.1630 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.1840 - val_sparse_categorical_accuracy: 0.4716\n",
            "Epoch 106/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 305.0647 - sparse_categorical_accuracy: 0.9600 - val_loss: 306.0863 - val_sparse_categorical_accuracy: 0.4736\n",
            "Epoch 107/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.9666 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.9899 - val_sparse_categorical_accuracy: 0.4744\n",
            "Epoch 108/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.8688 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.8939 - val_sparse_categorical_accuracy: 0.4744\n",
            "Epoch 109/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.7709 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.7980 - val_sparse_categorical_accuracy: 0.4750\n",
            "Epoch 110/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.6730 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.7021 - val_sparse_categorical_accuracy: 0.4764\n",
            "Epoch 111/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.5748 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.6043 - val_sparse_categorical_accuracy: 0.4772\n",
            "Epoch 112/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.4774 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.5083 - val_sparse_categorical_accuracy: 0.4768\n",
            "Epoch 113/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.3791 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.4113 - val_sparse_categorical_accuracy: 0.4786\n",
            "Epoch 114/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.2816 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.3149 - val_sparse_categorical_accuracy: 0.4792\n",
            "Epoch 115/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.1838 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.2182 - val_sparse_categorical_accuracy: 0.4798\n",
            "Epoch 116/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.0862 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.1222 - val_sparse_categorical_accuracy: 0.4790\n",
            "Epoch 117/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.9888 - sparse_categorical_accuracy: 0.9600 - val_loss: 305.0267 - val_sparse_categorical_accuracy: 0.4790\n",
            "Epoch 118/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.8915 - sparse_categorical_accuracy: 0.9600 - val_loss: 304.9307 - val_sparse_categorical_accuracy: 0.4792\n",
            "Epoch 119/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.7940 - sparse_categorical_accuracy: 0.9600 - val_loss: 304.8346 - val_sparse_categorical_accuracy: 0.4788\n",
            "Epoch 120/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.6965 - sparse_categorical_accuracy: 0.9600 - val_loss: 304.7379 - val_sparse_categorical_accuracy: 0.4808\n",
            "Epoch 121/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.5990 - sparse_categorical_accuracy: 0.9800 - val_loss: 304.6411 - val_sparse_categorical_accuracy: 0.4810\n",
            "Epoch 122/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.5017 - sparse_categorical_accuracy: 0.9800 - val_loss: 304.5454 - val_sparse_categorical_accuracy: 0.4818\n",
            "Epoch 123/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.4045 - sparse_categorical_accuracy: 1.0000 - val_loss: 304.4496 - val_sparse_categorical_accuracy: 0.4818\n",
            "Epoch 124/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.3075 - sparse_categorical_accuracy: 1.0000 - val_loss: 304.3541 - val_sparse_categorical_accuracy: 0.4824\n",
            "Epoch 125/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.2104 - sparse_categorical_accuracy: 1.0000 - val_loss: 304.2581 - val_sparse_categorical_accuracy: 0.4828\n",
            "Epoch 126/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.1136 - sparse_categorical_accuracy: 1.0000 - val_loss: 304.1613 - val_sparse_categorical_accuracy: 0.4844\n",
            "Epoch 127/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.0165 - sparse_categorical_accuracy: 1.0000 - val_loss: 304.0667 - val_sparse_categorical_accuracy: 0.4844\n",
            "Epoch 128/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.9198 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.9697 - val_sparse_categorical_accuracy: 0.4854\n",
            "Epoch 129/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.8227 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.8740 - val_sparse_categorical_accuracy: 0.4860\n",
            "Epoch 130/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.7259 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.7779 - val_sparse_categorical_accuracy: 0.4878\n",
            "Epoch 131/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.6292 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.6815 - val_sparse_categorical_accuracy: 0.4890\n",
            "Epoch 132/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.5326 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.5859 - val_sparse_categorical_accuracy: 0.4896\n",
            "Epoch 133/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.4356 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.4893 - val_sparse_categorical_accuracy: 0.4910\n",
            "Epoch 134/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.3390 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.3930 - val_sparse_categorical_accuracy: 0.4920\n",
            "Epoch 135/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.2425 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.2979 - val_sparse_categorical_accuracy: 0.4922\n",
            "Epoch 136/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.1460 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.2021 - val_sparse_categorical_accuracy: 0.4922\n",
            "Epoch 137/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.0495 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.1068 - val_sparse_categorical_accuracy: 0.4926\n",
            "Epoch 138/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.9533 - sparse_categorical_accuracy: 1.0000 - val_loss: 303.0121 - val_sparse_categorical_accuracy: 0.4926\n",
            "Epoch 139/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.8568 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.9160 - val_sparse_categorical_accuracy: 0.4944\n",
            "Epoch 140/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.7603 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.8205 - val_sparse_categorical_accuracy: 0.4950\n",
            "Epoch 141/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.6638 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.7253 - val_sparse_categorical_accuracy: 0.4956\n",
            "Epoch 142/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.5676 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.6298 - val_sparse_categorical_accuracy: 0.4960\n",
            "Epoch 143/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.4716 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.5341 - val_sparse_categorical_accuracy: 0.4972\n",
            "Epoch 144/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.3752 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.4387 - val_sparse_categorical_accuracy: 0.4972\n",
            "Epoch 145/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.2790 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.3429 - val_sparse_categorical_accuracy: 0.4976\n",
            "Epoch 146/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.1831 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.2479 - val_sparse_categorical_accuracy: 0.4980\n",
            "Epoch 147/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.0872 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.1526 - val_sparse_categorical_accuracy: 0.4984\n",
            "Epoch 148/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.9912 - sparse_categorical_accuracy: 1.0000 - val_loss: 302.0575 - val_sparse_categorical_accuracy: 0.4990\n",
            "Epoch 149/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.8952 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.9622 - val_sparse_categorical_accuracy: 0.4990\n",
            "Epoch 150/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.7992 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.8672 - val_sparse_categorical_accuracy: 0.4994\n",
            "Epoch 151/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.7033 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.7712 - val_sparse_categorical_accuracy: 0.4996\n",
            "Epoch 152/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.6075 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.6769 - val_sparse_categorical_accuracy: 0.4994\n",
            "Epoch 153/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.5116 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.5810 - val_sparse_categorical_accuracy: 0.5002\n",
            "Epoch 154/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.4160 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.4852 - val_sparse_categorical_accuracy: 0.5016\n",
            "Epoch 155/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.3203 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.3899 - val_sparse_categorical_accuracy: 0.5020\n",
            "Epoch 156/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.2244 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.2953 - val_sparse_categorical_accuracy: 0.5032\n",
            "Epoch 157/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.1284 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.2007 - val_sparse_categorical_accuracy: 0.5026\n",
            "Epoch 158/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.0328 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.1053 - val_sparse_categorical_accuracy: 0.5034\n",
            "Epoch 159/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.9372 - sparse_categorical_accuracy: 1.0000 - val_loss: 301.0106 - val_sparse_categorical_accuracy: 0.5046\n",
            "Epoch 160/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.8418 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.9164 - val_sparse_categorical_accuracy: 0.5040\n",
            "Epoch 161/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.7464 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.8218 - val_sparse_categorical_accuracy: 0.5042\n",
            "Epoch 162/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.6508 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.7276 - val_sparse_categorical_accuracy: 0.5046\n",
            "Epoch 163/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.5554 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.6324 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 164/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.4599 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.5380 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 165/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.3647 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.4431 - val_sparse_categorical_accuracy: 0.5056\n",
            "Epoch 166/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.2692 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.3484 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 167/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.1740 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.2537 - val_sparse_categorical_accuracy: 0.5056\n",
            "Epoch 168/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.0788 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.1596 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 169/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.9836 - sparse_categorical_accuracy: 1.0000 - val_loss: 300.0657 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 170/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.8884 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.9719 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 171/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.7932 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.8771 - val_sparse_categorical_accuracy: 0.5064\n",
            "Epoch 172/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.6981 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.7820 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 173/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.6029 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.6879 - val_sparse_categorical_accuracy: 0.5064\n",
            "Epoch 174/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.5079 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.5927 - val_sparse_categorical_accuracy: 0.5064\n",
            "Epoch 175/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.4127 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.4978 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 176/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.3175 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.4036 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 177/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.2226 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.3090 - val_sparse_categorical_accuracy: 0.5072\n",
            "Epoch 178/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.1278 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.2154 - val_sparse_categorical_accuracy: 0.5064\n",
            "Epoch 179/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.0326 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.1206 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 180/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.9378 - sparse_categorical_accuracy: 1.0000 - val_loss: 299.0273 - val_sparse_categorical_accuracy: 0.5072\n",
            "Epoch 181/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.8429 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.9334 - val_sparse_categorical_accuracy: 0.5074\n",
            "Epoch 182/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.7482 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.8387 - val_sparse_categorical_accuracy: 0.5072\n",
            "Epoch 183/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.6534 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.7434 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 184/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.5586 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.6495 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 185/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.4637 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.5556 - val_sparse_categorical_accuracy: 0.5078\n",
            "Epoch 186/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.3692 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.4617 - val_sparse_categorical_accuracy: 0.5088\n",
            "Epoch 187/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.2744 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.3670 - val_sparse_categorical_accuracy: 0.5086\n",
            "Epoch 188/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.1796 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.2727 - val_sparse_categorical_accuracy: 0.5082\n",
            "Epoch 189/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.0851 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.1787 - val_sparse_categorical_accuracy: 0.5090\n",
            "Epoch 190/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.9905 - sparse_categorical_accuracy: 1.0000 - val_loss: 298.0849 - val_sparse_categorical_accuracy: 0.5094\n",
            "Epoch 191/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.8959 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.9904 - val_sparse_categorical_accuracy: 0.5096\n",
            "Epoch 192/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.8013 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.8961 - val_sparse_categorical_accuracy: 0.5094\n",
            "Epoch 193/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.7068 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.8023 - val_sparse_categorical_accuracy: 0.5100\n",
            "Epoch 194/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.6122 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.7087 - val_sparse_categorical_accuracy: 0.5096\n",
            "Epoch 195/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.5178 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.6148 - val_sparse_categorical_accuracy: 0.5098\n",
            "Epoch 196/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.4232 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.5206 - val_sparse_categorical_accuracy: 0.5098\n",
            "Epoch 197/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.3287 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.4265 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 198/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.2343 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.3330 - val_sparse_categorical_accuracy: 0.5100\n",
            "Epoch 199/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.1397 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.2390 - val_sparse_categorical_accuracy: 0.5098\n",
            "Epoch 200/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.0455 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.1454 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 201/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.9513 - sparse_categorical_accuracy: 1.0000 - val_loss: 297.0512 - val_sparse_categorical_accuracy: 0.5122\n",
            "Epoch 202/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.8568 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.9573 - val_sparse_categorical_accuracy: 0.5122\n",
            "Epoch 203/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.7624 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.8635 - val_sparse_categorical_accuracy: 0.5120\n",
            "Epoch 204/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.6683 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.7699 - val_sparse_categorical_accuracy: 0.5116\n",
            "Epoch 205/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.5741 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.6755 - val_sparse_categorical_accuracy: 0.5118\n",
            "Epoch 206/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.4800 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.5820 - val_sparse_categorical_accuracy: 0.5114\n",
            "Epoch 207/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.3859 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.4879 - val_sparse_categorical_accuracy: 0.5130\n",
            "Epoch 208/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.2917 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.3940 - val_sparse_categorical_accuracy: 0.5124\n",
            "Epoch 209/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.1974 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.3007 - val_sparse_categorical_accuracy: 0.5128\n",
            "Epoch 210/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.1034 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.2064 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 211/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.0092 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.1125 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 212/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.9153 - sparse_categorical_accuracy: 1.0000 - val_loss: 296.0189 - val_sparse_categorical_accuracy: 0.5140\n",
            "Epoch 213/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.8213 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.9254 - val_sparse_categorical_accuracy: 0.5146\n",
            "Epoch 214/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.7272 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.8319 - val_sparse_categorical_accuracy: 0.5150\n",
            "Epoch 215/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.6332 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.7386 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 216/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.5392 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.6454 - val_sparse_categorical_accuracy: 0.5166\n",
            "Epoch 217/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.4453 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.5512 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 218/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.3514 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.4574 - val_sparse_categorical_accuracy: 0.5162\n",
            "Epoch 219/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.2576 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.3646 - val_sparse_categorical_accuracy: 0.5164\n",
            "Epoch 220/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.1637 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.2710 - val_sparse_categorical_accuracy: 0.5162\n",
            "Epoch 221/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.0699 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.1775 - val_sparse_categorical_accuracy: 0.5164\n",
            "Epoch 222/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.9762 - sparse_categorical_accuracy: 1.0000 - val_loss: 295.0833 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 223/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.8823 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.9894 - val_sparse_categorical_accuracy: 0.5168\n",
            "Epoch 224/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.7884 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.8961 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 225/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.6946 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.8025 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 226/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.6010 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.7092 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 227/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.5072 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.6162 - val_sparse_categorical_accuracy: 0.5182\n",
            "Epoch 228/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.4136 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.5226 - val_sparse_categorical_accuracy: 0.5184\n",
            "Epoch 229/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.3199 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.4291 - val_sparse_categorical_accuracy: 0.5184\n",
            "Epoch 230/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.2263 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.3357 - val_sparse_categorical_accuracy: 0.5184\n",
            "Epoch 231/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.1326 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.2420 - val_sparse_categorical_accuracy: 0.5188\n",
            "Epoch 232/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.0391 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.1487 - val_sparse_categorical_accuracy: 0.5184\n",
            "Epoch 233/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.9454 - sparse_categorical_accuracy: 1.0000 - val_loss: 294.0558 - val_sparse_categorical_accuracy: 0.5188\n",
            "Epoch 234/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.8520 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.9621 - val_sparse_categorical_accuracy: 0.5186\n",
            "Epoch 235/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.7585 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.8682 - val_sparse_categorical_accuracy: 0.5190\n",
            "Epoch 236/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.6649 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.7754 - val_sparse_categorical_accuracy: 0.5188\n",
            "Epoch 237/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.5714 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.6823 - val_sparse_categorical_accuracy: 0.5190\n",
            "Epoch 238/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.4778 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.5891 - val_sparse_categorical_accuracy: 0.5194\n",
            "Epoch 239/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.3846 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.4959 - val_sparse_categorical_accuracy: 0.5186\n",
            "Epoch 240/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.2910 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.4028 - val_sparse_categorical_accuracy: 0.5194\n",
            "Epoch 241/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.1978 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.3096 - val_sparse_categorical_accuracy: 0.5202\n",
            "Epoch 242/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.1042 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.2163 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 243/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.0109 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.1238 - val_sparse_categorical_accuracy: 0.5200\n",
            "Epoch 244/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.9175 - sparse_categorical_accuracy: 1.0000 - val_loss: 293.0308 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 245/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.8242 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.9378 - val_sparse_categorical_accuracy: 0.5204\n",
            "Epoch 246/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.7310 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.8448 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 247/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.6376 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.7517 - val_sparse_categorical_accuracy: 0.5204\n",
            "Epoch 248/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.5443 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.6585 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 249/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.4510 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.5657 - val_sparse_categorical_accuracy: 0.5214\n",
            "Epoch 250/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.3579 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.4729 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 251/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.2648 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.3798 - val_sparse_categorical_accuracy: 0.5220\n",
            "Epoch 252/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.1717 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.2864 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 253/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.0784 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.1939 - val_sparse_categorical_accuracy: 0.5224\n",
            "Epoch 254/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.9854 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.1013 - val_sparse_categorical_accuracy: 0.5226\n",
            "Epoch 255/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.8922 - sparse_categorical_accuracy: 1.0000 - val_loss: 292.0081 - val_sparse_categorical_accuracy: 0.5226\n",
            "Epoch 256/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.7992 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.9157 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 257/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.7061 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.8229 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 258/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.6132 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.7301 - val_sparse_categorical_accuracy: 0.5226\n",
            "Epoch 259/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.5201 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.6372 - val_sparse_categorical_accuracy: 0.5228\n",
            "Epoch 260/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.4271 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.5443 - val_sparse_categorical_accuracy: 0.5220\n",
            "Epoch 261/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.3340 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.4518 - val_sparse_categorical_accuracy: 0.5228\n",
            "Epoch 262/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.2410 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.3588 - val_sparse_categorical_accuracy: 0.5232\n",
            "Epoch 263/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.1480 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.2661 - val_sparse_categorical_accuracy: 0.5232\n",
            "Epoch 264/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.0552 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.1740 - val_sparse_categorical_accuracy: 0.5236\n",
            "Epoch 265/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.9623 - sparse_categorical_accuracy: 1.0000 - val_loss: 291.0807 - val_sparse_categorical_accuracy: 0.5244\n",
            "Epoch 266/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.8694 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.9882 - val_sparse_categorical_accuracy: 0.5242\n",
            "Epoch 267/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.7767 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.8950 - val_sparse_categorical_accuracy: 0.5248\n",
            "Epoch 268/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.6837 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.8026 - val_sparse_categorical_accuracy: 0.5252\n",
            "Epoch 269/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.5910 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.7099 - val_sparse_categorical_accuracy: 0.5256\n",
            "Epoch 270/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.4982 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.6168 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 271/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.4055 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.5245 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 272/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.3127 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.4320 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 273/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.2198 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.3395 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 274/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.1272 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.2472 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 275/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.0344 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.1548 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 276/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.9417 - sparse_categorical_accuracy: 1.0000 - val_loss: 290.0627 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 277/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.8492 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.9706 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 278/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.7565 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.8785 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 279/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.6639 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.7860 - val_sparse_categorical_accuracy: 0.5268\n",
            "Epoch 280/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.5713 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.6938 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 281/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.4788 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.6019 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 282/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.3862 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.5091 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 283/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.2936 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.4163 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 284/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.2010 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.3238 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 285/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.1086 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.2320 - val_sparse_categorical_accuracy: 0.5274\n",
            "Epoch 286/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.0161 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.1394 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 287/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.9235 - sparse_categorical_accuracy: 1.0000 - val_loss: 289.0474 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 288/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.8312 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.9547 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 289/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.7387 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.8625 - val_sparse_categorical_accuracy: 0.5280\n",
            "Epoch 290/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.6464 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.7705 - val_sparse_categorical_accuracy: 0.5274\n",
            "Epoch 291/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.5538 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.6782 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 292/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.4615 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.5860 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 293/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.3690 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.4938 - val_sparse_categorical_accuracy: 0.5280\n",
            "Epoch 294/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.2768 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.4019 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 295/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.1845 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.3098 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 296/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.0921 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.2177 - val_sparse_categorical_accuracy: 0.5280\n",
            "Epoch 297/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.9998 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.1256 - val_sparse_categorical_accuracy: 0.5282\n",
            "Epoch 298/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.9074 - sparse_categorical_accuracy: 1.0000 - val_loss: 288.0334 - val_sparse_categorical_accuracy: 0.5284\n",
            "Epoch 299/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.8151 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.9415 - val_sparse_categorical_accuracy: 0.5280\n",
            "Epoch 300/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.7230 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.8489 - val_sparse_categorical_accuracy: 0.5292\n",
            "Epoch 301/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.6308 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.7567 - val_sparse_categorical_accuracy: 0.5290\n",
            "Epoch 302/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.5385 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.6650 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 303/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.4464 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.5726 - val_sparse_categorical_accuracy: 0.5298\n",
            "Epoch 304/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.3542 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.4805 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 305/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.2621 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.3887 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 306/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.1700 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.2969 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 307/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.0779 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.2050 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 308/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.9858 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.1130 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 309/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.8936 - sparse_categorical_accuracy: 1.0000 - val_loss: 287.0211 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 310/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.8017 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.9289 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 311/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.7096 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.8369 - val_sparse_categorical_accuracy: 0.5300\n",
            "Epoch 312/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.6176 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.7448 - val_sparse_categorical_accuracy: 0.5300\n",
            "Epoch 313/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.5256 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.6532 - val_sparse_categorical_accuracy: 0.5300\n",
            "Epoch 314/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.4336 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.5616 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 315/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.3416 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.4695 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 316/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.2497 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.3776 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 317/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.1578 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.2855 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 318/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.0659 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.1938 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 319/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.9740 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.1022 - val_sparse_categorical_accuracy: 0.5300\n",
            "Epoch 320/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.8821 - sparse_categorical_accuracy: 1.0000 - val_loss: 286.0103 - val_sparse_categorical_accuracy: 0.5308\n",
            "Epoch 321/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.7903 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.9188 - val_sparse_categorical_accuracy: 0.5312\n",
            "Epoch 322/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.6985 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.8269 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 323/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.6067 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.7351 - val_sparse_categorical_accuracy: 0.5308\n",
            "Epoch 324/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.5149 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.6435 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 325/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.4231 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.5518 - val_sparse_categorical_accuracy: 0.5320\n",
            "Epoch 326/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.3312 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.4600 - val_sparse_categorical_accuracy: 0.5322\n",
            "Epoch 327/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.2395 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.3684 - val_sparse_categorical_accuracy: 0.5326\n",
            "Epoch 328/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.1478 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.2765 - val_sparse_categorical_accuracy: 0.5328\n",
            "Epoch 329/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.0562 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.1854 - val_sparse_categorical_accuracy: 0.5324\n",
            "Epoch 330/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.9644 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.0937 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 331/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.8727 - sparse_categorical_accuracy: 1.0000 - val_loss: 285.0022 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 332/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.7811 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.9107 - val_sparse_categorical_accuracy: 0.5332\n",
            "Epoch 333/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.6895 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.8191 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 334/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.5978 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.7271 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 335/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.5062 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.6353 - val_sparse_categorical_accuracy: 0.5336\n",
            "Epoch 336/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.4146 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.5440 - val_sparse_categorical_accuracy: 0.5336\n",
            "Epoch 337/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.3230 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.4522 - val_sparse_categorical_accuracy: 0.5336\n",
            "Epoch 338/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.2315 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.3611 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 339/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.1399 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.2697 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 340/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.0484 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.1779 - val_sparse_categorical_accuracy: 0.5336\n",
            "Epoch 341/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.9568 - sparse_categorical_accuracy: 1.0000 - val_loss: 284.0866 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 342/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.8654 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.9954 - val_sparse_categorical_accuracy: 0.5344\n",
            "Epoch 343/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.7739 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.9043 - val_sparse_categorical_accuracy: 0.5342\n",
            "Epoch 344/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.6824 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.8127 - val_sparse_categorical_accuracy: 0.5344\n",
            "Epoch 345/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.5909 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.7213 - val_sparse_categorical_accuracy: 0.5346\n",
            "Epoch 346/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.4996 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.6303 - val_sparse_categorical_accuracy: 0.5348\n",
            "Epoch 347/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.4082 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.5393 - val_sparse_categorical_accuracy: 0.5346\n",
            "Epoch 348/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.3168 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.4482 - val_sparse_categorical_accuracy: 0.5352\n",
            "Epoch 349/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.2255 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.3564 - val_sparse_categorical_accuracy: 0.5350\n",
            "Epoch 350/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.1341 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.2650 - val_sparse_categorical_accuracy: 0.5352\n",
            "Epoch 351/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.0428 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.1738 - val_sparse_categorical_accuracy: 0.5354\n",
            "Epoch 352/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.9514 - sparse_categorical_accuracy: 1.0000 - val_loss: 283.0826 - val_sparse_categorical_accuracy: 0.5352\n",
            "Epoch 353/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.8601 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.9914 - val_sparse_categorical_accuracy: 0.5352\n",
            "Epoch 354/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.7689 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.8998 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 355/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.6776 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.8088 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 356/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.5862 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.7178 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 357/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.4951 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.6261 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 358/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.4038 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.5349 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 359/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.3126 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.4439 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 360/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.2214 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.3529 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 361/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.1302 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.2619 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 362/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.0392 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.1710 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 363/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.9479 - sparse_categorical_accuracy: 1.0000 - val_loss: 282.0800 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 364/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.8568 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.9894 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 365/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.7658 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.8985 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 366/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.6747 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.8075 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 367/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.5836 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.7166 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 368/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 280.4925 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.6255 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 369/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.4015 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.5345 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.3105 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.4433 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.2194 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.3525 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.1284 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.2620 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.0374 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.1710 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.9465 - sparse_categorical_accuracy: 1.0000 - val_loss: 281.0799 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.8555 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.9889 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.7646 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.8983 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 279.6736 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.8073 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 279.5827 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.7162 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.4919 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.6253 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.4011 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.5345 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.3102 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.4438 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.2194 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.3528 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.1286 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.2619 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.0378 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.1714 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.9470 - sparse_categorical_accuracy: 1.0000 - val_loss: 280.0806 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.8563 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.9900 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.7654 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.8990 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.6747 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.8080 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.5840 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.7175 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.4933 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.6269 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.4026 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.5363 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 392/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.3119 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.4460 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 393/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.2213 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.3554 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 394/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.1307 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.2646 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 395/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.0400 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.1739 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 277.9494 - sparse_categorical_accuracy: 1.0000 - val_loss: 279.0832 - val_sparse_categorical_accuracy: 0.5372\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.8589 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.9924 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.7683 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.9020 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.6777 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.8119 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 400/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.5872 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.7212 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 401/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.4966 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.6308 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.4061 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.5405 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.3156 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.4503 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.2252 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.3602 - val_sparse_categorical_accuracy: 0.5372\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.1347 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.2701 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.0442 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.1795 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.9538 - sparse_categorical_accuracy: 1.0000 - val_loss: 278.0891 - val_sparse_categorical_accuracy: 0.5378\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.8634 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.9991 - val_sparse_categorical_accuracy: 0.5378\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.7730 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.9087 - val_sparse_categorical_accuracy: 0.5382\n",
            "Epoch 410/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.6826 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.8183 - val_sparse_categorical_accuracy: 0.5384\n",
            "Epoch 411/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.5923 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.7281 - val_sparse_categorical_accuracy: 0.5384\n",
            "Epoch 412/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.5019 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.6379 - val_sparse_categorical_accuracy: 0.5392\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.4116 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.5476 - val_sparse_categorical_accuracy: 0.5384\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.3213 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.4572 - val_sparse_categorical_accuracy: 0.5388\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.2310 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.3677 - val_sparse_categorical_accuracy: 0.5384\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.1407 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.2773 - val_sparse_categorical_accuracy: 0.5384\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.0504 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.1869 - val_sparse_categorical_accuracy: 0.5392\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.9601 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.0969 - val_sparse_categorical_accuracy: 0.5382\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.8699 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.0068 - val_sparse_categorical_accuracy: 0.5386\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.7797 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.9168 - val_sparse_categorical_accuracy: 0.5386\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.6895 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.8268 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.5992 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.7365 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.5091 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.6464 - val_sparse_categorical_accuracy: 0.5396\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.4189 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.5560 - val_sparse_categorical_accuracy: 0.5396\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.3288 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.4659 - val_sparse_categorical_accuracy: 0.5400\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.2387 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.3757 - val_sparse_categorical_accuracy: 0.5398\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.1486 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.2853 - val_sparse_categorical_accuracy: 0.5402\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.0585 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.1956 - val_sparse_categorical_accuracy: 0.5398\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.9684 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.1056 - val_sparse_categorical_accuracy: 0.5402\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.8783 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.0155 - val_sparse_categorical_accuracy: 0.5404\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.7883 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.9254 - val_sparse_categorical_accuracy: 0.5406\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.6982 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.8355 - val_sparse_categorical_accuracy: 0.5410\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.6082 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.7454 - val_sparse_categorical_accuracy: 0.5412\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.5183 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.6556 - val_sparse_categorical_accuracy: 0.5414\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.4283 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.5656 - val_sparse_categorical_accuracy: 0.5410\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.3383 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.4759 - val_sparse_categorical_accuracy: 0.5410\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.2483 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.3858 - val_sparse_categorical_accuracy: 0.5412\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.1584 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.2958 - val_sparse_categorical_accuracy: 0.5416\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.0685 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.2054 - val_sparse_categorical_accuracy: 0.5414\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.9785 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.1160 - val_sparse_categorical_accuracy: 0.5416\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.8886 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.0260 - val_sparse_categorical_accuracy: 0.5414\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.7987 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.9360 - val_sparse_categorical_accuracy: 0.5418\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.7090 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.8458 - val_sparse_categorical_accuracy: 0.5420\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.6191 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.7562 - val_sparse_categorical_accuracy: 0.5420\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.5294 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.6667 - val_sparse_categorical_accuracy: 0.5418\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.4395 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.5771 - val_sparse_categorical_accuracy: 0.5422\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.3497 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.4872 - val_sparse_categorical_accuracy: 0.5422\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.2599 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.3975 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.1702 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.3081 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.0804 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.2184 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.9907 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.1289 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 272.9010 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.0393 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.8113 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.9493 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.7216 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.8600 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.6319 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.7702 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.5422 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.6805 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.4527 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.5910 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.3630 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.5011 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.2734 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.4120 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.1839 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.3228 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.0943 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.2331 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.0047 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.1438 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.9152 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.0543 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.8257 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.9650 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.7361 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.8755 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.6466 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.7860 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.5572 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.6968 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.4677 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.6074 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.3783 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.5176 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.2888 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.4282 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.1995 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.3388 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.1101 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.2497 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.0207 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.1599 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.9313 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.0705 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.8419 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.9809 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.7526 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.8915 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.6633 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.8018 - val_sparse_categorical_accuracy: 0.5442\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.5740 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.7128 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.4847 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.6233 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.3954 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.5342 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.3061 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.4449 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.2169 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.3556 - val_sparse_categorical_accuracy: 0.5444\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.1277 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.2664 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.0384 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.1774 - val_sparse_categorical_accuracy: 0.5442\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.9492 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.0884 - val_sparse_categorical_accuracy: 0.5444\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.8601 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.9992 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.7709 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.9099 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.6818 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.8207 - val_sparse_categorical_accuracy: 0.5446\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 269.5927 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.7314 - val_sparse_categorical_accuracy: 0.5452\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 269.5035 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.6423 - val_sparse_categorical_accuracy: 0.5452\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.4144 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.5532 - val_sparse_categorical_accuracy: 0.5452\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.3253 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.4643 - val_sparse_categorical_accuracy: 0.5454\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.2362 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.3751 - val_sparse_categorical_accuracy: 0.5460\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.1473 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.2856 - val_sparse_categorical_accuracy: 0.5460\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.0582 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.1962 - val_sparse_categorical_accuracy: 0.5460\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.9691 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.1077 - val_sparse_categorical_accuracy: 0.5460\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.8802 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.0185 - val_sparse_categorical_accuracy: 0.5458\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.7911 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.9296 - val_sparse_categorical_accuracy: 0.5460\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.7021 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.8405 - val_sparse_categorical_accuracy: 0.5460\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.6132 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.7519 - val_sparse_categorical_accuracy: 0.5460\n",
            "Model Evaluation\n",
            "313/313 [==============================] - 5s 16ms/step - loss: 269.7886 - sparse_categorical_accuracy: 0.5269\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CgnCGYUvDqbC",
        "colab_type": "text"
      },
      "source": [
        "### L2-norm\n",
        "\n",
        "L2-norm regularization is also known as weight decay. This strategy drives the weights closer to the origin by adding the regularization term omega which is defined as:\n",
        "$$\n",
        " \\Omega(\\theta)=\\frac{1}{2}||\\mathbf{W}||_2^2\n",
        "$$\n",
        "\n",
        "Let's find the values for the $\\alpha$ parameters that allow removing remove the overfitting effect with L2-norm."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qqNhu350DtJo",
        "colab_type": "code",
        "outputId": "8434cb80-11c8-4751-fd13-907f2c38367e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "\n",
        "#To complete: insert the model that you previously define that overfit the \n",
        "#trainnig data, and add the L2-norm regularization. Use the proper value for the\n",
        "#alpha hyper-parameter, that prevents the model from overfitting. In general, a\n",
        "#layer that exploits L2-norm regularization is defined as follow:\n",
        "\n",
        "#model.add(keras.layers.Dense(units=To_complete, activation=To_complete,kernel_regularizer=tf.keras.regularizers.l2(To_complete))) \n",
        "l2_alpha = 0.11\n",
        "\n",
        "model.add(keras.layers.Dense(3000, activation=\"relu\",kernel_regularizer=tf.keras.regularizers.l2(l2_alpha)))\n",
        "model.add(keras.layers.Dense(1000, activation=\"relu\",kernel_regularizer=tf.keras.regularizers.l2(l2_alpha)))\n",
        "\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr),\n",
        "              metrics=[\"sparse_categorical_accuracy\"]) #To complete: learning rate\n",
        "\n",
        "history = model.fit(X_train_small, y_train_small, epochs=epochs, #To complete: that prevents the model from overfitting\n",
        "                    validation_data=(X_valid_scaled, y_valid))\n",
        "print(\"Model Evaluation\")\n",
        "model.evaluate(X_test_scaled, y_test)\n",
        "plot_learning_loss(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 304.0166 - sparse_categorical_accuracy: 0.1200 - val_loss: 303.8503 - val_sparse_categorical_accuracy: 0.1048\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.6596 - sparse_categorical_accuracy: 0.1200 - val_loss: 303.5439 - val_sparse_categorical_accuracy: 0.1104\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 303.3122 - sparse_categorical_accuracy: 0.1600 - val_loss: 303.2392 - val_sparse_categorical_accuracy: 0.1172\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.9708 - sparse_categorical_accuracy: 0.2000 - val_loss: 302.9419 - val_sparse_categorical_accuracy: 0.1258\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.6367 - sparse_categorical_accuracy: 0.2400 - val_loss: 302.6434 - val_sparse_categorical_accuracy: 0.1364\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 302.3015 - sparse_categorical_accuracy: 0.2800 - val_loss: 302.3459 - val_sparse_categorical_accuracy: 0.1546\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.9728 - sparse_categorical_accuracy: 0.3800 - val_loss: 302.0529 - val_sparse_categorical_accuracy: 0.1694\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.6501 - sparse_categorical_accuracy: 0.4000 - val_loss: 301.7611 - val_sparse_categorical_accuracy: 0.1852\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.3297 - sparse_categorical_accuracy: 0.4200 - val_loss: 301.4727 - val_sparse_categorical_accuracy: 0.2022\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 301.0160 - sparse_categorical_accuracy: 0.5000 - val_loss: 301.1855 - val_sparse_categorical_accuracy: 0.2166\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.7027 - sparse_categorical_accuracy: 0.5400 - val_loss: 300.9003 - val_sparse_categorical_accuracy: 0.2280\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.3936 - sparse_categorical_accuracy: 0.5400 - val_loss: 300.6161 - val_sparse_categorical_accuracy: 0.2408\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 300.0856 - sparse_categorical_accuracy: 0.6000 - val_loss: 300.3329 - val_sparse_categorical_accuracy: 0.2528\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.7812 - sparse_categorical_accuracy: 0.6200 - val_loss: 300.0522 - val_sparse_categorical_accuracy: 0.2624\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.4790 - sparse_categorical_accuracy: 0.6800 - val_loss: 299.7704 - val_sparse_categorical_accuracy: 0.2706\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 299.1803 - sparse_categorical_accuracy: 0.7000 - val_loss: 299.4925 - val_sparse_categorical_accuracy: 0.2796\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.8823 - sparse_categorical_accuracy: 0.7000 - val_loss: 299.2153 - val_sparse_categorical_accuracy: 0.2870\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.5857 - sparse_categorical_accuracy: 0.7000 - val_loss: 298.9389 - val_sparse_categorical_accuracy: 0.2948\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.2929 - sparse_categorical_accuracy: 0.7200 - val_loss: 298.6638 - val_sparse_categorical_accuracy: 0.3012\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 298.0001 - sparse_categorical_accuracy: 0.7200 - val_loss: 298.3886 - val_sparse_categorical_accuracy: 0.3044\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.7096 - sparse_categorical_accuracy: 0.7200 - val_loss: 298.1136 - val_sparse_categorical_accuracy: 0.3120\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.4214 - sparse_categorical_accuracy: 0.7400 - val_loss: 297.8393 - val_sparse_categorical_accuracy: 0.3182\n",
            "Epoch 23/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 297.1329 - sparse_categorical_accuracy: 0.7400 - val_loss: 297.5660 - val_sparse_categorical_accuracy: 0.3248\n",
            "Epoch 24/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.8466 - sparse_categorical_accuracy: 0.7600 - val_loss: 297.2957 - val_sparse_categorical_accuracy: 0.3296\n",
            "Epoch 25/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.5628 - sparse_categorical_accuracy: 0.7800 - val_loss: 297.0233 - val_sparse_categorical_accuracy: 0.3338\n",
            "Epoch 26/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 296.2797 - sparse_categorical_accuracy: 0.7800 - val_loss: 296.7542 - val_sparse_categorical_accuracy: 0.3384\n",
            "Epoch 27/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.9977 - sparse_categorical_accuracy: 0.7800 - val_loss: 296.4838 - val_sparse_categorical_accuracy: 0.3416\n",
            "Epoch 28/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.7170 - sparse_categorical_accuracy: 0.8000 - val_loss: 296.2158 - val_sparse_categorical_accuracy: 0.3442\n",
            "Epoch 29/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.4377 - sparse_categorical_accuracy: 0.8000 - val_loss: 295.9478 - val_sparse_categorical_accuracy: 0.3494\n",
            "Epoch 30/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 295.1579 - sparse_categorical_accuracy: 0.8000 - val_loss: 295.6799 - val_sparse_categorical_accuracy: 0.3538\n",
            "Epoch 31/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.8799 - sparse_categorical_accuracy: 0.8200 - val_loss: 295.4121 - val_sparse_categorical_accuracy: 0.3558\n",
            "Epoch 32/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.6026 - sparse_categorical_accuracy: 0.8200 - val_loss: 295.1454 - val_sparse_categorical_accuracy: 0.3576\n",
            "Epoch 33/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.3264 - sparse_categorical_accuracy: 0.8200 - val_loss: 294.8795 - val_sparse_categorical_accuracy: 0.3600\n",
            "Epoch 34/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 294.0507 - sparse_categorical_accuracy: 0.8200 - val_loss: 294.6143 - val_sparse_categorical_accuracy: 0.3628\n",
            "Epoch 35/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.7764 - sparse_categorical_accuracy: 0.8200 - val_loss: 294.3499 - val_sparse_categorical_accuracy: 0.3650\n",
            "Epoch 36/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.5030 - sparse_categorical_accuracy: 0.8200 - val_loss: 294.0857 - val_sparse_categorical_accuracy: 0.3678\n",
            "Epoch 37/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 293.2309 - sparse_categorical_accuracy: 0.8200 - val_loss: 293.8207 - val_sparse_categorical_accuracy: 0.3714\n",
            "Epoch 38/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.9577 - sparse_categorical_accuracy: 0.8200 - val_loss: 293.5570 - val_sparse_categorical_accuracy: 0.3730\n",
            "Epoch 39/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.6863 - sparse_categorical_accuracy: 0.8200 - val_loss: 293.2937 - val_sparse_categorical_accuracy: 0.3754\n",
            "Epoch 40/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.4158 - sparse_categorical_accuracy: 0.8200 - val_loss: 293.0301 - val_sparse_categorical_accuracy: 0.3774\n",
            "Epoch 41/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 292.1461 - sparse_categorical_accuracy: 0.8200 - val_loss: 292.7683 - val_sparse_categorical_accuracy: 0.3798\n",
            "Epoch 42/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.8772 - sparse_categorical_accuracy: 0.8200 - val_loss: 292.5061 - val_sparse_categorical_accuracy: 0.3838\n",
            "Epoch 43/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 291.6088 - sparse_categorical_accuracy: 0.8400 - val_loss: 292.2451 - val_sparse_categorical_accuracy: 0.3840\n",
            "Epoch 44/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.3400 - sparse_categorical_accuracy: 0.8400 - val_loss: 291.9832 - val_sparse_categorical_accuracy: 0.3864\n",
            "Epoch 45/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 291.0725 - sparse_categorical_accuracy: 0.8400 - val_loss: 291.7239 - val_sparse_categorical_accuracy: 0.3878\n",
            "Epoch 46/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.8052 - sparse_categorical_accuracy: 0.8400 - val_loss: 291.4645 - val_sparse_categorical_accuracy: 0.3896\n",
            "Epoch 47/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.5387 - sparse_categorical_accuracy: 0.8800 - val_loss: 291.2042 - val_sparse_categorical_accuracy: 0.3898\n",
            "Epoch 48/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.2738 - sparse_categorical_accuracy: 0.8800 - val_loss: 290.9448 - val_sparse_categorical_accuracy: 0.3920\n",
            "Epoch 49/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 290.0085 - sparse_categorical_accuracy: 0.8800 - val_loss: 290.6861 - val_sparse_categorical_accuracy: 0.3918\n",
            "Epoch 50/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.7440 - sparse_categorical_accuracy: 0.9000 - val_loss: 290.4266 - val_sparse_categorical_accuracy: 0.3922\n",
            "Epoch 51/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.4795 - sparse_categorical_accuracy: 0.9000 - val_loss: 290.1681 - val_sparse_categorical_accuracy: 0.3938\n",
            "Epoch 52/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 289.2160 - sparse_categorical_accuracy: 0.9000 - val_loss: 289.9087 - val_sparse_categorical_accuracy: 0.3962\n",
            "Epoch 53/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.9527 - sparse_categorical_accuracy: 0.9200 - val_loss: 289.6523 - val_sparse_categorical_accuracy: 0.3960\n",
            "Epoch 54/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.6896 - sparse_categorical_accuracy: 0.9200 - val_loss: 289.3954 - val_sparse_categorical_accuracy: 0.3970\n",
            "Epoch 55/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.4281 - sparse_categorical_accuracy: 0.9000 - val_loss: 289.1366 - val_sparse_categorical_accuracy: 0.3998\n",
            "Epoch 56/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 288.1653 - sparse_categorical_accuracy: 0.9200 - val_loss: 288.8801 - val_sparse_categorical_accuracy: 0.3998\n",
            "Epoch 57/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.9044 - sparse_categorical_accuracy: 0.9200 - val_loss: 288.6225 - val_sparse_categorical_accuracy: 0.4006\n",
            "Epoch 58/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.6423 - sparse_categorical_accuracy: 0.9400 - val_loss: 288.3672 - val_sparse_categorical_accuracy: 0.4014\n",
            "Epoch 59/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.3826 - sparse_categorical_accuracy: 0.9400 - val_loss: 288.1122 - val_sparse_categorical_accuracy: 0.4034\n",
            "Epoch 60/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 287.1222 - sparse_categorical_accuracy: 0.9400 - val_loss: 287.8560 - val_sparse_categorical_accuracy: 0.4050\n",
            "Epoch 61/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.8632 - sparse_categorical_accuracy: 0.9400 - val_loss: 287.5995 - val_sparse_categorical_accuracy: 0.4046\n",
            "Epoch 62/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.6034 - sparse_categorical_accuracy: 0.9400 - val_loss: 287.3450 - val_sparse_categorical_accuracy: 0.4056\n",
            "Epoch 63/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.3451 - sparse_categorical_accuracy: 0.9400 - val_loss: 287.0918 - val_sparse_categorical_accuracy: 0.4054\n",
            "Epoch 64/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 286.0864 - sparse_categorical_accuracy: 0.9400 - val_loss: 286.8376 - val_sparse_categorical_accuracy: 0.4074\n",
            "Epoch 65/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.8284 - sparse_categorical_accuracy: 0.9400 - val_loss: 286.5823 - val_sparse_categorical_accuracy: 0.4090\n",
            "Epoch 66/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.5706 - sparse_categorical_accuracy: 0.9400 - val_loss: 286.3289 - val_sparse_categorical_accuracy: 0.4092\n",
            "Epoch 67/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.3137 - sparse_categorical_accuracy: 0.9400 - val_loss: 286.0735 - val_sparse_categorical_accuracy: 0.4118\n",
            "Epoch 68/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 285.0561 - sparse_categorical_accuracy: 0.9400 - val_loss: 285.8204 - val_sparse_categorical_accuracy: 0.4116\n",
            "Epoch 69/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.8003 - sparse_categorical_accuracy: 0.9400 - val_loss: 285.5686 - val_sparse_categorical_accuracy: 0.4126\n",
            "Epoch 70/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.5435 - sparse_categorical_accuracy: 0.9400 - val_loss: 285.3147 - val_sparse_categorical_accuracy: 0.4144\n",
            "Epoch 71/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.2876 - sparse_categorical_accuracy: 0.9400 - val_loss: 285.0617 - val_sparse_categorical_accuracy: 0.4146\n",
            "Epoch 72/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 284.0325 - sparse_categorical_accuracy: 0.9400 - val_loss: 284.8103 - val_sparse_categorical_accuracy: 0.4158\n",
            "Epoch 73/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.7768 - sparse_categorical_accuracy: 0.9400 - val_loss: 284.5588 - val_sparse_categorical_accuracy: 0.4160\n",
            "Epoch 74/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.5225 - sparse_categorical_accuracy: 0.9400 - val_loss: 284.3072 - val_sparse_categorical_accuracy: 0.4172\n",
            "Epoch 75/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.2678 - sparse_categorical_accuracy: 0.9600 - val_loss: 284.0559 - val_sparse_categorical_accuracy: 0.4184\n",
            "Epoch 76/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 283.0141 - sparse_categorical_accuracy: 0.9600 - val_loss: 283.8055 - val_sparse_categorical_accuracy: 0.4186\n",
            "Epoch 77/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.7606 - sparse_categorical_accuracy: 0.9600 - val_loss: 283.5547 - val_sparse_categorical_accuracy: 0.4202\n",
            "Epoch 78/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.5074 - sparse_categorical_accuracy: 0.9600 - val_loss: 283.3051 - val_sparse_categorical_accuracy: 0.4196\n",
            "Epoch 79/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.2538 - sparse_categorical_accuracy: 0.9600 - val_loss: 283.0534 - val_sparse_categorical_accuracy: 0.4202\n",
            "Epoch 80/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 282.0006 - sparse_categorical_accuracy: 0.9600 - val_loss: 282.8037 - val_sparse_categorical_accuracy: 0.4216\n",
            "Epoch 81/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.7487 - sparse_categorical_accuracy: 0.9600 - val_loss: 282.5542 - val_sparse_categorical_accuracy: 0.4224\n",
            "Epoch 82/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.4962 - sparse_categorical_accuracy: 0.9600 - val_loss: 282.3039 - val_sparse_categorical_accuracy: 0.4230\n",
            "Epoch 83/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 281.2440 - sparse_categorical_accuracy: 0.9600 - val_loss: 282.0547 - val_sparse_categorical_accuracy: 0.4242\n",
            "Epoch 84/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.9929 - sparse_categorical_accuracy: 0.9600 - val_loss: 281.8058 - val_sparse_categorical_accuracy: 0.4252\n",
            "Epoch 85/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.7420 - sparse_categorical_accuracy: 0.9600 - val_loss: 281.5580 - val_sparse_categorical_accuracy: 0.4262\n",
            "Epoch 86/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.4912 - sparse_categorical_accuracy: 0.9600 - val_loss: 281.3088 - val_sparse_categorical_accuracy: 0.4270\n",
            "Epoch 87/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 280.2404 - sparse_categorical_accuracy: 0.9600 - val_loss: 281.0600 - val_sparse_categorical_accuracy: 0.4272\n",
            "Epoch 88/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.9901 - sparse_categorical_accuracy: 0.9600 - val_loss: 280.8125 - val_sparse_categorical_accuracy: 0.4280\n",
            "Epoch 89/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.7401 - sparse_categorical_accuracy: 0.9600 - val_loss: 280.5648 - val_sparse_categorical_accuracy: 0.4288\n",
            "Epoch 90/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.4905 - sparse_categorical_accuracy: 0.9600 - val_loss: 280.3165 - val_sparse_categorical_accuracy: 0.4298\n",
            "Epoch 91/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 279.2411 - sparse_categorical_accuracy: 0.9600 - val_loss: 280.0695 - val_sparse_categorical_accuracy: 0.4294\n",
            "Epoch 92/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.9922 - sparse_categorical_accuracy: 0.9600 - val_loss: 279.8236 - val_sparse_categorical_accuracy: 0.4296\n",
            "Epoch 93/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.7432 - sparse_categorical_accuracy: 0.9600 - val_loss: 279.5767 - val_sparse_categorical_accuracy: 0.4302\n",
            "Epoch 94/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.4946 - sparse_categorical_accuracy: 0.9600 - val_loss: 279.3295 - val_sparse_categorical_accuracy: 0.4302\n",
            "Epoch 95/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 278.2465 - sparse_categorical_accuracy: 0.9600 - val_loss: 279.0829 - val_sparse_categorical_accuracy: 0.4318\n",
            "Epoch 96/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.9988 - sparse_categorical_accuracy: 0.9600 - val_loss: 278.8369 - val_sparse_categorical_accuracy: 0.4320\n",
            "Epoch 97/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.7506 - sparse_categorical_accuracy: 0.9600 - val_loss: 278.5912 - val_sparse_categorical_accuracy: 0.4326\n",
            "Epoch 98/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.5036 - sparse_categorical_accuracy: 0.9600 - val_loss: 278.3454 - val_sparse_categorical_accuracy: 0.4340\n",
            "Epoch 99/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.2563 - sparse_categorical_accuracy: 0.9600 - val_loss: 278.1006 - val_sparse_categorical_accuracy: 0.4350\n",
            "Epoch 100/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 277.0098 - sparse_categorical_accuracy: 0.9600 - val_loss: 277.8554 - val_sparse_categorical_accuracy: 0.4358\n",
            "Epoch 101/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.7629 - sparse_categorical_accuracy: 0.9600 - val_loss: 277.6108 - val_sparse_categorical_accuracy: 0.4370\n",
            "Epoch 102/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.5167 - sparse_categorical_accuracy: 0.9800 - val_loss: 277.3669 - val_sparse_categorical_accuracy: 0.4376\n",
            "Epoch 103/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.2704 - sparse_categorical_accuracy: 1.0000 - val_loss: 277.1226 - val_sparse_categorical_accuracy: 0.4386\n",
            "Epoch 104/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 276.0247 - sparse_categorical_accuracy: 0.9800 - val_loss: 276.8788 - val_sparse_categorical_accuracy: 0.4394\n",
            "Epoch 105/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.7794 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.6354 - val_sparse_categorical_accuracy: 0.4400\n",
            "Epoch 106/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.5341 - sparse_categorical_accuracy: 1.0000 - val_loss: 276.3911 - val_sparse_categorical_accuracy: 0.4398\n",
            "Epoch 107/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.2892 - sparse_categorical_accuracy: 0.9800 - val_loss: 276.1480 - val_sparse_categorical_accuracy: 0.4410\n",
            "Epoch 108/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 275.0450 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.9057 - val_sparse_categorical_accuracy: 0.4402\n",
            "Epoch 109/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.8007 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.6632 - val_sparse_categorical_accuracy: 0.4410\n",
            "Epoch 110/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.5565 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.4211 - val_sparse_categorical_accuracy: 0.4414\n",
            "Epoch 111/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.3125 - sparse_categorical_accuracy: 1.0000 - val_loss: 275.1776 - val_sparse_categorical_accuracy: 0.4422\n",
            "Epoch 112/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 274.0694 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.9357 - val_sparse_categorical_accuracy: 0.4426\n",
            "Epoch 113/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.8253 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.6933 - val_sparse_categorical_accuracy: 0.4438\n",
            "Epoch 114/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.5824 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.4518 - val_sparse_categorical_accuracy: 0.4442\n",
            "Epoch 115/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.3394 - sparse_categorical_accuracy: 1.0000 - val_loss: 274.2099 - val_sparse_categorical_accuracy: 0.4450\n",
            "Epoch 116/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 273.0967 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.9690 - val_sparse_categorical_accuracy: 0.4452\n",
            "Epoch 117/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.8545 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.7289 - val_sparse_categorical_accuracy: 0.4452\n",
            "Epoch 118/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.6127 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.4884 - val_sparse_categorical_accuracy: 0.4460\n",
            "Epoch 119/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.3708 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.2481 - val_sparse_categorical_accuracy: 0.4462\n",
            "Epoch 120/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 272.1290 - sparse_categorical_accuracy: 1.0000 - val_loss: 273.0074 - val_sparse_categorical_accuracy: 0.4482\n",
            "Epoch 121/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.8875 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.7668 - val_sparse_categorical_accuracy: 0.4498\n",
            "Epoch 122/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.6463 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.5274 - val_sparse_categorical_accuracy: 0.4502\n",
            "Epoch 123/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.4055 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.2880 - val_sparse_categorical_accuracy: 0.4506\n",
            "Epoch 124/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 271.1649 - sparse_categorical_accuracy: 1.0000 - val_loss: 272.0491 - val_sparse_categorical_accuracy: 0.4502\n",
            "Epoch 125/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.9246 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.8101 - val_sparse_categorical_accuracy: 0.4518\n",
            "Epoch 126/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.6847 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.5705 - val_sparse_categorical_accuracy: 0.4532\n",
            "Epoch 127/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.4447 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.3328 - val_sparse_categorical_accuracy: 0.4536\n",
            "Epoch 128/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 270.2054 - sparse_categorical_accuracy: 1.0000 - val_loss: 271.0934 - val_sparse_categorical_accuracy: 0.4550\n",
            "Epoch 129/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.9657 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.8552 - val_sparse_categorical_accuracy: 0.4556\n",
            "Epoch 130/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.7266 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.6166 - val_sparse_categorical_accuracy: 0.4566\n",
            "Epoch 131/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.4878 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.3784 - val_sparse_categorical_accuracy: 0.4566\n",
            "Epoch 132/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.2493 - sparse_categorical_accuracy: 1.0000 - val_loss: 270.1410 - val_sparse_categorical_accuracy: 0.4574\n",
            "Epoch 133/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 269.0107 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.9026 - val_sparse_categorical_accuracy: 0.4586\n",
            "Epoch 134/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.7725 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.6650 - val_sparse_categorical_accuracy: 0.4594\n",
            "Epoch 135/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.5346 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.4286 - val_sparse_categorical_accuracy: 0.4604\n",
            "Epoch 136/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.2970 - sparse_categorical_accuracy: 1.0000 - val_loss: 269.1917 - val_sparse_categorical_accuracy: 0.4610\n",
            "Epoch 137/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 268.0594 - sparse_categorical_accuracy: 1.0000 - val_loss: 268.9554 - val_sparse_categorical_accuracy: 0.4604\n",
            "Epoch 138/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 267.8225 - sparse_categorical_accuracy: 1.0000 - val_loss: 268.7201 - val_sparse_categorical_accuracy: 0.4612\n",
            "Epoch 139/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 267.5854 - sparse_categorical_accuracy: 1.0000 - val_loss: 268.4835 - val_sparse_categorical_accuracy: 0.4624\n",
            "Epoch 140/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 267.3485 - sparse_categorical_accuracy: 1.0000 - val_loss: 268.2475 - val_sparse_categorical_accuracy: 0.4632\n",
            "Epoch 141/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 267.1119 - sparse_categorical_accuracy: 1.0000 - val_loss: 268.0123 - val_sparse_categorical_accuracy: 0.4630\n",
            "Epoch 142/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 266.8756 - sparse_categorical_accuracy: 1.0000 - val_loss: 267.7768 - val_sparse_categorical_accuracy: 0.4638\n",
            "Epoch 143/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 266.6398 - sparse_categorical_accuracy: 1.0000 - val_loss: 267.5415 - val_sparse_categorical_accuracy: 0.4638\n",
            "Epoch 144/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 266.4038 - sparse_categorical_accuracy: 1.0000 - val_loss: 267.3066 - val_sparse_categorical_accuracy: 0.4648\n",
            "Epoch 145/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 266.1682 - sparse_categorical_accuracy: 1.0000 - val_loss: 267.0714 - val_sparse_categorical_accuracy: 0.4646\n",
            "Epoch 146/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 265.9330 - sparse_categorical_accuracy: 1.0000 - val_loss: 266.8370 - val_sparse_categorical_accuracy: 0.4656\n",
            "Epoch 147/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 265.6981 - sparse_categorical_accuracy: 1.0000 - val_loss: 266.6032 - val_sparse_categorical_accuracy: 0.4658\n",
            "Epoch 148/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 265.4633 - sparse_categorical_accuracy: 1.0000 - val_loss: 266.3695 - val_sparse_categorical_accuracy: 0.4666\n",
            "Epoch 149/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 265.2284 - sparse_categorical_accuracy: 1.0000 - val_loss: 266.1356 - val_sparse_categorical_accuracy: 0.4672\n",
            "Epoch 150/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 264.9940 - sparse_categorical_accuracy: 1.0000 - val_loss: 265.9020 - val_sparse_categorical_accuracy: 0.4678\n",
            "Epoch 151/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 264.7599 - sparse_categorical_accuracy: 1.0000 - val_loss: 265.6678 - val_sparse_categorical_accuracy: 0.4688\n",
            "Epoch 152/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 264.5259 - sparse_categorical_accuracy: 1.0000 - val_loss: 265.4358 - val_sparse_categorical_accuracy: 0.4684\n",
            "Epoch 153/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 264.2921 - sparse_categorical_accuracy: 1.0000 - val_loss: 265.2021 - val_sparse_categorical_accuracy: 0.4698\n",
            "Epoch 154/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 264.0588 - sparse_categorical_accuracy: 1.0000 - val_loss: 264.9685 - val_sparse_categorical_accuracy: 0.4718\n",
            "Epoch 155/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 263.8254 - sparse_categorical_accuracy: 1.0000 - val_loss: 264.7360 - val_sparse_categorical_accuracy: 0.4732\n",
            "Epoch 156/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 263.5922 - sparse_categorical_accuracy: 1.0000 - val_loss: 264.5040 - val_sparse_categorical_accuracy: 0.4738\n",
            "Epoch 157/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 263.3591 - sparse_categorical_accuracy: 1.0000 - val_loss: 264.2724 - val_sparse_categorical_accuracy: 0.4730\n",
            "Epoch 158/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 263.1266 - sparse_categorical_accuracy: 1.0000 - val_loss: 264.0401 - val_sparse_categorical_accuracy: 0.4752\n",
            "Epoch 159/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 262.8943 - sparse_categorical_accuracy: 1.0000 - val_loss: 263.8088 - val_sparse_categorical_accuracy: 0.4746\n",
            "Epoch 160/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 262.6622 - sparse_categorical_accuracy: 1.0000 - val_loss: 263.5780 - val_sparse_categorical_accuracy: 0.4740\n",
            "Epoch 161/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 262.4303 - sparse_categorical_accuracy: 1.0000 - val_loss: 263.3473 - val_sparse_categorical_accuracy: 0.4740\n",
            "Epoch 162/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 262.1985 - sparse_categorical_accuracy: 1.0000 - val_loss: 263.1169 - val_sparse_categorical_accuracy: 0.4738\n",
            "Epoch 163/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 261.9670 - sparse_categorical_accuracy: 1.0000 - val_loss: 262.8859 - val_sparse_categorical_accuracy: 0.4744\n",
            "Epoch 164/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 261.7356 - sparse_categorical_accuracy: 1.0000 - val_loss: 262.6554 - val_sparse_categorical_accuracy: 0.4748\n",
            "Epoch 165/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 261.5048 - sparse_categorical_accuracy: 1.0000 - val_loss: 262.4250 - val_sparse_categorical_accuracy: 0.4752\n",
            "Epoch 166/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 261.2738 - sparse_categorical_accuracy: 1.0000 - val_loss: 262.1948 - val_sparse_categorical_accuracy: 0.4756\n",
            "Epoch 167/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 261.0433 - sparse_categorical_accuracy: 1.0000 - val_loss: 261.9649 - val_sparse_categorical_accuracy: 0.4764\n",
            "Epoch 168/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 260.8129 - sparse_categorical_accuracy: 1.0000 - val_loss: 261.7358 - val_sparse_categorical_accuracy: 0.4762\n",
            "Epoch 169/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 260.5827 - sparse_categorical_accuracy: 1.0000 - val_loss: 261.5070 - val_sparse_categorical_accuracy: 0.4762\n",
            "Epoch 170/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 260.3528 - sparse_categorical_accuracy: 1.0000 - val_loss: 261.2786 - val_sparse_categorical_accuracy: 0.4762\n",
            "Epoch 171/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 260.1230 - sparse_categorical_accuracy: 1.0000 - val_loss: 261.0493 - val_sparse_categorical_accuracy: 0.4764\n",
            "Epoch 172/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 259.8935 - sparse_categorical_accuracy: 1.0000 - val_loss: 260.8202 - val_sparse_categorical_accuracy: 0.4772\n",
            "Epoch 173/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 259.6642 - sparse_categorical_accuracy: 1.0000 - val_loss: 260.5920 - val_sparse_categorical_accuracy: 0.4778\n",
            "Epoch 174/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 259.4352 - sparse_categorical_accuracy: 1.0000 - val_loss: 260.3629 - val_sparse_categorical_accuracy: 0.4788\n",
            "Epoch 175/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 259.2061 - sparse_categorical_accuracy: 1.0000 - val_loss: 260.1345 - val_sparse_categorical_accuracy: 0.4790\n",
            "Epoch 176/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 258.9774 - sparse_categorical_accuracy: 1.0000 - val_loss: 259.9068 - val_sparse_categorical_accuracy: 0.4796\n",
            "Epoch 177/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 258.7490 - sparse_categorical_accuracy: 1.0000 - val_loss: 259.6789 - val_sparse_categorical_accuracy: 0.4806\n",
            "Epoch 178/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 258.5209 - sparse_categorical_accuracy: 1.0000 - val_loss: 259.4521 - val_sparse_categorical_accuracy: 0.4802\n",
            "Epoch 179/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 258.2926 - sparse_categorical_accuracy: 1.0000 - val_loss: 259.2243 - val_sparse_categorical_accuracy: 0.4806\n",
            "Epoch 180/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 258.0650 - sparse_categorical_accuracy: 1.0000 - val_loss: 258.9982 - val_sparse_categorical_accuracy: 0.4804\n",
            "Epoch 181/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 257.8374 - sparse_categorical_accuracy: 1.0000 - val_loss: 258.7719 - val_sparse_categorical_accuracy: 0.4804\n",
            "Epoch 182/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 257.6101 - sparse_categorical_accuracy: 1.0000 - val_loss: 258.5446 - val_sparse_categorical_accuracy: 0.4812\n",
            "Epoch 183/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 257.3829 - sparse_categorical_accuracy: 1.0000 - val_loss: 258.3173 - val_sparse_categorical_accuracy: 0.4812\n",
            "Epoch 184/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 257.1560 - sparse_categorical_accuracy: 1.0000 - val_loss: 258.0912 - val_sparse_categorical_accuracy: 0.4820\n",
            "Epoch 185/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 256.9292 - sparse_categorical_accuracy: 1.0000 - val_loss: 257.8654 - val_sparse_categorical_accuracy: 0.4820\n",
            "Epoch 186/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 256.7028 - sparse_categorical_accuracy: 1.0000 - val_loss: 257.6400 - val_sparse_categorical_accuracy: 0.4816\n",
            "Epoch 187/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 256.4764 - sparse_categorical_accuracy: 1.0000 - val_loss: 257.4137 - val_sparse_categorical_accuracy: 0.4822\n",
            "Epoch 188/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 256.2502 - sparse_categorical_accuracy: 1.0000 - val_loss: 257.1880 - val_sparse_categorical_accuracy: 0.4824\n",
            "Epoch 189/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 256.0244 - sparse_categorical_accuracy: 1.0000 - val_loss: 256.9628 - val_sparse_categorical_accuracy: 0.4830\n",
            "Epoch 190/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 255.7988 - sparse_categorical_accuracy: 1.0000 - val_loss: 256.7379 - val_sparse_categorical_accuracy: 0.4832\n",
            "Epoch 191/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 255.5733 - sparse_categorical_accuracy: 1.0000 - val_loss: 256.5129 - val_sparse_categorical_accuracy: 0.4834\n",
            "Epoch 192/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 255.3481 - sparse_categorical_accuracy: 1.0000 - val_loss: 256.2880 - val_sparse_categorical_accuracy: 0.4848\n",
            "Epoch 193/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 255.1229 - sparse_categorical_accuracy: 1.0000 - val_loss: 256.0638 - val_sparse_categorical_accuracy: 0.4848\n",
            "Epoch 194/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 254.8980 - sparse_categorical_accuracy: 1.0000 - val_loss: 255.8399 - val_sparse_categorical_accuracy: 0.4848\n",
            "Epoch 195/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 254.6735 - sparse_categorical_accuracy: 1.0000 - val_loss: 255.6160 - val_sparse_categorical_accuracy: 0.4842\n",
            "Epoch 196/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 254.4490 - sparse_categorical_accuracy: 1.0000 - val_loss: 255.3918 - val_sparse_categorical_accuracy: 0.4846\n",
            "Epoch 197/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 254.2247 - sparse_categorical_accuracy: 1.0000 - val_loss: 255.1682 - val_sparse_categorical_accuracy: 0.4848\n",
            "Epoch 198/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 254.0007 - sparse_categorical_accuracy: 1.0000 - val_loss: 254.9453 - val_sparse_categorical_accuracy: 0.4850\n",
            "Epoch 199/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 253.7766 - sparse_categorical_accuracy: 1.0000 - val_loss: 254.7219 - val_sparse_categorical_accuracy: 0.4854\n",
            "Epoch 200/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 253.5532 - sparse_categorical_accuracy: 1.0000 - val_loss: 254.4990 - val_sparse_categorical_accuracy: 0.4850\n",
            "Epoch 201/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 253.3299 - sparse_categorical_accuracy: 1.0000 - val_loss: 254.2756 - val_sparse_categorical_accuracy: 0.4860\n",
            "Epoch 202/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 253.1064 - sparse_categorical_accuracy: 1.0000 - val_loss: 254.0529 - val_sparse_categorical_accuracy: 0.4862\n",
            "Epoch 203/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 252.8834 - sparse_categorical_accuracy: 1.0000 - val_loss: 253.8304 - val_sparse_categorical_accuracy: 0.4870\n",
            "Epoch 204/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 252.6607 - sparse_categorical_accuracy: 1.0000 - val_loss: 253.6085 - val_sparse_categorical_accuracy: 0.4874\n",
            "Epoch 205/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 252.4382 - sparse_categorical_accuracy: 1.0000 - val_loss: 253.3859 - val_sparse_categorical_accuracy: 0.4874\n",
            "Epoch 206/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 252.2159 - sparse_categorical_accuracy: 1.0000 - val_loss: 253.1644 - val_sparse_categorical_accuracy: 0.4874\n",
            "Epoch 207/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 251.9938 - sparse_categorical_accuracy: 1.0000 - val_loss: 252.9423 - val_sparse_categorical_accuracy: 0.4884\n",
            "Epoch 208/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 251.7718 - sparse_categorical_accuracy: 1.0000 - val_loss: 252.7207 - val_sparse_categorical_accuracy: 0.4890\n",
            "Epoch 209/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 251.5499 - sparse_categorical_accuracy: 1.0000 - val_loss: 252.4998 - val_sparse_categorical_accuracy: 0.4886\n",
            "Epoch 210/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 251.3284 - sparse_categorical_accuracy: 1.0000 - val_loss: 252.2782 - val_sparse_categorical_accuracy: 0.4894\n",
            "Epoch 211/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 251.1069 - sparse_categorical_accuracy: 1.0000 - val_loss: 252.0571 - val_sparse_categorical_accuracy: 0.4902\n",
            "Epoch 212/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 250.8858 - sparse_categorical_accuracy: 1.0000 - val_loss: 251.8365 - val_sparse_categorical_accuracy: 0.4896\n",
            "Epoch 213/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 250.6649 - sparse_categorical_accuracy: 1.0000 - val_loss: 251.6161 - val_sparse_categorical_accuracy: 0.4898\n",
            "Epoch 214/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 250.4441 - sparse_categorical_accuracy: 1.0000 - val_loss: 251.3962 - val_sparse_categorical_accuracy: 0.4904\n",
            "Epoch 215/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 250.2235 - sparse_categorical_accuracy: 1.0000 - val_loss: 251.1762 - val_sparse_categorical_accuracy: 0.4912\n",
            "Epoch 216/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 250.0032 - sparse_categorical_accuracy: 1.0000 - val_loss: 250.9565 - val_sparse_categorical_accuracy: 0.4912\n",
            "Epoch 217/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 249.7830 - sparse_categorical_accuracy: 1.0000 - val_loss: 250.7363 - val_sparse_categorical_accuracy: 0.4922\n",
            "Epoch 218/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 249.5630 - sparse_categorical_accuracy: 1.0000 - val_loss: 250.5166 - val_sparse_categorical_accuracy: 0.4926\n",
            "Epoch 219/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 249.3433 - sparse_categorical_accuracy: 1.0000 - val_loss: 250.2979 - val_sparse_categorical_accuracy: 0.4930\n",
            "Epoch 220/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 249.1238 - sparse_categorical_accuracy: 1.0000 - val_loss: 250.0790 - val_sparse_categorical_accuracy: 0.4934\n",
            "Epoch 221/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 248.9044 - sparse_categorical_accuracy: 1.0000 - val_loss: 249.8600 - val_sparse_categorical_accuracy: 0.4936\n",
            "Epoch 222/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 248.6854 - sparse_categorical_accuracy: 1.0000 - val_loss: 249.6407 - val_sparse_categorical_accuracy: 0.4946\n",
            "Epoch 223/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 248.4663 - sparse_categorical_accuracy: 1.0000 - val_loss: 249.4216 - val_sparse_categorical_accuracy: 0.4946\n",
            "Epoch 224/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 248.2475 - sparse_categorical_accuracy: 1.0000 - val_loss: 249.2035 - val_sparse_categorical_accuracy: 0.4944\n",
            "Epoch 225/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 248.0289 - sparse_categorical_accuracy: 1.0000 - val_loss: 248.9852 - val_sparse_categorical_accuracy: 0.4952\n",
            "Epoch 226/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 247.8106 - sparse_categorical_accuracy: 1.0000 - val_loss: 248.7672 - val_sparse_categorical_accuracy: 0.4952\n",
            "Epoch 227/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 247.5925 - sparse_categorical_accuracy: 1.0000 - val_loss: 248.5500 - val_sparse_categorical_accuracy: 0.4956\n",
            "Epoch 228/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 247.3746 - sparse_categorical_accuracy: 1.0000 - val_loss: 248.3322 - val_sparse_categorical_accuracy: 0.4956\n",
            "Epoch 229/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 247.1567 - sparse_categorical_accuracy: 1.0000 - val_loss: 248.1146 - val_sparse_categorical_accuracy: 0.4962\n",
            "Epoch 230/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 246.9392 - sparse_categorical_accuracy: 1.0000 - val_loss: 247.8974 - val_sparse_categorical_accuracy: 0.4962\n",
            "Epoch 231/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 246.7218 - sparse_categorical_accuracy: 1.0000 - val_loss: 247.6801 - val_sparse_categorical_accuracy: 0.4962\n",
            "Epoch 232/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 246.5046 - sparse_categorical_accuracy: 1.0000 - val_loss: 247.4635 - val_sparse_categorical_accuracy: 0.4964\n",
            "Epoch 233/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 246.2876 - sparse_categorical_accuracy: 1.0000 - val_loss: 247.2471 - val_sparse_categorical_accuracy: 0.4968\n",
            "Epoch 234/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 246.0709 - sparse_categorical_accuracy: 1.0000 - val_loss: 247.0305 - val_sparse_categorical_accuracy: 0.4970\n",
            "Epoch 235/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 245.8544 - sparse_categorical_accuracy: 1.0000 - val_loss: 246.8136 - val_sparse_categorical_accuracy: 0.4984\n",
            "Epoch 236/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 245.6379 - sparse_categorical_accuracy: 1.0000 - val_loss: 246.5980 - val_sparse_categorical_accuracy: 0.4980\n",
            "Epoch 237/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 245.4218 - sparse_categorical_accuracy: 1.0000 - val_loss: 246.3824 - val_sparse_categorical_accuracy: 0.4978\n",
            "Epoch 238/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 245.2057 - sparse_categorical_accuracy: 1.0000 - val_loss: 246.1667 - val_sparse_categorical_accuracy: 0.4984\n",
            "Epoch 239/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 244.9901 - sparse_categorical_accuracy: 1.0000 - val_loss: 245.9514 - val_sparse_categorical_accuracy: 0.4986\n",
            "Epoch 240/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 244.7744 - sparse_categorical_accuracy: 1.0000 - val_loss: 245.7363 - val_sparse_categorical_accuracy: 0.4988\n",
            "Epoch 241/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 244.5591 - sparse_categorical_accuracy: 1.0000 - val_loss: 245.5211 - val_sparse_categorical_accuracy: 0.4992\n",
            "Epoch 242/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 244.3437 - sparse_categorical_accuracy: 1.0000 - val_loss: 245.3060 - val_sparse_categorical_accuracy: 0.4994\n",
            "Epoch 243/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 244.1287 - sparse_categorical_accuracy: 1.0000 - val_loss: 245.0922 - val_sparse_categorical_accuracy: 0.4992\n",
            "Epoch 244/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 243.9139 - sparse_categorical_accuracy: 1.0000 - val_loss: 244.8778 - val_sparse_categorical_accuracy: 0.4994\n",
            "Epoch 245/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 243.6992 - sparse_categorical_accuracy: 1.0000 - val_loss: 244.6636 - val_sparse_categorical_accuracy: 0.4994\n",
            "Epoch 246/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 243.4849 - sparse_categorical_accuracy: 1.0000 - val_loss: 244.4499 - val_sparse_categorical_accuracy: 0.4990\n",
            "Epoch 247/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 243.2706 - sparse_categorical_accuracy: 1.0000 - val_loss: 244.2361 - val_sparse_categorical_accuracy: 0.4996\n",
            "Epoch 248/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 243.0565 - sparse_categorical_accuracy: 1.0000 - val_loss: 244.0219 - val_sparse_categorical_accuracy: 0.5000\n",
            "Epoch 249/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 242.8426 - sparse_categorical_accuracy: 1.0000 - val_loss: 243.8087 - val_sparse_categorical_accuracy: 0.5006\n",
            "Epoch 250/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 242.6290 - sparse_categorical_accuracy: 1.0000 - val_loss: 243.5956 - val_sparse_categorical_accuracy: 0.5004\n",
            "Epoch 251/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 242.4156 - sparse_categorical_accuracy: 1.0000 - val_loss: 243.3822 - val_sparse_categorical_accuracy: 0.5008\n",
            "Epoch 252/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 242.2024 - sparse_categorical_accuracy: 1.0000 - val_loss: 243.1689 - val_sparse_categorical_accuracy: 0.5006\n",
            "Epoch 253/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 241.9893 - sparse_categorical_accuracy: 1.0000 - val_loss: 242.9565 - val_sparse_categorical_accuracy: 0.5004\n",
            "Epoch 254/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 241.7764 - sparse_categorical_accuracy: 1.0000 - val_loss: 242.7442 - val_sparse_categorical_accuracy: 0.5006\n",
            "Epoch 255/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 241.5638 - sparse_categorical_accuracy: 1.0000 - val_loss: 242.5316 - val_sparse_categorical_accuracy: 0.5012\n",
            "Epoch 256/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 241.3513 - sparse_categorical_accuracy: 1.0000 - val_loss: 242.3200 - val_sparse_categorical_accuracy: 0.5012\n",
            "Epoch 257/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 241.1389 - sparse_categorical_accuracy: 1.0000 - val_loss: 242.1081 - val_sparse_categorical_accuracy: 0.5012\n",
            "Epoch 258/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 240.9269 - sparse_categorical_accuracy: 1.0000 - val_loss: 241.8963 - val_sparse_categorical_accuracy: 0.5014\n",
            "Epoch 259/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 240.7151 - sparse_categorical_accuracy: 1.0000 - val_loss: 241.6846 - val_sparse_categorical_accuracy: 0.5010\n",
            "Epoch 260/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 240.5032 - sparse_categorical_accuracy: 1.0000 - val_loss: 241.4731 - val_sparse_categorical_accuracy: 0.5014\n",
            "Epoch 261/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 240.2916 - sparse_categorical_accuracy: 1.0000 - val_loss: 241.2619 - val_sparse_categorical_accuracy: 0.5018\n",
            "Epoch 262/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 240.0802 - sparse_categorical_accuracy: 1.0000 - val_loss: 241.0507 - val_sparse_categorical_accuracy: 0.5020\n",
            "Epoch 263/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 239.8691 - sparse_categorical_accuracy: 1.0000 - val_loss: 240.8401 - val_sparse_categorical_accuracy: 0.5022\n",
            "Epoch 264/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 239.6581 - sparse_categorical_accuracy: 1.0000 - val_loss: 240.6299 - val_sparse_categorical_accuracy: 0.5026\n",
            "Epoch 265/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 239.4474 - sparse_categorical_accuracy: 1.0000 - val_loss: 240.4190 - val_sparse_categorical_accuracy: 0.5030\n",
            "Epoch 266/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 239.2368 - sparse_categorical_accuracy: 1.0000 - val_loss: 240.2088 - val_sparse_categorical_accuracy: 0.5028\n",
            "Epoch 267/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 239.0265 - sparse_categorical_accuracy: 1.0000 - val_loss: 239.9980 - val_sparse_categorical_accuracy: 0.5032\n",
            "Epoch 268/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 238.8162 - sparse_categorical_accuracy: 1.0000 - val_loss: 239.7884 - val_sparse_categorical_accuracy: 0.5040\n",
            "Epoch 269/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 238.6062 - sparse_categorical_accuracy: 1.0000 - val_loss: 239.5788 - val_sparse_categorical_accuracy: 0.5038\n",
            "Epoch 270/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 238.3965 - sparse_categorical_accuracy: 1.0000 - val_loss: 239.3689 - val_sparse_categorical_accuracy: 0.5044\n",
            "Epoch 271/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 238.1868 - sparse_categorical_accuracy: 1.0000 - val_loss: 239.1596 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 272/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 237.9773 - sparse_categorical_accuracy: 1.0000 - val_loss: 238.9507 - val_sparse_categorical_accuracy: 0.5056\n",
            "Epoch 273/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 237.7679 - sparse_categorical_accuracy: 1.0000 - val_loss: 238.7417 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 274/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 237.5590 - sparse_categorical_accuracy: 1.0000 - val_loss: 238.5333 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 275/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 237.3500 - sparse_categorical_accuracy: 1.0000 - val_loss: 238.3248 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 276/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 237.1413 - sparse_categorical_accuracy: 1.0000 - val_loss: 238.1168 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 277/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 236.9330 - sparse_categorical_accuracy: 1.0000 - val_loss: 237.9090 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 278/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 236.7246 - sparse_categorical_accuracy: 1.0000 - val_loss: 237.7013 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 279/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 236.5164 - sparse_categorical_accuracy: 1.0000 - val_loss: 237.4934 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 280/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 236.3085 - sparse_categorical_accuracy: 1.0000 - val_loss: 237.2859 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 281/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 236.1008 - sparse_categorical_accuracy: 1.0000 - val_loss: 237.0791 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 282/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 235.8932 - sparse_categorical_accuracy: 1.0000 - val_loss: 236.8716 - val_sparse_categorical_accuracy: 0.5046\n",
            "Epoch 283/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 235.6857 - sparse_categorical_accuracy: 1.0000 - val_loss: 236.6639 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 284/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 235.4785 - sparse_categorical_accuracy: 1.0000 - val_loss: 236.4567 - val_sparse_categorical_accuracy: 0.5050\n",
            "Epoch 285/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 235.2715 - sparse_categorical_accuracy: 1.0000 - val_loss: 236.2505 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 286/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 235.0647 - sparse_categorical_accuracy: 1.0000 - val_loss: 236.0438 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 287/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 234.8580 - sparse_categorical_accuracy: 1.0000 - val_loss: 235.8377 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 288/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 234.6516 - sparse_categorical_accuracy: 1.0000 - val_loss: 235.6311 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 289/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 234.4452 - sparse_categorical_accuracy: 1.0000 - val_loss: 235.4253 - val_sparse_categorical_accuracy: 0.5048\n",
            "Epoch 290/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 234.2393 - sparse_categorical_accuracy: 1.0000 - val_loss: 235.2198 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 291/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 234.0332 - sparse_categorical_accuracy: 1.0000 - val_loss: 235.0141 - val_sparse_categorical_accuracy: 0.5056\n",
            "Epoch 292/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 233.8274 - sparse_categorical_accuracy: 1.0000 - val_loss: 234.8085 - val_sparse_categorical_accuracy: 0.5058\n",
            "Epoch 293/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 233.6219 - sparse_categorical_accuracy: 1.0000 - val_loss: 234.6034 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 294/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 233.4166 - sparse_categorical_accuracy: 1.0000 - val_loss: 234.3987 - val_sparse_categorical_accuracy: 0.5054\n",
            "Epoch 295/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 233.2114 - sparse_categorical_accuracy: 1.0000 - val_loss: 234.1940 - val_sparse_categorical_accuracy: 0.5052\n",
            "Epoch 296/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 233.0064 - sparse_categorical_accuracy: 1.0000 - val_loss: 233.9892 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 297/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 232.8015 - sparse_categorical_accuracy: 1.0000 - val_loss: 233.7847 - val_sparse_categorical_accuracy: 0.5062\n",
            "Epoch 298/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 232.5968 - sparse_categorical_accuracy: 1.0000 - val_loss: 233.5804 - val_sparse_categorical_accuracy: 0.5066\n",
            "Epoch 299/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 232.3923 - sparse_categorical_accuracy: 1.0000 - val_loss: 233.3763 - val_sparse_categorical_accuracy: 0.5070\n",
            "Epoch 300/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 232.1881 - sparse_categorical_accuracy: 1.0000 - val_loss: 233.1718 - val_sparse_categorical_accuracy: 0.5070\n",
            "Epoch 301/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 231.9840 - sparse_categorical_accuracy: 1.0000 - val_loss: 232.9680 - val_sparse_categorical_accuracy: 0.5072\n",
            "Epoch 302/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 231.7800 - sparse_categorical_accuracy: 1.0000 - val_loss: 232.7646 - val_sparse_categorical_accuracy: 0.5074\n",
            "Epoch 303/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 231.5764 - sparse_categorical_accuracy: 1.0000 - val_loss: 232.5608 - val_sparse_categorical_accuracy: 0.5072\n",
            "Epoch 304/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 231.3729 - sparse_categorical_accuracy: 1.0000 - val_loss: 232.3574 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 305/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 231.1695 - sparse_categorical_accuracy: 1.0000 - val_loss: 232.1543 - val_sparse_categorical_accuracy: 0.5078\n",
            "Epoch 306/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 230.9663 - sparse_categorical_accuracy: 1.0000 - val_loss: 231.9517 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 307/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 230.7634 - sparse_categorical_accuracy: 1.0000 - val_loss: 231.7489 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 308/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 230.5605 - sparse_categorical_accuracy: 1.0000 - val_loss: 231.5464 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 309/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 230.3578 - sparse_categorical_accuracy: 1.0000 - val_loss: 231.3442 - val_sparse_categorical_accuracy: 0.5080\n",
            "Epoch 310/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 230.1555 - sparse_categorical_accuracy: 1.0000 - val_loss: 231.1418 - val_sparse_categorical_accuracy: 0.5084\n",
            "Epoch 311/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 229.9531 - sparse_categorical_accuracy: 1.0000 - val_loss: 230.9395 - val_sparse_categorical_accuracy: 0.5090\n",
            "Epoch 312/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 229.7511 - sparse_categorical_accuracy: 1.0000 - val_loss: 230.7375 - val_sparse_categorical_accuracy: 0.5092\n",
            "Epoch 313/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 229.5491 - sparse_categorical_accuracy: 1.0000 - val_loss: 230.5360 - val_sparse_categorical_accuracy: 0.5090\n",
            "Epoch 314/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 229.3474 - sparse_categorical_accuracy: 1.0000 - val_loss: 230.3349 - val_sparse_categorical_accuracy: 0.5090\n",
            "Epoch 315/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 229.1458 - sparse_categorical_accuracy: 1.0000 - val_loss: 230.1333 - val_sparse_categorical_accuracy: 0.5090\n",
            "Epoch 316/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 228.9445 - sparse_categorical_accuracy: 1.0000 - val_loss: 229.9321 - val_sparse_categorical_accuracy: 0.5092\n",
            "Epoch 317/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 228.7433 - sparse_categorical_accuracy: 1.0000 - val_loss: 229.7308 - val_sparse_categorical_accuracy: 0.5094\n",
            "Epoch 318/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 228.5424 - sparse_categorical_accuracy: 1.0000 - val_loss: 229.5302 - val_sparse_categorical_accuracy: 0.5094\n",
            "Epoch 319/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 228.3415 - sparse_categorical_accuracy: 1.0000 - val_loss: 229.3297 - val_sparse_categorical_accuracy: 0.5098\n",
            "Epoch 320/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 228.1409 - sparse_categorical_accuracy: 1.0000 - val_loss: 229.1290 - val_sparse_categorical_accuracy: 0.5100\n",
            "Epoch 321/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 227.9403 - sparse_categorical_accuracy: 1.0000 - val_loss: 228.9291 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 322/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 227.7400 - sparse_categorical_accuracy: 1.0000 - val_loss: 228.7287 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 323/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 227.5400 - sparse_categorical_accuracy: 1.0000 - val_loss: 228.5288 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 324/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 227.3401 - sparse_categorical_accuracy: 1.0000 - val_loss: 228.3293 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 325/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 227.1402 - sparse_categorical_accuracy: 1.0000 - val_loss: 228.1295 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 326/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 226.9406 - sparse_categorical_accuracy: 1.0000 - val_loss: 227.9301 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 327/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 226.7412 - sparse_categorical_accuracy: 1.0000 - val_loss: 227.7310 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 328/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 226.5420 - sparse_categorical_accuracy: 1.0000 - val_loss: 227.5316 - val_sparse_categorical_accuracy: 0.5114\n",
            "Epoch 329/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 226.3430 - sparse_categorical_accuracy: 1.0000 - val_loss: 227.3333 - val_sparse_categorical_accuracy: 0.5110\n",
            "Epoch 330/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 226.1441 - sparse_categorical_accuracy: 1.0000 - val_loss: 227.1346 - val_sparse_categorical_accuracy: 0.5114\n",
            "Epoch 331/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 225.9454 - sparse_categorical_accuracy: 1.0000 - val_loss: 226.9364 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 332/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 225.7469 - sparse_categorical_accuracy: 1.0000 - val_loss: 226.7380 - val_sparse_categorical_accuracy: 0.5116\n",
            "Epoch 333/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 225.5485 - sparse_categorical_accuracy: 1.0000 - val_loss: 226.5397 - val_sparse_categorical_accuracy: 0.5118\n",
            "Epoch 334/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 225.3504 - sparse_categorical_accuracy: 1.0000 - val_loss: 226.3412 - val_sparse_categorical_accuracy: 0.5130\n",
            "Epoch 335/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 225.1523 - sparse_categorical_accuracy: 1.0000 - val_loss: 226.1431 - val_sparse_categorical_accuracy: 0.5134\n",
            "Epoch 336/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 224.9545 - sparse_categorical_accuracy: 1.0000 - val_loss: 225.9456 - val_sparse_categorical_accuracy: 0.5132\n",
            "Epoch 337/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 224.7569 - sparse_categorical_accuracy: 1.0000 - val_loss: 225.7479 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 338/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 224.5594 - sparse_categorical_accuracy: 1.0000 - val_loss: 225.5511 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 339/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 224.3621 - sparse_categorical_accuracy: 1.0000 - val_loss: 225.3540 - val_sparse_categorical_accuracy: 0.5140\n",
            "Epoch 340/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 224.1651 - sparse_categorical_accuracy: 1.0000 - val_loss: 225.1569 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 341/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 223.9680 - sparse_categorical_accuracy: 1.0000 - val_loss: 224.9602 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 342/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 223.7713 - sparse_categorical_accuracy: 1.0000 - val_loss: 224.7636 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 343/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 223.5747 - sparse_categorical_accuracy: 1.0000 - val_loss: 224.5677 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 344/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 223.3783 - sparse_categorical_accuracy: 1.0000 - val_loss: 224.3711 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 345/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 223.1820 - sparse_categorical_accuracy: 1.0000 - val_loss: 224.1750 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 346/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 222.9860 - sparse_categorical_accuracy: 1.0000 - val_loss: 223.9797 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 347/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 222.7901 - sparse_categorical_accuracy: 1.0000 - val_loss: 223.7842 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 348/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 222.5944 - sparse_categorical_accuracy: 1.0000 - val_loss: 223.5889 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 349/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 222.3989 - sparse_categorical_accuracy: 1.0000 - val_loss: 223.3931 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 350/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 222.2035 - sparse_categorical_accuracy: 1.0000 - val_loss: 223.1976 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 351/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 222.0083 - sparse_categorical_accuracy: 1.0000 - val_loss: 223.0029 - val_sparse_categorical_accuracy: 0.5140\n",
            "Epoch 352/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 221.8133 - sparse_categorical_accuracy: 1.0000 - val_loss: 222.8082 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 353/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 221.6184 - sparse_categorical_accuracy: 1.0000 - val_loss: 222.6134 - val_sparse_categorical_accuracy: 0.5148\n",
            "Epoch 354/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 221.4238 - sparse_categorical_accuracy: 1.0000 - val_loss: 222.4186 - val_sparse_categorical_accuracy: 0.5150\n",
            "Epoch 355/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 221.2293 - sparse_categorical_accuracy: 1.0000 - val_loss: 222.2243 - val_sparse_categorical_accuracy: 0.5148\n",
            "Epoch 356/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 221.0349 - sparse_categorical_accuracy: 1.0000 - val_loss: 222.0303 - val_sparse_categorical_accuracy: 0.5148\n",
            "Epoch 357/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 220.8408 - sparse_categorical_accuracy: 1.0000 - val_loss: 221.8358 - val_sparse_categorical_accuracy: 0.5152\n",
            "Epoch 358/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 220.6468 - sparse_categorical_accuracy: 1.0000 - val_loss: 221.6420 - val_sparse_categorical_accuracy: 0.5160\n",
            "Epoch 359/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 220.4530 - sparse_categorical_accuracy: 1.0000 - val_loss: 221.4486 - val_sparse_categorical_accuracy: 0.5152\n",
            "Epoch 360/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 220.2594 - sparse_categorical_accuracy: 1.0000 - val_loss: 221.2552 - val_sparse_categorical_accuracy: 0.5152\n",
            "Epoch 361/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 220.0659 - sparse_categorical_accuracy: 1.0000 - val_loss: 221.0619 - val_sparse_categorical_accuracy: 0.5152\n",
            "Epoch 362/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 219.8727 - sparse_categorical_accuracy: 1.0000 - val_loss: 220.8689 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 363/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 219.6794 - sparse_categorical_accuracy: 1.0000 - val_loss: 220.6763 - val_sparse_categorical_accuracy: 0.5158\n",
            "Epoch 364/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 219.4865 - sparse_categorical_accuracy: 1.0000 - val_loss: 220.4839 - val_sparse_categorical_accuracy: 0.5154\n",
            "Epoch 365/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 219.2938 - sparse_categorical_accuracy: 1.0000 - val_loss: 220.2914 - val_sparse_categorical_accuracy: 0.5152\n",
            "Epoch 366/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 219.1012 - sparse_categorical_accuracy: 1.0000 - val_loss: 220.0990 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 367/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 218.9088 - sparse_categorical_accuracy: 1.0000 - val_loss: 219.9068 - val_sparse_categorical_accuracy: 0.5158\n",
            "Epoch 368/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 218.7164 - sparse_categorical_accuracy: 1.0000 - val_loss: 219.7146 - val_sparse_categorical_accuracy: 0.5160\n",
            "Epoch 369/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 218.5243 - sparse_categorical_accuracy: 1.0000 - val_loss: 219.5227 - val_sparse_categorical_accuracy: 0.5160\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 218.3324 - sparse_categorical_accuracy: 1.0000 - val_loss: 219.3306 - val_sparse_categorical_accuracy: 0.5168\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 218.1406 - sparse_categorical_accuracy: 1.0000 - val_loss: 219.1392 - val_sparse_categorical_accuracy: 0.5168\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 217.9491 - sparse_categorical_accuracy: 1.0000 - val_loss: 218.9482 - val_sparse_categorical_accuracy: 0.5166\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 217.7576 - sparse_categorical_accuracy: 1.0000 - val_loss: 218.7568 - val_sparse_categorical_accuracy: 0.5164\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 217.5664 - sparse_categorical_accuracy: 1.0000 - val_loss: 218.5656 - val_sparse_categorical_accuracy: 0.5166\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 217.3752 - sparse_categorical_accuracy: 1.0000 - val_loss: 218.3745 - val_sparse_categorical_accuracy: 0.5168\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 217.1844 - sparse_categorical_accuracy: 1.0000 - val_loss: 218.1840 - val_sparse_categorical_accuracy: 0.5172\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 216.9935 - sparse_categorical_accuracy: 1.0000 - val_loss: 217.9931 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 216.8029 - sparse_categorical_accuracy: 1.0000 - val_loss: 217.8026 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 216.6126 - sparse_categorical_accuracy: 1.0000 - val_loss: 217.6122 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 216.4224 - sparse_categorical_accuracy: 1.0000 - val_loss: 217.4220 - val_sparse_categorical_accuracy: 0.5182\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 216.2323 - sparse_categorical_accuracy: 1.0000 - val_loss: 217.2323 - val_sparse_categorical_accuracy: 0.5182\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 216.0425 - sparse_categorical_accuracy: 1.0000 - val_loss: 217.0422 - val_sparse_categorical_accuracy: 0.5186\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 215.8527 - sparse_categorical_accuracy: 1.0000 - val_loss: 216.8526 - val_sparse_categorical_accuracy: 0.5192\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 215.6632 - sparse_categorical_accuracy: 1.0000 - val_loss: 216.6635 - val_sparse_categorical_accuracy: 0.5192\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 215.4737 - sparse_categorical_accuracy: 1.0000 - val_loss: 216.4740 - val_sparse_categorical_accuracy: 0.5196\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 215.2845 - sparse_categorical_accuracy: 1.0000 - val_loss: 216.2851 - val_sparse_categorical_accuracy: 0.5200\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 215.0954 - sparse_categorical_accuracy: 1.0000 - val_loss: 216.0960 - val_sparse_categorical_accuracy: 0.5202\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 214.9066 - sparse_categorical_accuracy: 1.0000 - val_loss: 215.9069 - val_sparse_categorical_accuracy: 0.5202\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 214.7178 - sparse_categorical_accuracy: 1.0000 - val_loss: 215.7183 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 214.5293 - sparse_categorical_accuracy: 1.0000 - val_loss: 215.5301 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 214.3409 - sparse_categorical_accuracy: 1.0000 - val_loss: 215.3420 - val_sparse_categorical_accuracy: 0.5200\n",
            "Epoch 392/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 214.1526 - sparse_categorical_accuracy: 1.0000 - val_loss: 215.1542 - val_sparse_categorical_accuracy: 0.5202\n",
            "Epoch 393/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 213.9646 - sparse_categorical_accuracy: 1.0000 - val_loss: 214.9662 - val_sparse_categorical_accuracy: 0.5200\n",
            "Epoch 394/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 213.7767 - sparse_categorical_accuracy: 1.0000 - val_loss: 214.7781 - val_sparse_categorical_accuracy: 0.5204\n",
            "Epoch 395/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 213.5889 - sparse_categorical_accuracy: 1.0000 - val_loss: 214.5906 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 213.4014 - sparse_categorical_accuracy: 1.0000 - val_loss: 214.4031 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 213.2141 - sparse_categorical_accuracy: 1.0000 - val_loss: 214.2155 - val_sparse_categorical_accuracy: 0.5208\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 213.0268 - sparse_categorical_accuracy: 1.0000 - val_loss: 214.0285 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 212.8398 - sparse_categorical_accuracy: 1.0000 - val_loss: 213.8419 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 400/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 212.6528 - sparse_categorical_accuracy: 1.0000 - val_loss: 213.6550 - val_sparse_categorical_accuracy: 0.5214\n",
            "Epoch 401/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 212.4661 - sparse_categorical_accuracy: 1.0000 - val_loss: 213.4686 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 212.2795 - sparse_categorical_accuracy: 1.0000 - val_loss: 213.2824 - val_sparse_categorical_accuracy: 0.5208\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 212.0931 - sparse_categorical_accuracy: 1.0000 - val_loss: 213.0963 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 211.9070 - sparse_categorical_accuracy: 1.0000 - val_loss: 212.9106 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 211.7209 - sparse_categorical_accuracy: 1.0000 - val_loss: 212.7249 - val_sparse_categorical_accuracy: 0.5208\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 211.5349 - sparse_categorical_accuracy: 1.0000 - val_loss: 212.5390 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 211.3492 - sparse_categorical_accuracy: 1.0000 - val_loss: 212.3534 - val_sparse_categorical_accuracy: 0.5210\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 211.1636 - sparse_categorical_accuracy: 1.0000 - val_loss: 212.1685 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 210.9782 - sparse_categorical_accuracy: 1.0000 - val_loss: 211.9832 - val_sparse_categorical_accuracy: 0.5214\n",
            "Epoch 410/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 210.7929 - sparse_categorical_accuracy: 1.0000 - val_loss: 211.7980 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 411/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 210.6079 - sparse_categorical_accuracy: 1.0000 - val_loss: 211.6132 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 412/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 210.4229 - sparse_categorical_accuracy: 1.0000 - val_loss: 211.4286 - val_sparse_categorical_accuracy: 0.5220\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 210.2382 - sparse_categorical_accuracy: 1.0000 - val_loss: 211.2438 - val_sparse_categorical_accuracy: 0.5218\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 210.0536 - sparse_categorical_accuracy: 1.0000 - val_loss: 211.0594 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 209.8692 - sparse_categorical_accuracy: 1.0000 - val_loss: 210.8757 - val_sparse_categorical_accuracy: 0.5214\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 209.6848 - sparse_categorical_accuracy: 1.0000 - val_loss: 210.6915 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 209.5008 - sparse_categorical_accuracy: 1.0000 - val_loss: 210.5073 - val_sparse_categorical_accuracy: 0.5218\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 209.3169 - sparse_categorical_accuracy: 1.0000 - val_loss: 210.3237 - val_sparse_categorical_accuracy: 0.5224\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 209.1330 - sparse_categorical_accuracy: 1.0000 - val_loss: 210.1402 - val_sparse_categorical_accuracy: 0.5230\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 208.9494 - sparse_categorical_accuracy: 1.0000 - val_loss: 209.9570 - val_sparse_categorical_accuracy: 0.5232\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 208.7660 - sparse_categorical_accuracy: 1.0000 - val_loss: 209.7739 - val_sparse_categorical_accuracy: 0.5230\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 208.5827 - sparse_categorical_accuracy: 1.0000 - val_loss: 209.5906 - val_sparse_categorical_accuracy: 0.5234\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 208.3996 - sparse_categorical_accuracy: 1.0000 - val_loss: 209.4076 - val_sparse_categorical_accuracy: 0.5234\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 208.2165 - sparse_categorical_accuracy: 1.0000 - val_loss: 209.2245 - val_sparse_categorical_accuracy: 0.5238\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 208.0338 - sparse_categorical_accuracy: 1.0000 - val_loss: 209.0419 - val_sparse_categorical_accuracy: 0.5240\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 207.8511 - sparse_categorical_accuracy: 1.0000 - val_loss: 208.8592 - val_sparse_categorical_accuracy: 0.5244\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 207.6687 - sparse_categorical_accuracy: 1.0000 - val_loss: 208.6765 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 207.4863 - sparse_categorical_accuracy: 1.0000 - val_loss: 208.4947 - val_sparse_categorical_accuracy: 0.5246\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 207.3042 - sparse_categorical_accuracy: 1.0000 - val_loss: 208.3127 - val_sparse_categorical_accuracy: 0.5250\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 207.1221 - sparse_categorical_accuracy: 1.0000 - val_loss: 208.1306 - val_sparse_categorical_accuracy: 0.5252\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 206.9403 - sparse_categorical_accuracy: 1.0000 - val_loss: 207.9490 - val_sparse_categorical_accuracy: 0.5252\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 206.7586 - sparse_categorical_accuracy: 1.0000 - val_loss: 207.7675 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 206.5771 - sparse_categorical_accuracy: 1.0000 - val_loss: 207.5859 - val_sparse_categorical_accuracy: 0.5250\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 206.3959 - sparse_categorical_accuracy: 1.0000 - val_loss: 207.4051 - val_sparse_categorical_accuracy: 0.5252\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 206.2146 - sparse_categorical_accuracy: 1.0000 - val_loss: 207.2238 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 206.0335 - sparse_categorical_accuracy: 1.0000 - val_loss: 207.0431 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 205.8526 - sparse_categorical_accuracy: 1.0000 - val_loss: 206.8622 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 205.6719 - sparse_categorical_accuracy: 1.0000 - val_loss: 206.6814 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 205.4913 - sparse_categorical_accuracy: 1.0000 - val_loss: 206.5005 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 205.3109 - sparse_categorical_accuracy: 1.0000 - val_loss: 206.3205 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 205.1307 - sparse_categorical_accuracy: 1.0000 - val_loss: 206.1403 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 204.9506 - sparse_categorical_accuracy: 1.0000 - val_loss: 205.9603 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 204.7708 - sparse_categorical_accuracy: 1.0000 - val_loss: 205.7802 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 204.5910 - sparse_categorical_accuracy: 1.0000 - val_loss: 205.6006 - val_sparse_categorical_accuracy: 0.5268\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 204.4115 - sparse_categorical_accuracy: 1.0000 - val_loss: 205.4216 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 204.2319 - sparse_categorical_accuracy: 1.0000 - val_loss: 205.2424 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 204.0527 - sparse_categorical_accuracy: 1.0000 - val_loss: 205.0630 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 203.8735 - sparse_categorical_accuracy: 1.0000 - val_loss: 204.8842 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 203.6946 - sparse_categorical_accuracy: 1.0000 - val_loss: 204.7057 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 203.5157 - sparse_categorical_accuracy: 1.0000 - val_loss: 204.5271 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 203.3371 - sparse_categorical_accuracy: 1.0000 - val_loss: 204.3487 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 203.1586 - sparse_categorical_accuracy: 1.0000 - val_loss: 204.1704 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 202.9803 - sparse_categorical_accuracy: 1.0000 - val_loss: 203.9919 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 202.8021 - sparse_categorical_accuracy: 1.0000 - val_loss: 203.8141 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 202.6240 - sparse_categorical_accuracy: 1.0000 - val_loss: 203.6360 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 202.4462 - sparse_categorical_accuracy: 1.0000 - val_loss: 203.4582 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 202.2686 - sparse_categorical_accuracy: 1.0000 - val_loss: 203.2807 - val_sparse_categorical_accuracy: 0.5268\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 202.0910 - sparse_categorical_accuracy: 1.0000 - val_loss: 203.1030 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 201.9136 - sparse_categorical_accuracy: 1.0000 - val_loss: 202.9261 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 201.7364 - sparse_categorical_accuracy: 1.0000 - val_loss: 202.7495 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 201.5593 - sparse_categorical_accuracy: 1.0000 - val_loss: 202.5725 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 201.3824 - sparse_categorical_accuracy: 1.0000 - val_loss: 202.3958 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 201.2056 - sparse_categorical_accuracy: 1.0000 - val_loss: 202.2192 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 201.0290 - sparse_categorical_accuracy: 1.0000 - val_loss: 202.0430 - val_sparse_categorical_accuracy: 0.5274\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 200.8525 - sparse_categorical_accuracy: 1.0000 - val_loss: 201.8666 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 200.6762 - sparse_categorical_accuracy: 1.0000 - val_loss: 201.6905 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 200.5001 - sparse_categorical_accuracy: 1.0000 - val_loss: 201.5148 - val_sparse_categorical_accuracy: 0.5274\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 200.3241 - sparse_categorical_accuracy: 1.0000 - val_loss: 201.3389 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 469/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 200.1483 - sparse_categorical_accuracy: 1.0000 - val_loss: 201.1628 - val_sparse_categorical_accuracy: 0.5280\n",
            "Epoch 470/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 199.9726 - sparse_categorical_accuracy: 1.0000 - val_loss: 200.9871 - val_sparse_categorical_accuracy: 0.5282\n",
            "Epoch 471/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 199.7971 - sparse_categorical_accuracy: 1.0000 - val_loss: 200.8114 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 472/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 199.6218 - sparse_categorical_accuracy: 1.0000 - val_loss: 200.6365 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 473/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 199.4466 - sparse_categorical_accuracy: 1.0000 - val_loss: 200.4610 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 474/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 199.2715 - sparse_categorical_accuracy: 1.0000 - val_loss: 200.2861 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 475/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 199.0966 - sparse_categorical_accuracy: 1.0000 - val_loss: 200.1111 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 476/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 198.9218 - sparse_categorical_accuracy: 1.0000 - val_loss: 199.9363 - val_sparse_categorical_accuracy: 0.5298\n",
            "Epoch 477/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 198.7473 - sparse_categorical_accuracy: 1.0000 - val_loss: 199.7615 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 478/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 198.5728 - sparse_categorical_accuracy: 1.0000 - val_loss: 199.5875 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 479/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 198.3986 - sparse_categorical_accuracy: 1.0000 - val_loss: 199.4130 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 480/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 198.2244 - sparse_categorical_accuracy: 1.0000 - val_loss: 199.2390 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 481/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 198.0504 - sparse_categorical_accuracy: 1.0000 - val_loss: 199.0652 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 482/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 197.8766 - sparse_categorical_accuracy: 1.0000 - val_loss: 198.8914 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 483/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 197.7029 - sparse_categorical_accuracy: 1.0000 - val_loss: 198.7180 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 484/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 197.5294 - sparse_categorical_accuracy: 1.0000 - val_loss: 198.5447 - val_sparse_categorical_accuracy: 0.5302\n",
            "Epoch 485/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 197.3560 - sparse_categorical_accuracy: 1.0000 - val_loss: 198.3716 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 486/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 197.1829 - sparse_categorical_accuracy: 1.0000 - val_loss: 198.1983 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 487/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 197.0098 - sparse_categorical_accuracy: 1.0000 - val_loss: 198.0254 - val_sparse_categorical_accuracy: 0.5308\n",
            "Epoch 488/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 196.8369 - sparse_categorical_accuracy: 1.0000 - val_loss: 197.8526 - val_sparse_categorical_accuracy: 0.5306\n",
            "Epoch 489/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 196.6642 - sparse_categorical_accuracy: 1.0000 - val_loss: 197.6797 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 490/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 196.4915 - sparse_categorical_accuracy: 1.0000 - val_loss: 197.5070 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 491/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 196.3191 - sparse_categorical_accuracy: 1.0000 - val_loss: 197.3348 - val_sparse_categorical_accuracy: 0.5312\n",
            "Epoch 492/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 196.1467 - sparse_categorical_accuracy: 1.0000 - val_loss: 197.1626 - val_sparse_categorical_accuracy: 0.5312\n",
            "Epoch 493/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 195.9746 - sparse_categorical_accuracy: 1.0000 - val_loss: 196.9904 - val_sparse_categorical_accuracy: 0.5314\n",
            "Epoch 494/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 195.8027 - sparse_categorical_accuracy: 1.0000 - val_loss: 196.8182 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 495/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 195.6309 - sparse_categorical_accuracy: 1.0000 - val_loss: 196.6461 - val_sparse_categorical_accuracy: 0.5314\n",
            "Epoch 496/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 195.4591 - sparse_categorical_accuracy: 1.0000 - val_loss: 196.4748 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 497/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 195.2877 - sparse_categorical_accuracy: 1.0000 - val_loss: 196.3032 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 498/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 195.1163 - sparse_categorical_accuracy: 1.0000 - val_loss: 196.1320 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 499/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 194.9450 - sparse_categorical_accuracy: 1.0000 - val_loss: 195.9608 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 500/500\n",
            "2/2 [==============================] - 2s 1s/step - loss: 194.7739 - sparse_categorical_accuracy: 1.0000 - val_loss: 195.7901 - val_sparse_categorical_accuracy: 0.5316\n",
            "Model Evaluation\n",
            "313/313 [==============================] - 5s 16ms/step - loss: 195.8219 - sparse_categorical_accuracy: 0.5160\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvyKPb4DD25G",
        "colab_type": "text"
      },
      "source": [
        "### Early stopping\n",
        "\n",
        "Early Stopping is a form of regularization used to avoid overfitting. It is designed to monitor the generalization error of one model and stop training when generalization error begins to degrade. In order to evaluate the generalization error, early stopping requires that a validation dataset is evaluated during training. Then, when the validation error does not improve for a specific number of epochs (patience hyper-parameter), it stops the training phase."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqNOOZ11D7cq",
        "colab_type": "code",
        "outputId": "dd844248-e0a9-4e99-9c1e-3971faa283fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = keras.models.Sequential()\n",
        "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n",
        "\n",
        "#To complete: insert the model that you previously define that overfit the \n",
        "#trainnig data\n",
        "model.add(keras.layers.Dense(3000, activation=\"relu\"))\n",
        "model.add(keras.layers.Dense(1000, activation=\"relu\"))\n",
        "\n",
        "model.add(keras.layers.Dense(10, activation=\"softmax\"))\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr=lr),\n",
        "              metrics=[\"sparse_categorical_accuracy\"])\n",
        "\n",
        "#Let's define a log dir in order to save the checkpoint file\n",
        "logdir = os.path.join(os.curdir, \"my_logs\", \"run_{}\".format(time.time()))\n",
        "patiente = 10\n",
        "\n",
        "#In Keras the Eraly stopping is manage by using the callbacks argument.\n",
        "callbacks = [\n",
        "    keras.callbacks.TensorBoard(logdir),\n",
        "    keras.callbacks.EarlyStopping(patience=patiente), #To complete: play with patience and find the value that prevents the model from overfitting\n",
        "    #Saving the checkpoints file allows to load the \"best\" model when the Early \n",
        "    #stopping detect that the generalization error degrade (after 'patience' epochs)\n",
        "    keras.callbacks.ModelCheckpoint(\"my_mnist_model.h5\", save_best_only=True),\n",
        "]\n",
        "\n",
        "history = model.fit(X_train_small, y_train_small, epochs=epochs, #To complete: number of epochs\n",
        "                    validation_data=(X_valid_scaled, y_valid),\n",
        "                    callbacks=callbacks)\n",
        "\n",
        "print(\"Model Evaluation\")\n",
        "#The early stopping stopped training after few epochs without progress, so your\n",
        "#model may already have started to overfit the training set. Since the \n",
        "#ModelCheckpoint callback only saved the best models (on the validation set) the\n",
        "#last saved model is the best on the validation set.\n",
        "model = keras.models.load_model(\"my_mnist_model.h5\")\n",
        "model.evaluate(X_test_scaled, y_test)\n",
        "plot_learning_loss(history)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "2/2 [==============================] - 2s 963ms/step - loss: 2.4246 - sparse_categorical_accuracy: 0.0800 - val_loss: 2.4905 - val_sparse_categorical_accuracy: 0.1028\n",
            "Epoch 2/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 2.3283 - sparse_categorical_accuracy: 0.1200 - val_loss: 2.4465 - val_sparse_categorical_accuracy: 0.1132\n",
            "Epoch 3/500\n",
            "2/2 [==============================] - 2s 872ms/step - loss: 2.2408 - sparse_categorical_accuracy: 0.1200 - val_loss: 2.4044 - val_sparse_categorical_accuracy: 0.1314\n",
            "Epoch 4/500\n",
            "2/2 [==============================] - 2s 855ms/step - loss: 2.1598 - sparse_categorical_accuracy: 0.1400 - val_loss: 2.3669 - val_sparse_categorical_accuracy: 0.1480\n",
            "Epoch 5/500\n",
            "2/2 [==============================] - 2s 850ms/step - loss: 2.0858 - sparse_categorical_accuracy: 0.2000 - val_loss: 2.3310 - val_sparse_categorical_accuracy: 0.1726\n",
            "Epoch 6/500\n",
            "2/2 [==============================] - 2s 854ms/step - loss: 2.0148 - sparse_categorical_accuracy: 0.2200 - val_loss: 2.2989 - val_sparse_categorical_accuracy: 0.2002\n",
            "Epoch 7/500\n",
            "2/2 [==============================] - 2s 843ms/step - loss: 1.9502 - sparse_categorical_accuracy: 0.2600 - val_loss: 2.2661 - val_sparse_categorical_accuracy: 0.2250\n",
            "Epoch 8/500\n",
            "2/2 [==============================] - 2s 855ms/step - loss: 1.8871 - sparse_categorical_accuracy: 0.3600 - val_loss: 2.2369 - val_sparse_categorical_accuracy: 0.2504\n",
            "Epoch 9/500\n",
            "2/2 [==============================] - 2s 829ms/step - loss: 1.8277 - sparse_categorical_accuracy: 0.4200 - val_loss: 2.2084 - val_sparse_categorical_accuracy: 0.2674\n",
            "Epoch 10/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 1.7740 - sparse_categorical_accuracy: 0.5000 - val_loss: 2.1831 - val_sparse_categorical_accuracy: 0.2806\n",
            "Epoch 11/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 1.7216 - sparse_categorical_accuracy: 0.5200 - val_loss: 2.1582 - val_sparse_categorical_accuracy: 0.2902\n",
            "Epoch 12/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 1.6729 - sparse_categorical_accuracy: 0.5800 - val_loss: 2.1347 - val_sparse_categorical_accuracy: 0.2996\n",
            "Epoch 13/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 1.6258 - sparse_categorical_accuracy: 0.6600 - val_loss: 2.1121 - val_sparse_categorical_accuracy: 0.3088\n",
            "Epoch 14/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 1.5823 - sparse_categorical_accuracy: 0.6800 - val_loss: 2.0919 - val_sparse_categorical_accuracy: 0.3150\n",
            "Epoch 15/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 1.5408 - sparse_categorical_accuracy: 0.6800 - val_loss: 2.0704 - val_sparse_categorical_accuracy: 0.3214\n",
            "Epoch 16/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 1.5022 - sparse_categorical_accuracy: 0.6800 - val_loss: 2.0512 - val_sparse_categorical_accuracy: 0.3258\n",
            "Epoch 17/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 1.4648 - sparse_categorical_accuracy: 0.7000 - val_loss: 2.0341 - val_sparse_categorical_accuracy: 0.3288\n",
            "Epoch 18/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 1.4289 - sparse_categorical_accuracy: 0.7000 - val_loss: 2.0175 - val_sparse_categorical_accuracy: 0.3322\n",
            "Epoch 19/500\n",
            "2/2 [==============================] - 2s 821ms/step - loss: 1.3958 - sparse_categorical_accuracy: 0.7000 - val_loss: 2.0012 - val_sparse_categorical_accuracy: 0.3354\n",
            "Epoch 20/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 1.3630 - sparse_categorical_accuracy: 0.7000 - val_loss: 1.9844 - val_sparse_categorical_accuracy: 0.3406\n",
            "Epoch 21/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 1.3317 - sparse_categorical_accuracy: 0.7200 - val_loss: 1.9691 - val_sparse_categorical_accuracy: 0.3450\n",
            "Epoch 22/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 1.3030 - sparse_categorical_accuracy: 0.7200 - val_loss: 1.9539 - val_sparse_categorical_accuracy: 0.3508\n",
            "Epoch 23/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 1.2738 - sparse_categorical_accuracy: 0.7600 - val_loss: 1.9390 - val_sparse_categorical_accuracy: 0.3578\n",
            "Epoch 24/500\n",
            "2/2 [==============================] - 2s 961ms/step - loss: 1.2460 - sparse_categorical_accuracy: 0.7600 - val_loss: 1.9257 - val_sparse_categorical_accuracy: 0.3620\n",
            "Epoch 25/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 1.2206 - sparse_categorical_accuracy: 0.7800 - val_loss: 1.9111 - val_sparse_categorical_accuracy: 0.3654\n",
            "Epoch 26/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 1.1956 - sparse_categorical_accuracy: 0.7800 - val_loss: 1.8992 - val_sparse_categorical_accuracy: 0.3670\n",
            "Epoch 27/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 1.1717 - sparse_categorical_accuracy: 0.8000 - val_loss: 1.8863 - val_sparse_categorical_accuracy: 0.3690\n",
            "Epoch 28/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 1.1488 - sparse_categorical_accuracy: 0.8000 - val_loss: 1.8747 - val_sparse_categorical_accuracy: 0.3716\n",
            "Epoch 29/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 1.1267 - sparse_categorical_accuracy: 0.8000 - val_loss: 1.8644 - val_sparse_categorical_accuracy: 0.3744\n",
            "Epoch 30/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 1.1049 - sparse_categorical_accuracy: 0.8000 - val_loss: 1.8530 - val_sparse_categorical_accuracy: 0.3754\n",
            "Epoch 31/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 1.0838 - sparse_categorical_accuracy: 0.8000 - val_loss: 1.8414 - val_sparse_categorical_accuracy: 0.3768\n",
            "Epoch 32/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 1.0637 - sparse_categorical_accuracy: 0.8200 - val_loss: 1.8305 - val_sparse_categorical_accuracy: 0.3790\n",
            "Epoch 33/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 1.0439 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.8206 - val_sparse_categorical_accuracy: 0.3812\n",
            "Epoch 34/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 1.0252 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.8107 - val_sparse_categorical_accuracy: 0.3830\n",
            "Epoch 35/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 1.0069 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.8013 - val_sparse_categorical_accuracy: 0.3854\n",
            "Epoch 36/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.9895 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.7922 - val_sparse_categorical_accuracy: 0.3894\n",
            "Epoch 37/500\n",
            "2/2 [==============================] - 2s 845ms/step - loss: 0.9732 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.7823 - val_sparse_categorical_accuracy: 0.3928\n",
            "Epoch 38/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.9559 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.7732 - val_sparse_categorical_accuracy: 0.3950\n",
            "Epoch 39/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.9399 - sparse_categorical_accuracy: 0.8400 - val_loss: 1.7649 - val_sparse_categorical_accuracy: 0.3956\n",
            "Epoch 40/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.9248 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.7560 - val_sparse_categorical_accuracy: 0.3980\n",
            "Epoch 41/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.9102 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.7487 - val_sparse_categorical_accuracy: 0.4006\n",
            "Epoch 42/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.8960 - sparse_categorical_accuracy: 0.8600 - val_loss: 1.7407 - val_sparse_categorical_accuracy: 0.4026\n",
            "Epoch 43/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.8821 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.7330 - val_sparse_categorical_accuracy: 0.4036\n",
            "Epoch 44/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.8677 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.7247 - val_sparse_categorical_accuracy: 0.4054\n",
            "Epoch 45/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.8545 - sparse_categorical_accuracy: 0.8800 - val_loss: 1.7193 - val_sparse_categorical_accuracy: 0.4070\n",
            "Epoch 46/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.8412 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7130 - val_sparse_categorical_accuracy: 0.4088\n",
            "Epoch 47/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.8285 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.7058 - val_sparse_categorical_accuracy: 0.4090\n",
            "Epoch 48/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.8170 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6994 - val_sparse_categorical_accuracy: 0.4106\n",
            "Epoch 49/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.8053 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6933 - val_sparse_categorical_accuracy: 0.4114\n",
            "Epoch 50/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.7939 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6864 - val_sparse_categorical_accuracy: 0.4110\n",
            "Epoch 51/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.7822 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6803 - val_sparse_categorical_accuracy: 0.4138\n",
            "Epoch 52/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.7715 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6728 - val_sparse_categorical_accuracy: 0.4150\n",
            "Epoch 53/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.7604 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6686 - val_sparse_categorical_accuracy: 0.4158\n",
            "Epoch 54/500\n",
            "2/2 [==============================] - 2s 787ms/step - loss: 0.7497 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6639 - val_sparse_categorical_accuracy: 0.4164\n",
            "Epoch 55/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.7402 - sparse_categorical_accuracy: 0.9000 - val_loss: 1.6561 - val_sparse_categorical_accuracy: 0.4178\n",
            "Epoch 56/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.7293 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6510 - val_sparse_categorical_accuracy: 0.4192\n",
            "Epoch 57/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.7199 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6442 - val_sparse_categorical_accuracy: 0.4194\n",
            "Epoch 58/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.7089 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6395 - val_sparse_categorical_accuracy: 0.4212\n",
            "Epoch 59/500\n",
            "2/2 [==============================] - 2s 819ms/step - loss: 0.7004 - sparse_categorical_accuracy: 0.9200 - val_loss: 1.6353 - val_sparse_categorical_accuracy: 0.4214\n",
            "Epoch 60/500\n",
            "2/2 [==============================] - 2s 839ms/step - loss: 0.6909 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6296 - val_sparse_categorical_accuracy: 0.4230\n",
            "Epoch 61/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.6828 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6237 - val_sparse_categorical_accuracy: 0.4242\n",
            "Epoch 62/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.6736 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6198 - val_sparse_categorical_accuracy: 0.4246\n",
            "Epoch 63/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.6656 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6165 - val_sparse_categorical_accuracy: 0.4242\n",
            "Epoch 64/500\n",
            "2/2 [==============================] - 2s 819ms/step - loss: 0.6570 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6121 - val_sparse_categorical_accuracy: 0.4256\n",
            "Epoch 65/500\n",
            "2/2 [==============================] - 2s 849ms/step - loss: 0.6491 - sparse_categorical_accuracy: 0.9400 - val_loss: 1.6065 - val_sparse_categorical_accuracy: 0.4274\n",
            "Epoch 66/500\n",
            "2/2 [==============================] - 2s 851ms/step - loss: 0.6410 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.6024 - val_sparse_categorical_accuracy: 0.4288\n",
            "Epoch 67/500\n",
            "2/2 [==============================] - 2s 862ms/step - loss: 0.6336 - sparse_categorical_accuracy: 0.9600 - val_loss: 1.5958 - val_sparse_categorical_accuracy: 0.4306\n",
            "Epoch 68/500\n",
            "2/2 [==============================] - 2s 831ms/step - loss: 0.6252 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5915 - val_sparse_categorical_accuracy: 0.4314\n",
            "Epoch 69/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.6185 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5883 - val_sparse_categorical_accuracy: 0.4324\n",
            "Epoch 70/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.6108 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5829 - val_sparse_categorical_accuracy: 0.4354\n",
            "Epoch 71/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.6034 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5782 - val_sparse_categorical_accuracy: 0.4370\n",
            "Epoch 72/500\n",
            "2/2 [==============================] - 2s 821ms/step - loss: 0.5966 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5751 - val_sparse_categorical_accuracy: 0.4376\n",
            "Epoch 73/500\n",
            "2/2 [==============================] - 2s 817ms/step - loss: 0.5892 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5713 - val_sparse_categorical_accuracy: 0.4386\n",
            "Epoch 74/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.5830 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5676 - val_sparse_categorical_accuracy: 0.4394\n",
            "Epoch 75/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.5761 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5639 - val_sparse_categorical_accuracy: 0.4392\n",
            "Epoch 76/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.5701 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5607 - val_sparse_categorical_accuracy: 0.4412\n",
            "Epoch 77/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.5638 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5569 - val_sparse_categorical_accuracy: 0.4422\n",
            "Epoch 78/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.5578 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5542 - val_sparse_categorical_accuracy: 0.4424\n",
            "Epoch 79/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.5511 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5492 - val_sparse_categorical_accuracy: 0.4452\n",
            "Epoch 80/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.5448 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5460 - val_sparse_categorical_accuracy: 0.4460\n",
            "Epoch 81/500\n",
            "2/2 [==============================] - 2s 826ms/step - loss: 0.5393 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5427 - val_sparse_categorical_accuracy: 0.4468\n",
            "Epoch 82/500\n",
            "2/2 [==============================] - 2s 819ms/step - loss: 0.5332 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5383 - val_sparse_categorical_accuracy: 0.4488\n",
            "Epoch 83/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.5271 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5352 - val_sparse_categorical_accuracy: 0.4498\n",
            "Epoch 84/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.5220 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5321 - val_sparse_categorical_accuracy: 0.4508\n",
            "Epoch 85/500\n",
            "2/2 [==============================] - 2s 821ms/step - loss: 0.5168 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5296 - val_sparse_categorical_accuracy: 0.4504\n",
            "Epoch 86/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.5114 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5258 - val_sparse_categorical_accuracy: 0.4506\n",
            "Epoch 87/500\n",
            "2/2 [==============================] - 2s 835ms/step - loss: 0.5058 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5223 - val_sparse_categorical_accuracy: 0.4518\n",
            "Epoch 88/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.5005 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5197 - val_sparse_categorical_accuracy: 0.4532\n",
            "Epoch 89/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.4955 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5170 - val_sparse_categorical_accuracy: 0.4536\n",
            "Epoch 90/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.4905 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5130 - val_sparse_categorical_accuracy: 0.4536\n",
            "Epoch 91/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.4856 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5101 - val_sparse_categorical_accuracy: 0.4550\n",
            "Epoch 92/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.4808 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5086 - val_sparse_categorical_accuracy: 0.4548\n",
            "Epoch 93/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.4759 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5057 - val_sparse_categorical_accuracy: 0.4568\n",
            "Epoch 94/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.4711 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.5022 - val_sparse_categorical_accuracy: 0.4578\n",
            "Epoch 95/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.4665 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4993 - val_sparse_categorical_accuracy: 0.4600\n",
            "Epoch 96/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.4623 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4965 - val_sparse_categorical_accuracy: 0.4610\n",
            "Epoch 97/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.4574 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4940 - val_sparse_categorical_accuracy: 0.4632\n",
            "Epoch 98/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.4533 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4911 - val_sparse_categorical_accuracy: 0.4642\n",
            "Epoch 99/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.4488 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4889 - val_sparse_categorical_accuracy: 0.4656\n",
            "Epoch 100/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.4448 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4862 - val_sparse_categorical_accuracy: 0.4670\n",
            "Epoch 101/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.4403 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4837 - val_sparse_categorical_accuracy: 0.4684\n",
            "Epoch 102/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.4362 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4817 - val_sparse_categorical_accuracy: 0.4684\n",
            "Epoch 103/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.4319 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4793 - val_sparse_categorical_accuracy: 0.4692\n",
            "Epoch 104/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.4279 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4774 - val_sparse_categorical_accuracy: 0.4698\n",
            "Epoch 105/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.4241 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4756 - val_sparse_categorical_accuracy: 0.4700\n",
            "Epoch 106/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.4202 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4728 - val_sparse_categorical_accuracy: 0.4708\n",
            "Epoch 107/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.4164 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4708 - val_sparse_categorical_accuracy: 0.4716\n",
            "Epoch 108/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.4131 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4694 - val_sparse_categorical_accuracy: 0.4722\n",
            "Epoch 109/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.4095 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4680 - val_sparse_categorical_accuracy: 0.4718\n",
            "Epoch 110/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.4058 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4664 - val_sparse_categorical_accuracy: 0.4724\n",
            "Epoch 111/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.4020 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4630 - val_sparse_categorical_accuracy: 0.4742\n",
            "Epoch 112/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.3990 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4611 - val_sparse_categorical_accuracy: 0.4746\n",
            "Epoch 113/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.3948 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4585 - val_sparse_categorical_accuracy: 0.4754\n",
            "Epoch 114/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.3916 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4566 - val_sparse_categorical_accuracy: 0.4762\n",
            "Epoch 115/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.3882 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4543 - val_sparse_categorical_accuracy: 0.4762\n",
            "Epoch 116/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.3849 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4527 - val_sparse_categorical_accuracy: 0.4770\n",
            "Epoch 117/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.3817 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4513 - val_sparse_categorical_accuracy: 0.4784\n",
            "Epoch 118/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.3787 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4497 - val_sparse_categorical_accuracy: 0.4792\n",
            "Epoch 119/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.3754 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4480 - val_sparse_categorical_accuracy: 0.4784\n",
            "Epoch 120/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.3721 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4454 - val_sparse_categorical_accuracy: 0.4804\n",
            "Epoch 121/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.3687 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4431 - val_sparse_categorical_accuracy: 0.4824\n",
            "Epoch 122/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.3656 - sparse_categorical_accuracy: 0.9800 - val_loss: 1.4417 - val_sparse_categorical_accuracy: 0.4826\n",
            "Epoch 123/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.3626 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4402 - val_sparse_categorical_accuracy: 0.4820\n",
            "Epoch 124/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.3596 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4388 - val_sparse_categorical_accuracy: 0.4830\n",
            "Epoch 125/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.3567 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4371 - val_sparse_categorical_accuracy: 0.4838\n",
            "Epoch 126/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.3540 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4346 - val_sparse_categorical_accuracy: 0.4854\n",
            "Epoch 127/500\n",
            "2/2 [==============================] - 2s 855ms/step - loss: 0.3511 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4340 - val_sparse_categorical_accuracy: 0.4852\n",
            "Epoch 128/500\n",
            "2/2 [==============================] - 2s 882ms/step - loss: 0.3485 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4313 - val_sparse_categorical_accuracy: 0.4868\n",
            "Epoch 129/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.3455 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4296 - val_sparse_categorical_accuracy: 0.4878\n",
            "Epoch 130/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.3427 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4273 - val_sparse_categorical_accuracy: 0.4896\n",
            "Epoch 131/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.3401 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4251 - val_sparse_categorical_accuracy: 0.4898\n",
            "Epoch 132/500\n",
            "2/2 [==============================] - 2s 824ms/step - loss: 0.3375 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4235 - val_sparse_categorical_accuracy: 0.4910\n",
            "Epoch 133/500\n",
            "2/2 [==============================] - 2s 821ms/step - loss: 0.3347 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4209 - val_sparse_categorical_accuracy: 0.4922\n",
            "Epoch 134/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.3321 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4189 - val_sparse_categorical_accuracy: 0.4926\n",
            "Epoch 135/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.3296 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4177 - val_sparse_categorical_accuracy: 0.4934\n",
            "Epoch 136/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.3270 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4160 - val_sparse_categorical_accuracy: 0.4942\n",
            "Epoch 137/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.3245 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4146 - val_sparse_categorical_accuracy: 0.4944\n",
            "Epoch 138/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.3222 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4139 - val_sparse_categorical_accuracy: 0.4944\n",
            "Epoch 139/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.3197 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4118 - val_sparse_categorical_accuracy: 0.4960\n",
            "Epoch 140/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.3171 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4100 - val_sparse_categorical_accuracy: 0.4966\n",
            "Epoch 141/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.3147 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4091 - val_sparse_categorical_accuracy: 0.4964\n",
            "Epoch 142/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.3123 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4076 - val_sparse_categorical_accuracy: 0.4964\n",
            "Epoch 143/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.3101 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4060 - val_sparse_categorical_accuracy: 0.4974\n",
            "Epoch 144/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.3076 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4046 - val_sparse_categorical_accuracy: 0.4978\n",
            "Epoch 145/500\n",
            "2/2 [==============================] - 2s 829ms/step - loss: 0.3053 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4028 - val_sparse_categorical_accuracy: 0.4978\n",
            "Epoch 146/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.3033 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4015 - val_sparse_categorical_accuracy: 0.4990\n",
            "Epoch 147/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.3012 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.4004 - val_sparse_categorical_accuracy: 0.4988\n",
            "Epoch 148/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.2991 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3994 - val_sparse_categorical_accuracy: 0.4992\n",
            "Epoch 149/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.2968 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3979 - val_sparse_categorical_accuracy: 0.4992\n",
            "Epoch 150/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.2947 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3967 - val_sparse_categorical_accuracy: 0.4998\n",
            "Epoch 151/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.2926 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3946 - val_sparse_categorical_accuracy: 0.5006\n",
            "Epoch 152/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.2906 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3944 - val_sparse_categorical_accuracy: 0.5004\n",
            "Epoch 153/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.2884 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3924 - val_sparse_categorical_accuracy: 0.5018\n",
            "Epoch 154/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.2865 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3904 - val_sparse_categorical_accuracy: 0.5028\n",
            "Epoch 155/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.2845 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3890 - val_sparse_categorical_accuracy: 0.5044\n",
            "Epoch 156/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.2822 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3880 - val_sparse_categorical_accuracy: 0.5054\n",
            "Epoch 157/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.2801 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3871 - val_sparse_categorical_accuracy: 0.5058\n",
            "Epoch 158/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.2782 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3855 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 159/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.2763 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3847 - val_sparse_categorical_accuracy: 0.5060\n",
            "Epoch 160/500\n",
            "2/2 [==============================] - 2s 787ms/step - loss: 0.2745 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3842 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 161/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.2727 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3831 - val_sparse_categorical_accuracy: 0.5068\n",
            "Epoch 162/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.2707 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3828 - val_sparse_categorical_accuracy: 0.5062\n",
            "Epoch 163/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.2690 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3812 - val_sparse_categorical_accuracy: 0.5070\n",
            "Epoch 164/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.2672 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3802 - val_sparse_categorical_accuracy: 0.5070\n",
            "Epoch 165/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.2656 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3791 - val_sparse_categorical_accuracy: 0.5070\n",
            "Epoch 166/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.2637 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3779 - val_sparse_categorical_accuracy: 0.5076\n",
            "Epoch 167/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.2620 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3768 - val_sparse_categorical_accuracy: 0.5082\n",
            "Epoch 168/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.2603 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3762 - val_sparse_categorical_accuracy: 0.5092\n",
            "Epoch 169/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.2586 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3758 - val_sparse_categorical_accuracy: 0.5088\n",
            "Epoch 170/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.2570 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3753 - val_sparse_categorical_accuracy: 0.5096\n",
            "Epoch 171/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.2553 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3741 - val_sparse_categorical_accuracy: 0.5102\n",
            "Epoch 172/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.2537 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3728 - val_sparse_categorical_accuracy: 0.5096\n",
            "Epoch 173/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.2520 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3724 - val_sparse_categorical_accuracy: 0.5094\n",
            "Epoch 174/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.2504 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3709 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 175/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.2486 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3697 - val_sparse_categorical_accuracy: 0.5104\n",
            "Epoch 176/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.2469 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3691 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 177/500\n",
            "2/2 [==============================] - 2s 829ms/step - loss: 0.2455 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3681 - val_sparse_categorical_accuracy: 0.5108\n",
            "Epoch 178/500\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.2440 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3682 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 179/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.2423 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3668 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 180/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.2409 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3668 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 181/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.2393 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3665 - val_sparse_categorical_accuracy: 0.5106\n",
            "Epoch 182/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.2380 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3651 - val_sparse_categorical_accuracy: 0.5112\n",
            "Epoch 183/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.2365 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3637 - val_sparse_categorical_accuracy: 0.5118\n",
            "Epoch 184/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.2351 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3630 - val_sparse_categorical_accuracy: 0.5118\n",
            "Epoch 185/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.2336 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3625 - val_sparse_categorical_accuracy: 0.5122\n",
            "Epoch 186/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.2323 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3620 - val_sparse_categorical_accuracy: 0.5126\n",
            "Epoch 187/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.2308 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3608 - val_sparse_categorical_accuracy: 0.5132\n",
            "Epoch 188/500\n",
            "2/2 [==============================] - 2s 867ms/step - loss: 0.2293 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3599 - val_sparse_categorical_accuracy: 0.5134\n",
            "Epoch 189/500\n",
            "2/2 [==============================] - 2s 851ms/step - loss: 0.2281 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3592 - val_sparse_categorical_accuracy: 0.5136\n",
            "Epoch 190/500\n",
            "2/2 [==============================] - 2s 853ms/step - loss: 0.2267 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3586 - val_sparse_categorical_accuracy: 0.5134\n",
            "Epoch 191/500\n",
            "2/2 [==============================] - 2s 855ms/step - loss: 0.2254 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3577 - val_sparse_categorical_accuracy: 0.5138\n",
            "Epoch 192/500\n",
            "2/2 [==============================] - 2s 836ms/step - loss: 0.2240 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3567 - val_sparse_categorical_accuracy: 0.5142\n",
            "Epoch 193/500\n",
            "2/2 [==============================] - 2s 844ms/step - loss: 0.2226 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3563 - val_sparse_categorical_accuracy: 0.5140\n",
            "Epoch 194/500\n",
            "2/2 [==============================] - 2s 824ms/step - loss: 0.2212 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3559 - val_sparse_categorical_accuracy: 0.5144\n",
            "Epoch 195/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.2200 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3555 - val_sparse_categorical_accuracy: 0.5146\n",
            "Epoch 196/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.2187 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3544 - val_sparse_categorical_accuracy: 0.5146\n",
            "Epoch 197/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.2173 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3536 - val_sparse_categorical_accuracy: 0.5150\n",
            "Epoch 198/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.2160 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3535 - val_sparse_categorical_accuracy: 0.5150\n",
            "Epoch 199/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.2146 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3527 - val_sparse_categorical_accuracy: 0.5148\n",
            "Epoch 200/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.2135 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3522 - val_sparse_categorical_accuracy: 0.5148\n",
            "Epoch 201/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.2123 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3510 - val_sparse_categorical_accuracy: 0.5156\n",
            "Epoch 202/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.2109 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3503 - val_sparse_categorical_accuracy: 0.5160\n",
            "Epoch 203/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.2097 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3497 - val_sparse_categorical_accuracy: 0.5154\n",
            "Epoch 204/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.2086 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3493 - val_sparse_categorical_accuracy: 0.5160\n",
            "Epoch 205/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.2075 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3481 - val_sparse_categorical_accuracy: 0.5166\n",
            "Epoch 206/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.2063 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3478 - val_sparse_categorical_accuracy: 0.5162\n",
            "Epoch 207/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.2052 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3466 - val_sparse_categorical_accuracy: 0.5166\n",
            "Epoch 208/500\n",
            "2/2 [==============================] - 2s 825ms/step - loss: 0.2040 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3459 - val_sparse_categorical_accuracy: 0.5166\n",
            "Epoch 209/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.2027 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3454 - val_sparse_categorical_accuracy: 0.5170\n",
            "Epoch 210/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.2017 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3443 - val_sparse_categorical_accuracy: 0.5174\n",
            "Epoch 211/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.2005 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3437 - val_sparse_categorical_accuracy: 0.5168\n",
            "Epoch 212/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.1995 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3432 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 213/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.1984 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3427 - val_sparse_categorical_accuracy: 0.5176\n",
            "Epoch 214/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.1973 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3422 - val_sparse_categorical_accuracy: 0.5172\n",
            "Epoch 215/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.1962 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3419 - val_sparse_categorical_accuracy: 0.5172\n",
            "Epoch 216/500\n",
            "2/2 [==============================] - 2s 817ms/step - loss: 0.1952 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3414 - val_sparse_categorical_accuracy: 0.5174\n",
            "Epoch 217/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.1941 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3402 - val_sparse_categorical_accuracy: 0.5182\n",
            "Epoch 218/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.1930 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3395 - val_sparse_categorical_accuracy: 0.5186\n",
            "Epoch 219/500\n",
            "2/2 [==============================] - 2s 817ms/step - loss: 0.1921 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3394 - val_sparse_categorical_accuracy: 0.5184\n",
            "Epoch 220/500\n",
            "2/2 [==============================] - 2s 830ms/step - loss: 0.1910 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3390 - val_sparse_categorical_accuracy: 0.5182\n",
            "Epoch 221/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.1900 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3383 - val_sparse_categorical_accuracy: 0.5184\n",
            "Epoch 222/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.1891 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3371 - val_sparse_categorical_accuracy: 0.5190\n",
            "Epoch 223/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.1880 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3361 - val_sparse_categorical_accuracy: 0.5194\n",
            "Epoch 224/500\n",
            "2/2 [==============================] - 2s 836ms/step - loss: 0.1869 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3356 - val_sparse_categorical_accuracy: 0.5202\n",
            "Epoch 225/500\n",
            "2/2 [==============================] - 2s 832ms/step - loss: 0.1859 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3349 - val_sparse_categorical_accuracy: 0.5206\n",
            "Epoch 226/500\n",
            "2/2 [==============================] - 2s 833ms/step - loss: 0.1850 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3343 - val_sparse_categorical_accuracy: 0.5212\n",
            "Epoch 227/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.1840 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3341 - val_sparse_categorical_accuracy: 0.5214\n",
            "Epoch 228/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.1831 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3332 - val_sparse_categorical_accuracy: 0.5220\n",
            "Epoch 229/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.1821 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3326 - val_sparse_categorical_accuracy: 0.5214\n",
            "Epoch 230/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.1812 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3319 - val_sparse_categorical_accuracy: 0.5216\n",
            "Epoch 231/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1802 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3310 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 232/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.1793 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3306 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 233/500\n",
            "2/2 [==============================] - 2s 821ms/step - loss: 0.1783 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3303 - val_sparse_categorical_accuracy: 0.5222\n",
            "Epoch 234/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.1775 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3293 - val_sparse_categorical_accuracy: 0.5228\n",
            "Epoch 235/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.1766 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3281 - val_sparse_categorical_accuracy: 0.5234\n",
            "Epoch 236/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.1756 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3279 - val_sparse_categorical_accuracy: 0.5238\n",
            "Epoch 237/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.1748 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3275 - val_sparse_categorical_accuracy: 0.5238\n",
            "Epoch 238/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.1738 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3269 - val_sparse_categorical_accuracy: 0.5242\n",
            "Epoch 239/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.1731 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3266 - val_sparse_categorical_accuracy: 0.5236\n",
            "Epoch 240/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.1721 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3262 - val_sparse_categorical_accuracy: 0.5234\n",
            "Epoch 241/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.1713 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3256 - val_sparse_categorical_accuracy: 0.5242\n",
            "Epoch 242/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.1703 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3249 - val_sparse_categorical_accuracy: 0.5248\n",
            "Epoch 243/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.1695 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3250 - val_sparse_categorical_accuracy: 0.5246\n",
            "Epoch 244/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.1686 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3245 - val_sparse_categorical_accuracy: 0.5246\n",
            "Epoch 245/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.1678 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3241 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 246/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.1670 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3238 - val_sparse_categorical_accuracy: 0.5254\n",
            "Epoch 247/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.1662 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3235 - val_sparse_categorical_accuracy: 0.5256\n",
            "Epoch 248/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.1653 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3226 - val_sparse_categorical_accuracy: 0.5262\n",
            "Epoch 249/500\n",
            "2/2 [==============================] - 2s 834ms/step - loss: 0.1645 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3223 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 250/500\n",
            "2/2 [==============================] - 2s 831ms/step - loss: 0.1638 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3221 - val_sparse_categorical_accuracy: 0.5260\n",
            "Epoch 251/500\n",
            "2/2 [==============================] - 2s 829ms/step - loss: 0.1630 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3215 - val_sparse_categorical_accuracy: 0.5258\n",
            "Epoch 252/500\n",
            "2/2 [==============================] - 2s 836ms/step - loss: 0.1623 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3206 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 253/500\n",
            "2/2 [==============================] - 2s 825ms/step - loss: 0.1615 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3204 - val_sparse_categorical_accuracy: 0.5264\n",
            "Epoch 254/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.1607 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3201 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 255/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.1600 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3195 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 256/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1592 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3194 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 257/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.1585 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3192 - val_sparse_categorical_accuracy: 0.5268\n",
            "Epoch 258/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.1578 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3186 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 259/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1571 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3180 - val_sparse_categorical_accuracy: 0.5268\n",
            "Epoch 260/500\n",
            "2/2 [==============================] - 2s 837ms/step - loss: 0.1563 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3174 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 261/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.1555 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3170 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 262/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.1548 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3164 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 263/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.1541 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3162 - val_sparse_categorical_accuracy: 0.5272\n",
            "Epoch 264/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.1534 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3164 - val_sparse_categorical_accuracy: 0.5266\n",
            "Epoch 265/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.1527 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3156 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 266/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.1521 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3152 - val_sparse_categorical_accuracy: 0.5270\n",
            "Epoch 267/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1515 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3141 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 268/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.1508 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3138 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 269/500\n",
            "2/2 [==============================] - 2s 819ms/step - loss: 0.1501 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3133 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 270/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1495 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3125 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 271/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1489 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3122 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 272/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.1482 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3120 - val_sparse_categorical_accuracy: 0.5278\n",
            "Epoch 273/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.1474 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3117 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 274/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1469 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3116 - val_sparse_categorical_accuracy: 0.5276\n",
            "Epoch 275/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1462 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3115 - val_sparse_categorical_accuracy: 0.5282\n",
            "Epoch 276/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.1455 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3114 - val_sparse_categorical_accuracy: 0.5282\n",
            "Epoch 277/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.1450 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3116 - val_sparse_categorical_accuracy: 0.5282\n",
            "Epoch 278/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.1444 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3115 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 279/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1437 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3111 - val_sparse_categorical_accuracy: 0.5282\n",
            "Epoch 280/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.1432 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.5286\n",
            "Epoch 281/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.1426 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3110 - val_sparse_categorical_accuracy: 0.5288\n",
            "Epoch 282/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.1420 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3105 - val_sparse_categorical_accuracy: 0.5288\n",
            "Epoch 283/500\n",
            "2/2 [==============================] - 2s 826ms/step - loss: 0.1413 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3095 - val_sparse_categorical_accuracy: 0.5290\n",
            "Epoch 284/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1407 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3090 - val_sparse_categorical_accuracy: 0.5290\n",
            "Epoch 285/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.1402 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3092 - val_sparse_categorical_accuracy: 0.5288\n",
            "Epoch 286/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1396 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3086 - val_sparse_categorical_accuracy: 0.5292\n",
            "Epoch 287/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.1389 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3085 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 288/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1385 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3080 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 289/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.1378 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3077 - val_sparse_categorical_accuracy: 0.5298\n",
            "Epoch 290/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.1373 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3077 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 291/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.1366 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3073 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 292/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.1361 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3069 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 293/500\n",
            "2/2 [==============================] - 2s 825ms/step - loss: 0.1355 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3068 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 294/500\n",
            "2/2 [==============================] - 2s 833ms/step - loss: 0.1350 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3068 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 295/500\n",
            "2/2 [==============================] - 2s 831ms/step - loss: 0.1345 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3064 - val_sparse_categorical_accuracy: 0.5300\n",
            "Epoch 296/500\n",
            "2/2 [==============================] - 2s 846ms/step - loss: 0.1339 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3063 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 297/500\n",
            "2/2 [==============================] - 2s 825ms/step - loss: 0.1333 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3061 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 298/500\n",
            "2/2 [==============================] - 2s 827ms/step - loss: 0.1327 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3058 - val_sparse_categorical_accuracy: 0.5296\n",
            "Epoch 299/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.1321 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3056 - val_sparse_categorical_accuracy: 0.5294\n",
            "Epoch 300/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.1317 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3048 - val_sparse_categorical_accuracy: 0.5298\n",
            "Epoch 301/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.1311 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3045 - val_sparse_categorical_accuracy: 0.5298\n",
            "Epoch 302/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.1306 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3044 - val_sparse_categorical_accuracy: 0.5304\n",
            "Epoch 303/500\n",
            "2/2 [==============================] - 2s 790ms/step - loss: 0.1301 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3038 - val_sparse_categorical_accuracy: 0.5310\n",
            "Epoch 304/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.1296 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3034 - val_sparse_categorical_accuracy: 0.5314\n",
            "Epoch 305/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.1291 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3032 - val_sparse_categorical_accuracy: 0.5318\n",
            "Epoch 306/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.1286 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3032 - val_sparse_categorical_accuracy: 0.5318\n",
            "Epoch 307/500\n",
            "2/2 [==============================] - 2s 815ms/step - loss: 0.1281 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3028 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 308/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.1276 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3025 - val_sparse_categorical_accuracy: 0.5316\n",
            "Epoch 309/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.1270 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3024 - val_sparse_categorical_accuracy: 0.5326\n",
            "Epoch 310/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.1266 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3019 - val_sparse_categorical_accuracy: 0.5332\n",
            "Epoch 311/500\n",
            "2/2 [==============================] - 2s 828ms/step - loss: 0.1261 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3015 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 312/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1256 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3010 - val_sparse_categorical_accuracy: 0.5334\n",
            "Epoch 313/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.1251 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3009 - val_sparse_categorical_accuracy: 0.5336\n",
            "Epoch 314/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.1246 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3008 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 315/500\n",
            "2/2 [==============================] - 2s 819ms/step - loss: 0.1241 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.3004 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 316/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.1236 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2999 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 317/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.1232 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2994 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 318/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.1228 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2993 - val_sparse_categorical_accuracy: 0.5338\n",
            "Epoch 319/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.1223 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2991 - val_sparse_categorical_accuracy: 0.5340\n",
            "Epoch 320/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.1218 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2986 - val_sparse_categorical_accuracy: 0.5342\n",
            "Epoch 321/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.1214 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2986 - val_sparse_categorical_accuracy: 0.5344\n",
            "Epoch 322/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.1209 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2982 - val_sparse_categorical_accuracy: 0.5352\n",
            "Epoch 323/500\n",
            "2/2 [==============================] - 2s 827ms/step - loss: 0.1205 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2979 - val_sparse_categorical_accuracy: 0.5352\n",
            "Epoch 324/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.1201 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2976 - val_sparse_categorical_accuracy: 0.5350\n",
            "Epoch 325/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.1196 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2973 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 326/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.1191 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2969 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 327/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.1187 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2967 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 328/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.1182 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2961 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 329/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.1179 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2964 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 330/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.1174 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2959 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 331/500\n",
            "2/2 [==============================] - 2s 821ms/step - loss: 0.1170 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2959 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 332/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.1165 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2956 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 333/500\n",
            "2/2 [==============================] - 2s 825ms/step - loss: 0.1161 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2952 - val_sparse_categorical_accuracy: 0.5356\n",
            "Epoch 334/500\n",
            "2/2 [==============================] - 2s 832ms/step - loss: 0.1157 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2946 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 335/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1153 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2940 - val_sparse_categorical_accuracy: 0.5364\n",
            "Epoch 336/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.1149 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2939 - val_sparse_categorical_accuracy: 0.5358\n",
            "Epoch 337/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.1145 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2935 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 338/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.1140 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2936 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 339/500\n",
            "2/2 [==============================] - 2s 798ms/step - loss: 0.1136 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2934 - val_sparse_categorical_accuracy: 0.5360\n",
            "Epoch 340/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.1132 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2929 - val_sparse_categorical_accuracy: 0.5362\n",
            "Epoch 341/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.1128 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2927 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 342/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.1124 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2926 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 343/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.1120 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2927 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 344/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.1116 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2923 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 345/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.1112 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2920 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 346/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.1108 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2921 - val_sparse_categorical_accuracy: 0.5366\n",
            "Epoch 347/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.1104 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2922 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 348/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.1101 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2922 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 349/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.1097 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2917 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 350/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.1093 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2912 - val_sparse_categorical_accuracy: 0.5378\n",
            "Epoch 351/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.1090 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2911 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 352/500\n",
            "2/2 [==============================] - 2s 811ms/step - loss: 0.1086 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2910 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 353/500\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.1082 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2908 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 354/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.1078 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2902 - val_sparse_categorical_accuracy: 0.5370\n",
            "Epoch 355/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.1074 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2901 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 356/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.1070 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2901 - val_sparse_categorical_accuracy: 0.5374\n",
            "Epoch 357/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.1067 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2895 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 358/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.1063 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2893 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 359/500\n",
            "2/2 [==============================] - 2s 822ms/step - loss: 0.1060 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2892 - val_sparse_categorical_accuracy: 0.5376\n",
            "Epoch 360/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.1056 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2891 - val_sparse_categorical_accuracy: 0.5380\n",
            "Epoch 361/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.1053 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2890 - val_sparse_categorical_accuracy: 0.5380\n",
            "Epoch 362/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.1050 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2889 - val_sparse_categorical_accuracy: 0.5386\n",
            "Epoch 363/500\n",
            "2/2 [==============================] - 2s 793ms/step - loss: 0.1046 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2889 - val_sparse_categorical_accuracy: 0.5384\n",
            "Epoch 364/500\n",
            "2/2 [==============================] - 2s 775ms/step - loss: 0.1042 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2891 - val_sparse_categorical_accuracy: 0.5386\n",
            "Epoch 365/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.1039 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2890 - val_sparse_categorical_accuracy: 0.5386\n",
            "Epoch 366/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.1036 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2888 - val_sparse_categorical_accuracy: 0.5390\n",
            "Epoch 367/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.1032 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2887 - val_sparse_categorical_accuracy: 0.5388\n",
            "Epoch 368/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.1028 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2884 - val_sparse_categorical_accuracy: 0.5392\n",
            "Epoch 369/500\n",
            "2/2 [==============================] - 2s 835ms/step - loss: 0.1025 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2881 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 370/500\n",
            "2/2 [==============================] - 2s 834ms/step - loss: 0.1022 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2877 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 371/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.1018 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2875 - val_sparse_categorical_accuracy: 0.5396\n",
            "Epoch 372/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.1015 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2877 - val_sparse_categorical_accuracy: 0.5396\n",
            "Epoch 373/500\n",
            "2/2 [==============================] - 2s 831ms/step - loss: 0.1011 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2875 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 374/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.1009 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2870 - val_sparse_categorical_accuracy: 0.5392\n",
            "Epoch 375/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.1005 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2868 - val_sparse_categorical_accuracy: 0.5392\n",
            "Epoch 376/500\n",
            "2/2 [==============================] - 2s 826ms/step - loss: 0.1002 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2868 - val_sparse_categorical_accuracy: 0.5390\n",
            "Epoch 377/500\n",
            "2/2 [==============================] - 2s 838ms/step - loss: 0.0998 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2863 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 378/500\n",
            "2/2 [==============================] - 2s 851ms/step - loss: 0.0995 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2861 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 379/500\n",
            "2/2 [==============================] - 2s 863ms/step - loss: 0.0992 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2858 - val_sparse_categorical_accuracy: 0.5396\n",
            "Epoch 380/500\n",
            "2/2 [==============================] - 2s 842ms/step - loss: 0.0989 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2855 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 381/500\n",
            "2/2 [==============================] - 2s 838ms/step - loss: 0.0986 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2853 - val_sparse_categorical_accuracy: 0.5396\n",
            "Epoch 382/500\n",
            "2/2 [==============================] - 2s 824ms/step - loss: 0.0983 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2850 - val_sparse_categorical_accuracy: 0.5394\n",
            "Epoch 383/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.0980 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2848 - val_sparse_categorical_accuracy: 0.5398\n",
            "Epoch 384/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.0977 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2848 - val_sparse_categorical_accuracy: 0.5400\n",
            "Epoch 385/500\n",
            "2/2 [==============================] - 2s 878ms/step - loss: 0.0973 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2845 - val_sparse_categorical_accuracy: 0.5400\n",
            "Epoch 386/500\n",
            "2/2 [==============================] - 2s 920ms/step - loss: 0.0971 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2844 - val_sparse_categorical_accuracy: 0.5400\n",
            "Epoch 387/500\n",
            "2/2 [==============================] - 2s 827ms/step - loss: 0.0967 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2840 - val_sparse_categorical_accuracy: 0.5404\n",
            "Epoch 388/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.0964 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2836 - val_sparse_categorical_accuracy: 0.5404\n",
            "Epoch 389/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.0961 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2834 - val_sparse_categorical_accuracy: 0.5404\n",
            "Epoch 390/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.0958 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2833 - val_sparse_categorical_accuracy: 0.5408\n",
            "Epoch 391/500\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.0955 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2833 - val_sparse_categorical_accuracy: 0.5410\n",
            "Epoch 392/500\n",
            "2/2 [==============================] - 2s 980ms/step - loss: 0.0952 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2833 - val_sparse_categorical_accuracy: 0.5408\n",
            "Epoch 393/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.0949 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2830 - val_sparse_categorical_accuracy: 0.5412\n",
            "Epoch 394/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.0946 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2826 - val_sparse_categorical_accuracy: 0.5412\n",
            "Epoch 395/500\n",
            "2/2 [==============================] - 2s 803ms/step - loss: 0.0943 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2824 - val_sparse_categorical_accuracy: 0.5414\n",
            "Epoch 396/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.0940 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2821 - val_sparse_categorical_accuracy: 0.5414\n",
            "Epoch 397/500\n",
            "2/2 [==============================] - 2s 808ms/step - loss: 0.0938 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2816 - val_sparse_categorical_accuracy: 0.5422\n",
            "Epoch 398/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.0934 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2815 - val_sparse_categorical_accuracy: 0.5422\n",
            "Epoch 399/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.0931 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2817 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 400/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.0928 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2814 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 401/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.0926 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2813 - val_sparse_categorical_accuracy: 0.5424\n",
            "Epoch 402/500\n",
            "2/2 [==============================] - 2s 825ms/step - loss: 0.0923 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2813 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 403/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.0920 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2813 - val_sparse_categorical_accuracy: 0.5426\n",
            "Epoch 404/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.0918 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2814 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 405/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.0915 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2815 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 406/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.0912 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2811 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 407/500\n",
            "2/2 [==============================] - 2s 826ms/step - loss: 0.0909 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2810 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 408/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.0906 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2813 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 409/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.0903 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2810 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 410/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.0900 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2809 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 411/500\n",
            "2/2 [==============================] - 2s 797ms/step - loss: 0.0898 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2808 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 412/500\n",
            "2/2 [==============================] - 2s 818ms/step - loss: 0.0895 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2807 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 413/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.0893 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2804 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 414/500\n",
            "2/2 [==============================] - 2s 824ms/step - loss: 0.0890 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2802 - val_sparse_categorical_accuracy: 0.5428\n",
            "Epoch 415/500\n",
            "2/2 [==============================] - 2s 783ms/step - loss: 0.0887 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2805 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 416/500\n",
            "2/2 [==============================] - 2s 774ms/step - loss: 0.0884 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2804 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 417/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.0882 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2801 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 418/500\n",
            "2/2 [==============================] - 2s 788ms/step - loss: 0.0879 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2802 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 419/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.0876 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2801 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 420/500\n",
            "2/2 [==============================] - 2s 805ms/step - loss: 0.0874 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2801 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 421/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.0872 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2802 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 422/500\n",
            "2/2 [==============================] - 2s 814ms/step - loss: 0.0869 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2799 - val_sparse_categorical_accuracy: 0.5430\n",
            "Epoch 423/500\n",
            "2/2 [==============================] - 2s 813ms/step - loss: 0.0866 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2798 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 424/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.0863 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2795 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 425/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.0861 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2795 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 426/500\n",
            "2/2 [==============================] - 2s 810ms/step - loss: 0.0859 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2792 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 427/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.0856 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2787 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 428/500\n",
            "2/2 [==============================] - 2s 801ms/step - loss: 0.0854 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2789 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 429/500\n",
            "2/2 [==============================] - 2s 799ms/step - loss: 0.0851 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2788 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 430/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.0848 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2785 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 431/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.0846 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2785 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 432/500\n",
            "2/2 [==============================] - 2s 809ms/step - loss: 0.0844 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2783 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 433/500\n",
            "2/2 [==============================] - 2s 855ms/step - loss: 0.0841 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2781 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 434/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.0839 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2782 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 435/500\n",
            "2/2 [==============================] - 2s 847ms/step - loss: 0.0836 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2780 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 436/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.0834 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2780 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 437/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.0832 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2777 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 438/500\n",
            "2/2 [==============================] - 2s 795ms/step - loss: 0.0829 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2775 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 439/500\n",
            "2/2 [==============================] - 2s 824ms/step - loss: 0.0827 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2769 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 440/500\n",
            "2/2 [==============================] - 2s 827ms/step - loss: 0.0824 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2771 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 441/500\n",
            "2/2 [==============================] - 2s 816ms/step - loss: 0.0822 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2768 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 442/500\n",
            "2/2 [==============================] - 2s 807ms/step - loss: 0.0820 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2766 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 443/500\n",
            "2/2 [==============================] - 2s 804ms/step - loss: 0.0818 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2761 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 444/500\n",
            "2/2 [==============================] - 2s 802ms/step - loss: 0.0815 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2761 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 445/500\n",
            "2/2 [==============================] - 2s 794ms/step - loss: 0.0814 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2763 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 446/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.0811 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2764 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 447/500\n",
            "2/2 [==============================] - 2s 792ms/step - loss: 0.0809 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2762 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 448/500\n",
            "2/2 [==============================] - 2s 812ms/step - loss: 0.0806 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2761 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 449/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.0804 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2763 - val_sparse_categorical_accuracy: 0.5434\n",
            "Epoch 450/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.0802 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2762 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 451/500\n",
            "2/2 [==============================] - 2s 781ms/step - loss: 0.0800 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2763 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 452/500\n",
            "2/2 [==============================] - 2s 784ms/step - loss: 0.0798 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2762 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 453/500\n",
            "2/2 [==============================] - 2s 796ms/step - loss: 0.0796 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2759 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 454/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.0793 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2759 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 455/500\n",
            "2/2 [==============================] - 2s 806ms/step - loss: 0.0791 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2757 - val_sparse_categorical_accuracy: 0.5432\n",
            "Epoch 456/500\n",
            "2/2 [==============================] - 2s 800ms/step - loss: 0.0789 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2754 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 457/500\n",
            "2/2 [==============================] - 2s 791ms/step - loss: 0.0787 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2754 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 458/500\n",
            "2/2 [==============================] - 2s 823ms/step - loss: 0.0785 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2750 - val_sparse_categorical_accuracy: 0.5442\n",
            "Epoch 459/500\n",
            "2/2 [==============================] - 2s 789ms/step - loss: 0.0783 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2752 - val_sparse_categorical_accuracy: 0.5444\n",
            "Epoch 460/500\n",
            "2/2 [==============================] - 2s 773ms/step - loss: 0.0781 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2755 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 461/500\n",
            "2/2 [==============================] - 2s 780ms/step - loss: 0.0779 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2753 - val_sparse_categorical_accuracy: 0.5436\n",
            "Epoch 462/500\n",
            "2/2 [==============================] - 2s 785ms/step - loss: 0.0777 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2753 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 463/500\n",
            "2/2 [==============================] - 2s 779ms/step - loss: 0.0774 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2752 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 464/500\n",
            "2/2 [==============================] - 2s 770ms/step - loss: 0.0773 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2753 - val_sparse_categorical_accuracy: 0.5438\n",
            "Epoch 465/500\n",
            "2/2 [==============================] - 2s 782ms/step - loss: 0.0770 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2752 - val_sparse_categorical_accuracy: 0.5440\n",
            "Epoch 466/500\n",
            "2/2 [==============================] - 2s 778ms/step - loss: 0.0768 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2751 - val_sparse_categorical_accuracy: 0.5442\n",
            "Epoch 467/500\n",
            "2/2 [==============================] - 2s 776ms/step - loss: 0.0766 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2752 - val_sparse_categorical_accuracy: 0.5440\n",
            "Epoch 468/500\n",
            "2/2 [==============================] - 2s 786ms/step - loss: 0.0764 - sparse_categorical_accuracy: 1.0000 - val_loss: 1.2751 - val_sparse_categorical_accuracy: 0.5440\n",
            "Model Evaluation\n",
            "313/313 [==============================] - 3s 11ms/step - loss: 1.3172 - sparse_categorical_accuracy: 0.5232\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cex989Dz87/fgK6+BOe6fLSIiEhMJeQUucM41Tg/62FnZgzBOHgTF98O2d2Dj6/ErTkREpJ2EDWNjDCOyU9he0YMwBuf2irmnwYJ/1oVARESkVyRsGAOMyE5lZ8WRnn3I64cr/xNq9sPCH8anMBERkXYSO4yznKtwhcLNPftgwTQ4605Y+RRsXxyf4kRERCISOoyLslMJNVv2HKrv+YfP/y4MLoLX7oGmKI/IFhEROQEJHcanZDunN+2o7OFUNUAgFS5/GCo/hZKfxLgyERGRNgkdxkXZqQA9P4irxahimPoVWPIIfPq3mNUlIiLSXkKHcW56EkG/p+cHcbV3yX/AkPHwytfgsKsXFRMRkQSV0GHs8RhOyTqB05vaC6TCl56Bplp4+asQDsWuQBERERI8jKHl9KaTCGNwRsaX/QJ2vgeL/j02hYmIiEQkfhhnpbCj8gjOFTtPwpnzYeqNsPjnsHFBbIoTERFhIIRxTir1Tc2UVTec/MYu/SnkT4ZXvwZlG05+eyIiIgyAMB4ZOaL60wM1J78xfzJc+zz4U+CFa+FI+clvU0REBryED+OxeWkAfFoWgzAGyCyAa5+D6n3w/HxoPMn90SIiMuAlfBjnpieRHvSxOVZhDFA4E67+DexeCa/cAs3h2G1bREQGnIQPY2MMY3PT2LS/OrYbPvVyuPRn8MkCWHAfnOwBYiIiMmD53C6gN4zNTeftjftjv+FZt8HhUljyMKQPhfO+FfvvEBGRhJfwI2Nw9huX1zRSeaQx9huf+wCceZ1z/vGSR2K/fRERSXgDIozH5DoHcW2J5X7jFh4PXPFLOO0LsPD7sOzx2H+HiIgktAERxuPy0gHYXBbj/cYtvD74whMw4TJ441uw+CHtQxYRkagNiDDOzwySGvCyeX8cRsYtvH744lNw+hfhrQfgr9+B5ub4fZ+IiCSMAXEAlzGGMXnp8RsZt/AF4Av/BWm5sPRXUFsOVz3mjJxFRESOYcCkxNjcNN7ZdCD+X+TxwMU/htQh8Pa/Qk0ZXPVr52IhIiIiXRgQ09TghHFZdQNVtU3x/zJjYPY/OQd2la6AX5+tm0uIiMgxDZwwjlwWc8uBOE9Vtzf1y3D7u5A1Cl68Dhb9WFfrEhGRowycMM6NHFEdz4O4upI9Gm7+K0y+Hv7+U/j9F5ypaxERkYgBE8YFg5IJ+j2xvUZ1tPxBuPJRuPwR2LkUHvssbF/S+3WIiEifNGDC2OMxjInHNaqjZQxMuxFufRuSMuCZK50rdjXVu1OPiIj0GQMmjAHG52XwyT6XwrjF0NPh1oUweo5zxa7/nAY7l7lbk4iIuGpAhfGp+emUVTdwoLrB3UKSB8P1L8GN/+Ocm/zMFbD4F7pIiIjIADWgwnjisAwANuw97HIlESPPha/+L4y5AN76ITz3Raj41O2qRESklw2sMM53wnh9XwljgLQhMP/38Lmfw65l8OgsePN7UF/ldmUiItJLBlQYD0oJUDAomfV7+lAYg3Nw14xb4B9XwZnXwvuPwq/PgdUvQrgXLlIiIiKuGlBhDHBqfkbfGhm3l54HV/4SblkISenwx6/Dw2fC0scgHHK7OhERiZMBF8YTh2Ww9UANdY19+EpYhTPg9iXwDy/B4CL467fh8XNhy9tuVyYiInEw8MI4P4NmC5+4db5xtDweGHcx3PQ6XPM7CNU5V+/6n3vh4Ha3qxMRkRgakGEM9L39xsdiDJx2FdzxHsz8Onz4HDwyFf54OxzY5HZ1IiISAwMujAuzkkkP+li7p58drexPhnk/g3tXw6yvw7o/waMz4aWvwJ4P3a5OREROwoALY2MMZw4fxOpdh9wu5cRkFsAlP4H/s9a5TeOni+CJYvjNhbDmZQg1ul2hiIj00IALY4AzCzPZuK+a+qY+fBBXd1JzYO4PnFC+5D+gthxeuQUeOg3euB+q97ldoYiIRGlghvHwQYSbLev621R1V4KZcNYdcPdKuP4VKJwJK56EhyfDq1+HXR+4XaGIiHTD53YBbphcOAiAD3ceYtqILJeriRGPB8Ze4CwVn8J7/wlrX4GPX4TCWTDpGph4lXPFLxER6VO6HRkbY35rjCkzxqw9xvvFxpgqY8xHkeUHsS8ztnIzggzLDLK6NAFGxl3JHg2X/wK+uREu+ndoqIEF/wz/33j4/dXw0QtQ30+OJhcRGQCiGRk/DfwSeOY467xrrb0sJhX1kjML+/FBXNEKpMJn7naW/eucA7zWvAx/uh18Qec85knXwJgLwR90u1oRkQGr2zC21r5jjCmKfym9a3LhIN5Yu4/KI41kpQbcLif+8k5zlrk/gNLlsOa/Ye2rsP7PkJQJE+bBxCud+yz7ktyuVkRkQInVPuOzjTGrgT3AP1tr18Vou3FzZmS/8Ue7DjJnQp7L1fQiY5yDvApnwsU/gW1/d/Ytb/wLrH4BAukw/lInmMfMdc5vFhGRuDLW2u5XckbGf7HWnt7FexlAs7W2xhgzD3jYWjv2GNu5DbgNIC8vb9qLL754EqV3VFNTQ1paWtTrN4Qtd75Vy6Uj/Xxx3AAYGXfDNDcx+OAahhxYQk75MvyhakLeIJVZ09mTehq1+bNoTMp2u8yE09N/txI99W38qG9P3Pnnn7/SWju9c/tJh3EX624Hpltry4+33vTp0+2KFSu6/e5olZSUUFxc3KPPXPnLxST5vbz09bNjVkdCCDfB9nedKeyNr8ORA057zngYcTac8hnncdAp7taZAE7k361ER30bP+rbE2eM6TKMT3qa2hgzFNhvrbXGmJk4R2hXnOx2e8P0oix+v3QHjaFmAr4Becp117x+Z9/x6DnwuYdY8frTTM+qhu2LYe0fYeXTznqZhXDK2W0BPWS8Mw0uIiI90m0YG2NeAIqBHGNMKfBDwA9grX0M+CJwhzEmBNQB19pohtt9wPQRg3ly8TbW7qli6imD3S6nb/J4qEkfBecUwzn3QnPYOTJ75/uw4z1nn/Oal5x1U4fAyPNg1HnOuc3ZY53zn0VE5LiiOZr6um7e/yXOqU/9zrQiJ4BXbj+oMI6Wxwv5ZzjLrK+DtVC5NRLM78DWElj7srNuIB2GTYaCqVAwHYbPgIx8V8sXEemLBuQVuFrkpgcZkZ3C8u2VfO3cUW6X0z8Z41xkJHs0TP2yE84HPoHdK2HPKufx/V9Bc5Ozfmquc4rV0NMh73QomAbZYzS9LSID2oAOY4BpIwbz908OYK3FKBBOnjGQO8FZplzvtDXVw741TjDvWwP718KyJyDc4Lyfng9Fs6HoszByNgweqXAWkQFlwIfxrJFZvLpqN5vLahiXl+52OYnJH4TCGc7SIhyCii2wayls/bszvd2y7zljuHMedO6pMGSC8zh4JHgH/D9XEUlQA/6/bp8ZnQPAki3lCuPe5PW1jaCn3eRMb5dvck6p2vauM4pe92q79ZMgZ6xzxHZLQA85FbJGOvuxRUT6sQEfxoVZKZySlcKSLRXcfM5It8sZuIyJBO14mHGr09ZQA+WfQNlGOLABDmyC0hXOZTyJHLDvCzpHbWePgqzRMHQSDBrh7MNOHuTanyMi0hMDPowBzhmTzV9W7yUUbsbn1ak4fUZSmnOAV8G0ju2NR5yDxMo2QNl65/m+tc4FSppDbetlj4H8yTCoEDIKIGNY5LEAUnO0X1pE+gyFMc5U9Qsf7GLN7iqm6BSnvi+QGjldamrH9nAT7PsYasqcc6F3r4TSD5wribUczd3CG+gYzq3PhzmnX6UPg7Q8nSctIr1CYQx8ZrRzzeX3Pq1QGPdnXn/bKHr8pW3tzc3OJT0P744se5zHqsjzXcucx6MCO8m55OfgEU4wp2RBSrazJEeet4S4Di4TkZOg/4IA2WlJTBiazpIt5dx1/hi3y5FY83ggPc9ZOo+mWzQ3Q205VJVCzX4nrA/ugIPb4dBOZ791bTmE6o/+rPE44Zya41yFLH2oE94tj8mDIZjZcdHdsESkHYVxxDljcnh26Q7qm8IE/To6d8DxeCAt11mOp7EWaisiS7kzoj600xl5Hyl3Hnd94AR6V8Hdwp8CqUOYGk6CPaOdIE/LdcK885KSpSPGRRKcwjjinDHZPLl4Gyu2H+SzY3PcLkf6qkCKswwqPP561kJ9lbP/ur4qshxqe6ythCMHCO36xBmN7/nQCXIbPnpbxgPBQU4oJw+OLJHn7dtaRuYpOc5rn24NKtJfKIwjZo7MxucxLPm0XGEsJ88Y59Sqbk6v+rj9reiam52gPlIOR8raRts1ZVBXCXUHnRCv2Q8HNkLtQWisPvbGA2mR0G4J8qy20E7JgWCGs04gtd1ju+e+JB1xLtJLFMYRaUk+po0YzKKNZXz7kglulyMDkccTOUgsC4aMi+4z4aa2kK4tbwvwukNOgNdGQryuEg7tioT6IVrP0z4e43VOLwsOigR3uvM6kAZJ6R2X1rYMZ53O7V7/SXWNSKJTGLdz4cQ8fvT6BnZV1lKYleJ2OSLd8/qj29fdXnPYCemGw845261LzdHPG6oj0+uHnbaa/dDwqdPeWANNtdF9py/YMaD9yeDxdbF4nb/JF4xMw2c7I3RvwHn0BSOvk5xpeG+nxR909sf7kzHtzzkX6eMUxu3MPdUJ47c27NfVuCRxebyQNsRZTlY45IRyQ3Xb0ljtXD2t9XWNE/zt20L1zo+CUAM0H3Eu1tIcdk4vCzc57bUVEKo74dLOA3jX2xrOzpLS7jEYeZ7qHAfgT2lr7xD6kef+ZOd5V48tzzWtLydIYdzOyJxUxuSmKYxFouX1RbVv/IQ11TnBHG50HkMNzt2+QvVOaIcbIdToPIYbnDuEheqgqY6tm9YxavhQp62p1tlWy2Oo3pkdaKqDpiPOY2NtZKQfxRT+sfiC7ZZAW5h7/ZEl4MwAeP3g8be1H/Xc53y25YdBS1vrDILf+VHVuq1OMwztv8+bFJltiMwweP1tbfrx0GcojDu54NQ8fvPuVqrqmshM1n4uEVe1jDpPwM6GEka1HBwXLWudoG79ARB5HqpvF/Rtgd8a7J0fQ/WRHwkNbdsKNzkzAI01bc/bt3d+HmrgpH4YRKPzNL83cPT0vy+pY4B7A0w4UAGHXnLC3Hi6WaJYp/X7fG3Pj7Ubo8OPjaS2duNpe9+0nAponf9NW//WTtvv/GPEdtHfvfSDRWHcyYUT83js759S8kkZV04ucLscEelNxpzUD4CYsjYyZV8fmcaPLC2B3aGt/eumyOvIdH+4yflR0GEWod3SfmahZRdByw+Dlrb6wx0+M+hIDdRvAdt8nMUe//3+4v6dzoV64kxh3MnkwkHkpAV4a4PCWERcZIwzSu2D54svbX9K3olqCeuWHxitMwPtn4fb/cgId/zB0TLz0BxyTgu0YWcdG253wxjTNrINNx29/Q4i67UfCVvrjL57gcK4E6/HMGdCLm+s3UdjqJmATzcKEBGJOWOc6WSP15kKH+CUNF244NQ8qutDfLCt0u1SRERkAFAYd2H22CEk+Ty8tWG/26WIiMgAoDDuQnLAy+yxOSxcvx/b1dF1IiIiMaQwPoYLJ+ax+1Ada3ZXuV2KiIgkOIXxMVxyWj4Br4c/f7TH7VJERCTBKYyPITPFT/H4IfzP6j2EmzVVLSIi8aMwPo4rJxdQVt3A+59WuF2KiIgkMIXxccw9NZe0JB9/+mi326WIiEgCUxgfR9Dv5ZLTh/LXtfuobwq7XY6IiCQohXE3rppcQE1DiL9tLHO7FBERSVAK426cPTqb3PQkXl2lqWoREYkPhXE3vB7D56cWsOiTMsoO17tdjoiIJCCFcRTmTy8k3Gx5eVWp26WIiEgCUhhHYdSQNGaNzOIPy3fp8pgiIhJzCuMoXTuzkB0Vtby/Vecci4hIbCmMo3Tp6flkBH38Yfkut0sREZEEozCOUtDv5fNTCnhj7T4O1Ta6XY6IiCQQhXEPXDvzFBpDzfz3Ch3IJSIisaMw7oFT8zOYOTKLp9/bTijc7HY5IiKSIBTGPfTVc4rYfaiOtzbsd7sUERFJEArjHrpw4lCGD07mt0u2u12KiIgkCIVxD3k9hhvPLuKDbZWs3V3ldjkiIpIAug1jY8xvjTFlxpi1x3jfGGMeMcZsMcZ8bIyZGvsy+5YvzSgkJeDlKY2ORYplkGIAABeVSURBVEQkBqIZGT8NXHKc9y8FxkaW24Bfn3xZfVtmsp8vThvOa6t3s7eqzu1yRESkn+s2jK217wCVx1nlSuAZ61gKDDLG5MeqwL7qa7NH0WzhN+9uc7sUERHp52Kxz7gAaH9ZqtJIW0IrzErhijOH8cIHOzl4RBcBERGRE+frzS8zxtyGM5VNXl4eJSUlMdt2TU1NTLcXjekpzfyxMcwPny/hC2MDvfrdvcmNvh0o1Lfxo76NH/Vt7MUijHcDhe1eD4+0HcVa+wTwBMD06dNtcXFxDL7eUVJSQiy3F63FVSt5e9MBfnjd2WSnJfX69/cGt/p2IFDfxo/6Nn7Ut7EXi2nq14CvRI6qPguostbujcF2+4VvXjSeuqYwv1y0xe1SRESkn4rm1KYXgPeB8caYUmPMLcaY240xt0dWWQBsBbYA/wXcGbdq+6AxuWl8aXohzy3dya7KWrfLERGRfqjbaWpr7XXdvG+Bu2JWUT/0jQvG8ccPd/PQwk38fP5kt8sREZF+RlfgioGhmUFuOqeIP360mw17D7tdjoiI9DMK4xi587wxpCf5+NlfN7pdioiI9DMK4xjJTPFzR/EYFn1ygGVbK9wuR0RE+hGFcQzd9Jki8jKS+I+/bsTZlS4iItI9hXEMJQe8fPPC8Xy48xCvrOryVGsREZGjKIxj7IvThjP1lEH8eMEGXSZTRESiojCOMY/H8O+fn0RVXRM/e1MHc4mISPcUxnFwan4GXz2niBc+2MXKHce74ZWIiIjCOG6+ccE48jODfO+Pa2kKN7tdjoiI9GEK4zhJTfLxw8tPY+O+ap5est3tckREpA9TGMfRxaflMXdCLg+9tYndh+rcLkdERPoohXEcGWN44IrTALjvv1fT3Kxzj0VE5GgK4zgrzErhh5dP5L1PK3hy8Ta3yxERkT5IYdwLvjS9kIsm5vHgm5+wdneV2+WIiEgfozDuBcYYfnr1GWSnBbjr+VVU1ze5XZKIiPQhCuNeMjg1wCPXTaH0YB3feXWNrl0tIiKtFMa9aEZRFv904Tj+8vFeXvhgl9vliIhIH6Ew7mV3nDea2WNzeOB/1rF+z2G3yxERkT5AYdzLPB7DQ/MnMyjZz53PreSw9h+LiAx4CmMX5KQl8ej1Uyk9WMc3X9L5xyIiA53C2CUzirL47rxTWbh+P794a5Pb5YiIiIt8bhcwkN18ThEb9x3mkb9toSgnlS9MHe52SSIi4gKFsYuMMfzoqknsqqzj2698TMGgZGaNyna7LBER6WWapnZZwOfhsRumcUpWCrf/fqVuKCEiMgApjPuAzBQ/v7lxBqGw5Zanl1N5pNHtkkREpBcpjPuIkTmp/OqGqWwrP8I//NdSKmoa3C5JRER6icK4D5k9dghP3jgjEsjLKFcgi4gMCArjPuazY3N46qYZ7Kg8wnVPLOVAtQJZRCTRKYz7oM+MyeHpm2ey+1Ad1z7xPmWH690uSURE4khh3EedNSqbp2+eyb6qeq59Yin7qhTIIiKJSmHch80cmcXvvjqTsuoGrn3iffZW6bQnEZFEpDDu46YXZfHMLTOpqGlk/uNLdR6yiEgCUhj3A1NPGcyzt87iYG0j8x9/n12VtW6XJCIiMaQw7icmFw7i+VvPoro+xFWPLmHljoNulyQiIjGiMO5HJg3P5NU7P0Na0Md1/7WUP3+02+2SREQkBhTG/czoIWn86c5zmFw4iHtf/IhfvLUJa3U/ZBGR/kxh3A8NTg3w7C0zuXrqcH7x1mbufv5DDtc3uV2WiIicIIVxP5Xk8/L/rjmD+y+dwF/X7WPew+9qP7KISD+lMO7HjDHcft5o/vv2szEGvvT4+zzy9mbCzZq2FhHpTxTGCWDqKYN5/Z7ZXHZGPj9fuInrntD5yCIi/YnCOEFkBP08fO0UHpp/Juv2VHHpL95hwZq9bpclIiJRUBgnmM9PGc6Ce2czckgadz63im+//DG1jSG3yxIRkeOIKoyNMZcYYz4xxmwxxtzfxfs3GWMOGGM+iiy3xr5UidaI7FRevv1s7iwezUsrd3HZI4tZu7vK7bJEROQYug1jY4wXeBS4FJgIXGeMmdjFqn+w1k6OLL+JcZ3SQ36vh29dMoHnbp1FbWOYz/9qCT9fuIn6prDbpYmISCfRjIxnAlustVuttY3Ai8CV8S1LYuUzo3N4497ZXHp6Po+8vZkLH/o7b2/Y73ZZIiLSTjRhXADsave6NNLW2dXGmI+NMS8bYwpjUp3ExODUAI9cN4XnvzaLJJ+XW363glt/t1w3nBAR6SNMd5dSNMZ8EbjEWntr5PWXgVnW2rvbrZMN1FhrG4wxXwfmW2vndLGt24DbAPLy8qa9+OKLMftDampqSEtLi9n2ElWo2fK/O5r485Ymmi1cNsrPpSP9BLzmmJ9R38aP+jZ+1Lfxo749ceeff/5Ka+30zu3RhPHZwAPW2osjr78DYK39yTHW9wKV1trM4213+vTpdsWKFVGW372SkhKKi4tjtr1Et7eqjh/9ZQOvr9nLiOwUHrjiNM4fn9vluurb+FHfxo/6Nn7UtyfOGNNlGEczTb0cGGuMGWmMCQDXAq912nh+u5dXABtOpliJv/zMZB69firP3jITrzHc/NRyvvzkMtbt0VHXIiK9rdswttaGgLuBN3FC9iVr7TpjzL8ZY66IrHaPMWadMWY1cA9wU7wKltiaPXYIb3xjNt+bdyofl1Zx2X8u5p/+8JH2J4uI9CJfNCtZaxcACzq1/aDd8+8A34ltadJbknxevnbuKL40vZBf/X0LTy3Zzmur93DVlALuLB7tdnkiIgkvqjCWgSEzxc93Lj2Vmz5TxON/38oLH+zk1VWlzMjzMnTCYSYMzXC7RBGRhKTLYcpR8jOTeeCK01j87Tncdu5oVh8Ic8kv3uVrz6xg9a5DbpcnIpJwNDKWYxqSnsT9l07gdO9eNpsCnlqyjYXr9zN7bA53nz+GmSOzMObYp0SJiEh0NDKWbqUFDP/nwnEsuX8O375kAhv2Hmb+E0u5/JeLefGDnboRhYjISVIYS9TSg37uKB7Nu9+aw/+98jSaQpb7X13DrB+/zQOvrWNLWY3bJYqI9EuappYeSw54+fLZRdxw1giWbz/I75fu4LllO3j6ve2cO24I1886hTkTcvF79VtPRCQaCmM5YcYYZo7MYubILMprJvL8sp38fukOvv7sSnLSkrh6agHXTC9kTK4umycicjwKY4mJnLQk7pk7ljuLR1PyyQH+sGIXv1m8jcff2cq0EYOZP72QeWfkk5akf3IiIp3pv4wSUz6vhwsm5nHBxDzKquv546rd/GHFLr71ysd8/89rmXtqLpefMYzzJ+QS9HvdLldEpE9QGEvc5KYH+fp5o7nt3FGs2nmQ1z7aw+tr9rJgzT7SknxcNDGPeZPyOWdMDskBBbOIDFwKY4k7YwzTRmQxbUQW379sIku3VvLa6t28sXYfr364m6Dfw2fH5DD31DzmTsglNyPodskiIr1KYSy9yuf18NmxOXx2bA7/96rTWba1krc37OetDWW8taEMgDOHZzrBfGouE/MzdGEREUl4CmNxTZLPy7njhnDuuCE8cIXlk/3VvLXeCeaH3trEzxduYlhmsDWYzx6dTZJP09kikngUxtInGGOYMDSDCUMzuHvOWA5UN7BoYxkLN+zn5ZWlPLt0BykBL7PH5nDBqXmcN34IuemazhaRxKAwlj5pSHoSX5pRyJdmFFLfFOb9Tyt4a8N+3t5Qxpvr9gMwLi+Nc8bkcM7oHGYUZZGZ4ne5ahGRE6Mwlj4v6Pdy/oRczp+Qy4+usqzbc5h3N5fz3qflPL9sJ08t2Y4xMD4vnRlFWcwYmcXMoiyGZmrkLCL9g8JY+hVjDKcXZHJ6QSZ3FI+mIRTmw52HWL6tkg+2V/LqKmdKG6AwK5mZRdnMHDmYGUVZjMxJ1cFgItInKYylX0vyeTlrVDZnjcoGIBRuZsPeaj7YXskH2ypY9EkZr6wqBZyrhM0ocoJ55sgsxuWlE/Dp+tki4j6FsSQUn9fDpOGZTBqeyS2fHYm1lk8PHGH59kqWb6tk2bZK3li7D4CA18OE/HRnpD0sk0kFmYwbmqYjtkWk1ymMJaEZYxiTm8aY3DSum3kKAHsO1bFyx0HW7q5ize4q/rJ6D88v2wmA32sYPzSdSZGp8NOHZTJ+aLou3SkicaUwlgFn2KBkhg1K5vIzhwFgrWVnZS1rdlexdvdh1u6uYsGafbzwwS4AfB7DuLx0Ti/IYFxeOmPz0hmbm0Z+ZlD7oEUkJhTGMuAZYxiRncqI7FQuO6MtoEsP1kUC2hlBv72hjJdWlLZ+Li3Jx5jcNMbmpjE2L601pIdlJuPxKKRFJHoKY5EuGGMozEqhMCuFeZPyW9srahrYXFbD5rIatuyvZnNZDYs+OcB/r2wL6ZSAl7G5aYzJTXdCOjeNcXnpFAxSSItI1xTGIj2QnZZEdlpS69HbLQ4eaWTLgRo27a9m8/4atpTVsHjLgdYjuQGCfk9kJJ3OmNw0RmSnUHYozKSaBrJSA5ryFhnAFMYiMTA4NcCM1CxmFGV1aK+qbWLLASegN5c5Yb10awV//HB36zr/tvQtUgJehg9OZvjgFApbHrNaXqfo6mIiCU5hLBJHmSn+1ttHtlfTEKL0YC1vvPMBgwpGs6uyjl0Hayk9WMfybZVUN4Q6rJ8e9HUd1JHHtCT9X1mkP9P/g0VckJbkY8LQDPbl+ig+Z2SH96y1HK4LRcK5tkNQbys/wruby6lrCnf4zOAUP4VZKQwfnEzhYOdxeFZbeOvULJG+TWEs0scYY8hM8ZOZ4pzr3Jm1loojjZQerGNXZW1rUO+qrGXj3mreWl9GY7i5w2cGpfgZmhEkLyNIXkaS8zwz2K4tSHZqQAeYibhEYSzSzxhjyElLIicticmFg456v7nZcqCmgV2VTkiXHqxl3+F69lU1sP9wPev3Hqa8pgFrO37O7zXkpjthnZcRbP2O7LQAOWkBstu9Tk/y6YAzkRhSGIskGI/HtI52pxd1vU5TuJnymgb2VdWz/3A9+6rq2XfYCev9h+v5ZH81731aQVVdU5efD3g9ZKcFIkGdRHZqUiSwI6/TkshOdZ5npQZ0DXCRbiiMRQYgv9dDfmYy+ZnJx12vMdTMwdpGymsaKK9ppKKmgYqaRsqPRB4jrzftq6a8pvGo6fEWmcl+slMDDErxMyglwKBkP5kpfgYlBxic6iczua19UKQ9PejTtLkMGApjETmmgM/TOsrujrWWmoZQa0iX1zRSEQntipoGyo80UlXbRFl1PZv2V1NV23TUUePteQytIZ3ZGtKR0G73PDPyPDPZT0ayn/SgTzf7kH5HYSwiMWGMIT3oJz3opygnNarPNIWbqapr4lBtE1V1jRyqdZ4frG1sbT9U18Sh2kYqjzSy9cARDtY2Ul1/7BAHSPJ5CHot2StLyAg6IZ0R9JEe9JOR7HPagj7Sgj7SkvykJflIDzpLWpLTrkCX3qQwFhHX+L2e1gPFeiIUbuZwfYhDtY2tYX24LsTh+iaq60Mcrmti49adpA3O4HB9E1V1TZRW1nK4vonDdaFjTqe3F/B6SGsf0JHATgn4SE3ykuyPPAa8pPi9pCT5SAl4SQ34nLaAl5RAxzbtO5djURiLSL/j83rISg2QlRo45jolJfspLp7a5Xv1TWGq60PUNISoqQ9R3dDkPLa0NbQ8d9pbXu85VE9tY4jaxnBkCdFsu/yKruv2mLaQTooEtr/d80h4J0cCvKu25ICX1CTncy3Pgz6v9q/3cwpjERlwgn4vQb+XIek9G5F3Zq2lIdTcGsztQ7q2IUxtU5jaBqe9rinMkYZQh3XrGsMcaQxx8EgjpQed17WNIY40hmkMdT96by8lMhrvENoBX+Rv9bQ+Jvkir33eDm1Jret4Cfo8JPmPsZ5G93GhMBYROUHGmNYAO94o/USEws3UNrUEtBPkdU2RsO8U6sd7XnGkkYZQmIamZuqbws4SaibckyF9Jz4PpJS82fq3J/k8HUK/NdwjwR/wOUuS1wn5gLetLeD1kOT3dGhL8nkIeL1Htbe950m489wVxiIifZDP6yHD6yEjGJ+bhDSFm2kItQvoSFi3Bneora3tPedx89bt5OYXtH62bTvN1DSEKK/p+AOgMdRMQ7i5x6P94zkqvH1dBLrPCf6k46zn97a99ns9+L2mdR2/18O544b0yr5+hbGIyADkj4TNidxkpKRkD8XFp/X4c9ZaGiOh3Bhqbn3eEOr42BhupqEpfNS6DU2Rx1Bbe0Mo3GFb7detqmuKtIU7fL5lvVAUswMfP3CRwlhERBKHMSay37lvnDbW3Oz8OGgKN9MUtjSF28K8KdxMU8iSGuidmFQYi4jIgOTxGIIeb5+4q5kOixMREXFZVGFsjLnEGPOJMWaLMeb+Lt5PMsb8IfL+MmNMUawLFRERSVTdhrExxgs8ClwKTASuM8ZM7LTaLcBBa+0Y4CHgp7EuVEREJFFFMzKeCWyx1m611jYCLwJXdlrnSuB3kecvA3NNop0EJiIiEifRhHEBsKvd69JIW5frWGtDQBWQHYsCRUREEl2vHk1tjLkNuA0gLy+PkpKSmG27pqYmptuTNurb+FHfxo/6Nn7Ut7EXTRjvBgrbvR4eaetqnVJjjA/IBCo6b8ha+wTwBMD06dNtcXHxCZTctZKSEmK5PWmjvo0f9W38qG/jR30be9FMUy8HxhpjRhpjAsC1wGud1nkNuDHy/IvA36y1J37hUxERkQGk25GxtTZkjLkbeBPwAr+11q4zxvwbsMJa+xrwJPCsMWYLUIkT2CIiIhKFqPYZW2sXAAs6tf2g3fN64JrYliYiIjIwGLdmk40xB4AdMdxkDlAew+1JG/Vt/Khv40d9Gz/q2xM3wlo7pHOja2Eca8aYFdba6W7XkYjUt/Gjvo0f9W38qG9jT9emFhERcZnCWERExGWJFMZPuF1AAlPfxo/6Nn7Ut/Gjvo2xhNlnLCIi0l8l0shYRESkX0qIMO7ufstyfMaY3xpjyowxa9u1ZRljFhpjNkceB0fajTHmkUhff2yMmepe5X2fMabQGLPIGLPeGLPOGHNvpF39e5KMMUFjzAfGmNWRvv3XSPvIyH3Vt0Tusx6ItOu+6z1gjPEaYz40xvwl8lr9Gkf9PoyjvN+yHN/TwCWd2u4H3rbWjgXejrwGp5/HRpbbgF/3Uo39VQj4prV2InAWcFfk36f69+Q1AHOstWcCk4FLjDFn4dxP/aHI/dUP4txvHXTf9Z66F9jQ7rX6NY76fRgT3f2W5Tiste/gXMa0vfb3qP4dcFW79mesYykwyBiT3zuV9j/W2r3W2lWR59U4/3ErQP170iJ9VBN56Y8sFpiDc191OLpvdd/1KBhjhgOfA34TeW1Qv8ZVIoRxNPdblp7Ls9bujTzfB+RFnqu/T1Bk+m4KsAz1b0xEplI/AsqAhcCnwKHIfdWhY//pvuvR+wXwLaA58job9WtcJUIYS5xF7sClw+5PgjEmDXgF+Ia19nD799S/J85aG7bWTsa5tetMYILLJfV7xpjLgDJr7Uq3axlIEiGMo7nfsvTc/pbp0chjWaRd/d1Dxhg/ThA/Z619NdKs/o0ha+0hYBFwNs7UfstNcNr3X2vfHu++68I5wBXGmO04u/3mAA+jfo2rRAjjaO63LD3X/h7VNwJ/btf+lchRv2cBVe2mW6WTyL6zJ4EN1tqft3tL/XuSjDFDjDGDIs+TgQtx9skvwrmvOhzdt7rvejestd+x1g631hbh/Pf0b9ba61G/xlVCXPTDGDMPZx9Hy/2W/93lkvoVY8wLQDHOnVj2Az8E/gS8BJyCc3etL1lrKyPh8kuco69rgZuttSvcqLs/MMZ8FngXWEPb/rfv4uw3Vv+eBGPMGTgHDnlxBhYvWWv/zRgzCmdElwV8CNxgrW0wxgSBZ3H221cC11prt7pTff9gjCkG/tlae5n6Nb4SIoxFRET6s0SYphYREenXFMYiIiIuUxiLiIi4TGEsIiLiMoWxiIiIyxTGIiIiLlMYi4iIuExhLCIi4rL/H7zS1ruOz8nAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kK4HkJpCS4Xz3lUFJAXgKnM/WNAvSmjuf1V/mfA94ITEZmrSGX33XIM0fP2GsYWoRcVswCDbgBJINQMkO2LsFElMhOR0SEiGjrRN6FcVOWHg94C3Z/8tf4QScr8w5RvmeUHhVfgX2hVkw8OPtQX8oaMtCAVTmnOugDKQ2g6CPzr5y2JYGSalO7YkpkJjkfE9Idn5OSnNCbf3HzvakNEhKgYQk51jGHPx7QtKh96nte1Kq035JqU4A+8ucdqvwQHIadBrihHBKhtP+FcWQlO78GSSnO/UGfM7naCBxk0zF5bq1SURCgkGnx1TZ8/OHgqmyRxfwgt8Lnh+gYJXzj3llL7D6dxt0Aiox2fnHfs8mJ0yDgVCQhsLTV+p82WAYP4RxAi6jrdOzS0gAkwgmwflKSKz2PXnfdpPgBF1ymhNUSWlOAKU2g7RmoUCqtj0hCdodDxntnHNYy8cff6wlFMMsbpJJ9xmLNHLWhkKywgmIxGTAhIZGvft6gKFeZ9PidbBsB+xYA7s2OMdITHKGJzd+4QzJ1kVyhjPkWdkLTE4L9fLSnGAr3uacMzkN2vZyhnOT052QTKkcHs0I9biSncCsDM4mrULDuH4ntAM+KNvlhGJqZmh4talzjJSM0M+hoExwaf6LMe6cN8bFTTKVVPhpkpJIYoL+IonUWdkuZwg0OcP5x3/HGqfnuHdb3d4fqIDC1U5YVgZl0F/te+irotj5bowz7OordXqmAe++3qq/vF6lDwb4Gpzrlkc54Rf0Ob3I7AnQ6ph9w5KV4ZpUOeSa4gyppreCll0VQBJxcRPGngo/GeoVS7wJBqBsN5TuhLIip9dWuhNKdzih5y93ema+Mti6ZN8Em8KV+ybyHKmkdKfXWHkdsOorcd/PKU2c79Y6IZmS4fQiq65HJodCM9W5JmkDoZmw1vldQnJoOHZfr3PFuq30GTluX+iKRLG4Saficj+ZCmOJFZWTURKTnQDbmQ8FK6BgpfO1d6sTvmW7caaYHkBlT7B8jzPc2q6302u0Qeg91vk5vRVktAkFcwW07u70GJt3rltv0SRA86MbdCJMpUJPLmT1afDzihyOuEmnHZ4KWjTRbU3ikmDodovKmbTBAJTvpk3h57BitxN2O1Y7vcjyPeDZ7nwlpzs9WV/Zvp5k+R4oWs8BQzalqXPdskN/57aMJq2cQG3SGpq0dL5Xvk7JcAK1wuOEZkqTBm8WEXHETRjnF5Qwqmdbt8uQWGEtFP8Ahav2Df/u3eJcSy3e5oScp8AZCq68DmoDPzpMNsCK0IuEJOe6aXIGNG3nfJUWQXpL55pn5XXW5kdDv59ARut9Q7UtuzkzXlvUscdaXWrTI2wMETlScRHGu0u97PBU0CNL/+hIDdbCzrWw6UvnwQYmAbYtdXqfzTrCjv/tfw9nMLjvPk1/2f7HSkiCzKMgsz1VAdlpsPO7jLbO7FiTuK+Hm9yErzeXM2jwEKcH3Lq7c+xEjeCIxJu4COP8Ag8A3dspjOOKpwBWvAVbv4FNXzg9Ts8Pzu+Smzj3hFY+uQeciUI26FxnzGjrDAW3Ohaad9z/Hk2T6PQ+W3R2rrNmtHV6rxltnclD9VC8NxeOGlBtix7XKhKP4iqMe7TLdLkSiShvCWxf4dyO8+0rsPJd51aWpu2da6gBr/PdGKe32/ei0ASjTnD0idDmOOc49QxUEZEjFRdhvG5HCSlJCXRsodsbGi1PoXMtNjEFflgGRWthz2Zn1vDeLaFJTwX7rsumNYehV8MJk6FdL3drFxE5hLgI4+JyH83Tk0nQAz+in7VO6G77DrZ87fRyC1fBhk9r7GicCU7NOjrXWtNbQGYHOOoEZ5ZwpyGaHSwijUZchHGZN0CTFF2Li1r+Cli7AH74zhleLlrrbDeJzmMB01vCqJnQ9jjnaUytukFWX+d+VxGRGBAXYVzqDZCerDCOKpW3AuW9Dd/Ohj0bne1dTnaGlzv0c67vpmS4W6eISAOIizAu8wVIUxi7JxiAVe85D7FITHEmVq39yLm+axKg6ylw7gPQcZAz9CwiEmfiIozLfRqmbnB7t8LqOc4Sdctec+7drdS8M4y4HrKy4aiB0PpY9+oUEYkCcRHGpd4A7ZvpQQoRU1oEmxfBtm9h61IoKXQmX1XObG6aBRP+Dl1OcmY9t+2lVXBERKqJizAu8wVIV8/4yFkLXo8z03nTl/smWq14y9kOzr26TdrAiOkw4DLnmm9ai30zm5sd5U7tIiJRLC7CuFwTuA6P3wsr3qTnqtmw6VGnt1u+Z9/vm7QBLPQYDUOugvb9nNnPIiJSL3ERxqXqGddP4f/gm386txmVFNA6uTnYztB7nHNbUdvjnft4M1q7XamISEyIizAu8yqM66RwNSx6Ghb93bmm2+NMGHIln21KIGfUaW5XJyISs2I+jINBS4U/qGHq2gSDsHMNfP44LH3Z2db/Ujjjzn23GW3Oda08EZF4EPNhXOZzZvQqjEPK98DyN6BgpXP70Zr/QKDCWdGo38Vwxt3QVOs+i4g0pLgJ47i8z9hayJ8Hy/+1bwnBorXgK3UWUkhrAf1/4iwFeMIVCmEREZfEfhh7nTCOqydw7drgDDnnz4Mti53g7XoKBP3QeZhzy9FRJ+heXxGRKBH7YVw5TB3rPeNgAFa+AxsWOsPQ5XugfV845wEY/DNI1ENPRESiVeyHsTdGh6l95c6yghs+hY1fOMsO7t4Iqc0gqw+M+wu0OsbtKkVEpA5iP4x9MThMveY/8PYvnYUXEpKd5zu36wOn3wl9LoSEGPqsIiJxIPbD2Bsjs6l95c6DOFa/D+tyod3xcMHj0PXkfY+aFBGRRin2w7hqNnUj/ajeUljyPHz2Z9i7GVr3gBN/ASNn6NGTIiIxopEmVN01up6xr8xZcnDZa+ApcL7KiqDzcBj7OBw7yu0KRUQkzGI+jEsrrxmnJLhcyUFs+86ZiPXdbPhhubP0YNte0Lq7Mxw99Gpn+UEREYlJMR/GZV4/EGXD1MEgbPsG8ufD2o9g4+fO9pZdnaUHjx3l3Bes+4BFROJCFCVUZHjK/RgDGW7f2hQMwvZlsGcLzL8bClcBxukBn3k/9L0IMtpBQhT34EVEJCLqFMbGmLOBR4FE4Glr7e9r2W8C8DowxFq7OGxVHoHiCj9NU5MwbvUyt34DX/8Dvv8Mdqx2tqW3grFPQM9zoUkrd+oSEZGoccgwNsYkAk8Ao4HNwCJjzDvW2rwa+2UC04EvI1Ho4fKUO2Hc4ApXQ+7/gxVvQkomtOvl3IrUqhu06annQIuISJW6pNRQIN9auw7AGPMKMBbIq7HfvcAfgN+GtcIj5Klo4DDevQne+oUzISs5A069CU66TrchiYhIreqSUh2BTdVebwaGVd/BGHMCcLS19j1jTPSFcVoDhLG1kPc2vHu985zo0fc6KyJVrgksIiJSiyNOKWNMAvAwMKUO+14NXA2QlZVFbm7ukZ6+isfjOeDxthaUkZ5kwnqumrJ++Ihj1j1PqncXezN7kNf715T7OsDiPH48gND41Na2cuTUtpGjto0ctW341SWMtwBHV3vdKbStUiaQDeSGJkm1B94xxlxQcxKXtXYWMAtg8ODBNicn5/ArryE3N5cDHe/+JR9zdLum5OQMCtu59rPoaVj1KBx9Igy4lGYDJnFijK2QVFvbypFT20aO2jZy1LbhV5cwXgT0MMZ0wwnhnwCXVf7SWrsHaFP52hiTC/wmWmZTR/Sa8ao5MOe3cNzZcMmLkBjzd4qJiEgEHPKmVmutH7gW+BBYCbxqrV1hjLnHGHNBpAs8Up7yCFwz9pXBh7fBK5dB+35w0TMKYhEROWx1ShBr7RxgTo1td9Syb86RlxUe1lo8Xj+Z4ewZb/wS3r4GdubDoKkw+h5IyQjf8UVEJO7EdHeu1BvAWo68Z1w5U/rr55zlC5sfDZe/pUUbREQkLGI6jD0VznOpM46kZ+z3wpzfwJJ/QPPOztKFJ10LqZlhqlJEROJdTIdxcbkTxoc9gWvjl/DBzbB1CZzyGxh1m54dLSIiYRfTYVzZM86s7zC1tTD/Hlj4MDRtDxc9C9njI1ChiIhIrIdxVc+4nvf9fvmkE8QDL4dz/qAJWiIiElExHcYlVWsZ12P5xB+Ww9yZ0PM8OP8xDUuLiEjExXTSVPiDAKQl1+NjLnwYktJh7OMKYhERaRAxnTblvgAAqUl17BnvyHeWPBw8VesMi4hIg4npMK7sGafWtWf8nzsguYmz5KGIiEgDie0wrk/P+NvZsPo9OOXXWvZQREQaVGyHcV2vGRf/4DzYo/NwOOn6BqhMRERkn9gO41DPOCXxIB/T74W3fwn+Chj7hBZ8EBGRBhfTyVPhD5KalEBoneUf81fAq1dA/jwY8wi0PrZhCxQRESEOwjgtuZbrxf4KmH05rPkQzn0IBv+sYYsTEREJifEwDpCaVMsQ9Qe3OEE85hEFsYiIuCqmrxmX+4IHvq1p/Sew+O/OLUwKYhERcVlMh7HTM64xTG0tzL8XMo+CUTPdKUxERKSa2A5jX/DHtzXlz4PNX8HI30JymjuFiYiIVBPTYVxes2dsLSy4H1p0hgE/da8wERGRamI6jCt8wf0ncBXkwdZvYPh1kJTiXmEiIiLVxHYY17y1afkbYBKhz4XuFSUiIlJDjIdxtVubrHXC+JiR0LStu4WJiIhUE9NhXF59mHrrEti1AbInuFqTiIhITTEdxhX+wL5h6mVvQEIy9BrjblEiIiI1xHgYh3rGwSCs+Bf0GA3pLdwuS0REZD+xHca+IKnJibDxcyjepiFqERGJSjEbxtba0H3GCbDyHUhKh+POdrssERGRH4nZMPYFLNbiXDPesBA6D4PUpm6XJSIi8iMxG8YV/gAAzWwxbF8BXU52uSIREZEDi+EwDgLQqfhbwELXEe4WJCIiUouYDeNyn9Mzbl+8DBKS4KgTXK5IRETkwGI2jCt7xq2KV0PbXlqhSUREolbMhrGn3A9Aiz2roH1fl6sRERGpXcyGcVGJl7bsJrW8ENr3c7scERGRWsVsGO8s8XJ8wvfOC/WMRUQkisVsGO8q8dLFbHdetO7ubjEiIiIHEbNhvLPES5eEQmxiKjTNcrscERGRWsVsGBeVVHBs0g5Mi86QELMfU0REYkDMplRRiZejE3ZAyy5ulyIiInJQMR3GHYLboWVXt0sRERE5qJgNY6+niAzrgRbqGYuISHSL2TBOL93i/KBhahERiXIxGcZef5BW3m3OC/WMRUQkysVkGG8sKuFoU+i8UM9YRESiXJ3C2BhztjFmtTEm3xhz8wF+f6MxJs8Y850xZr4xxtUEzC/wcLQpIJDSDNJbulmKiIjIIR0yjI0xicATwDlAb+BSY0zvGrt9Awy21vYDXgceCHeh9eGEcaF6xSIi0ijUpWc8FMi31q6z1nqBV4Cx1Xew1i6w1paGXn4BdApvmfWzpsBDt6QdJCqMRUSkEahLGHcENlV7vTm0rTZXAu8fSVFHKn97MUdRqHuMRUSkUUgK58GMMT8FBgMja/n91cDVAFlZWeTm5obt3B6Pp+p4xTu2kppYwZrCCraE8RzxqnrbSnipbSNHbRs5atvwq0sYbwGOrva6U2jbfowxZwC3ASOttRUHOpC1dhYwC2Dw4ME2JyenvvXWKjc3l5ycHKy1tP3PnyERegw5gx49w3eOeFXZthJ+atvIUdtGjto2/OoyTL0I6GGM6WaMSQF+ArxTfQdjzEDgKeACa21B+Musuwp/kA6VJbQ4+uA7i4iIRIFDhrG11g9cC3wIrARetdauMMbcY4y5ILTbg0BT4DVjzFJjzDu1HC7iisv9dDQ7nBfNFcYiIhL96nTN2Fo7B5hTY9sd1X4+I8x1HbaSCj+dTCEVyc1JTWvmdjkiIiKHFNYJXNHAUxnGGZ1IdbsYkRjn8/nYvHkz5eXlbpfyI82bN2flypVulxGT1LYHl5aWRqdOnUhOTq7ze2IujIvL/XQyO/Bl9nG7FJGYt3nzZjIzM+natSvGGLfL2U9xcTGZmZlulxGT1La1s9ayc+dONm/eTLdu3er8vph7NrWn3EdHswPbvLPbpYjEvPLyclq3bh11QSziFmMMrVu3rvdoUcyFsbe4gCamAtNSYSzSECAocr4AABdZSURBVBTEIvs7nP8mYi6MzR7nYWFJrfQoTBERaRxiLowTQ2Gc2qbuY/UiIm5ZunQpc+bMOfSOYXDVVVeRl5dX7/fl5uYyZsyYCFQklWIujFNLNjvf22iYWkTqx+/3N/g5GyqMA4EATz/9NL1711x0r3EJBAJulxARMTebOr1kC3tpQjOtYyzSoO5+dwV5W/eG9Zi9j2rGnefXfmdESUkJF198MZs3byYQCHD77bczY8YMLr74Yt577z0yMjJ46aWX6N69O++++y733XcfXq+X1q1b8+KLL5KVlcVdd93F2rVrWbduHZ07d2bmzJlMnToVr9dLMBjkjTfeoEePHrzwwgs89thjeL1ehg0bxl/+8hcSExMPWNcHH3zArbfeSiAQoE2bNsyfP5+vvvqK6dOnU15eTnp6Os8++yzdunXjjjvuoKysjIULF3LLLbcwZswYrrvuOpYvX47P5+Ouu+5i7NixlJaWMmXKFJYvX07Pnj3ZunUrTzzxBIMHD+bll1/md7/7HdZazjvvPP7whz8A0LRpU/7v//6PefPm8cQTTzBz5kweeughBg8eXOcae/bsecg/p9reFwgEmDFjBh988AEJCQlMmzaN6667jkWLFjF9+nRKSkpITU1l/vz5vPHGGyxevJjHH38cgDFjxvCb3/yGnJycH32Ojz76iHfffZeysjJOOukknnrqKYwx5Ofn8/Of/5zCwkISExN57bXXuPvuuxk/fjzjxo0DYNKkSVx88cWMHTv2YB+pwcVcGDct38Z20w497kMk9n3wwQccddRRvPfeewDs2bOHGTNm0Lx5c7744gvefPNNfvWrX/Hvf/+bk08+mS+++AJjDE8//TQPPPAAf/zjHwHIy8tj4cKFpKenc9111zF9+nQmTZqE1+slEAiwcuVKZs+ezX//+1+Sk5O55pprePHFF5k8efKPaiosLGTatGl88skndOvWjaKiIgB69erFp59+SlJSEvPmzePWW2/ljTfe4J577tkvhG699VZOO+00nnnmGXbv3s3QoUM544wz+Otf/0rLli3Jy8tj+fLlDBgwAICtW7cyY8YMvv76a1q2bMmZZ57JW2+9xbhx4ygpKWHYsGFVn/NwazyU2t43a9YsNmzYwNKlS0lKSqKoqAiv18sll1zC7NmzGTJkCHv37iU9Pf2gx6/5OXr37s0ddzjPnbr88sv597//zfnnn8+kSZO4+eabufDCCykvLycYDHLllVfyyCOPMG7cOPbs2cNnn33GP/7xj0N+poYWc2HcpmIja5I608PtQkTizMF6sJHSt29ffv3rXzNjxgzGjBnDKaecAsCll15a9f2GG24AnHuiL7nkErZt24bX693vHtALLrigKhCGDx/O/fffz+bNmxk/fjw9evRg/vz5fP311wwZMgSAsrIy2rVrd8CavvjiC0499dSq47dq1Qpw/kfhiiuuYM2aNRhj8Pl8B3z/3Llzeeedd3jooYcA5/axjRs3snDhQqZPnw5AdnY2/fr1A2DRokXk5OTQtm1bwOn5ffLJJ4wbN47ExEQmTJgQ9hprqu198+bN4+c//zlJSUlV51m2bBkdOnSoastmzQ7ddar5ORYsWMADDzxAaWkpRUVF9OnTh5ycHLZs2cKFF14IOA/eABg5ciTXXHMNhYWFvPHGG0yYMKGqnmgSW9eMK4pp69vKppTublciIg3guOOOY8mSJfTt25eZM2dyzz33APvfWlL583XXXce1117LsmXLeOqpp/a7DzQjI6Pq58suu4x33nmH9PR0zj33XD766COstVxxxRUsXbqUpUuXsnr1au6666561Xr77bczatQoli9fzrvvvlvrfajWWt54442qc23cuJHjjz++XueqlJaWVutQ+pHUGK73VZeUlEQwGKx6Xf0Y1T9HeXk511xzDa+//jrLli1j2rRphzzf5MmTeeGFF3j22Wf52c9+Vu/aGkJshfH2PBKwFGaoXywSD7Zu3UqTJk346U9/ym9/+1uWLFkCwOzZs6u+Dx8+HHB6bx07dgQ46DDlunXrOOaYY7j++usZO3Ys3333Haeffjqvv/46BQXOinBFRUV8//33B3z/iSeeyCeffML69eur9q15/ueee65q/8zMTIqLi6ten3XWWfz5z3/GWgvAN998A8CIESN49dVXAWdYfdmyZQAMHTqUjz/+mB07dhAIBHj55ZcZOfKAS8ofdo2HUtv7Ro8ezVNPPVU1Ma6oqIiePXuybds2Fi1aBDhP8/L7/XTt2pWlS5cSDAbZtGkTX3311QHPVRm8bdq0wePx8PrrrwNOO3bq1Im33noLgIqKCkpLSwGYMmUKf/rTnwCidgJbTIVx8AfnL2dCh34uVyIiDWHZsmUMHTqUAQMGcPfddzNz5kwAdu3axfDhw3n00Ud55JFHALjrrruYOHEigwYNok2bNrUe89VXXyU7O5sBAwawfPlyJk+eTO/evbnvvvs488wz6devH6NHj2bbtm0HfH/btm2ZNWsW48ePp3///lxyySUA3HTTTdxyyy0MHDhwv1nbo0aNIi8vjwEDBjB79mxuv/12fD4f/fr1o0+fPtx+++0AVUOtvXv3ZubMmfTp04fmzZvToUMHfv/73zNq1Cj69+/PoEGDDjk5qb41Hkpt77vqqqvo3Lkz/fr1o3///rz00kukpKQwe/ZsrrvuOvr378/o0aMpLy9nxIgRdOvWjd69e3P99ddzwgknHPBcLVq0YNq0aWRnZ3PWWWdVDXcD/POf/+Sxxx6jX79+nHTSSfzwww8AZGVlcfzxxzN16tQ6f6aGZir/76uhDR482C5evDhsx8vNzWVQ4WsEl/+LD8/7kouH6tamcNFC4pHT2Nt25cqVhz2EGildu3Zl8eLFpKamxtTzkwOBAD6fj7S0NNauXcsZZ5zB6tWrSUlJafBaGtuzqUtLS+nbty9LliyhefPmDXLOA/23YYz52lo7+ED7R99V7CMQ3LaMvGBXurdvPH9JRETqorS0lFGjRuHz+bDW8pe//MWVIG5s5s2bx5VXXskNN9zQYEF8OGInjG2AJrtXkWdPY2K7pm5XIyIu2bBhA8B+12EjZdiwYVRUVOy37Z///Cd9+/YN+7kyMzMJ52ji4Xj22Wd59NFHCQaDJCQ4VzlHjBjBE0884WpdB3PGGWfUen0/msRMGC9ft4mcYAVbU4+lWVrd15AUETlcX375pdslNKipU6cyderURjdM3RjEzASuxN3OrMCx55ztciUiIiL1EzNhfLR3DV6S6DdgqNuliIiI1EtshLG1nOBdzIqUAZCU6nY1IiIi9RIbYVy4iqPsdvKanex2JSIiIvUWG2G8YSEA37c9+FNnRCS+NW0a+3da5Obm8tlnnzXIuc4991x2795d7/c999xzXHvttRGoqPGKiTAODr6KkyseJbnFUW6XIiJSxY31kRsijK21BINB5syZQ4sWLSJ6rkiq/BzRICbCeG+5j822La0ydL1YxDXv3wzPnhfer/dvPugpb7755v3ucb3rrru47777OP300znllFPo27cvb7/9dp3K37ZtG6eeeioDBgwgOzubTz/9FHB60zfccAN9+vTh9NNPp7CwEIC//e1vDBkyhP79+zNhwoT9noP885//nGHDhnHTTTfx8ccfM2DAAAYMGMDAgQOr7n9+8MEHGTJkCP369ePOO+88aG3PP/981SMlL7/8cgDeffddhg0bxsCBAznjjDPYvn07GzZs4Mknn+SRRx5hwIABfPrppxQWFjJhwgSGDBnCkCFD+O9//ws4yyiOHj2aPn36cNVVV9GlSxd27NgBwMMPP0x2djbZ2dlVz3TesGEDPXv2ZPLkyQwbNoxNmzbRtWvXqvfUtca6qO19Ho+HqVOn0rdvX/r161e1vOMHH3zACSecQP/+/Tn99NMB5+9C5cpX4Kx0tWHDhv0+R3Z2Nps2beIXv/gFgwcPpk+fPvv9WSxatIiTTjqJ/v37M3ToUIqLizn11FNZunRp1T4nn3wy3377bZ0+10FZa135GjRokA2X/IJi22XGv+2/lmwK2zFlnwULFrhdQsxq7G2bl5e378WcGdY+c254v+bMOOj5lyxZYk899dSq18cff7zduHGj3bNnj927d68tLCy0xx57rA0Gg9ZaazMyMmo91kMPPWTvu+8+a621fr/f7t2711prLWBfeOEFa621d999t/3lL39prbV2x44dVe+97bbb7GOPPWattfaKK66w5513nvX7/dZaa8eMGWMXLlxorbW2uLjY+nw+++GHH9pp06bZYDBoA4GAPe+88+zHH398wLqWL19ue/ToYQsLC6211u7cudNaa21RUVHV5/rb3/5mb7zxRmuttXfeead98MEHq95/6aWX2k8//dRaa+33339ve/XqZa219pe//KX93e9+Z6219v3337eALSwstIsXL7bZ2dnW4/HY4uJi27t3b7tkyRK7fv16a4yxn3/+eVXbdOnSxRYWFta7xmeffbaqHQ+ktvfddNNNdvr06fvtV1BQYDt16mTXrVu337lrtkOfPn3s+vXr9/sclSrf4/f77ciRI+23335rKyoqbLdu3exXX31lrbV2z5491ufz2eeee66qhtWrV9vasmy//zZCgMW2lkyMiYd+7CrxAqhnLOKmc37f4KccOHAgBQUFbN26lcLCQlq2bEn79u254YYbyM3NJSkpiS1btrB9+3bat29/0GMNGTKEn/3sZ/h8PsaNG8eAAQMASEhIqFpI4ac//Snjx48HYPny5cycOZPdu3fj8Xg466yzqo41ceLEqiX/RowYwY033sikSZMYP348nTp1Yu7cucydO5eBAwcCTo9vzZo1nHrqqT+q66OPPmLixIlVi1tUrj18sPWZq5s3bx55eXlVr/fu3YvH42HhwoW8+eabAJx99tm0bNkSgIULF3LhhRdWLSs5fvx4Pv30Uy644AK6dOnCiSee+KOnmx1pjTXV9r558+bxyiuvVO3XsmVL3n333QOuzXwwlZ+j0quvvsqsWbPw+/1s27aNvLw8jDEHXHd54sSJ3HvvvTz44IM888wzTJkypU6f6VBiYph6ZyiMW2foOa0i8WbixIm8/vrrzJ49m0suuYQXX3yRwsJCPvnkE5YuXUpWVlad1tc99dRT+eSTT+jYsSNTpkzh+eefP+B+lesjT5kyhccff5xly5Zx55131ro+8s0338zTTz9NWVkZI0aMYNWqVVhrueWWW6rWLM7Pz+fKK6+s1+c+2PrM1QWDQb744ouqc23ZsuWwJ7JV/1zhrDFc76vuYOsjV/8c69ev56GHHmL+/Pl89913nHfeeQc9X5MmTRg9ejRvv/02r776KpMmTap3bQcSE2FcVNUzVhiLxJtLLrmEV155hddff52JEyeyZ88e2rVrR3JyMgsWLKjzc4m///57srKymDZtGldddVXV2sjBYLBqzdyXXnqJk092bqEsLi6mQ4cO+Hw+XnzxxVqPu3btWvr27cuMGTMYMmQIq1at4qyzzuKZZ57B4/EAsGXLlqq1kms67bTTeO2119i5cydw4LWHq6/PXHN95DPPPJM///nPVa8rr3dWXx957ty57Nq1C4BTTjmFt956i9LSUkpKSnjzzTc55ZRTDtp29a3xUGp73+jRo/ebI7Br165a12bu2rVr1Z/hkiVLqn5f0969e8nIyKB58+Zs376d999/H6DWdZfBWRry+uuvZ8iQIVUjCkcqJsL43OwO3DE8jbaZGqYWiTd9+vShuLiYjh070qFDByZNmsTixYs58cQTef755+nVq1edjpObm0v//v0ZOHAgs2fPZvr06YDTi/rqq6/Izs7mo48+4o477gDg3nvvZdiwYYwYMeKg5/jTn/5EdnY2/fr1Izk5mXPOOYczzzyTyy67jOHDh9O3b18uuuiiWhe26NOnD7fddhsjR46kf//+3HjjjUDt6zOff/75vPnmm1UTuB577DEWL15Mv3796N27N08++SQAd955J3PnziU7O5vXXnuN9u3bk5mZyQknnMCUKVMYOnQow4YN46qrrqoaTj/Yn0F9ajyU2t43c+ZMdu3aRXZ2Nv3792fBggW1rs08YcIEioqK6NOnD48//jjHHXfcAc9V+Wfeq1cvLrvsMkaMGAFQ67rLAIMGDaJZs2ZhXR85ptYzbszrwkYztW3kNPa2jcb1jCuFazGDpk2bVvVgY0lFRQWJiYkkJSXx+eef84tf/GK/WcIHE+8LRWzdupWcnBxWrVpVtXpVTXG9nrGIiNTNxo0bufjiiwkGg6SkpPC3v/3N7ZIaheeff57bbruNhx9+uNYgPhwKYxGJK8uWLau6D7ZSampqrcshNlSveOfOnVX3yFY3f/58WrduHfbz9ejRg2+++Sbsx62P+++/n9dee22/bRMnTuS2225zqaJDmzx5MpMnTw77cRXGIhJX+vbtW+fh2IbUunXrqKwrkm677baoDt6GFBMTuETEPW7NOxGJVofz34TCWEQOW1paGjt37lQgi4RYa9m5cydpaWn1ep+GqUXksHXq1InNmzdXPa85mpSXl9f7H0SpG7XtwaWlpdGpU6d6vUdhLCKHLTk5uc6POGxoubm5h7w/Vg6P2jb8NEwtIiLiMoWxiIiIyxTGIiIiLnPtcZjGmEKgbk9wr5s2wI4wHk/2UdtGjto2ctS2kaO2PTxdrLVtD/QL18I43Iwxi2t75qccGbVt5KhtI0dtGzlq2/DTMLWIiIjLFMYiIiIui6UwnuV2ATFMbRs5atvIUdtGjto2zGLmmrGIiEhjFUs9YxERkUYpJsLYGHO2MWa1MSbfGHOz2/U0NsaYZ4wxBcaY5dW2tTLG/McYsyb0vWVouzHGPBZq6++MMSe4V3l0M8YcbYxZYIzJM8asMMZMD21X2x4hY0yaMeYrY8y3oba9O7S9mzHmy1AbzjbGpIS2p4Ze54d+39XN+hsDY0yiMeYbY8y/Q6/VthHU6MPYGJMIPAGcA/QGLjXG9Ha3qkbnOeDsGttuBuZba3sA80OvwWnnHqGvq4G/NlCNjZEf+LW1tjdwIvDL0N9Nte2RqwBOs9b2BwYAZxtjTgT+ADxire0O7AKuDO1/JbArtP2R0H5ycNOBldVeq20jqNGHMTAUyLfWrrPWeoFXgLEu19SoWGs/AYpqbB4L/CP08z+AcdW2P28dXwAtjDEdGqbSxsVau81auyT0czHOP2wdUdsesVAbeUIvk0NfFjgNeD20vWbbVrb568DpxhjTQOU2OsaYTsB5wNOh1wa1bUTFQhh3BDZVe705tE2OTJa1dlvo5x+ArNDPau/DEBq6Gwh8ido2LELDqEuBAuA/wFpgt7XWH9qlevtVtW3o93uA1g1bcaPyJ+AmIBh63Rq1bUTFQhhLhFlnyr2m3R8mY0xT4A3gV9bavdV/p7Y9fNbagLV2ANAJZ4Ssl8slxQRjzBigwFr7tdu1xJNYCOMtwNHVXncKbZMjs71yiDT0vSC0Xe1dD8aYZJwgftFa+6/QZrVtGFlrdwMLgOE4Q/uV67RXb7+qtg39vjmws4FLbSxGABcYYzbgXPY7DXgUtW1ExUIYLwJ6hGb6pQA/Ad5xuaZY8A5wRejnK4C3q22fHJr5eyKwp9qQq1QTum72d2Cltfbhar9S2x4hY0xbY0yL0M/pwGica/ILgItCu9Vs28o2vwj4yOohCwdkrb3FWtvJWtsV59/Tj6y1k1DbRlRMPPTDGHMuzjWOROAZa+39LpfUqBhjXgZycFZi2Q7cCbwFvAp0xlld62JrbVEoYB7HmX1dCky11i52o+5oZ4w5GfgUWMa+a2+34lw3VtseAWNMP5xJQ4k4nYpXrbX3GGOOwenNtQK+AX5qra0wxqQB/8S5bl8E/MRau86d6hsPY0wO8Btr7Ri1bWTFRBiLiIg0ZrEwTC0iItKoKYxFRERcpjAWERFxmcJYRETEZQpjERERlymMRUREXKYwFhERcZnCWERExGX/H83lv8Qq0/80AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCV8pwVzl9GK",
        "colab_type": "text"
      },
      "source": [
        "## Exercise 2.3: Model Selection\n",
        "\n",
        "Hyperparameters are the parameters of the learning method itself which we have to specify a priori, i.e., before model fitting. In contrast, model parameters are parameters which arise as a result of the fit. The aim of the model selection is selecting the best hyperparameters for our deep network. Finding the right hyperparameters for a model can be crucial for the model performance on given data. For istance lets consider our model trained by using different values for the learnign rate: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fw8Hs-AEmLKh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learning_rates = [1e-4, 1e-2, 1e-1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mym4GOgUmWqD",
        "colab_type": "code",
        "outputId": "08d15db7-ec51-4cbf-be68-cd1a572013ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 919
        }
      },
      "source": [
        "histories = []\n",
        "for learning_rate in learning_rates:\n",
        "    model = keras.models.Sequential([\n",
        "                                    keras.layers.Flatten(input_shape=[28, 28]),\n",
        "                                    keras.layers.Dense(300, activation=\"relu\"),\n",
        "                                    keras.layers.Dense(10, activation=\"softmax\")])\n",
        "\n",
        "    model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "              optimizer=keras.optimizers.SGD(lr=learning_rate),\n",
        "              metrics=[\"accuracy\"])\n",
        "              \n",
        "    callbacks = [keras.callbacks.EarlyStopping(patience=2)]\n",
        "\n",
        "    history = model.fit(X_train_scaled, y_train,\n",
        "                        validation_data=(X_valid_scaled, y_valid), epochs=10,\n",
        "                        callbacks=callbacks)\n",
        "    histories.append(history)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 1.7495 - accuracy: 0.3915 - val_loss: 1.2615 - val_accuracy: 0.6026\n",
            "Epoch 2/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 1.1027 - accuracy: 0.6401 - val_loss: 0.9795 - val_accuracy: 0.6872\n",
            "Epoch 3/10\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.9182 - accuracy: 0.6936 - val_loss: 0.8581 - val_accuracy: 0.7250\n",
            "Epoch 4/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.8249 - accuracy: 0.7229 - val_loss: 0.7872 - val_accuracy: 0.7424\n",
            "Epoch 5/10\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.7663 - accuracy: 0.7418 - val_loss: 0.7397 - val_accuracy: 0.7574\n",
            "Epoch 6/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.7248 - accuracy: 0.7549 - val_loss: 0.7047 - val_accuracy: 0.7668\n",
            "Epoch 7/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.6931 - accuracy: 0.7643 - val_loss: 0.6774 - val_accuracy: 0.7748\n",
            "Epoch 8/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.6679 - accuracy: 0.7721 - val_loss: 0.6549 - val_accuracy: 0.7802\n",
            "Epoch 9/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.6470 - accuracy: 0.7785 - val_loss: 0.6364 - val_accuracy: 0.7842\n",
            "Epoch 10/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.6293 - accuracy: 0.7841 - val_loss: 0.6204 - val_accuracy: 0.7892\n",
            "Epoch 1/10\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.5160 - accuracy: 0.8203 - val_loss: 0.4038 - val_accuracy: 0.8586\n",
            "Epoch 2/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3785 - accuracy: 0.8650 - val_loss: 0.3673 - val_accuracy: 0.8714\n",
            "Epoch 3/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3426 - accuracy: 0.8785 - val_loss: 0.3638 - val_accuracy: 0.8696\n",
            "Epoch 4/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3185 - accuracy: 0.8857 - val_loss: 0.3404 - val_accuracy: 0.8762\n",
            "Epoch 5/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3015 - accuracy: 0.8925 - val_loss: 0.3353 - val_accuracy: 0.8796\n",
            "Epoch 6/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2853 - accuracy: 0.8988 - val_loss: 0.3316 - val_accuracy: 0.8822\n",
            "Epoch 7/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2726 - accuracy: 0.9033 - val_loss: 0.3261 - val_accuracy: 0.8844\n",
            "Epoch 8/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2602 - accuracy: 0.9081 - val_loss: 0.3291 - val_accuracy: 0.8772\n",
            "Epoch 9/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2491 - accuracy: 0.9103 - val_loss: 0.3159 - val_accuracy: 0.8860\n",
            "Epoch 10/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2394 - accuracy: 0.9145 - val_loss: 0.3152 - val_accuracy: 0.8858\n",
            "Epoch 1/10\n",
            "1719/1719 [==============================] - 5s 3ms/step - loss: 0.4726 - accuracy: 0.8344 - val_loss: 0.4013 - val_accuracy: 0.8550\n",
            "Epoch 2/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3474 - accuracy: 0.8739 - val_loss: 0.3639 - val_accuracy: 0.8726\n",
            "Epoch 3/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.3065 - accuracy: 0.8888 - val_loss: 0.4711 - val_accuracy: 0.8434\n",
            "Epoch 4/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2841 - accuracy: 0.8991 - val_loss: 0.3390 - val_accuracy: 0.8840\n",
            "Epoch 5/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2537 - accuracy: 0.9073 - val_loss: 0.3537 - val_accuracy: 0.8882\n",
            "Epoch 6/10\n",
            "1719/1719 [==============================] - 4s 3ms/step - loss: 0.2310 - accuracy: 0.9164 - val_loss: 0.3528 - val_accuracy: 0.8890\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RzTSjfwptkHJ",
        "colab_type": "text"
      },
      "source": [
        "Let's plot the results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIlN1vlioIYh",
        "colab_type": "code",
        "outputId": "23150e66-02e8-428a-d61f-6a4bd3e2bfe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        }
      },
      "source": [
        "for learning_rate, history in zip(learning_rates, histories):\n",
        "    print(\"Learning rate:\", learning_rate)\n",
        "    plot_learning_curves(history)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate: 0.0001\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Learning rate: 0.01\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Learning rate: 0.1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "atzcLJWuIE1Q",
        "colab_type": "text"
      },
      "source": [
        "Note that the learning rate influence significantly the training phase. That behavior suggests that is crucial to validate the hyper-parameters to obtain good results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_JntujSWq3e5",
        "colab_type": "text"
      },
      "source": [
        "### GRID Search:\n",
        "\n",
        "Since a deep net has many hyperparameters, in order to find the best ones, we have to consider all the possible combinations of all of the possible values. One common method to perform this complex task is Grid-Search.\n",
        "Given a set of values for each hyper-parameter, Grid-Search will build a model on each parameter combination possible. It iterates through every parameter combination and stores a model for each combination. Finally, the model that obtained the best result on the validation set will be select.\n",
        "\n",
        "In order to perfrom Grid-Search we will use GridSearchCV method from scikit-learn."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CPz629YSrDhN",
        "colab_type": "text"
      },
      "source": [
        "Let's Create a build_model() function that takes two arguments, n_units and learning_rate, and builds, compiles and returns a model with the given number of of neurons and the given learning rate.\n",
        "In order to limit the time demends of the proces we will consider only this two hyper-paramters and a small 3 layer model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGSKWTTtqxZ4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_model(n_units=30, learning_rate=1e-3):\n",
        "\n",
        "  #To complete:\n",
        "  #HINT: the function has to build the model with 3 layers:\n",
        "  #- a Flatten layer to convert each 28x28 image to a single row \n",
        "  #- a Danse layer composed of n_units and that exploits Relu activation function \n",
        "  #- the output layer that uses the softmax activation function\n",
        "  #Then, has to be defined how the model will be compiled.\n",
        "  model = keras.models.Sequential([\n",
        "                                  keras.layers.Flatten(input_shape=[28, 28]),\n",
        "                                  keras.layers.Dense(n_units, activation=\"relu\"),\n",
        "                                  keras.layers.Dense(10, activation=\"softmax\")])\n",
        "\n",
        "  model.compile(loss=\"sparse_categorical_crossentropy\",\n",
        "            optimizer=keras.optimizers.SGD(lr=learning_rate),\n",
        "            metrics=[\"sparse_categorical_accuracy\"])\n",
        "\n",
        "  return model\n",
        "\n",
        "    \n",
        "#Create a keras.wrappers.scikit_learn.KerasRegressor and pass the build_model \n",
        "#function to the constructor. This gives you a Scikit-Learn compatible predictor\n",
        "keras_reg = keras.wrappers.scikit_learn.KerasRegressor(build_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PS6W3A2LxDg5",
        "colab_type": "text"
      },
      "source": [
        "Let's define the lists of hyper-parameters' values. Also in this case, we use a very limited size lists, but in a real-world scenario a reasonable amount of possible values should be considered (try to add some values and check how much the time required to perform the Grid-Search increases)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-uldCW3ru24",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "param_distribs = {\n",
        "    \"n_units\": [1000,5000,15000],# To_complete: insert a few (2 or 3) reasonable values  \n",
        "    \"learning_rate\": [0.01, 0.001] # To_complete: insert a few (2 or 3) reasonable values\n",
        "    #Check how much increase the time required to perform GRID search increasing\n",
        "    #the number of the values for each hyper-parameter.\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEiBxVYtx_sK",
        "colab_type": "text"
      },
      "source": [
        "Use a sklearn.model_selection.GridSearchCV to search the hyperparameter space of your KerasRegressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iobF2Bvdr9ER",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "grid_search = GridSearchCV(keras_reg, param_distribs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVGMcZz_yKSX",
        "colab_type": "text"
      },
      "source": [
        "Run the Grid-Search"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BA3vgTjr-hw",
        "colab_type": "code",
        "outputId": "c0b4f1c8-321e-4019-eb5e-b194070877e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "grid_search.fit(X_train_scaled, y_train, epochs=10, #The number of epochs can be modified (check what happens by increasing it) \n",
        "                  validation_data=(X_valid_scaled, y_valid))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.5073 - sparse_categorical_accuracy: 0.8255 - val_loss: 0.4074 - val_sparse_categorical_accuracy: 0.8598\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3712 - sparse_categorical_accuracy: 0.8684 - val_loss: 0.3682 - val_sparse_categorical_accuracy: 0.8726\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3326 - sparse_categorical_accuracy: 0.8822 - val_loss: 0.3552 - val_sparse_categorical_accuracy: 0.8784\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3078 - sparse_categorical_accuracy: 0.8917 - val_loss: 0.3560 - val_sparse_categorical_accuracy: 0.8762\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2873 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3384 - val_sparse_categorical_accuracy: 0.8854\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2715 - sparse_categorical_accuracy: 0.9033 - val_loss: 0.3373 - val_sparse_categorical_accuracy: 0.8834\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2561 - sparse_categorical_accuracy: 0.9106 - val_loss: 0.3290 - val_sparse_categorical_accuracy: 0.8884\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2436 - sparse_categorical_accuracy: 0.9142 - val_loss: 0.3335 - val_sparse_categorical_accuracy: 0.8868\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2317 - sparse_categorical_accuracy: 0.9184 - val_loss: 0.3318 - val_sparse_categorical_accuracy: 0.8852\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2208 - sparse_categorical_accuracy: 0.9232 - val_loss: 0.3282 - val_sparse_categorical_accuracy: 0.8864\n",
            "344/344 [==============================] - 1s 3ms/step - loss: 0.3222 - sparse_categorical_accuracy: 0.8849\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.5059 - sparse_categorical_accuracy: 0.8242 - val_loss: 0.4072 - val_sparse_categorical_accuracy: 0.8600\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3717 - sparse_categorical_accuracy: 0.8689 - val_loss: 0.3673 - val_sparse_categorical_accuracy: 0.8724\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3327 - sparse_categorical_accuracy: 0.8827 - val_loss: 0.3436 - val_sparse_categorical_accuracy: 0.8808\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3061 - sparse_categorical_accuracy: 0.8925 - val_loss: 0.3430 - val_sparse_categorical_accuracy: 0.8800\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2868 - sparse_categorical_accuracy: 0.8989 - val_loss: 0.3263 - val_sparse_categorical_accuracy: 0.8882\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2695 - sparse_categorical_accuracy: 0.9051 - val_loss: 0.3338 - val_sparse_categorical_accuracy: 0.8808\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2550 - sparse_categorical_accuracy: 0.9110 - val_loss: 0.3286 - val_sparse_categorical_accuracy: 0.8826\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2416 - sparse_categorical_accuracy: 0.9157 - val_loss: 0.3257 - val_sparse_categorical_accuracy: 0.8878\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2305 - sparse_categorical_accuracy: 0.9190 - val_loss: 0.3239 - val_sparse_categorical_accuracy: 0.8870\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2190 - sparse_categorical_accuracy: 0.9236 - val_loss: 0.3190 - val_sparse_categorical_accuracy: 0.8830\n",
            "344/344 [==============================] - 1s 3ms/step - loss: 0.3235 - sparse_categorical_accuracy: 0.8848\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.5090 - sparse_categorical_accuracy: 0.8211 - val_loss: 0.4058 - val_sparse_categorical_accuracy: 0.8592\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3720 - sparse_categorical_accuracy: 0.8687 - val_loss: 0.3668 - val_sparse_categorical_accuracy: 0.8702\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3332 - sparse_categorical_accuracy: 0.8816 - val_loss: 0.3538 - val_sparse_categorical_accuracy: 0.8768\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3070 - sparse_categorical_accuracy: 0.8908 - val_loss: 0.3453 - val_sparse_categorical_accuracy: 0.8778\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2872 - sparse_categorical_accuracy: 0.8981 - val_loss: 0.3305 - val_sparse_categorical_accuracy: 0.8846\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2709 - sparse_categorical_accuracy: 0.9047 - val_loss: 0.3378 - val_sparse_categorical_accuracy: 0.8774\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2563 - sparse_categorical_accuracy: 0.9086 - val_loss: 0.3334 - val_sparse_categorical_accuracy: 0.8834\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2428 - sparse_categorical_accuracy: 0.9138 - val_loss: 0.3226 - val_sparse_categorical_accuracy: 0.8876\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2313 - sparse_categorical_accuracy: 0.9182 - val_loss: 0.3163 - val_sparse_categorical_accuracy: 0.8876\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2200 - sparse_categorical_accuracy: 0.9232 - val_loss: 0.3148 - val_sparse_categorical_accuracy: 0.8870\n",
            "344/344 [==============================] - 1s 3ms/step - loss: 0.3119 - sparse_categorical_accuracy: 0.8895\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.5088 - sparse_categorical_accuracy: 0.8240 - val_loss: 0.4039 - val_sparse_categorical_accuracy: 0.8604\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3726 - sparse_categorical_accuracy: 0.8687 - val_loss: 0.3695 - val_sparse_categorical_accuracy: 0.8692\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3334 - sparse_categorical_accuracy: 0.8815 - val_loss: 0.3513 - val_sparse_categorical_accuracy: 0.8766\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3075 - sparse_categorical_accuracy: 0.8910 - val_loss: 0.3410 - val_sparse_categorical_accuracy: 0.8784\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2873 - sparse_categorical_accuracy: 0.8982 - val_loss: 0.3308 - val_sparse_categorical_accuracy: 0.8824\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2706 - sparse_categorical_accuracy: 0.9030 - val_loss: 0.3356 - val_sparse_categorical_accuracy: 0.8798\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2564 - sparse_categorical_accuracy: 0.9099 - val_loss: 0.3222 - val_sparse_categorical_accuracy: 0.8816\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2431 - sparse_categorical_accuracy: 0.9141 - val_loss: 0.3306 - val_sparse_categorical_accuracy: 0.8862\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2315 - sparse_categorical_accuracy: 0.9190 - val_loss: 0.3128 - val_sparse_categorical_accuracy: 0.8848\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2195 - sparse_categorical_accuracy: 0.9233 - val_loss: 0.3155 - val_sparse_categorical_accuracy: 0.8866\n",
            "344/344 [==============================] - 1s 3ms/step - loss: 0.3091 - sparse_categorical_accuracy: 0.8905\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.5071 - sparse_categorical_accuracy: 0.8243 - val_loss: 0.4072 - val_sparse_categorical_accuracy: 0.8606\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3731 - sparse_categorical_accuracy: 0.8675 - val_loss: 0.3758 - val_sparse_categorical_accuracy: 0.8676\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3332 - sparse_categorical_accuracy: 0.8809 - val_loss: 0.3565 - val_sparse_categorical_accuracy: 0.8754\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.3084 - sparse_categorical_accuracy: 0.8890 - val_loss: 0.3523 - val_sparse_categorical_accuracy: 0.8776\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2883 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3391 - val_sparse_categorical_accuracy: 0.8804\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2695 - sparse_categorical_accuracy: 0.9044 - val_loss: 0.3404 - val_sparse_categorical_accuracy: 0.8830\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.9103 - val_loss: 0.3307 - val_sparse_categorical_accuracy: 0.8854\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2431 - sparse_categorical_accuracy: 0.9146 - val_loss: 0.3241 - val_sparse_categorical_accuracy: 0.8896\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2302 - sparse_categorical_accuracy: 0.9205 - val_loss: 0.3261 - val_sparse_categorical_accuracy: 0.8862\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 7s 5ms/step - loss: 0.2198 - sparse_categorical_accuracy: 0.9242 - val_loss: 0.3212 - val_sparse_categorical_accuracy: 0.8920\n",
            "344/344 [==============================] - 1s 3ms/step - loss: 0.3240 - sparse_categorical_accuracy: 0.8856\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 28s 20ms/step - loss: 0.4856 - sparse_categorical_accuracy: 0.8311 - val_loss: 0.3868 - val_sparse_categorical_accuracy: 0.8646\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 26s 19ms/step - loss: 0.3572 - sparse_categorical_accuracy: 0.8750 - val_loss: 0.3537 - val_sparse_categorical_accuracy: 0.8738\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.3179 - sparse_categorical_accuracy: 0.8885 - val_loss: 0.3391 - val_sparse_categorical_accuracy: 0.8806\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 26s 19ms/step - loss: 0.2925 - sparse_categorical_accuracy: 0.8979 - val_loss: 0.3430 - val_sparse_categorical_accuracy: 0.8792\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.2713 - sparse_categorical_accuracy: 0.9052 - val_loss: 0.3244 - val_sparse_categorical_accuracy: 0.8890\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.2548 - sparse_categorical_accuracy: 0.9112 - val_loss: 0.3231 - val_sparse_categorical_accuracy: 0.8878\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.2389 - sparse_categorical_accuracy: 0.9178 - val_loss: 0.3143 - val_sparse_categorical_accuracy: 0.8912\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.2259 - sparse_categorical_accuracy: 0.9222 - val_loss: 0.3211 - val_sparse_categorical_accuracy: 0.8904\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 24s 18ms/step - loss: 0.2135 - sparse_categorical_accuracy: 0.9267 - val_loss: 0.3205 - val_sparse_categorical_accuracy: 0.8902\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2023 - sparse_categorical_accuracy: 0.9315 - val_loss: 0.3159 - val_sparse_categorical_accuracy: 0.8894\n",
            "344/344 [==============================] - 3s 7ms/step - loss: 0.3142 - sparse_categorical_accuracy: 0.8873\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.4853 - sparse_categorical_accuracy: 0.8314 - val_loss: 0.4030 - val_sparse_categorical_accuracy: 0.8618\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 25s 18ms/step - loss: 0.3593 - sparse_categorical_accuracy: 0.8734 - val_loss: 0.3594 - val_sparse_categorical_accuracy: 0.8754\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.3194 - sparse_categorical_accuracy: 0.8874 - val_loss: 0.3397 - val_sparse_categorical_accuracy: 0.8834\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2925 - sparse_categorical_accuracy: 0.8989 - val_loss: 0.3382 - val_sparse_categorical_accuracy: 0.8816\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2724 - sparse_categorical_accuracy: 0.9050 - val_loss: 0.3237 - val_sparse_categorical_accuracy: 0.8892\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 24s 18ms/step - loss: 0.2552 - sparse_categorical_accuracy: 0.9108 - val_loss: 0.3317 - val_sparse_categorical_accuracy: 0.8844\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2395 - sparse_categorical_accuracy: 0.9175 - val_loss: 0.3236 - val_sparse_categorical_accuracy: 0.8860\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2257 - sparse_categorical_accuracy: 0.9228 - val_loss: 0.3210 - val_sparse_categorical_accuracy: 0.8916\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2140 - sparse_categorical_accuracy: 0.9255 - val_loss: 0.3245 - val_sparse_categorical_accuracy: 0.8880\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2019 - sparse_categorical_accuracy: 0.9314 - val_loss: 0.3183 - val_sparse_categorical_accuracy: 0.8848\n",
            "344/344 [==============================] - 2s 7ms/step - loss: 0.3209 - sparse_categorical_accuracy: 0.8837\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.4847 - sparse_categorical_accuracy: 0.8330 - val_loss: 0.3985 - val_sparse_categorical_accuracy: 0.8654\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.3571 - sparse_categorical_accuracy: 0.8735 - val_loss: 0.3641 - val_sparse_categorical_accuracy: 0.8744\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.3179 - sparse_categorical_accuracy: 0.8880 - val_loss: 0.3503 - val_sparse_categorical_accuracy: 0.8792\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2913 - sparse_categorical_accuracy: 0.8968 - val_loss: 0.3401 - val_sparse_categorical_accuracy: 0.8798\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2709 - sparse_categorical_accuracy: 0.9042 - val_loss: 0.3262 - val_sparse_categorical_accuracy: 0.8898\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 26s 19ms/step - loss: 0.2543 - sparse_categorical_accuracy: 0.9107 - val_loss: 0.3414 - val_sparse_categorical_accuracy: 0.8768\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2388 - sparse_categorical_accuracy: 0.9155 - val_loss: 0.3311 - val_sparse_categorical_accuracy: 0.8802\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2250 - sparse_categorical_accuracy: 0.9219 - val_loss: 0.3207 - val_sparse_categorical_accuracy: 0.8888\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2131 - sparse_categorical_accuracy: 0.9260 - val_loss: 0.3139 - val_sparse_categorical_accuracy: 0.8872\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2011 - sparse_categorical_accuracy: 0.9322 - val_loss: 0.3150 - val_sparse_categorical_accuracy: 0.8864\n",
            "344/344 [==============================] - 2s 7ms/step - loss: 0.3035 - sparse_categorical_accuracy: 0.8927\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.4842 - sparse_categorical_accuracy: 0.8331 - val_loss: 0.3914 - val_sparse_categorical_accuracy: 0.8630\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.3589 - sparse_categorical_accuracy: 0.8724 - val_loss: 0.3646 - val_sparse_categorical_accuracy: 0.8732\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 24s 18ms/step - loss: 0.3198 - sparse_categorical_accuracy: 0.8867 - val_loss: 0.3481 - val_sparse_categorical_accuracy: 0.8776\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2937 - sparse_categorical_accuracy: 0.8966 - val_loss: 0.3346 - val_sparse_categorical_accuracy: 0.8826\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2723 - sparse_categorical_accuracy: 0.9034 - val_loss: 0.3229 - val_sparse_categorical_accuracy: 0.8884\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2542 - sparse_categorical_accuracy: 0.9108 - val_loss: 0.3249 - val_sparse_categorical_accuracy: 0.8842\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2394 - sparse_categorical_accuracy: 0.9162 - val_loss: 0.3159 - val_sparse_categorical_accuracy: 0.8880\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2257 - sparse_categorical_accuracy: 0.9213 - val_loss: 0.3225 - val_sparse_categorical_accuracy: 0.8870\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2136 - sparse_categorical_accuracy: 0.9262 - val_loss: 0.3080 - val_sparse_categorical_accuracy: 0.8906\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2014 - sparse_categorical_accuracy: 0.9320 - val_loss: 0.3103 - val_sparse_categorical_accuracy: 0.8932\n",
            "344/344 [==============================] - 3s 7ms/step - loss: 0.3040 - sparse_categorical_accuracy: 0.8955\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 24s 18ms/step - loss: 0.4844 - sparse_categorical_accuracy: 0.8326 - val_loss: 0.3928 - val_sparse_categorical_accuracy: 0.8666\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.3584 - sparse_categorical_accuracy: 0.8731 - val_loss: 0.3650 - val_sparse_categorical_accuracy: 0.8752\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.3180 - sparse_categorical_accuracy: 0.8866 - val_loss: 0.3461 - val_sparse_categorical_accuracy: 0.8788\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2926 - sparse_categorical_accuracy: 0.8966 - val_loss: 0.3428 - val_sparse_categorical_accuracy: 0.8792\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2717 - sparse_categorical_accuracy: 0.9056 - val_loss: 0.3256 - val_sparse_categorical_accuracy: 0.8848\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2525 - sparse_categorical_accuracy: 0.9120 - val_loss: 0.3294 - val_sparse_categorical_accuracy: 0.8864\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2383 - sparse_categorical_accuracy: 0.9171 - val_loss: 0.3205 - val_sparse_categorical_accuracy: 0.8854\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2250 - sparse_categorical_accuracy: 0.9227 - val_loss: 0.3153 - val_sparse_categorical_accuracy: 0.8888\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 23s 17ms/step - loss: 0.2116 - sparse_categorical_accuracy: 0.9287 - val_loss: 0.3164 - val_sparse_categorical_accuracy: 0.8874\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 24s 17ms/step - loss: 0.2010 - sparse_categorical_accuracy: 0.9322 - val_loss: 0.3099 - val_sparse_categorical_accuracy: 0.8930\n",
            "344/344 [==============================] - 3s 7ms/step - loss: 0.3145 - sparse_categorical_accuracy: 0.8879\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 71s 51ms/step - loss: 0.4809 - sparse_categorical_accuracy: 0.8327 - val_loss: 0.3870 - val_sparse_categorical_accuracy: 0.8666\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3541 - sparse_categorical_accuracy: 0.8749 - val_loss: 0.3534 - val_sparse_categorical_accuracy: 0.8778\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3151 - sparse_categorical_accuracy: 0.8902 - val_loss: 0.3387 - val_sparse_categorical_accuracy: 0.8836\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2896 - sparse_categorical_accuracy: 0.8994 - val_loss: 0.3418 - val_sparse_categorical_accuracy: 0.8824\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2683 - sparse_categorical_accuracy: 0.9056 - val_loss: 0.3213 - val_sparse_categorical_accuracy: 0.8898\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2521 - sparse_categorical_accuracy: 0.9120 - val_loss: 0.3219 - val_sparse_categorical_accuracy: 0.8866\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2358 - sparse_categorical_accuracy: 0.9189 - val_loss: 0.3128 - val_sparse_categorical_accuracy: 0.8926\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2226 - sparse_categorical_accuracy: 0.9242 - val_loss: 0.3187 - val_sparse_categorical_accuracy: 0.8906\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2099 - sparse_categorical_accuracy: 0.9283 - val_loss: 0.3194 - val_sparse_categorical_accuracy: 0.8878\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 70s 51ms/step - loss: 0.1987 - sparse_categorical_accuracy: 0.9326 - val_loss: 0.3151 - val_sparse_categorical_accuracy: 0.8912\n",
            "344/344 [==============================] - 7s 22ms/step - loss: 0.3148 - sparse_categorical_accuracy: 0.8873\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.4804 - sparse_categorical_accuracy: 0.8343 - val_loss: 0.4001 - val_sparse_categorical_accuracy: 0.8630\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3548 - sparse_categorical_accuracy: 0.8755 - val_loss: 0.3581 - val_sparse_categorical_accuracy: 0.8742\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3155 - sparse_categorical_accuracy: 0.8892 - val_loss: 0.3340 - val_sparse_categorical_accuracy: 0.8858\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2885 - sparse_categorical_accuracy: 0.8997 - val_loss: 0.3355 - val_sparse_categorical_accuracy: 0.8796\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2679 - sparse_categorical_accuracy: 0.9065 - val_loss: 0.3194 - val_sparse_categorical_accuracy: 0.8880\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2510 - sparse_categorical_accuracy: 0.9122 - val_loss: 0.3278 - val_sparse_categorical_accuracy: 0.8836\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2356 - sparse_categorical_accuracy: 0.9190 - val_loss: 0.3208 - val_sparse_categorical_accuracy: 0.8848\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 70s 51ms/step - loss: 0.2213 - sparse_categorical_accuracy: 0.9235 - val_loss: 0.3182 - val_sparse_categorical_accuracy: 0.8902\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2097 - sparse_categorical_accuracy: 0.9273 - val_loss: 0.3218 - val_sparse_categorical_accuracy: 0.8886\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.1975 - sparse_categorical_accuracy: 0.9332 - val_loss: 0.3139 - val_sparse_categorical_accuracy: 0.8862\n",
            "344/344 [==============================] - 7s 22ms/step - loss: 0.3176 - sparse_categorical_accuracy: 0.8858\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.4809 - sparse_categorical_accuracy: 0.8345 - val_loss: 0.3947 - val_sparse_categorical_accuracy: 0.8652\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3559 - sparse_categorical_accuracy: 0.8744 - val_loss: 0.3589 - val_sparse_categorical_accuracy: 0.8750\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3162 - sparse_categorical_accuracy: 0.8883 - val_loss: 0.3465 - val_sparse_categorical_accuracy: 0.8796\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2894 - sparse_categorical_accuracy: 0.8981 - val_loss: 0.3362 - val_sparse_categorical_accuracy: 0.8828\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.2688 - sparse_categorical_accuracy: 0.9054 - val_loss: 0.3222 - val_sparse_categorical_accuracy: 0.8892\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.2519 - sparse_categorical_accuracy: 0.9118 - val_loss: 0.3367 - val_sparse_categorical_accuracy: 0.8776\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 70s 51ms/step - loss: 0.2363 - sparse_categorical_accuracy: 0.9176 - val_loss: 0.3265 - val_sparse_categorical_accuracy: 0.8838\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2223 - sparse_categorical_accuracy: 0.9226 - val_loss: 0.3186 - val_sparse_categorical_accuracy: 0.8900\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.2102 - sparse_categorical_accuracy: 0.9272 - val_loss: 0.3101 - val_sparse_categorical_accuracy: 0.8898\n",
            "Epoch 10/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.1981 - sparse_categorical_accuracy: 0.9332 - val_loss: 0.3120 - val_sparse_categorical_accuracy: 0.8876\n",
            "344/344 [==============================] - 7s 21ms/step - loss: 0.3013 - sparse_categorical_accuracy: 0.8935\n",
            "Epoch 1/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.4785 - sparse_categorical_accuracy: 0.8353 - val_loss: 0.3915 - val_sparse_categorical_accuracy: 0.8658\n",
            "Epoch 2/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3557 - sparse_categorical_accuracy: 0.8743 - val_loss: 0.3659 - val_sparse_categorical_accuracy: 0.8714\n",
            "Epoch 3/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.3169 - sparse_categorical_accuracy: 0.8882 - val_loss: 0.3467 - val_sparse_categorical_accuracy: 0.8752\n",
            "Epoch 4/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2902 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3356 - val_sparse_categorical_accuracy: 0.8814\n",
            "Epoch 5/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.2691 - sparse_categorical_accuracy: 0.9048 - val_loss: 0.3239 - val_sparse_categorical_accuracy: 0.8896\n",
            "Epoch 6/10\n",
            "1375/1375 [==============================] - 70s 51ms/step - loss: 0.2513 - sparse_categorical_accuracy: 0.9119 - val_loss: 0.3261 - val_sparse_categorical_accuracy: 0.8824\n",
            "Epoch 7/10\n",
            "1375/1375 [==============================] - 68s 50ms/step - loss: 0.2359 - sparse_categorical_accuracy: 0.9178 - val_loss: 0.3165 - val_sparse_categorical_accuracy: 0.8870\n",
            "Epoch 8/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.2227 - sparse_categorical_accuracy: 0.9225 - val_loss: 0.3249 - val_sparse_categorical_accuracy: 0.8876\n",
            "Epoch 9/10\n",
            "1375/1375 [==============================] - 68s 49ms/step - loss: 0.2102 - sparse_categorical_accuracy: 0.9275 - val_loss: 0.3077 - val_sparse_categorical_accuracy: 0.8908\n",
            "Epoch 10/10\n",
            "  78/1375 [>.............................] - ETA: 1:00 - loss: 0.1984 - sparse_categorical_accuracy: 0.9387Buffered data was truncated after reaching the output size limit."
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aadmkiOoyM94",
        "colab_type": "text"
      },
      "source": [
        "Print the best hyper-parameters, and evealuate the best model on the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0nL5OZkylNX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(grid_search.best_params_)\n",
        "\n",
        "model = grid_search.best_estimator_.model\n",
        "model.evaluate(X_test_scaled, y_test)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}