l04c01_image_classification_with_cnns.ipynb

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      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "id": "vasWnqRgy1H4"
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      "source": [
        "#@title MIT License\n",
        "#\n",
        "# Copyright (c) 2017 François Chollet\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jYysdyb-CaWM"
      },
      "source": [
        "# Image Classification with Convolutional Neural Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S5Uhzt6vVIB2"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l04c01_image_classification_with_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l04c01_image_classification_with_cnns.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbVhjPpzn6BM"
      },
      "source": [
        "In this tutorial, we'll build and train a neural network to classify images of clothing, like sneakers and shirts.\n",
        "\n",
        "It's okay if you don't understand everything. This is a fast-paced overview of a complete TensorFlow program, with explanations along the way. The goal is to get the general sense of a TensorFlow project, not to catch every detail.\n",
        "\n",
        "This guide uses [tf.keras](https://www.tensorflow.org/guide/keras), a high-level API to build and train models in TensorFlow."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0tMfX2vR0uD"
      },
      "source": [
        "## Install and import dependencies\n",
        "\n",
        "We'll need [TensorFlow Datasets](https://www.tensorflow.org/datasets/), an API that simplifies downloading and accessing datasets, and provides several sample datasets to work with. We're also using a few helper libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5HDhfftMGc_i"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uusvhUp9Gg37"
      },
      "source": [
        "# Import TensorFlow Datasets\n",
        "import tensorflow_datasets as tfds\n",
        "tfds.disable_progress_bar()\n",
        "\n",
        "# Helper libraries\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXZ44qIaG0Ru"
      },
      "source": [
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yR0EdgrLCaWR"
      },
      "source": [
        "## Import the Fashion MNIST dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DLdCchMdCaWQ"
      },
      "source": [
        "This guide uses the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset, which contains 70,000 grayscale images in 10 categories. The images show individual articles of clothing at low resolution (28 $\\times$ 28 pixels), as seen here:\n",
        "\n",
        "<table>\n",
        "  <tr><td>\n",
        "    <img src=\"https://tensorflow.org/images/fashion-mnist-sprite.png\"\n",
        "         alt=\"Fashion MNIST sprite\" width=\"600\">\n",
        "  </td></tr>\n",
        "  <tr><td align=\"center\">\n",
        "    <b>Figure 1.</b> <a href=\"https://github.com/zalandoresearch/fashion-mnist\">Fashion-MNIST samples</a> (by Zalando, MIT License).<br/>&nbsp;\n",
        "  </td></tr>\n",
        "</table>\n",
        "\n",
        "Fashion MNIST is intended as a drop-in replacement for the classic [MNIST](http://yann.lecun.com/exdb/mnist/) dataset—often used as the \"Hello, World\" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc) in an identical format to the articles of clothing we'll use here.\n",
        "\n",
        "This guide uses Fashion MNIST for variety, and because it's a slightly more challenging problem than regular MNIST. Both datasets are relatively small and are used to verify that an algorithm works as expected. They're good starting points to test and debug code.\n",
        "\n",
        "We will use 60,000 images to train the network and 10,000 images to evaluate how accurately the network learned to classify images. You can access the Fashion MNIST directly from TensorFlow, using the [Datasets](https://www.tensorflow.org/datasets) API:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7MqDQO0KCaWS",
        "outputId": "ef7c82a6-00e8-4133-f9ac-1b3f8b56de07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "dataset, metadata = tfds.load('fashion_mnist', as_supervised=True, with_info=True)\n",
        "train_dataset, test_dataset = dataset['train'], dataset['test']"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1mDownloading and preparing dataset fashion_mnist/3.0.1 (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\u001b[0m\n",
            "Shuffling and writing examples to /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteMGBTV7/fashion_mnist-train.tfrecord\n",
            "Shuffling and writing examples to /root/tensorflow_datasets/fashion_mnist/3.0.1.incompleteMGBTV7/fashion_mnist-test.tfrecord\n",
            "\u001b[1mDataset fashion_mnist downloaded and prepared to /root/tensorflow_datasets/fashion_mnist/3.0.1. Subsequent calls will reuse this data.\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9FDsUlxCaWW"
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      "source": [
        "Loading the dataset returns metadata as well as a *training dataset* and *test dataset*.\n",
        "\n",
        "* The model is trained using `train_dataset`.\n",
        "* The model is tested against `test_dataset`.\n",
        "\n",
        "The images are 28 $\\times$ 28 arrays, with pixel values in the range `[0, 255]`. The *labels* are an array of integers, in the range `[0, 9]`. These correspond to the *class* of clothing the image represents:\n",
        "\n",
        "<table>\n",
        "  <tr>\n",
        "    <th>Label</th>\n",
        "    <th>Class</th>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>0</td>\n",
        "    <td>T-shirt/top</td>\n",
        "  </tr>\n",
        "  <tr>\n",
        "    <td>1</td>\n",
        "    <td>Trouser</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>2</td>\n",
        "    <td>Pullover</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>3</td>\n",
        "    <td>Dress</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>4</td>\n",
        "    <td>Coat</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>5</td>\n",
        "    <td>Sandal</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>6</td>\n",
        "    <td>Shirt</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>7</td>\n",
        "    <td>Sneaker</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>8</td>\n",
        "    <td>Bag</td>\n",
        "  </tr>\n",
        "    <tr>\n",
        "    <td>9</td>\n",
        "    <td>Ankle boot</td>\n",
        "  </tr>\n",
        "</table>\n",
        "\n",
        "Each image is mapped to a single label. Since the *class names* are not included with the dataset, store them here to use later when plotting the images:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjnLH5S2CaWx"
      },
      "source": [
        "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal',      'Shirt',   'Sneaker',  'Bag',   'Ankle boot']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Brm0b_KACaWX"
      },
      "source": [
        "### Explore the data\n",
        "\n",
        "Let's explore the format of the dataset before training the model. The following shows there are 60,000 images in the training set, and 10000 images in the test set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MaOTZxFzi48X",
        "outputId": "4ef28c7d-2d75-4e81-9198-0044dba1248d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "num_train_examples = metadata.splits['train'].num_examples\n",
        "num_test_examples = metadata.splits['test'].num_examples\n",
        "print(\"Number of training examples: {}\".format(num_train_examples))\n",
        "print(\"Number of test examples:     {}\".format(num_test_examples))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of training examples: 60000\n",
            "Number of test examples:     10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ES6uQoLKCaWr"
      },
      "source": [
        "## Preprocess the data\n",
        "\n",
        "The value of each pixel in the image data is an integer in the range `[0,255]`. For the model to work properly, these values need to be normalized to the range `[0,1]`. So here we create a normalization function, and then apply it to each image in the test and train datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAsH3Zm-76pB"
      },
      "source": [
        "def normalize(images, labels):\n",
        "  images = tf.cast(images, tf.float32)\n",
        "  images /= 255\n",
        "  return images, labels\n",
        "\n",
        "# The map function applies the normalize function to each element in the train\n",
        "# and test datasets\n",
        "train_dataset =  train_dataset.map(normalize)\n",
        "test_dataset  =  test_dataset.map(normalize)\n",
        "\n",
        "# The first time you use the dataset, the images will be loaded from disk\n",
        "# Caching will keep them in memory, making training faster\n",
        "train_dataset =  train_dataset.cache()\n",
        "test_dataset  =  test_dataset.cache()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIQbEiJGXM-q"
      },
      "source": [
        "### Explore the processed data\n",
        "\n",
        "Let's plot an image to see what it looks like."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSzE9l7PjHx0",
        "outputId": "e79ab494-4ed8-42c7-fd76-0283de2d9ffd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# Take a single image, and remove the color dimension by reshaping\n",
        "for image, label in test_dataset.take(1):\n",
        "  break\n",
        "image = image.numpy().reshape((28,28))\n",
        "\n",
        "# Plot the image - voila a piece of fashion clothing\n",
        "plt.figure()\n",
        "plt.imshow(image, cmap=plt.cm.binary)\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ee638AlnCaWz"
      },
      "source": [
        "Display the first 25 images from the *training set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZTImqg_CaW1",
        "outputId": "fb2810f2-7f77-49b6-c431-6e599203a829",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "i = 0\n",
        "for (image, label) in test_dataset.take(25):\n",
        "    image = image.numpy().reshape((28,28))\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(image, cmap=plt.cm.binary)\n",
        "    plt.xlabel(class_names[label])\n",
        "    i += 1\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59veuiEZCaW4"
      },
      "source": [
        "## Build the model\n",
        "\n",
        "Building the neural network requires configuring the layers of the model, then compiling the model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gxg1XGm0eOBy"
      },
      "source": [
        "### Setup the layers\n",
        "\n",
        "The basic building block of a neural network is the *layer*. A layer extracts a representation from the data fed into it. Hopefully, a series of connected layers results in a representation that is meaningful for the problem at hand.\n",
        "\n",
        "Much of deep learning consists of chaining together simple layers. Most layers, like `tf.keras.layers.Dense`, have internal parameters which are adjusted (\"learned\") during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ODch-OFCaW4"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), padding='same', activation=tf.nn.relu,\n",
        "                           input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu),\n",
        "    tf.keras.layers.MaxPooling2D((2, 2), strides=2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gut8A_7rCaW6"
      },
      "source": [
        "This network layers are:\n",
        "\n",
        "* **\"convolutions\"** `tf.keras.layers.Conv2D and MaxPooling2D`— Network start with two pairs of Conv/MaxPool. The first layer is a Conv2D filters (3,3) being applied to the input image, retaining the original image size by using padding, and creating 32 output (convoluted) images (so this layer creates 32 convoluted images of the same size as input). After that, the 32 outputs are reduced in size using a MaxPooling2D (2,2) with a stride of 2. The next Conv2D also has a (3,3) kernel, takes the 32 images as input and creates 64 outputs which are again reduced in size by a MaxPooling2D layer. So far in the course, we have described what a Convolution does, but we haven't yet covered how you chain multiples of these together. We will get back to this in lesson 4 when we use color images. At this point, it's enough if you understand the kind of operation a convolutional filter performs\n",
        "\n",
        "* **output** `tf.keras.layers.Dense` — A 128-neuron, followed by 10-node *softmax* layer. Each node represents a class of clothing. As in the previous layer, the final layer takes input from the 128 nodes in the layer before it, and outputs a value in the range `[0, 1]`, representing the probability that the image belongs to that class. The sum of all 10 node values is 1.\n",
        "\n",
        "> Note: Using `softmax` activation and `SparseCategoricalCrossentropy()` has issues and which are patched by the `tf.keras` model. A safer approach, in general, is to use a linear output (no activation function) with `SparseCategoricalCrossentropy(from_logits=True)`.\n",
        "\n",
        "\n",
        "### Compile the model\n",
        "\n",
        "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n",
        "\n",
        "\n",
        "* *Loss function* — An algorithm for measuring how far the model's outputs are from the desired output. The goal of training is this measures loss.\n",
        "* *Optimizer* —An algorithm for adjusting the inner parameters of the model in order to minimize loss.\n",
        "* *Metrics* —Used to monitor the training and testing steps. The following example uses *accuracy*, the fraction of the images that are correctly classified."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lhan11blCaW7"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qKF6uW-BCaW-"
      },
      "source": [
        "## Train the model\n",
        "\n",
        "First, we define the iteration behavior for the train dataset:\n",
        "1. Repeat forever by specifying `dataset.repeat()` (the `epochs` parameter described below limits how long we perform training).\n",
        "2. The `dataset.shuffle(60000)` randomizes the order so our model cannot learn anything from the order of the examples.\n",
        "3. And `dataset.batch(32)` tells `model.fit` to use batches of 32 images and labels when updating the model variables.\n",
        "\n",
        "Training is performed by calling the `model.fit` method:\n",
        "1. Feed the training data to the model using `train_dataset`.\n",
        "2. The model learns to associate images and labels.\n",
        "3. The `epochs=5` parameter limits training to 5 full iterations of the training dataset, so a total of 5 * 60000 = 300000 examples.\n",
        "\n",
        "(Don't worry about `steps_per_epoch`, the requirement to have this flag will soon be removed.)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_Dp8971McQ1"
      },
      "source": [
        "BATCH_SIZE = 32\n",
        "train_dataset = train_dataset.cache().repeat().shuffle(num_train_examples).batch(BATCH_SIZE)\n",
        "test_dataset = test_dataset.cache().batch(BATCH_SIZE)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvwvpA64CaW_",
        "outputId": "829ba4ad-6d35-44d0-e677-5d621e0b41b6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.fit(train_dataset, epochs=10, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 52s 6ms/step - loss: 0.3939 - accuracy: 0.8585\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.2575 - accuracy: 0.9068\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.2145 - accuracy: 0.9215\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1845 - accuracy: 0.9332\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1547 - accuracy: 0.9425\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1347 - accuracy: 0.9503\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1087 - accuracy: 0.9594\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0963 - accuracy: 0.9646\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0793 - accuracy: 0.9707\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0679 - accuracy: 0.9750\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9acb8d4650>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3ZVOhugCaXA"
      },
      "source": [
        "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 0.97 (or 97%) on the training data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEw4bZgGCaXB"
      },
      "source": [
        "## Evaluate accuracy\n",
        "\n",
        "Next, compare how the model performs on the test dataset. Use all examples we have in the test dataset to assess accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VflXLEeECaXC",
        "outputId": "eacc8bff-e239-46d0-e98c-2770887089ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_loss, test_accuracy = model.evaluate(test_dataset, steps=math.ceil(num_test_examples/32))\n",
        "print('Accuracy on test dataset:', test_accuracy)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 4s 11ms/step - loss: 0.3329 - accuracy: 0.9180\n",
            "Accuracy on test dataset: 0.9179999828338623\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWfgsmVXCaXG"
      },
      "source": [
        "As it turns out, the accuracy on the test dataset is smaller than the accuracy on the training dataset. This is completely normal, since the model was trained on the `train_dataset`. When the model sees images it has never seen during training, (that is, from the `test_dataset`), we can expect performance to go down. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsoS7CPDCaXH"
      },
      "source": [
        "## Make predictions and explore\n",
        "\n",
        "With the model trained, we can use it to make predictions about some images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ccoz4conNCpl"
      },
      "source": [
        "for test_images, test_labels in test_dataset.take(1):\n",
        "  test_images = test_images.numpy()\n",
        "  test_labels = test_labels.numpy()\n",
        "  predictions = model.predict(test_images)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl91RPhdCaXI",
        "outputId": "5f6fca17-5b83-45ee-e59a-438b07c69f9f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions.shape\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9Kk1voUCaXJ"
      },
      "source": [
        "Here, the model has predicted the probability of each label for each image in the testing set. Let's take a look at the first prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DmJEUinCaXK",
        "outputId": "dd043186-54e6-4816-a3a7-994727567d57",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.8474088e-09, 3.5318775e-12, 1.1473096e-05, 9.4113660e-13,\n",
              "       9.5932966e-01, 6.9410272e-10, 4.0658925e-02, 1.4194573e-17,\n",
              "       1.4645585e-12, 1.6268249e-11], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-hw1hgeSCaXN"
      },
      "source": [
        "A prediction is an array of 10 numbers. These describe the \"confidence\" of the model that the image corresponds to each of the 10 different articles of clothing. We can see which label has the highest confidence value:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsqenuPnCaXO",
        "outputId": "26537b72-d2f4-485e-f13b-963ca2425846",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E51yS7iCCaXO"
      },
      "source": [
        "So the model is usually most confident that this image is a Shirt, or `class_names[6]`. Let's check the label:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sd7Pgsu6CaXP",
        "outputId": "638d8dbd-1fdd-4c07-a915-2eab90c3d7ad",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygh2yYC972ne"
      },
      "source": [
        "We can graph this to look at the full set of 10 class predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvYmmrpIy6Y1"
      },
      "source": [
        "def plot_image(i, predictions_array, true_labels, images):\n",
        "  predictions_array, true_label, img = predictions_array[i], true_labels[i], images[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  \n",
        "  plt.imshow(img[...,0], cmap=plt.cm.binary)\n",
        "\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  if predicted_label == true_label:\n",
        "    color = 'blue'\n",
        "  else:\n",
        "    color = 'red'\n",
        "  \n",
        "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
        "                                100*np.max(predictions_array),\n",
        "                                class_names[true_label]),\n",
        "                                color=color)\n",
        "\n",
        "def plot_value_array(i, predictions_array, true_label):\n",
        "  predictions_array, true_label = predictions_array[i], true_label[i]\n",
        "  plt.grid(False)\n",
        "  plt.xticks([])\n",
        "  plt.yticks([])\n",
        "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
        "  plt.ylim([0, 1])\n",
        "  predicted_label = np.argmax(predictions_array)\n",
        "  \n",
        "  thisplot[predicted_label].set_color('red')\n",
        "  thisplot[true_label].set_color('blue')"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d4Ov9OFDMmOD"
      },
      "source": [
        "Let's look at the 0th image, predictions, and prediction array. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV5jw-5HwSmO",
        "outputId": "19edc873-be76-4c1e-8305-293a1ed77778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "i = 0\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions, test_labels)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko-uzOufSCSe",
        "outputId": "5ad47d3d-6827-49b9-d3fb-be770775f2ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "i = 12\n",
        "plt.figure(figsize=(6,3))\n",
        "plt.subplot(1,2,1)\n",
        "plot_image(i, predictions, test_labels, test_images)\n",
        "plt.subplot(1,2,2)\n",
        "plot_value_array(i, predictions, test_labels)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kgdvGD52CaXR"
      },
      "source": [
        "Let's plot several images with their predictions. Correct prediction labels are blue and incorrect prediction labels are red. The number gives the percent (out of 100) for the predicted label. Note that it can be wrong even when very confident. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hQlnbqaw2Qu_",
        "outputId": "647f2174-416a-4638-d597-1b34f9ac801b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "# Plot the first X test images, their predicted label, and the true label\n",
        "# Color correct predictions in blue, incorrect predictions in red\n",
        "num_rows = 5\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_image(i, predictions, test_labels, test_images)\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  plot_value_array(i, predictions, test_labels)\n"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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h+7+IGA/d1qlTJ7GdyOHEfW3SpEku5jKDQPJMcOGMrLyvDJdxeTUurcYpSIC/7+T1devWzWvHs8aOHz/exWEqBB8DfPTRR4nbx/0wTHls3bp1zmVhiTd+nyQ7dIZYRERERDJNB8QiIiIikmkVZnydq0xwigAATJ8+PeeyRYsWee14iCi8ipSHbXgYNLzSlYdjOT0hHC7mx/HzhkO98+fPz7m94fYtWbLExeEV9ry9SpmQssTf7XBWuLCfFuvQoYN3m1Mm+Gp0wO+Xn376aeK6efiT+0OY4hSmShXj1IzwubhKDKdWAf5QdTg8u2zZspzPJVISX3zxBSZOnAgAOPvss71l55xzjos5VTCsVtSiRYuc7fr37++147SD8HvMfYD7a9g3+PegUaNGLuZ0JMCvtMR9rUmTJl473u9x2kb4u8MzRIbLuL8uWLDAxZx2KdmlM8QiIiIikmk6IBYRERGRTNMBsYiIiIhkWoVJOOVZZYqKirxlPPvbZ5995uIJEyZ47bp37+7icEYsLhnD5WLCmW5Wr17tYs4TDss7cUknLh8TzrLHuUtcSmbw4MFeO85xDPMg169f72IuKyNyuPF3j/sG4OfBc257WE7p3XffdXGvXr28ZZwrzH0szJXnfsV5gmHOftJ2hH2KyzBxaakwT5ifi2fZytVW5FAsX74ct912GwBg+/bt3rIXX3zRxZzLG+bG8neer4fh7zjgl0YL95XcJ6dOnepizrsPH8e/BSeddJLXrnfv3i7m/XyYk8wl5Ph1ha+R971h+TTOG+Z85bB0m2STzhCLiIiISKbpgFhEREREMq3CpEzwUEc4DMJpEpzGEJaL4ZQJHi4C/BI0PEtcWP6MSzrxkE445MLlnnhYOZw5iJ+LZ9tas2aN145fczgDH5ekU8qElCUegm3Tpo23jL/PPMwalihcvHixi8O0Ax4a5sdxPwT8/sbDritWrPDa8TApp0+E/ZL7FJdGDGeQ5NcVpkiEJa9EDkWXLl3w5ptvAgCmTZvmLXv++eddzMvmzJnjtePvMvcTvh8AZs+e7WJOQQD8GeO4T4YzNXLJM075C9MGObWR+z+XGgX8/R6nOHJpRsD/LQjLpnL5N95nh8cKPOOtZIfOEIuIiIhIpumAWEREREQyrcKkTHCKQzic0aVLFxfz8EuYWsFDpOGQK8+4wzPihMOgPDzDV6LzECvgDyXzkFOYCsHbxDN4hVfY8pW04dB0+NwiZYXTAlatWuUt477D39+wT6X1S05J4Fm2wrQLTo3goeC0Gah27drl4rBfcqUZHnYNf1N4e/l5gf0r1IgcquLvWJ8+fbz7w9vFwpQBTmOaNWuWi8NZXTnNL0wH5PQiruISzvbI+7O6deu6OExD5PQHfq633nrLa/fggw+6uFmzZkiSNBsl4KdMcB8P+7Vkk84Qi4iIiEim6YBYRERERDJNB8QiIiIikmkVJoeYc5rCMiucTzhx4kQXc14w4Oc0TZkyxVvWuXNnFzdp0sTFnMMIAJ988omLeeYsXjfg50lyTvLKlSu9dpwrzGVlwpmDOnbsmLMdACxdutTFgwYNgkhZ4RzCMLf/448/dnHbtm1dHM58xTnEYVkz/q5zzj6XfgL8/F1eRzijV9JMXeGMdrwOzjUMZ8nkko/cDti/vJRIWQtnUOXbPBtdIRoyZEjqbZHSpjPEIiIiIpJpOiAWERERkUyrMCkTPMwazojDQ64ch+kOnLoQDpEmzcwTllLisjCcujFjxgyv3d69e13M5Ze4/AzgDwPzMG3YjpeFpaTCYWGRsjJ9+nQXh6XQOD2BZ4j76KOPvHacosSPAZLLpoVlCbkPcCpEmILB/ZfLP4XPy78xXNItTMPi5wpTJrjEk4iIFDadIRYRERGRTNMBsYiIiIhkmg6IRURERCTTKkwOMecGc44v4Of4cV7veeed57XjPOFQ0vSR/LyAP1UtC/MFk3KN582b57Xjaaf79u3r4jfffNNrd8YZZ7g4zGsOy9CJlBX+7oV9hfsAl13jvFsAmDt3rot5embAz/PlqZaXLFmS2I7jsG9waTjO7Q+njJ4/f76LW7dunfMxoTCXf8eOHYltRUSksOgMsYiIiIhkmg6IRURERCTTKkzKRK1atVwcllLicmqdOnVy8fXXX5+4Pp5hCgA2bdrk4g0bNri4du3aXrsFCxa4mId+wzJpPGzbsmVLF/PwK7B/Gadi48eP925zqka4TeHMdSJlhVMIOEUC8FN7OFXhvvvu89oNHz7cxTzDI+CnHXAZxTA9gftEWulF3iZOvQrLuCW57LLLvNv8+sN1JKVXiYhI4dEZYhERERHJtApzhlhERCR06623Ji574IEHynBLRKQiqzAHxHxVOc8CBwBLly51MV+JnqZNmzapt5P06dMnr3aHKkwL4Zn6QuEws0hZmTZtmovDChE8C+PatWvzWl+YdpBvKkNZCVOeVq9e7WKejQ/wU7lERKSwKWVCRERERDJNB8QiIiIikmkVJmVCpBAoX1FERKTyqTAHxF/96lddvGzZMm8Z59teffXVieuw1iYuC/OSi4WlpBiXcArLOyW1S1sfO/roo73bq1atcnE40xfPYidSln7/+9+7ePny5d6ymTNnujjtD4m02d94WVr/zVfYd4qFsz/ybY5/+ctfeu2OO+44F9epU8dbVlbXG4iIyKFTyoSIiIiIZJoOiEVEREQk00xpDEMe0gYYswbA0gM2lNLWzlrbtLRWps+x3OnzrBz0OVYe+iwrB32OlUvi51nuB8QiIiIiIuVJKRMiIiIikmk6IBYRERGRTCvoA2Jj0MIY/NMYfGIMphiDV41BlxKsp4ExuD5l+Q+NwWxjMMcY3BQsu9EYzI+X/Sq+72RjMNMYTDYGnek53jAm+T01Bi8Ygw5xXMcY/JVe2zhjcPzBvrZ4XT+juLoxeNuYilNSTyqW8u6XxuDS+L4vjUE/ul/9UsqFMbjQGFhjcEye7ZcYgyY57t96kM97UO1T1nOVMWiVsCxnf4uX3WYMFhqDj4zBV+j+s+L7FhqDW+n+Z+I+eh/dd4cxuDBl23obg8eD+0YZgwl5vrZTjcHLCa/5oXzWUZL2KevxfveMQVNj8NqhrlcOXcEeEBsDA+BFAOOsRUdr0RfAbQCal2B1DYDcO15j0B3ANQAGAOgJ4Fxj0CleNgTABQB6WotjAfwmftjNAL4K4CYA18b33QHgPmuRs6iqMTgWQFVrsSi+6zEA6wF0jl/b1cD+P5B5cjtea7ELwJsAvl7CdYkkKoR+CWA2gIsBvB08TP1SyssVAN6N/6+IrgJyHxAjob8Zg24ALgdwLICzAPzJGFQ1BlUBPAzgbADdAFxhDLoZgx4AtluLHgD6G4P6xqAlgOOtxaiUbfsZgAfpeRsA6AugfvEfshWM97tnLdYAWGEMTi6/TRKggA+IAQwBsNta/KX4Dmsxw1q8YwyMMfh1fPZoljHRTiY+u/OmMZga339B/NAHAHQ0BtONwa+D5+kKYKK12GYt9gAYj6jzA8B1AB6wFjvj518d378bQK34325j0BFAG2sxLuX1fAPAv+Pt7AjgeAB3FO+orcVia/FKvPzH8WubHZwZGxWftZpjDL4b3/cAgJrxa3smbjoqfj6R0lbu/dJazLMWH+XYNvVLKXPGoA6AgQC+g+gAsfj+U+MRhhdMNMr4TPwHJT+2pjEYYwyuybHenxiDD+Mzqj9Pef7fxd+9N41B0/i+XsZgQvzYF41Bw6T7jcElAPoBeCb+vtbk9af0twsA/NNa7LQWiwEsRPQH7AAAC63FovgPwX/GbXcj6hNVAFQDsBfAPQDuTnltdQH0sBYz6O6LAbwUr5ff7+HG4EFj8L4xWBS/rnB9/Y3BtLiv8/1NjcGI+P3+MOXgtE38mS4wZt92p/w25Lo/1++efhsKgbW2IP8B9geA/V3Csq8BdixgqwK2OWA/BWxLwB4B2HpxmyaAXQhYA9giwM5OWFdXwH4M2MaArQXYDwD7x3jZdMD+HLATATsesP3j+3sBdgJg3wLsUYD9J2A7H+D1jAfscXF8PmBfTGjXF7CzAFsbsHUAOwewveNljeL/awJ2NmAbx7e3BuuoCtg15f0Z6l/l+1cI/ZLajANsP7qtfql/Zf4PsN8A7ONx/D5g+8bxqYDdFH8Xq8Tf4YHxsiXx9/8/gP0mrWtr/P9QwD4S95MqgH0ZsINzPLcF7Dfi+C7APhTHMwF7ShzfA9jfH+B+ry8lvM6wvz0E2GF0+3HAXhL/e4zuv5K26/fxfvXmuL8+foDnHALYEcF9YwE7CLBdADuL7h8O2Ofj96sbYBfS5/AyYE8C7BTAto3vv4q26x/02bQF7Lwc23IVYFfEv0nFfb1f0m9Dyv37/e4BtjW/Fv0rn38VNZ9tIIBnrcVeAKuMwXgA/QGMAXCfMRgM4EsArXGAoVxrMc8Y/BLAGwC+ADAd0V+uQDS1dSMAJ8Tr/5cx6GAtpsf3IX6uFYhmeH0O0V/BN1uLVf4zoSWANXm+thetxRfx+kcCGARgGoAfGIOL4nZtAHQGsC7Ha9prDHYZg7rWYksezylSGsqqXyY9Rv1SysMVAP4Qx/+Mb0+Jb0+yFssAwBhMB1CEKLUCiEYmfmWtG0FgQ+N/0+LbdRB9r8I0oS8BPBfHfwcw0hjUB9DAWoyP738SwPNJ9x/cSz001npnT18C8D1jcDuitKix1uLR4CFe/zQGzRG9D+9aC2sMdhuD7tZidtxklI1Gd+bGbYt1BfAIgKHW4vMcm3YGgG5m3/n7esagjrX75WiPtTbq2/FvwEAAFrl/G0zC/aNzPP9qJKesSBkp5APiOcD+Qx4H8A0ATQH0tRa7jcESAEce6EHW4nEgSto3UbL/snjRMgAjrYUFMMkYfIkon3BN3NYgylG8HMAfAdyC6AfvBwBuD55mO23LHAA9jUHV+ODhgIzBqYg67YnWYpsxGHeA11YDwI581i1yEAqhX6ZSv5SyYgwaATgNwHHGwAKoCsAag5/ETXZS873w97nvATjLGPwj3sd4qwZwv7X460FuUllOLLAc0R+AxY6K70PK/QCAOG1qCqID/Y7W4jJj8LoxeMZabKOm3D8B4DIADQEsjg9e6yH6A6S4X/P7zekpK+L19AZyHhBXAXCCtQfsm+H7W1rv95GIXquUo0LOIf4vgBrFOXkAYAx6GINBAN4B8HUTJfA3BTAYwCQA9QGsjne6QwC0ix+6BUDdpCcyBs3i/9siyk/6R7xoFKKcSZjoKvrqANbSQ78J4FVrsR5R3uKX8b9aOZ5mHhBdFGQtPgEwGcDPi3PKjEGRMTgnfm0XGoNaxqA2gIvi++oD2BDvdI9BfCYsttsYVKPX0xjAWmuxO+k1i5RQIfTLA1G/lLJyCYCnrUU7a1FkLdoAWIzoTOCB3AVgA6IL0EKvA/i2ifKTYQxaF/eHQBXs+wP1fxCdOd0EYEPcJwHgSgDjk+6P49S+mGA0gMuNQQ1j0B7RmdtJAD4E0NkYtDcG1RH9YerOisZ94iYAvwJQE/sOKqsi2scy1z9jVwA4K36vixBdXHc5DmwjgHMA3B//ERt6A8CNtI29EtZzpjFoZKI86wsR/VGT9NuQdH+u97oL4M5ySzkp2APi+C/miwCcYaISSHMA3A9gJaKr3GcCmIFoB32LtVgJ4BkA/YzBLEQ7xfnxutYBeC9ObA8v3gGAEcZgLqJE/Rusxcb4/icAdDAGsxENhX2r+C95Y1AL0ZW5xT9mvwXwKoDfA/suOCKvAF5H/P8QDRsvjNc/HNFBw9Q4ngRgIoDHrMU0AK8BOMIYzEOUlM8lZx4BMNPsu3hnSPx8IqWqEPqlMbjIGCwDcCKAV4zB68UPUL+UMnYFou89G4H8q038ENGFZr/iO63FG4j+APwg7jcvIPcB6xcABsTf1dMQXaQGAN8C8GtjMBNArzzuHw7gLybHRXVJ/c1azAHwLwBzEfWDG6zFXhtdBPt9RAf18wD8K25b7AYAT8ZngmcCqBW/xim07y1+H+YjqiZR1xgUIfpjegItXwxgk8mjNGKcLnUugIdztP8Bot+omfFvzrX7rSAyCdHnOxPACGsxOem3IeX+XL97+m0oAJq6uYzEPzJvATg53+HYQ3iukUk4GT8AACAASURBVAButRYfH87nEano1C9FCpsx+BGALdbisfLelsPFGLwN4AJrsaG8tyXLCvYMcWVjLbYjKi/T+nA+TzxENUo7XZEDU78UKXh/hp8bXKnE6WW/1cFw+dMZYhERERHJNJ0hFhEREZFM0wGxiIiIiGSaDohFREREJNN0QCwiIiIimVbuM9U1adLEFhUVlfdmZM6UKVPWWmubltb69DlGZswA9uxJXn7EEUDPnqX/vPo8Kwd9jr60/nS4+lJpqSyf5e7d/jwyixcvdvGXX37p4j3BB8XLqlSpkjMGgKpVq7r4iCP2HZJ07NixhFtcugrhcyyv/UpllPZ5lvsBcVFRESZPnlzem5E5xpilpbk+fY4RY9KX79kDHI63SZ9n5aDP0ZfWnw5XXyotleWz/Pxzf6bjYcOGuXj79n2zDa9du9Zrt2PHvlmQa9eu7eJatfwJI+vW3TffSOPGjV08cuTIEm5x6SqEz7G89iuVUdrnWe4HxCIiIlKYnn76ae/29OnTXdy69b7y3XxgC/hnePls8bx587x2TZo0cfH8+fNdPGnSJK/dgAEDDmazRQ6acohFREREJNN0QCwiIiIimaYDYhERERHJNOUQi4iISE4bNmzwbnfp0sXF1loX79q1y2u3cePGnDHnDAN+ZQnOSQ4v5hM53HSGWEREREQyTQfEIiIiIpJpSpkQERGRnNatW+fd5vrC9erVS3wc1yg+8sgjc8aAn5Kxfv16F8+cOdNrd+GFF+a5xSIlozPEIiIiIpJpOiAWERERkUxTyoSISJ7CK+l5Nq2//vWv3rJzzjnHxZdffvnh3TCRw2Tnzp3eba4ssWfPnpxxiKtMVKnin4dr0KBBzphntxMpCzpDLCIiIiKZpgNiEREREck0HRCLiIiISKYph/gwWbJkiYvHjBnj4uuuuy6vx4e5ijybjzHGWxbeFpGDwyWiAOCZZ55x8fPPP+9i7tcAUFRU5OKPP/44cf3KIZaKKswN3r17t4u5hFrYhzgHmNvt3bs3cX1c0m3Lli0l3GKRktEZYhERERHJNB0Qi4iIiEimKWXiIPEwEJePCYeVBg8e7OLPPvvMxa+88orX7uWXX875PNWrVy/V7ROpzBYtWuRiHpKtU6eO1278+PEufvjhh10cpijx7ZYtW7r45JNPTtyGmjVrere5XNXq1atd3KxZM69dOIQsUkjCmeW47BqnO4T7G57hjlP+ateunbg+1rBhw4PfWJFDoCMmEREREck0HRCLiIiISKYpZeIw4aGkdu3auZhn7AGArl27unjIkCEuvvjii712PFQbDs0ypUlIZcKpEL/+9a9dHKYdJJk6dap3m/tf06ZNXRwOC7PGjRu7OJy1q2rVqi5u1KiRt+zCCy/Ma3t5HSKFpkWLFt7tGjVquLh169Yunjt3rtfujDPOcPG8efNcvHnzZq8dpyRxpYqOHTuWcItFSkZHTyIiIiKSaTogFhEREZFM0wGxiIiIiGSacohLCZeVAYC6deu6mMvPhGWg3nvvPRevWLHCxc8++6zXjnMf27Zt6y3j3ONvfvObLj7ttNPy2naRQsX5u9/+9rddHObkvvTSSy5euXKlixs0aOC147xEzsXfunWr1477bFr/ZZwzDADnn39+YluRiiLMjeeZUXnZmjVrvHZDhw51MZdWC0uN8vUxvA7O8RcpCzpDLCIiIiKZpgNiEREREck0pUwcJp988omLmzdv7uL69et77fg2DwOH5Z146Dec2Wf06NEunjVrlounTJlysJstUlC4f/Tv3z+x3fe//30Xc+mm1157zWs3ZswYFy9ZssTFnOIE+GXYuMwUpzUBwLXXXuvis846K3H7RCqqsOwalxRl4WytXE6NZ2Pk2VQBf1ZIjtu0aXPwGytyCHSGWEREREQyTQfEIiIiIpJpOiAWERERkUxTDvFB4pIzaThPinMQ165d67XjfGDOwQrzrHh617DEG+coc/6kSGXFOYmA3z+4nNpFF13ktTvllFNc/PDDD7uYc/4Bv/9xnwrLuA0cOPBgNhtA+raLFJp+/fp5t3k/xX0jzCFu166di7lcIecJA/5UztWrV3dxhw4dSrjFIiWjM8QiIiIikmk6IBYRERGRTFPKxEHKN2WCyzbxEO6yZcsS2/GQU1hajYdw07Zhw4YNeW2fSEWWlmbAKQlhO55Zi8ukPfXUU167VatW5Vz3UUcd5d1Om7kubTtEKoqjjz7au81l13iGxzBdr1WrVi7msoY7duzw2vG+rnbt2i6uVq1aCbdYpGR0hlhEREREMk0HxCIiIiKSaUqZOIAwdSEpXWHSpEnebR4W6tq1q4s/+OADrx2nTHBaRDhcxDPX8ZW4gJ9qEc5wJ1IIwsoKrLTTCfJNVeCZ78aPH+8tS0pzqlWrVt7boTQJqQzCfV7jxo1dzPu5sB33ldatWye2Y2EVF5GypDPEIiIiIpJpOiAWERERkUzTAbGIiIiIZJpyiGOcK8w5Tmn5TjwL1qhRo7xlJ598sos5r5dn5QGAevXq5Vx3+Lycjxgu45nruOzaxo0bvXbKz5KDkZT3W5Lc2LLMpy3Jc4Xl1Fq0aOFiLifVrFmzkm9YLNy+tPxqkULDM8itXr3axeEMqqxjx44ubtu2rbeMv/9dunQpjU0UKRGdIRYRERGRTNMBsYiIiIhkWqZSJpLSInLdLvbCCy94t6+44goXc7mza6+91mvHM1299dZbLm7atKnXjmf94aGjtO0LS8ElDRFPmTLFu3366afnbCeSS0UtG5a23Zz+wKXVatSo4bVbsGCBi7kE4vTp0/PejqVLl7p4xowZLh4zZozXTqlMUpE0b97cxZwykZb6E6YkMd6PlkZKkkhJ6QyxiIiIiGSaDohFREREJNMqZMpEmDLA0qpCpC1buHChi88880wXL1myxGvH1SMGDBjg4jfffNNrt2XLFhe3a9cucRu2bdvmYq5GwbPWhY8L18FDVTycNXr0aK+dUiakEIRDq6WRmpHv7HThLI/F+vTp492+9dZbXXzaaae5eOXKlV671157zcVTp071lvHslXxlfqtWrbx2p5xyiosfeOCBxG0XKQRJ+6LatWsf9GMAv0/yjHYiZU1niEVEREQk03RALCIiIiKZpgNiEREREcm0gsohDvNmWZUq+47d03KB04wYMcLFN998s7eMSyT179/fxeeee67Xbs6cOS5+5plnXNywYUOvXZ06dVzMucEhLunErz98zK5duxLXsWPHDhdzWamxY8cmPkbkQIrzctNmViuUWevyXWdSO87zB4DLL7/cxevXr3cxz2AHALfddpuLu3bt6i3r2bOni8O8YTZ48OCULRYpLLyf4v1SWLqQpc1ix/vzunXrHuLWiZSczhCLiIiISKbpgFhEREREMq2gUiY4LaKkeIap66+/3ls2YcIEF3PJNAA4++yzXfzJJ5+4mNMsAH9YiEuchekenMbAQ8xhybik2fPCEjabNm3Kub7wuXloav78+RApbUlpB2kzVeU7e1zYjksypa0/3+dKwv0L8Msmbty40cVcPg0AfvGLX7iYXwcA3H333S7moWUu6yhS0WzdutXF/L1OS3fgVCNOEwzXkZZ2IXK46QyxiIiIiGSaDohFREREJNMKKmVi9uzZ3u2nn37axV988YWLZ8yY4bVbvXq1i3nIJbwinCtGLF++3FvGM07xMG3Lli29dnv27HExD+Hy/YCfCsFDuOFwLqc7cMoED9MC/rBSeMUuPzdve5ieEc66J1ISSVUm8k1VCNMT6tevn9iWv/clSZ9IwylF99xzj7fsyCOPdPHtt9/u4mOOOSbv9XOlGU6n4JnvRCoaTpng/VdaykStWrVcHM4WGe47RcqLzhCLiIiISKbpgFhEREREMk0HxCIiIiKSaeWeQ7xx40aMHDkSAPCDH/zAW8aljzgHKcyh5WWcq8ezzwF+ObVwHZz/xOXftm3b5rXjnEbO0Q3zdTm3KmlmH8DPu+RyNFy2DfBzJvOd0S80ZsyYxGUioeLvZvidDXMAi3FuIeBfE8D5tLfccovXbt68eS6eOHGit6xZs2ap23aw/vvf/7r4N7/5jYsHDhzotfvZz36W8/H5vheA/5uiGbiksuD9ctosrOzTTz91cbhP3b17t4vDa2dEypLOEIuIiIhIpumAWEREREQyrdxTJmrWrIkePXoAAL7yla94y+bOnetiLrvGQywAsGHDBhdzKsS6deu8djzMGpZt4hQFLn9W2sLUCi7vxNuXlloRpkzwsG3aEG44k5ZIPtK+U4899piLw3KI3P+4tNq1117rtRs1apSLefZHAHjzzTddXJJyZTw7JeCnSZx11lkuDtO1GP9WhO9FUgk6IPptK6bSUlJZ1KtXz8ULFy50cbjPYpwmEaZMcApGSVOhREqDzhCLiIiISKbpgFhEREREMq3cUyZq1KiBTp06AQAef/zxxHY8pDl27FhvWfv27V3MQy7du3f32vFQDc9oB/hDmpySkG/1iBA/jlMwuJIE4FeFSEvV4MeF6+BhW04ZCYefwlQTkUMVzi7JeKbI9evXu3j48OFeO06VGjRokLfsd7/7nYt79erl4kaNGnntuA9w6sbdd9/ttfvGN77h4m9+85uJ285KOoxbVFTkYk75EqnIeJ/F+5S0CkecGhju53jfG84MK1KWdIZYRERERDJNB8QiIiIikmk6IBYRERGRTCv3HGK2adMm7zaXanrwwQdd3Lt3b69dWFopCecNc04T4Of88rIwLyrMKT7Q/YCfaxzm8ea7Ps6zCkvGcbkb3t4wx7l169aJ2yjCNm/e7HL1eXY3wC+nxn0vnLUqzPMtxjn/APDyyy+7OPxu9+nTx8Vcgu24447z2r3xxhsu5rzhP/7xj167YcOG5dymNGml1bjvcZk1QHnDUjlxiVL+/rdp0ybxMZwbHM4Sy/u6pJkpRcqCzhCLiIiISKbpgFhEREREMq2gUiY4RQLwh2N4hqhp06Z57XgIl8s7bd682Wu3YsUKF/PsOIA/LMpxmHbAw8JpaRJJy9JK07DweevUqZO4Dh6qbdq0qYvDIWsuQ3fllVfmtR2STfXq1UucGY5nPOzYsaOLP/roI6/dmjVrXMyl0BYvXuy1434azlQ3btw4F5944ok5YwBYvXp1zu0ojSHYtLJrYYoHW758uYs1S6RUFlxO8O2333Zxhw4dEh/TuHFjF9eqVctbxv0rKc1KpCzoDLGIiIiIZJoOiEVEREQk03RALCIiIiKZVlA5xCHOG07D+Ukci0jJFef2nXnmmYltzj///LLanILEuf2h+++/vwy3RKRscMlDvtYlzP9PEuYQ8zUx2n9LedIZYhERERHJNB0Qi4iIiEimFXTKhIiIiBSOzp07uzhphtc0YTtOuzDGHOLWiZSczhCLiIiISKbpgFhEREREMk0pEyIiIpKXsEpEsbRZG1m1atW825rFUQqFzhCLiIiISKbpgFhEREREMk0HxCIiIiKSacohFhERkbzUr1/fxTVr1nRxw4YN83p8o0aNvNurVq0qnQ0TOUQ6QywiIiIimaYDYhERERHJNMMzzZTLBhizBsDSct2IbGpnrW1aWivT51ju9HlWDvocKw99lpWDPsfKJfHzLPcDYhERERGR8qSUCRERERHJNB0Qi4iIiEim6YBYRERERDKt1A6IjcHtxmCOMZhpDKYbg+NLa93x+k81Bi+X0rqOMQYfGIOdxuB/g2VnGYOPjMFCY3Ar3d/eGEyM73/OGFSP77/RGMw2Bq/SfQONwe9Snr+mMRhvDKoagyrG4MF4HbOMwYfGoH1pvE56vquMwUOH8PjfGIPTSnObpHwdrv5qDMYZg34laWMMvh/3L2sMmtD9Ju4jC+Pt7UPLvmUMFsT/vhXfV8MYvBb3qeup7SP82BzPf6ExuCuOj463c7oxmGcMHjm4dyLxOQ74O8ZtjMG5xuCe0nhuKUzGoHH8PZtuDFYag+V0u3oBbN+l8W/Fl2G/NQa3xf3yI2PwFbo/aT/6TNyH76P77jAGF6Y8f29j8LgxuJrel13x/nK6MXigtF9zvoxBU2PwWnk9v5SuUjkgNgYnAjgXQB9r0QPAGQA+K411lwZj9puAZD2AHwD4TdCuKoCHAZwNoBuAK4xBt3jxLwH8zlp0ArABwHfi+78BoAeA9wF8xRgYAHcCuDdlk74NYKS12Avg6wBaAehhLY4DcBGAjSV5nYdD/J78Edj3oyYVWwH31/cQbUt4BfbZADrH/74L4M8AYAwaAbgbwPEABgC42xg0BPAVAO8i6pdXxm17AqhqLaamPP8tAP4Uxw8i6u+9rEVXRH2gPLwC4DxjUKucnl8OM2uxLv6e9QLwF+z73vWyFrty7L8Oq/g3n80GcDGAt4N23QBcDuBYAGcB+FN8kifnftQY9ACwPf7N6W8M6huDlgCOtxajUjbpZwAetBZ/o/fpcwBD4tt8wB1u+2FjDI6wFmsArDAGJ5fV88rhU1pniFsCWGstdgKAtVhrLT4HAGOwxBj83BhMjf+iOya+v7YxeMIYTDIG04zBBfH9Rcbgnbj9VGNwUvhkxqB//JiOxqCvic62TjEGr8cdrPgs1O+NwWQAP+THW4vV1uJDALuDVQ8AsNBaLLIWuwD8E8AF8UHuaQBeiNs9Cbi/aA2AagBqxesbBmCMtVif8n59A8C/6b1bYS2+jLdtmbXYEL+GrcbgF8ZghjGYYAyax/c3NQYjTHQ2+cPizmgMBpjozPc0Y/C+MTg6x3t3TtymiTEYGsdTjcHzxqAOfWa/NAZTAVxqLZYCaGwMWqS8Jqk40vrrXfF3araJzqia+P5x8XdikjH42BgMiu+vaQz+aaKzqC8CcFNXGYM/G4PJ8dmlnx9oo6zFNGuxJMeiCwA8ZS2stZgAoEHcz78CYKy1WB/3mbGIdsy7EfXHaoj6JxD9gXpn0nMbgy4AdlqLtfQeLaNtmxW3y/n7ZKKzuuOMwQvGYH58Jqz4vTsrvm8qogOL4uc8YH+1FhbAOER/wEhGGIPhxuAvxmAigF8Zg17xPmCmMXgx/sPPG22Jf9OXxPGxcV+dHj+mc3z/MLr/r8UHkPG+5v8ZgxkATuRtsRbzrMVHOTbzAgD/tBY7rcViAAsR7UNz7kcR9cuaxqAKor65F8A9iP6oTXof6iI6WTQjpY237cbgx/Hv12xjcFPcpsgYzKbH/K8x+L84/oExmBu/T/+M70s6PrnKGIw2Bv8F8Ga8ulGI9ulSwZXWAfEbANrEO8o/GYNTguVrrUUfRGd2ilMUbgfwX2sxAMAQAL82BrUBrAZwZtz+64jO1DjxDugviDrYp4jO3FxiLfoCeALAL6h5dWvRz1r8vzxfR2v4Z8qWxfc1BrDRWuwJ7geAhwBMANAW0RmuqxH9dZyTiYbAOtCO/1+IzgBNjzt1b2peG8AEa9ET0V/n18T3/wHRWYT+AL4G4LH4/vkABlmL3gDuAvYNS8XPfRGiM71fje+6A8AZ8Xs9GcCPqfk6a9HH2ugHAsBUQH8FVxJp/fUha9HfWnRHdHDLB2JHxP31JuzbiV0HYFt8FvVuAH2p/e3Woh+iM7WnmOgMUUkk9cuk+8cCKELULx80BucDmFp80J/gZMA7e/w7AP81BmOMwY+MQYP4/rTfp96I3ptuADoAONkYHAngUQDnIXpv+I/K1P5KJgPRHyCSKUcBOMla/BjAUwB+Gp9dnYWUg8jYtQD+EJ9N7QdgmTHoiug7e3J8/17sO5CrDWCitehpLd7Nc/sOql9ai3kA1iDqZy8B6ASgygFGbfoB+w5kE7htB7Ad0T74eAAnALgm2KfmciuA3vF7e218X9LxCQD0QXTMUfy7qf5ZSZTKUIy12GoM+iL6UgwB8JwxuNVaDI+bjIz/n4J9Z0iGAjjf7MvhPRLRQeXnAB4yxnXYLvRUXQE8AmCotfjcGHQH0B3AWBOdB6oKYAW1f640Xl8aa/E0gKeB6Owaoh3k2cbgm4h+FG4uPvsbawJKibAWy+IzQ6fF/940BpdaizcB7AJcvuEUAGfG8RkAuhnj1lnPRGd36wN4Mj4bYBH9FV7sNEQ/LkOtxWZjcC6iHfd78XqqA/iA2ofv3WpEqR1SwR2gvw4xBrcgOsPaCMAcRDsvwO/HRXE8GPFBobWYaQxm0lNdZgy+i+h3piWi7xsvPyziP1z/BwCMQTUAryMa6fktot+Yp6zF6OBhLRHtrIvX8Tdj8DqiM84XAPieidIuqiH592mStdFZZWMwHdF7tBXAYmuxIL7/74jSPoD0/srU97LpeWux1xjUB9DAWoyP738SwPMHeOwHAG43BkchSs9bYAxOR/RH2Yfxb35NRN8tIPoujyjtFxCyNjpjCwDG4CVE/ep2AD0RjfY8GjzE65cJeNsHAnjRWnwRP8dIRL9zYX9nMwE8YwxGAS51I+n4BPF28giw+mclUWq5SXE+7DgA44zBLADfAtwB8c74/730nAbA18KhmHgYYxWiDlIFwA5avALRF7M3ogNnA2COtf4QD/niIF/GcgBt6PZR8X3rEA3THhHvbIvv5+1uBWCAtbjHGIxHdAB6B4DTEZ2xKrY9fg1OPHQ9BsAYY7AKUTrGmwB2x0OmgP/eVQFwgrXeewMTXTj3lrW4yBgUIfo8in2C6KxVF0R/0RpEHfuKhPcifO+OjLddKoFc/TUeLvwTgH7W4rO4L/J3NVc/zslEF4b+L4D+1mKDMRgerOtgJPXL5QBODe4fFzz2ekRn104AsAnRGbL/Yv8d5HZEB6hOfEb5CQBPxMOt3RGd6U36fdpJ8QHfI0RpHEn9lanvZVM++6892DfS6/qXtfhHnG5xDoBXjcH3EP3mP2ktbsuxnh3xb8LBSOqXSLkfABCnIEwBUAdAR2txmYlSHp+xFtuo6X77yxJuO79PCNZ5DqI/7M9D9EfEcUg+Pjke2jdWWqV1Ud3RxTlKsV448NSErwO40ezLsyse1qiPfTm1VwJekvxGRF/e+43BqQA+AtDURBcJwRhUMwbHHsJL+RBAZxNVlKiO6IKB0fFB6VsALonbfQv7coCL3Yto2BOI/vK2AL4E/Ith4lzHqvFQKoxBn/hgGnFuVQ8c+L17A8CNxTfis1VA9N4V//BcFTxmKaL0iqfi92gCoiHdTvE6ahvjne0KdcGBh66kAkjpr8U7ibXxiMMl+z14f29j39nY7oBLi6iHaMexyUS572cfwiaPBvBNE1WbOAHAJmuxAtFvyFBj0NBEOZVD4/sQb09DRCkfTyHqh18i6pc1wycAMA/REG7xY8+Kzy7DRLnzjRH1rbTfp1zmAygyBh3j2/wHaFp/Zep7GWYtNgHYYIwblr8ScGeLl2BfmpLrr8agA4BF1uJBRPuqHohOslxiDJrFbRoZg3aHsGmjAVxuoqou7RFd9DoJCftR2rZqiFKLfoV9+0og6kthVQ2vX+bhHQAXGoNacYrDRfF9qwA0M1FFjxqIU8HifW4ba/EWgJ8i6pN1kHx8kov6ZyVRWjnEdRAN/c2Nh0y7AVHCeop7EQ0RzjQGc7CvKsOfEJ2tmgHgGAR/jVmLVYi+zA8jOlN8CYBfxu2nA/tfhBcyBi2MwTJEObN3GINlxqBefPb3+4g6wzwA/7IWc+KH/RTAj43BQkQ7x8dpfb3jbSvOhfoHojyvk4GcJVneQDS0AwDNALwUn4Gaiegv2QOVSPsBgH4mughgLvblPf0K0R8L05Dj7JS1mI8oZ+x5RAcsVwF4Nv7MPkD0fu8n/gHrhOjMslR8OfurtdiIKN91NqI+8GEe6/ozgDrGYB6iC2SmAEB8Ecw0RAeE/0CUX5/KRBe3LEN0RmmmMS43/lUAixBdtPMoorO+iIct742380MA9wRDmXcB+EV88Po6oqHTWYhTnAJvA+hdvANEdHA9O/5deR3AT6zFShzg9ykUj+J8F8ArJrqobjUtTu2vZAiiahOSXd9ClMc6E9EfsMWl+H4D4Lr4O9SE2l+G6Ps7HdHIxlPWYi6iUcs34vWMRZSSkMoYXBT3yxMRfY9fB4B43/gvAHMR7edusBZ7D7AfBYAbEJ2p3oZon1crHqWaEv8GOfE+q76JLq47oHgfPBzRgflEAI/FF+vuRvSeTYpf9/z4IVUB/D1+/mmIqllsRPLxSS7qn5WEsdYeuJWUKhPVQv2RtVFJqEJnoovx+libfJW+SEVnDP4A4CVr8Z/y3pZi8dn1f1iL08t7W0TKgzH4EYAt1ro/kAuKMXgbwAXx6K9UYJqprhzEf8W+ZcqwZuIhOgLIu1KHSEV1H1Bw9X7bAri5vDdCpBz9GX5+fsEwBk0B/FYHw5WDzhCLiIiISKbpDLGIiIiIZJoOiEVEREQk03RALCIiIiKZVmoTc5RUkyZNbFFRUamtj3OijTEpLZPt3buvxve6deu8ZVWr7rsO7ogj9r19YS72jh376vU3adIk52PK05QpU9Zaa5uW1vrCz3HGDGDPntxtjzgC6NmztJ5ZgMP/eUrZ0Ofoq8i/I/osKwd9jodPefTvtM+z3I/OioqKMHnyoZW35YPR3bt3u7h69bDGd342bdrk4qef9kuW1q+/bzIrPtDl5wWAOXP2lV383ve+5+JGjRqVaJu+/HLf7M/hgX5JDvyNMQea/OOghJ9j2ibt2QMc4kcugcP9eUrZ0Ofoq8i/I/osKwd9jodPefTvtM9TKRMiIiIikmnlfoa4JDilAfDTGNLOCnMawwsvvOAt4zPB7723b1KtWrX8sqTbtu2bZn3AgAEu/vjjj71269fvmzDrgQcecPFll13mtbviin0zuZ522mmJ216lSvLfLnz2OK2diIiIiOxPR08iIiIikmk6IBYRERGRTNMBsYiIiIhkWoXMIeac4dCIESNc/Je//MVbxnm9mzdv9pZx7m3nzp0T18/r4LzhXbt2ee2OOeYYF3OptXfeecdrx7cbNmzo4jAX+pprrnHxsGHDeWBfdgAAIABJREFUErdd+cQih46rxlSrVi2x3ZIlS1ysUkoiIhWXjphEREREJNN0QCwiIiIimVbQKRP5Dv/36tXLxatXr3Zx7dq1vXZ16tTJGYd4uDScWa5x48Yu3r59u4tr1KiRuO1cJi4s48apEVwWbsuWLV67O++808XPP/+8t+zFF190sdIkRPLDfXTnzp3espo1a7p46dJ9ddzPPfdcrx330+HDh3vLTj311JzPm1Y2UkREyoeOnkREREQk03RALCIiIiKZVtApE0nD/3fddZd3e9WqVS5u27ati8PKD+FQJeNhSx4+5fQJAGjatKmLmzVrlritW7dudfGyZctcHKZM8DbxOsJ29erVc/HMmTO9Zd/73vdc/Oijj0JEDoz7G6dIAMDs2bNdzDNIhr8hnFJ11VVXecsWLFjgYq5UYYwp2QaLiMhhozPEIiIiIpJpOiAWERERkUzTAbGIiIiIZFpB5xAnGTlypHe7QYMGLuYZ6MK8QM7d45JL4eP69Onj4hYtWnjtFi9e7GLOCwxLvLG+ffu6mPOJAWDdunUurlu3routtV47vs35xAAwefJkF+/Zs8fFYck4kazhXH4gudzi//3f/3m3//a3v7n4yCOPzGv9Dz30kLeMfx/UL0VECpvOEIuIiIhIpumAWEREREQyrcKM3d17770u3rRpk7eMZ49bv369i8OhTk6Z+OKLL7xl559/vou5tNLo0aO9dscff7yLOU2CS7ABwJQpU1zcvHlzF59++uleu/Hjx7uYX1fDhg29djyLHc9uFz7ut7/9rYtvueUWiFRGYUpRUkpC2oyUN954o4sffvhhb1m7du1c3KZNGxfPmTPHazdkyBAXX3rppYnPxdsUbrvKsImIlD+dIRYRERGRTNMBsYiIiIhkWoVJmXjkkUdcHKZC8Ix0XD0iTIvg2d/CdXC1h3Hjxrk4HM787LPPXMypC+EMVjyj3YwZM1xcv359r12XLl1c/P777yduH1enCIeBue0TTzzhYqVMSGXCfTucGZIrOrB33nnHu/31r3/dxR06dHAx90MAWLlypYu5n5988sleuxEjRiRuL/8m8EyYSpEQ2eeaa65xMe8377vvvsTHcNpRWn8K98vcD0VCOkMsIiIiIpmmA2IRERERyTQdEIuIiIhIphV0DvH27dtdzHmzYX5tUkmyjRs3eu34ccccc4y3bMGCBS4+44wzXMwllwBgy5YtLm7ZsqWLw1zFfv36uXjgwIEu5jxhwM9/6tGjh4snTZrkteOcyTB/kl8X50jNnz/faxe+ZpFCkG8+YPi9ZytWrHDxXXfd5eKnn3468TFHHXWUi8Mc4o8++sjFgwYNcnFYhpFx6TcgudQaX/MA7F9GUaSQ7N6927udlK8fyrdfr1271sWjRo1y8Xe+8x2vXceOHV3M1xOEecG8veEyzinm3xPl9QugM8QiIiIiknE6IBYRERGRTCvolIk///nPLuYySDzzG+APd/Aw5c6dO712PMwSzizHwyycqsHDOYBfyo3bhcNIvL7FixfnfAwA9O3b18UzZ850ce/evb12nNIRDs1yaghv3wsvvOC1u+OOOyBSaLj/pqUdzJo1y8VXXXWV127hwoU51x3+VnC/5JSijz/+2Gv3/PPPu/iSSy5J2nRPODzLfb1mzZourlGjRl7rEykE4b4t31SIpGVhGtPs2bNdXK9ePRenlUhLW5ZvSgfT7JEC6AyxiIiIiGScDohFREREJNN0QCwiIiIimVbQOcTnnXeei3n64zVr1njtXn/9dRdzLlCrVq28dlu3bnXxxIkTvWWc79etWzcXc+4y4Oc4co5ymHPEOYmdO3d2MZeIA/wybNOnT3cx5x0DwA9/+EMX/+tf//KW8RTNnO/Yv39/iBQ67rNppdU4B37atGnesnbt2rmY++KmTZu8dnwdAecrhr8p7du3P9Bm7yf8DeC8YfaHP/zBu71o0aKDfi6R8sL9Nd++e+ONN7qY99eAX5b0vffeczFfPxBKm8Z95MiRLn7ggQe8ZTx1+8033+zisO+GOcWSDTpDLCIiIiKZpgNiEREREcm0gk6Z4FSDJ5988qAfHw6XcBm3sPQRly7j9InGjRt77Tjtgsu7hMM7PCNOWmoF3+ZSMtu2bfPaPffcczljkYqO+0rYjzZv3uziKVOmuLhTp05eO+6zy5YtczHPRgf4/Yr7fNjPeabJkg6fPvXUUy7m354JEyZ47fIt6yZSVtJSEtJSI9hNN93k4kcffdTFV155pdeuT58+LuYUJ06fAPx0h7RtuO6661wcpkLx/vuGG25wcTj7rWSTzhCLiIiISKbpgFhEREREMq2gUyYOFc9KFeIhIQBo2LChi9u2beviMLWC0yR42CacOYfXwct27drlteOZ5XjdJZltB/Bfczj8rNl3pBClpUz8/e9/dzGnRTRq1Mhrx/3qvvvuc/Ftt92W+Lzcx7Zs2eIt4woRAwYMcPGkSZO8dn/7299c/J3vfMdbxv2NZ8Zs06aN1+7OO+90cTi7pAgrTt/J97c8TPdJSv8paVrE448/7uKf/exn3rIf//jHLn722WddHFZamjx5sou5j99///2J7YYMGeJiTs0AgFq1arl46NCh3jL+nfjpT3/q4rDyi/aV2aQzxCIiIiKSaTogFhEREZFM0wGxiIiIiGRapcgh5rxZzgts0KCB1y4tL4rzmubMmeNizm8Mb3MJl7BsC+cN16lTx8Vh7jKXZDv22GMTt4+FudGlkXssUl7S+uWZZ57p4kceecTFq1at8tr16NHDxWl5w4z7UTirHN/+/PPPXdyxY0ev3dq1a11cv359b1nTpk1dzOWkwusNeNtF0hTntqblBnMc7m9Ksn+4+OKLvdv8vZ47d66LwzKn77zzjou57/I+D/BnjGzRooWLw5lmuewiz0YXXufTvXt3F/M1OoB/jUJ4PYCIzhCLiIiISKbpgFhEREREMq1SpEwklUjhoR3AH5oNH8NDSTyDVVo7HlYNh7A4tYKHVatXr+614+GecCg1SUlnzhIpRGGpNcazVXJfDPvlBx984OITTzwx5/1pRo0a5d2+6KKLXMylmsIUKk7LCtMuGA/d/uhHP8prm0RCSWXXkvaB4T6FZ2r85S9/6eIxY8YkPi4sXcazv/3whz908fz58712//73v13M6UThrJAbNmxw8ZIlS1x8wgkneO14P9qtWzcXh6+d1xGWOPz0009dXLt2bYgwnSEWERERkUzTAbGIiIiIZFqlSJlISiHg2eeA/Gff4bSIcDgm6WrecN08DMzteBYdwB+C5aHZtLQIzaIjFc2ePXtcHF75zn0nLX2C+wQP/QJA8+bNXTxhwgQXX3311V47nlmOq8RceOGFXjtOf/jJT37i4muuucZr993vftfFfFU94FeX4dSoq666CiIlkfTbz/sOrpK0dOlSr91//vMfF0+cONHFYXohp0V88skn3jJOeXjqqady3g/4MzcuXLjQxdzvAKBnz54u5goUYYWIzZs3u5irtoQVnjhNIvyt4feP35twBtkwtVGyQWeIRURERCTTdEAsIiIiIpmmA2IRERERybRKkUOclFcVlkHifKJ8S5el5SCxcH3h44qFuUpc3iYpPzmkHGIpRGFJMv4Op+UG54vXF852xbfbtm3r4uHDh3vtuJzaJZdc4uJw9sdZs2a5uFOnTonb9Oyzz7q4Xbt23jL+DWjdurWLeTYukYNR3MceffRR7/6VK1e6mHPyw2tbeDbUU045xcXhvpJnghwxYoS3jPP1uW9w7jLgl0nj34awFBw/Fz9m9erVXrtly5a5mPtamGu8fPlyF4ez2J1xxhku5jJx/F4A+ZdrlMpFZ4hFREREJNN0QCwiIiIimVYpUiaShGkH+c4Ex0o7PSGtjBsvS9tWzVQnZSHpe8bfU05VCIcnk3z88cfe7fbt27uYSx6GXnnlFReHM1Bt2bIl53bwTHcAcNNNN7m4bt26Ln733Xe9dmlpEoyfKyypuH79ehe3bNkyr/WJJNm6dSvee+89AMDMmTO9ZR07dnQxpyeFpdBWrFjh4s8//9zFYX/itIPBgwd7yxYvXuxiTncIS7zxjK9hihM76qijcq6D0xsAv0xcq1atXDxp0iSv3dSpU13MaVEA0KRJk5zbMH369MRtl+zQGWIRERERyTQdEIuIiIhIplXqlIkQD/WmpR3km5KQ7/qSKk6Ey/KdSU9VJqQsFH/Pwu8vXzGeliZxzz33uPjuu+92cVhlgWedGjRokLfs+9//vovPO++8nNsA+ClGfMV8uO3bt2938Z133unirl27eu14Zr20q/ZZ7dq1vds821eXLl0SHyeSjy+++MKlB3z22WfeMq4ywbgyA+CnCXF1hrBd7969XRymU/Tv39/F/J3nlCbA719HH320i0eOHOm1mzFjhos5pSmcaTbJgAEDUm8zThmpV6+ei3v16uW1C2fTk2zQGWIRERERyTQdEIuIiIhIpumAWEREREQyrVLnEB955JHebc7/S5tVK98c3bTHhOsvlu/Md+HMQSJlyVrrZm/btm2bt6x+/fou/ve//+3iCy+8MLEdz27F9wN+Lt+0adO8Zeeff76LGzVq5OJ169btt73FOF/x008/9dpx/vItt9yCJCXJ0w9LxvGslGk5xGklqUSKNW3aFNdffz2A/Wc85T7EM7CdfPLJXjvuD7yPuu2227x2nFOfb/77FVdckVe7iy++OPV2Mc73B/zZJPnahXCmOt53hjNkcvnDtDzhOnXqJC6TyktniEVEREQk03RALCIiIiKZlqmUCRaWSePbaSXUkkqtpc1Ax8JUCh5m5WX5lmATORyMMe67GaY4ME6TCGeB4qFL7h9cZg3w04jCoUouE7Vo0aKc6waAzZs3u5hniAvTGMLSUMXCIejq1avn3L40/BjA78OcxhFSGUXJR5UqVdxsiD/5yU+8ZZMnT3Yxpzucfvrpea073F+lfSd37NiR8/7x48d7txs0aODi7t27u5jLEQLJ+zru++Ft3r7wt4D7critnCbBy3gWPMD/PZHs0FGXiIiIiGSaDohFREREJNMqdcpEWiWJfGeqyzcVIl9pj+eraMMhG56151C3QeRANm7c6CpI8HAsALz99tsu5soP4ax1W7Zsybks/P5ySkJ4ZfmGDRtcfMkll7iYq08AfhULfszVV1/ttevWrZuLubpD2ox7+UobZubtO5jHieQSpgKdeOKJLn7//fddPHXqVK/d3//+dxcvX77cxWGFiHHjxrmY+zHgpyhwhYewD/H+rGnTpi4OU6Z4fWeddZaLx44d67VbvXq1i0855RQXL1682GvH+85LL73UW8az5PE2cUoHAAwbNszFN9xwAyQbdIZYRERERDJNB8QiIiIikmk6IBYRERGRTKsUOcRJOXjh/ZyrmFZKqSQ5uiXNSWac8xzmbeX7XCKlYf369XjmmWcAAC+99JK3rF27di7mmaD27NnjtePZs/g7W7VqVa8d5/KGpRK5hNKHH37o4rBMEq+/ffv2Ln7iiSeQJNyOJPnm+Ia/KbxNbdu2zWsdIofqpJNOSlzWp0+fvNYRzjpZHoYOHVrq6xw4cGCpr1MqD50hFhEREZFM0wGxiIiIiGRapUiZ4PIuPAwaDuFySkI4DMpteRg431nr0pZxnJaqwct4GDmklAk53Bo3buxKloUzUHG5Jp6NKpxxKqkPpM3WGJZd49nfeAa62rVre+141qmvfe1r4ctxuExUuI4k+aY8hTPVcTmpfNMzRESkfOgMsYiIiIhkmg6IRURERCTTdEAsIiIiIplWKXKIw9zFYnXq1PFuc45fmIebb15uvtM/8zZx6aiwrBRLyy/OV775jiJp6tevj7PPPhsA8O6773rL7rvvPhcPHz7cxVxmDfC/i/y95xz98HaYh8vraNy4sYs3btzoteMc4l//+tdIwnm9pS0slcjb2KJFi8THqZ+KiJQ/nSEWERERkUzTAbGIiIiIZFqlTplo3bq1dzstXYHXwWWgQpzWkJaewMPAaUOi3K5GjRo541C+5d5ESkPnzp2923/72//P3pmHbVWV+/9zhyMiAjIIKhFDKSCgIM6zaWoOxxxTy07HTplplpb9Si09eTIb1Cw92jG1Mj0OmUPmrJEjg4CgGJMjKogCjji0fn/s9ez33otnb17whXd4vp/req/33nutvfd6hnuv9az13ff9u7r2ww8/XKh32WWX5fbdd9+d288///xKtcNLMpYsWVIou+uuu+oe46UUUH0PKKO5PpWGe/MZw6ruKQrJJoQQrY9miIUQQgghREOjAbEQQgghhGhoOrRkIn3q3Wejmz9/fqHMSxReeuml3E6fiPf1/FJqWs9LK3wmPf+kPBQzZ/ml086dO1NG2esVYlXgv79Q/P757+x2221XqJdu10gzSE6bNi23Fy5cWCibOXNmbnv5w+c///lCvd69e+d2WXSLtKy5UghfL40s48tOP/30Zp0vjSYjfxZCiNZHd2IhhBBCCNHQaEAshBBCCCEaGg2IhRBCCCFEQ9MhNMTN1QKeeeaZub148eJC2TvvvJPbS5cuze00vJOv5zWNqbbQZ9xaf/31c7tHjx6Fen7b6yAHDhxY8ioUpkmsXqpChq0Mqd5+1KhRpXX32GOPFT7/qgxL2BJhDaUZFkKItofuzEIIIYQQoqHRgFgIIYQQQjQ0li71r/YGmC0Anm3VRjQmHw8h9Gqpk+lzbHX0eXYM9Dl2HPRZdgz0OXYsSj/PVh8QCyGEEEII0ZpIMiGEEEIIIRoaDYiFEEIIIURDUzkgNmNDMybHv5fNeNFtr1Vx3AAzppWUnWXGniVlx5rRL9l3hBnfN2NXM7ZvzotqDmb8zYxFZtya7P+EGY+aMcuMa2uv04y14/asWD4g7t/BjKlmTDBjSNzXzYw7zcrfXzOuN2NgPNdkM54zY4F7fwc04zW8WbL/q2Z8oaTsIDOGJvu2NeMyM0aZse/yrlvRnl5m/G1ljxerj+hT0+N3d7IZ27TQee83Y8zK1DHjhOhfwYyebr+ZcWEsm2rGVq7si2bMjH9fjPvWjv49zYzjXd1L/bF1rn+QGWdE+1OxnZPNeMqMS1fsnSi9xq7pPaeqjhmfNeOslri2aDusKv9z51/u92wFzrWZGQ+bsdSMU5Kyz5jxdPTN09z+sn70G9Ev/+r27WjGLyuuv64ZD5jRyYyPxXvBNDOeMGO8GZ9oidfprnesGRd9hON/ZsbuLdkmsXqoHBCHwMIQGBUCo4BLgF/WtkPgvZW5YAicEQJ3p/vN6AQcC8UBMbAP8DdgV2i5ATFwHnBMnf3nkr3OwcDrwJfj/i8Dr8f9v4z1AL4N7At8E/hq3PcD4JwQ+Fe9C5sxDOgUAnNCYJv4/p4BXOve32dW9oWFwCUhcFWd664BHATFATFN7/Go+FpW9roLgJfM2GFlzyFWPWZsB3wW2CoERgB7As+3bqsAeJCsLekDJ/sAQ+LfV4CLAczoAZwJbAOMBc40ozuwN/APYATRx80YSeZzkyqu/x3gN9G+kKb73ebArz7yq1s5bgP2N6NzK11ftDBt2P+AvJ/wvAacCPwsqdcJ+DWZfw4FjnSTLWX96FFkfvkQsLcZBpwOnF3RpH8HbgyBD4HDycYII0JgC+DfgEUr8zpXBfE9+RU0/TgQ7YePLJkwY5gZj8VfuVNrs6RApzjrOD3Olq4b619hxiHRfsaMc82YBBwJjAH+GM+1bnSWUWQO+VXg5Fi2kxkDzLg3XvMeM/q7819i2YztP834bL12h8A9wBvJazFgd+D6uOtKsgEkwIFxm1i+R6z/PtA5/r1vxiBg0xC4v+JtOwr4y3LeWt+uvmb8Pb72aWbs5Mp+bMYUMx4xo0/c98PaL/k4y3W+GROA7wIHAOfFcw2Kp9kDuBs4Czg8lh1uRg8zborv8SNmjHDn/32cNZhpxnGuuTfF1yfaLn2BV0NgKUAIvBoC8wDMOCPOukyzbEbV4v77o68+Fv1qp7h/XTOusWwW9c+Q+Xksuzj64XQzfrS8RoXA4yU/BA8ErgqBEAKPAN3M6Es28L0rBF4LgdeBu4DP0OSTawK1TBpnk3W8dTHjk8DSEHjVvUcvuLY9EesNMGOcGZPi3/Zx/67xPbrejBlm/NG9d5+J+yYBB7trjo0+9LgZD5nxqTrvSQDuh/r3MdEuqfK/Z8z4UfxuPWHGZnH/emZcHv3vcTMOjPvrfh89ZmwdjxlkxmjLZlsnmnFH9KO0nzjJHx8C80NgPJlfecYCs+LEznvANcCBy+lHjcwvO8fzHQ3cHgKvVbxfvr/sC7xUm2wKgRei72PGmyX9YS8zboj3tfEWJ2ya439m7Bfr9DRjr2hPMuM6M7q4z6w2jjk0BJ4FNjRjo4rXJNogLaEh/ipwQZzlHENTJzIE+HUIDCP7Bfe5kuMXhsBWIfAHYAJwVJyVeQfYEpgSAnMpzlCPI/sVdmX8hf1HshmdGgPInHU/4BIz1mnma9kQWBQCH8TtF4CNo70x8Vd8LF8c6/83cBXwPeAi4MdkM8RV7ABMbGabAD4P3BHf45HA5Lh/PeCREBgJ/B0KA1PPWiEwJgR+DNwMnBrfx9mWLU2/HwKLKc5SXwv8CHg8vsf/L77OGiPIbnrbAWdYk9RlAjQN2EWb5E5g0ziw/Y0Zu7iyi0Jg6xAYTja49QOxNUJgLNlqSC3t49eAt+Ms6pnAaFf/+yEwhuy7skvtB9VKkPtepOaXZfvvIrsHPAJcaMYBwKTaoKOEHaAwe/xL4F4zbjfjZDO6xf3zgU+HwFZks1X+vrMl2XszFBgI7BDvPZcB+5O9N76TnAHsFAJbkvneOSVtk091LKr8D7LB8lZkKyE1icL3gXuj/+1GNqmxHtXfR+IA+RKyH5XPkfWbh4TAaOBysv6qRq2f+HkzX0eZ/1X1oxeR+WV/shWhL5HNMtfFMlnFQPdD+f/IVkwmm/FzM7Z01cv6wwvIxg5bk41Dfhv3V/qfGf9GNtNbWzX9AbBnfK8nAN9y1WvjmGvi9iTQSml7oyVSNz8MfN+MTciWNWZaNiczN4R84DYRSjWx11ac+zPA7SVl29E02/J74Keu7P/iL8iZZswBNqNpENmixNe4LYAZOwMvkWV4vZbsF/C3Q+CV5LC+wIIVuMx44HIz1gRucu/re5DrxCYCny45vuo93ovsBl2PHYk/ZELgXss05V1j2V/ij5Z3zLiP7AfITWQ36FT2ItoQIfCmGaPJBlm7AdeacVoIXAHsZsZ3yGZwegDTgVvioTfG/96fdyZ2wiEw1Yyp7lKHmfEVsvtMX7KBoi9fJcSO+PMA0WfuIJu5+gVZR3xVCNycHFbwyRD4nRl3kN2DDgT+0zLZxZrARWaMAj4EPunO8VgI2YSAGZPJ3qM3ye6FM+P+P5DJPgA2AK60bFUtxHPXQz7VgViO/0HRz2p93F7AAdak4V2H7Ls8j/Lv4+bApcBeITDPjOHAcOCu2Ed3IuuvalT1Ey1CCPyerL/GMr3+hcA+lj3z8jxZf+mlhj1xkogQeCHO5O4e/+4x49C44lvWH+4JDLWmrOtd4+xulf/tTjbBt1cILLFspXko8GA8z1pkY58a6Xsnn22HrPAMsRn/Zk0Pfo0JgavJluHfAf5qTWLype6wDykffL9VcbmqwVoVaXDl5gZbXki2HFtr6ybAi9F+EdgUco3VBrE+cZ+R/YI8m2ym7DtkM0Mn1rnOO1A+a23GNu49PiAE/k428HgRuMKaHph7Py6pwsq/xzX98IpS9h6vQ/b6RBsmBD4MgftD4EzgBOBzcTbzN2QzSFuQfX/997Tm01XfNSB7qIZsdmuPuMJwGxXf+eWQ+16k5pdl+z3Hk61sbEu2qnM4me4/ZRmfDIF5IXB5CBwIfEA2mDgZeIVspWYMFB4ubu49r8bZwH1xNn7/9PoO+VQHo57/ueJ6fmbA59wzJv1D4Cmqv48vAe9CPotqwHR3ji1CYC9Xv6qfqEeZ/1X1o1lDshXFsSFwE5k/Hk428N0juUY9v1waAreHwKlks7o1OUZZf/gxYFv3ujcOgTep9r/ZwPo0/cAwMnlW7RxDQ8h10bDseyefbYes8IA4BP7svhQTzBgIzAmBC8l0Piu7LAqZpnd9ADM2IFuiXZiWRR4Cjoj2UcA4V3aoZU+jDiJbuny6ORePznQfZBpn4Is0aZdujtvE8nud8wF8Afhr1EJ1Bv4V/+o9DPMUMLiiHY+69/hmMz4OvBICl5Et95Q+Kd8M/HtsZJ/X5LQsMo6oBzZjV7KlvCWx7EAz1jFjQ7IHHsfH/Z+E+hFGRNvAsggKQ9yuUWQPstU6hFfjDMohyxy8LH+naTZ2OE3+35Wsk1gctXz7fIQm3wx8wbJoE9sCi0PgJbKZ373M6G7Zw3R7xX3E9nQnk3xcRZNPBpzO2VHwSct0v2tGeyOyZeAXyX4I1zSMx5DNslUxAxhgTXr9I13ZBjQNFI6tOId8qgNR4X9V3AF8w5p06bVBbtX3cRGZbPC/4/37aaCXZQ/1Ycaalj3gvbKMB4ZYFlFiLbL++Obl9KM1ziaTKUDmj4E6/WXUB3eqyR7N2Komz7MsitMIlv/e3Ql8o7YRZ9Oh2v+eJfuRclV8jx4hk0ANjudYz6wwG58in22HtISG+DBgWlwiHA7LRjdYAa4g0/xOJpt19tEoboF8dnonsi/4l+IS7TEUHwR4DniMTG7x1RB4N72QGeOA68gejnvBjL1j0XeBb5kxi6wT/N+4/3/JhPKzyLRDPsRMZzKHqmmhfgH8FTifTL+VchvZILK57ApMMeNxsl/SF6zAsSnXAKfGc40l0wjXBvb3kS0tTTbjcOCHwOj4Hv+Eph8EkC1930d2ozjb6TN3I3t9ou3ShWyp8Mn42Q4FfhgCi8hmhaeRdcDjK85R42KgixlPkT2UOREgBKYAj5MNCK8m0wtWYsaJZrxANqMrhknJAAAgAElEQVQ01SzX+v0VmAPMiu07Pl7jNbKOdXz8OysUH845A/hxHCzcQbZE/QRxyTbh78CWtQEH2eB6mhlT4rGnhsDLZDPoX4z7N2M5s2rx3vMV4DbLHrqZ74p/SjZYeZzq2WT5VMeirv8t55izyZb0p5oxnaaoDJXfxyjX+yxZ37Ql2SD13Fh/Ms2I3GTGRtEvvwX8IPaXXaM06QQy/3iKTKo4PR5W1o/mg/nQFPHlajK/3IH6q5V3ksn3AHoDt1gW1nUq2crN8kKknQiMsezh8CdpigZV6X8hMINsQug6sh/4xwJ/ip/Zw2Tv9zLEH9KDyXTGoh3RZlM3x87wtyF7qnxFjrsCuDWE/AnXNodlETfuA3YIWSiZ1mrHD8ieEr5muZWLx/0QeDOEYhieWPZ34MD4y16IdoMZFwC3hDphIVuLOLt+dQjLLCUL0RBYFjv85BDqhkltc1j2MN5WIZRHtRFtk5Z4qG6VEAL/0dptWFWEwDtmnEn25O1zrdiO/2rJ85nRC/iFBsOinXIOtGyChBagP/U1z0I0BCEwyYz7zOjUmhNIK8Aa0OxIHaIN0WZniIUQQgghhFgdtISGWAghhBBCiHaLBsRCCCGEEKKh0YBYCCGEEEI0NBoQCyGEEEKIhqbVo0z07NkzDBgwoLWbwRtvvJHb77//fm6/9957hXr/+ldTVsm11147t5cuXVqot8YaTW/t+us35bpYd916OQFWPxMnTnw1hNCrpc7XVj7HMqZMgQ8+qF+2xhowcuTqbU9L09Y/z6qHd83lVE155plncrt///65/bGPrdxv+Vdeacqi3rlzUw4A76Mpvu1VbW0J2vrnKJpPR/0sV6c/tAU66ufYXmjpvrvq82z1AfGAAQOYMKH141ffd999uf3yyy/n9rPPFpPgvPNOUzbGQYMG5fbMmTML9Xr27Jnbe+zRFEJ0+PDhH72xLYCZLS+7zwrRVj7HMqru2x98AG246c2itT5P3zn6H4tQHLR++GF5tCT/4zHli19sygVz0UVN8ferBrBVnH/++bk9atSo3N51111Lj/nA3Y07dSompmvpAcGq/hw32gjcb4ICffqAu/WJj0hHvcd6f/Df/9Q3VgZ/n2iJ87UEHfVzbC+0dN9d9Xm2+oB4VfJB8rPCd7zjxxeTcM2YMSO3d9xxx9weMmRIod6CBQty+913mxLgDRtWzIC53nrr5fYZZ5yR2yeccEKh3u67757bfmZ6zTXXRIi2jh8Epx2YHyxXDXoXLlyY21deeWWhzM/o9u7dO7dHjx5dqLf11lvn9ltvNSXruuOOOwr1nnuuKez39773vbrXAdh///1z288kt3fKBsPLKxOiHlWD1rPOOiu3X3311dJjvva1r+X2Jz/ZlA05/RHdVgbIouMiDbEQQgghhGhoNCAWQgghhBANjQbEQgghhBCioenQGuIq3eKf/vSnwvZ2221X97hu3boV6nkdo3+gwGuQoRi14ktf+lJu//nPfy7U8xriqvYK0Rbxur40koT3j3/84x+5ffXVVxfqeV1vqhP02vyR7nHi6dOnF+o9/PDDub3OOuvk9pgxYwr1jj/++Nz2D8jedNNNhXp33313bnft2jW3vS9D8SHZqtcvRHsl/V6X9VPHHHNMYXvvvffO7dNOOy23va8CnHTSSbl98cUX53YaiaEtPnAnOhaaIRZCCCGEEA2NBsRCCCGEEKKhadg1eh/qCWDDDTfM7UWLFuX2xIkTC/X8Us1aa62V29dff32h3rHHHpvbm2++eW77WKopWmIV7Y2qZUwvjbjuuutyu3v37oV6PrRhGmppyZIlue3DqQ0dOrRQz0ub3n777bo2FH3bJ8kZPHhwoZ73bS+t+M1vflOod8QRR+T2zjvvXChrtAQGomOSxhf3fn7uuefm9n777Veo533Ds8suuxS2vcTpF7/4RW5feOGFhXorm4xHiOaib5gQQgghhGhoNCAWQgghhBANTUNJJrxcwT/1DvD000/n9plnnpnbBx10UKGej0bxzDPP5PYDDzxQqOdTOZ944om5vXTp0kK9c845J7ePO+643O7Vq5hqO33S16PlWNFa+OXTVO7g5UY+k5yXI0AxIkuaodEv1/pMjum1vBRi7bXXzm2fMRLgvffeq3tuHz0Giq/L23369CnUe/TRR3N7++23L5Qpaoxor1RJoby/vv7667n93e9+t1CvrM/yGV4Bttlmm9y+8cYbcztNb+wjxkiOJFYFmiEWQgghhBANjQbEQgghhBCiodGAWAghhBBCNDTtUuRWlRHqsccey+1TTjmlUM9rn/r27Vsomz17dm5/61vfyu00M9Wee+6Z2z7D1h/+8IdCvQcffDC3fbiY9ddfv1DvlltuqWufffbZpdcVoi0yZcqUwrbXy2+66aa5PW/evEI9r/n94IMPCmXet70eONU1es2j1yinYdd23XXXum36y1/+UqjnNfw+PFuqXV6wYEFup5rHbbfdFiHaI1Ua4ttvvz23fabGFK/X9z5eFT5t7NixuT1+/PhCmdcQ++cJ0mcShFhZNEMshBBCCCEaGg2IhRBCCCFEQ9MuJRNVYVZ8yLR0acaHTPLLOVDMJlcVSmafffbJbb+E+7vf/a5Qzy/v+PBLadYf3yafiSvN0iPJhGjrTJo0qbDtv9te5pRKnnymqlTi4H3H1/NLsLCslKFGutzrJQ5enpH65VNPPZXb/fr1y22f0RKKr2X69OmFMkkmRHsl9RuPD1F6wAEHlNYr66er+u+BAwfmdhoatbntE2Jl0QyxEEIIIYRoaDQgFkIIIYQQDU27lEykXHvttbm9cOHC3O7SpUuhnpdJVEWq8E+fjxgxolDPP93ql1/333//Qr0333wzt/1ybLq067P2+Cxd/twAjzzySG5rKVa0RZ5//vnCdo8ePXLbR5Z44YUXCvU222yz0nN62ZOXT6RLpr6e9ykvs0ivPWvWrNxOM0P6e4CPntG1a9dCPd+muXPnlrwKIdoXzZUkjBw5srTM+6Tvb6vOvcUWW+T2K6+80qxzC9FS6FslhBBCCCEaGg2IhRBCCCFEQ6MBsRBCCCGEaGg6hIb4t7/9bW77THCpTjgNrVRW5sOuvfbaa4V6XkPo9cSpLrJz5851z+01h+m21zGn9a677rrcloZYtBV89kev3YWiDvfuu+/O7UWLFhXqee18FT4jVVWmOv/sQKo17N27d257v/Safyj67wYbbJDbqa5xwIABue2zXULxOYBUoyxEa5P2j56q0Gg+3GgVvg9rroY47fc806ZNy22fIS99Lkch2cTKohliIYQQQgjR0GhALIQQQgghGpoOIZnwy5h+ySXNZrXuuuvmdrpM65dZ/HFp2KYyWUO6TOPreWlFutz0zjvv1H0d6XV9diAh2go+1FoqTyiTGqTZ6Ly/pX5U5pdVYZd8Brq0nt/25/ZyDCjeK/75z3/mduqXfuk2DfPoM9ftuuuupe0VorVJ5YTeN9IQoKk0qob3OyiXA6b4cKjeD70PQlHK6KmSfgixImiGWAghhBBCNDQaEAshhBBCiIZGA2IhhBBCCNHQtEsN8auvvlrY9hrdPn365Pb8+fML9bzGL9Uq+nN4TdLSpUsL9byuqSqMm9dPedvrpQAmTpyY2z40U9++fQv1HnvssdJrCdFa+HCDqT7ehyj0+kKfxhmKusHUP1JtbxneF6v0xb6ev1bqy97Py7SLUAwh5+89sGzIRiFWFanf1Eh9wX/PfT9XFe7MP+cCxfBnnlQn7PtOX1b1nIDH98kAixcvLm1jGamu2bfDh2tL36eq90N0XDRDLIQQQgghGhoNiIUQQgghREPTLtcFnnvuucK2X5rs2bNnbqfZpzbccMPc9hntoBhOyS/TpiFd/NKKX9JJw7j5JRd/TFrPyyT8MT4DWHrckiVLCmV+aVqI1YlfPk0lSt5Pq2RI/rudLnGmWahqpMuzftm1bFm43naNdFnYL0F7/03vPV4yki7ppvcfIVYVzZUWrQw+4yQsGw6tRnOlEM0llYGUSRSr5A1VZcpoJ1I0QyyEEEIIIRoaDYiFEEIIIURD0y4lE+lT6j169Mhtnx3LL2cCPP7447m9ww47FMp89qyq6BE+W1bZsioUl2b9sm+6DOSXmf3SVCqt8PKMNGvd1ltvXdpeIVYlY8eOze0hQ4YUyrwcaMyYMbmdLmP6bHep/Md/771MoioDnfffdCnZSyP8kmkqoZo9e3ZuH3300bnt7yFQXD7eY489CmXyS7G68NkUb7311tz22RKh+P33fWUqg/jEJz6R2y+99FKhzPvNQw89lNtp9Anf16VSKI+XAO688865PXXq1EK9wYMH57bv8x9++OFCPT8+ePTRRwtl/ri99947tw866KBCvVQmIhoDzRALIYQQQoiGRgNiIYQQQgjR0GhALIQQQgghGpp2qSF+6623Cts+vJG300w3PlNdqlX0WsM045anc+fOue0zWHltMRR1kj7MVKpp9Ppir2MuC22Tnk+I1mTHHXdsVr1tt902t7/3ve8VymbMmJHb22+/faHMhx/0PtHcDJJVWmOvcfT3Bij69jbbbJPbn/vc5xCireF17l6Tm2Y89bp+31e+8sorhXpeQ59mavSa4pkzZ+Z2VfjPqhBn/lkffy/YeOONC/VuvPHG3Pba4DTc4SabbFJ6Dp/l9owzzsjtm266qVDvnnvuKW2v6LhohlgIIYQQQjQ0GhALIYQQQoiGpl1KJnxmOijKEPr165fbc+bMKdTzy6wvvvhioezdd9/NbS+ZWLRoUaGeX3Lyy0DPPvtsoZ7PfOelEM0NOZVm4vKSjHR5S4jWoiyTHBQlCV7SkIYe7NatW26nGeO8v/jQTal/eHy9VDLh/cj7fJrBzssu0qyRnio5VFUbhWhJtthii9z2oQFTGYP3DR9aLZUM+e9yGrrMf68HDBiQ28OHDy/U875cJtWAok/6/vaBBx4o1PP3ic033zy3U3mhl4xUjQF8H522XTQmmiEWQgghhBANjQbEQgghhBCioWmXkok0U51fFvVPkaZRJjxeWgHF5VOfwad///6Fen4pyS8/+WOgKKfwS0JphAxfzz+9mz5Fr8gSoi1S9fR4KiGo4ZdqARYsWJDbCxcuLJT57FT+afJ0mdRLN7zcIZVn+ONSOYXHl/ml1RQv6ZBEQrQWffr0yW3fl6URInyf6CUN3bt3L62XZm4dNmxYbnvJXyob9FKj5557LrdTn/TbXl6YZo/0kgmfrTW9z/jXnLbJyyH9vWazzTZDCM0QCyGEEEKIhkYDYiGEEEII0dBoQCyEEEIIIRqadqkh9jojgA033DC3e/Tokdtp9jiv3507d26hzIdd8prGNAyU1zV5nWGqDV5zzTVz22uNq7LseVJdlNd4ea2xEG2VNJRZjVST63XI6TFe8+vDLaa6xrLzed9L8frf9Hze/6pCy/n2pm2XplisLrzW3n8P0zCf/jvpfcPrjqHoQ17/C8VQZl7X65/fgWL4M9+O5mr3Z82aVSjzfeVGG22U2+nzO95ffT8M5c/i9O7du7RNonHQDLEQQgghhGhoNCAWQgghhBANTbuUTKQyBr+U4iUJPkwTFCUUnTt3LpT5pRWf3Sdd3vFhW/zSVCqF8Ms4viw9nw8h98QTT1CGl4mkkhEhWgvvA6lEoEwykC5x+tBIacYsf34fCiqVMZRdK5VC+HOUZcGD4v2gKnxjc9ogxKpmxIgRue2/86lkwPuN7w/TvtJLhjbddNNCmZdTeB9KMzr6a/k+O5UW+Xq+f+zZs2ehns925/315ZdfLtTz8o9UNunLfNv79u2LEJohFkIIIYQQDY0GxEIIIYQQoqFpl5IJ/4QpFCNL+CgQXvoAMH/+/NxOs+X4yA3+ydl0KdWf3y/hpE/Epks1ZefzS0R+ydW3AWDRokWl5xCiPZH67+OPP57baXYqv5xaJZnwy5/+SfJ0edb7vY9gkcqwPPI30dYZM2ZMbnu5Qyr38XIHL61Ioxp5H0ojM/i+zfdZVVFW/DFpm7wvV123TD7l+0YoyhpTiaL3ZV+WykJEY6IZYiGEEEII0dBoQCyEEEIIIRoaDYiFEEIIIURD0y41xGmmNh/uxYdVqcoy53XHUNQT+dBPaVYtr0HyOqb+/fsX6vnseT7sWqqf8tl9fNnrr79eqOfL0oxAQrQWVaHGyjJSpcd4bWCqZfR4rWH6DIDH64lTrXFZZrk0o5fXL1ZlqlOoNdHW8GEN0xCdffr0yW3/vU7Dhno/TP3Y+54vS33In9/rlat80p8v7SvTsG410j7anyMNu+iv5UOvSkMsQDPEQgghhBCiwdGAWAghhBBCNDTtUjKRZrryIcq8LMKHX4HiconPbpdu+6w9afg0L6fwMokZM2YU6vlQMFXLr37byz18SCgoZu1RGCjRHvDLlT7cUyoH8lRJlPxSaOoDXrpQFsoQisu9PotXmtGrLGxiSpksRIjWwvtdKoUokzGkfuL7Iu+76bb3w/Ra3jf8ddPwbKmsoey6vk8skz6l7UglWL4dXtYoBGiGWAghhBBCNDgaEAshhBBCiIamXUom0idnvTzBL6ukT736yBKpZMIvrVQtpfplHP/Uq39iFYoyjqqlKd8Ov1ycPlHrX3O6RCREeyKNEOF9quq77X0nfQK9uT5RJplIJRK+TYoyIdoTBxxwQG5fcsklpfW8z1R9j1N/LfOH1AfTTHNllEkm0vP5/ty3qep+kpZ5OUUqzxJCM8RCCCGEEKKh0YBYCCGEEEI0NBoQCyGEEEKIhqZdaojTkEu9e/fObZ+J58knnyzU81rjfv36Fcq8Rre5WbAWLFiQ26km2WfJ8/riVC/l6/kwcal22b/G9FpCtCdS//X6/eZmsfJhoVKq9JDNDXNYFp5KiLbOPvvsk9sXX3xxoczren1flup9VyZMWpXW3pOer0z/n/bDZdnu0vBs/v6Slvlnc0466aRmtVc0DpohFkIIIYQQDY0GxEIIIYQQoqFpl5KJNOza/Pnzc9uHY/JZ5QC6d++e22mGKb9s65dwqpZjfKimtJ4/X1VYKd+OqmxeXk7hs/EJ0VYpy+KW+m9ZOKX0HH6ZNF2e9TIJX5aGXvR+6UMwpdkvPal8ydPc0FVCrC5GjhyZ215CCEXJQFX2Uy9PSMvKJA5V339/vlRyUZZ5Ne1Ty/rlqmyUaWg1Hw71uOOOK22vaEw0QyyEEEIIIRoaDYiFEEIIIURDowGxEEIIIYRoaNqlhrhv376F7Tlz5uS21wylodV82LXFixcXyspCnqV6Ka9R9tqnqjBQXseY6qf89qabbprbS5YsKW1fmiZaiPZEqv/1OuFUd+z9o0qL39wwaakusUYaytCfryrMYVn7hGgLDB48uLA9efLk3PZ9SqrD9d9/HxoUyvvHKg1xmU4Ymp+6ubnH+zFA+kzCwIEDc7t///7NOr9oHDRDLIQQQgghGhoNiIUQQgghREPTLiUTaUgyv8z62muv5bYPPwPw4x//OLf322+/QpkPx+TtNIOVx4dq8iGcoLis5LMAPf3004V65513Xm4/8MADuX3rrbcW6vnX6EPnCNHeSH3F40MZQnEZ1vtU1fKsr5fKM3yZDzuVyiJ8O7xMKkUyCdEWKJMu7LvvvoV648ePz20vNUi//77fS0OXlUkZqjLVpeEPy87npRWpFMK/Ln9MKlf0PunHAwC77757aTuE0AyxEEIIIYRoaDQgFkIIIYQQDU27lExURXTwyyppNAa/RProo4+2fMM+IvPmzcvtVBbhl36qlqaEaCuUyRrSCC++3uzZswtlXr7kl3UXLFhQqOefmPdLpqmkwUdv8WWpT/l7h/dLIdoiZdFOjjzyyEK9U089Nbd91KVUFuTPkUY8SqNO1Kjql1Ymi2NZpksoRo/w9wiArl275naaFfOYY46pe75UBqKsk42JZoiFEEIIIURDowGxEEIIIYRoaDQgFkIIIYQQDU271BAfcsghhe3LL788t33YljK9ELRNzdBXvvKV3F64cGGhbNSoUbm9zTbbrLY2CdHS7LzzzoVt74vdu3cvlJVpI1NdY58+fXLbawpTXaO/Vqo99HhNZZWWsSqbVlu4p4jGoCz8X7r/3HPPze0f/vCHuZ1mdKsKjdilS5fcrvINX5aGbvN4H/Lt9deB8ux5qf7ZP39z/PHHF8p23XXXum2QrwrQDLEQQgghhGhw2uUMsRBCCAFw2mmnlZb95Cc/WY0tEUK0Z6xqyW+1NMBsAfBsqzaiMfl4CKFXS51Mn2Oro8+zY6DPseOgz7JjoM+xY1H6ebb6gFgIIYQQQojWRBpiIYQQQgjR0GhALIQQQgghGhoNiIVox5ixoRmT49/LZrzotkvjipkxwIxpJWVnmbFnSdmxZvRL9h1hxvfN2NWM7T/aKyqc91wzpsW/w93+PcyYFF/jP8wYHPd/I9b9a+21m7GjGb+suMa6Zjxgxkj3vr1mxtxo392Mdu5qxq0lZb81Y2hJ2TfN6JzsO82Mo8w4qOy45mDGZ804a2WPF+0PMz6M39kp0T9axBfN2Dme7wMzDknKvmjGzPj3Rbd/tBlPmDHLjAvNsLj/XDOmmnGVq3u0Gd+suH7fmn9FX1scX+dUM+42o/dHeG13m9F9+TVFI9DmBsRmbGTGNWbMNmNi7Nw+uRLn6WbG8RXll5sxPx0UmNHDjLuig99VcxYzLDr2rOiIW8X9n4rtnGrGdnHfGtHROi975fw655uxc7TXNOMn8ZqTzHjYjH1W9DXHcxU6WTl8xyYEFobAqBAYBVwC/LK2HQLvLe/4knOeEcKyA0EzOgHHQnFADOwD/A3YFVqsE94P2AoYBWwDnGJG11h8MXBUfM1XAz+I+48CRgAPAXvHTvh04OyKS/07cGMITHHv483AqXG77g+D5hIC/xECT9Z5fZ2Ab8Iy94i9gTuBg2DlB8TAbcD+Vfcg0eF4J35nRwLfA/67hc77HJnfX+13mtEDOJPMP8cCZ7q+5mLgOGBI/PuMGRsAW4XACOA9M7YwY13gS8CvK67/LeAytz0uvs4RwHjg6x/htf0eyscJorFoUwPi2IH9Gbg/BAaFwGgyx+5TfWRdulH9Rb8C+Eyd/acB94TAEOCeuA1Zp19z7q+QOTzAfwInAfsCp8R9XwP+EAJv17uwGRsC24bA3+Ous4G+wPAQ2IqsM1x/Oa+vjLSTlcM3OGYMM+MxN6syJBZ1MuMyM6abcWfsnDDjitpMkBnPxFmdScCRwBjgj/Fc60afHQW8BnwVODmW7WTGADPujde8x4z+7vyXmDHBjH+a8dk6zR4K/D0EPgiBt4CpNPlrgHxwvAEwr/ZSgTXJvv/vA0cDt4fAaxVvz1HAX1bgvdzFmmaSHzfL/bSLGdebMcOMP7oZsfvNGBPtN834uRlTgO+T/bC4z4z7YnlXYC2ye8wBwHnxOoPMGGXGI/G9/LP7oX6/GRfEetPMGAsQAgG4H+q+t6Lj0xV4HcCMLtH/Jlk2a3tgrZIZp5vxtGUrLX8yy/uwnBB4JgSmAv9KivYG7gqB10LgdeAusoFvX6BrCDwSv4dXkfVp/wLWjL5R89FTgF+FwPuU8zmyH9wF4nnWd69zbJxMetyMh8z4VNzf2Yz/M+PJ6DuP1nyS7MfvkZXvpGgY2tSAGNgNeD8ELqntiDM34+IM7Xnxpv+ExSXUCmf/CTAodhTnpReKg9F6HeWBwJXRvpLMkWv7rwqBEAKPAN2i479P5tydgffN6AbsD01LQnXIHTzO4BwHfCMElsa2vRIC/xfLj4yva5oZeZohMy6OA4rpZvwo7juRpJNFDi+ygeoFcfZzDPBC3D8E+HUIDAMWkX0v67EwBLYKgT8AE4izsyHwDrAlMCUE5lKcoR4H/Aq4Ms7k/BG40J1zANms0n7AJWakqaymkHWunc3oSXZv2DSW/QfwVzNeAI4h83WAi4BHgP7Agyxn5skyWcXAEHimrE4dTgG+Ht/LnYBayqwtyX6MDgUGAjvUOXY94NEQGBkCZ5EN5HcLgd1i+Z5kP8YfojhLPZvsfvLd+F4+QTYzV6NzbM/xwOVu/4TYRtEYrBv7uxnAb2laGXkX+Lc42bIb8PPYn25N5vMjySZ8xtQ7aQUbA8+77Rfivo1pusfk+0PgDeCvwOPAS8BiYJsQuKnsAmZ8Ani91jdGdjJjMtnM9Z40fednADuFwJbAGcA5cf/x8RxDyVaMRtdOFAfya8dJKtHgtLXEHMOBiSVlB5PNRI0EegLjzfg7sIDM2ZfEjvMRM24mm9kdHjuKFaFPCLwU7Zdpmp0uc/5fk3VWa5PNFp8OnBPCMr+mPTsA10d7MPBcCCxJK1mm1TyXzIFfB+4046B4A/l+CLxm2dLrPWaMCIELzfgWWSf7KmQOb5Y5fAgsTK8hGoKHge+bsQmZPGCmZZlK54bA5FhnItkgtR7XVpz7M8DtJWXbkfktZCsVP3Vl/xd9ZKYZc4DNIG8LIXBn7LAfIvPxh4Fa7taTgX1D4FEzTgV+AfxHCPw+XgczziAbgO9jxhfIfPfbiV/2JPshsCI8CPzCjD+SvZcvxPfysRCyQUDsrAcA/0iO/RC4oeLcnwF+l+60bKm5Wwg8EHddCVznqvwJsh/5ZnQ1o1sILALms6y8RXRc3qn1d5bJ964yYzjZysk5lkn0/kXWb/Uh64f+EgLvAu+accuqbmAI/JR4HzDjt8AZZvwHsBcwNQT+KzmkL5n/e8aFkK18mPHdeL6vkq0WXWnZClggWy0C2BG4IF5/mhlTk/PV/ET9Y4PT1maIq9gR+FMIfBgCrwAPAFvT5OxTgbtpcvaPTFzuqQzUHALPhcCuIbAd8DawCfCUGb8341qrr3+u5+T12JpMPrIgBD4gm2XbOZYdZtky9uPAMKr1huoYGwgz/s0t648JgavJluDfIZtZ3T1W9bMuH1L+A/mtisvtRaZ5XVFSv1rGz0Lgx4wHTXcAACAASURBVHGG9NNkfv5PM3oBI0Pg0VjtWhLdcvwhOTb+cPw2cDjZwHeP5BLvwDIz0wXM+Lp7L/uFwE/IZqjXBR40Y7NYtTnv5bsh5IP6eowFHqtqTwll7+U6NM1giwYiBB4m+8HXi0wW1AsYHQfMr7Cc730zeZGmVRvI+r4X498mdfbnmLElmU8/DRwaAoeRregOocjyfPRmmvrEs4H7QmA42Sptc1+j/EQAbW9APB23nNFMWtrZX4lSCOL/+XF/mfN7fkz2gM+JZEtW36G4tFnDO/ksoL81PTC0XOIy0inAHnEJ9TaqX7McvoEIgT+7B+smmDEQmBMCF5LpZUd8hNO/QdS3x5nLNdzKQ14WeQg4ItpHAeNc2aFmfMyMQWQSg6f9RczoVFvGNGNEbPOdZCslG7gfmp8GnkraeDbZkilkA9dANjNWeMAsLpd2qiPX8HV+7d7LeWYMCoEnQuBcsgd6Nis7thn493IYMMMNmPOyEFgMvG6Wyx+OgXy2GMjlYzsCi2N9gE9C/UgiomMTf6h1Ipv13ACYHwLvm7Eb8PFY7UGyBy/XMaMLK643vwPYy4zulmna9wLuiCusS8zYNup8v8CyOv2zyVZT14zthDo+CvyT8pUryCbKZkd7A5r65GNdnQeBwwAsi9yyRa0gtm8jWCHZlOigtLUB8b1kep6v1HaYMSJ2BOOAw2NH2YvsV+FjlDt72jk3l5shDx/zRZoc+WbgC1F7tS1Zx1OTVmDGLsC8EJhJ5tT/or6DQ9aBDwaID979L3CBNYWK6mXGofH17WJGzyiNOJKsI+xKNmu32Iw+UIhIUXjdcnhB1hlMi0v5w6nWty+PK8g0v5PJZp19NIpbIJ+d3gn4BvCluHpzDNnDpzWeI/t+3w58NS7betYExpnxJHApcHR8wO4DMs39DZY9nHYMcGrtoDjzRAhMiruuJtPc7kCdB3PIBtk7rsDr/2bU808le36gTC7SHC4F/hb1/rVIHTWuAU617AGhQWT3ovPidUdBIaTau2Y8Tqbh/rLbvxvZj2XRGNQ0xJPJVk6+GH9g/REYY8YTZIPTGQAhMJ6sX5tK9j1+AvIfUzlmbB31+ocC/2PG9Hj8a2QD2/Hx7yz3AOvxZJNCs8gGrLe78x0ETAiBeVHaMzm2bZ0QmOKvHbIHamdbDK0Y2Sm+zpr/fzvu/ynw39EX/ArNb4Be8V7yX2QTb7XXORp4JN5XRKMT4lNibeUPQj8I/wdhNoTpEG6DMASCQTgPwjQIT0A4PNbvCeHhuO93EJ6CMCCWXR3rn1fnOn+C8BKE9yG8AOHLcf+GEO6BMBPC3RB6xP0G4dexXU9AGOPOZRDucnU3hzAJwlQIO9S59k4Q/uC214LwUwizYnsfhbB3LDsyXm8ahHPdMVdA+Gds640Qjo37vwHhaQj3xe0xEG5o7c9Vfx3vD8JvIWy7EsddAeGQ1m5/bMtWEH7fBtpxF4S+K3Hc/f5e5Pb3gXBPa78u/bXtPwhd4v/OECZA2Kq121Snjf8G4b8+wvGdIKwT7UEQ5kJYK25fAGGP1n6N+msbfxZCpURWrCLM+Afw2RBW+KGeFb3OBcDNIXDPqryOEM3FjCuAW0PIHyxtVcz4d7JoGFX63jaJGfcDp4TAhGT/1mQReybXPVAIwIyryZ4/WYfMB1oqdnGLYsZ/hMBvV/LY9YH7yFadjCxay+2x7LgQCjGORQOjAXErYcY2ZE8Fp0+8tvR15PBCCCGEEBVoQCyEEEIIIRqatvZQnRBCCCGEEKsVDYiFEEIIIURDowGxEEIIIYRoaFo9dXPPnj3DgAEDWrsZLFrUFOxh4cKmDI5rrbVWoV7Xrk35Mzp16pTbb7/9dqGeP8eaa66Z2xtuWEyZ3r1795Vs8Udj4sSJr4YQerXU+VbV5zhlCnxQEiFyjTVg5MgWv2S7pL18nsvjww+LgR7eeacpn4z3o/TZB1/21ltNifXWXnvt0vN/4L5Y//pXMdO69/vOneuFEl81dJTPEeS7HemzbGT0ObZtVvQ+U/V5tvqAeMCAAUyYMGH5FVcxf/lLUyKdK664Irc33XTTQr1Pf/rTud2tW7fcnjy5GN3oqquach/07ds3t48++uhCvcMOO2zlGvwRMbNnW/J8q+pzNCsv++ADaANfnTZBe/k8l8eSJUsK21OnNgVh8X703nvvFer5sscea8p+PGjQoEK9N954I7dfffXV3E5/0Pbr15TpfMyYMc1qe0vQUT5HkO92pM+ykdHn2LZZ0ftM1ecpyYQQQgghhGhoWn2GeFVyww03FLYvvPDC3P7HP/5Relz//v1zO/0ld+2119Y9Zp111ilsf+xjTb81Xnopz/DMrbfeWqh3xBFH5PZIN7f/5S9/uVDvhBNOKG2vEO2Z5557LrdPPPHEQlmPHj1ye/jw4bntJRIAb775Zm6//PLLud2rV3FlbP31m7K5b7zxxrntZ6IBFixYkNteMjF06NCSVyGE8HhZk/end98tZmn3sqh//vOfub106dJCvTXWaBquzJo1K7enTClke2b+/Pm57e8tUOxXTznllOoXIBoOzRALIYQQQoiGRgNiIYQQQgjR0GhALIQQQgghGpoOpyEeMWJEbnvdElRrAb0+ab311svtNEyaD7X2/vvv53aqIX7llVfqtmNkEgPE66l86LczzzyzUO+iiy7K7RkzZiBER8GHP/v5z39eKPPfe68VPPLIIwv1PvGJT+S2D4146aWXFuo9+uijuX3ggQfmdqprHD16dG7/7Gc/y+3LL7+85FUI0TFJQyGae6zfPyuTstNOO9Wt9+yzxYf8fWQZ74c+ilNa5vvo9HkC7/9enwzw4IMP5rY0xCJFM8RCCCGEEKKh0YBYCCGEEEI0NO1GMuEzSaXLNH5Z1Gep2myzzQr1vMQhDcTvz+/PkS4XedlFly5dcjuVZ/ig/35J54MkpYqXavgQUT7RAMALL7yQ2z58HCwbqkqItsy4ceMK208++WRuf/7zny+U/fu//3tu+5CF559/fqHeHnvskds+JFOagc5LpXbffffc9sk8oOjnPgnIZZddVqh33HHHIURHxssEodiH+b44lSf4vtPbabIrfz4vx0ilEL6elyimSXqqMlX6/laIFM0QCyGEEEKIhkYDYiGEEEII0dC0m/WDqqdZr7/++tz2magsSXLtM9+kSyf+/H55J12O8WVedpE+pe6XdLxUo+p1lC1FQfGp2iuuuKJQJsmEaE/4p8ChGJEllS74J9DnzZuX20cffXTpOXwGurFjxxbq+UyT3/72t3PbZ7cDmDt3bm77iBZbb701Qohlee211wrbvk/0/a2XNECxf/TZ7dI+0MsfvDzDZ6lMr5VKJsaPH1/3WulYQTQmmiEWQgghhBANjQbEQgghhBCiodGAWAghhBBCNDTtRkNcxRtvvJHbXgvktUlQ1CSl+iSvJ6ra77e9njjVIJVpodLr+rBQ/phUu+zPn+odhWhPpNka/fbJJ59cKBs0aFBue/9IM0N+9atfze2XXnopt9MQb8OHD8/tfv365fYtt9xSqOefRTj00EPrvAohGpM0DFuNF198sbBdFuLMhxAFGDZsWG57v5s6dWqh3rrrrpvbvj9MdcJrrbVWbvvQqFDsb31WTGWtE6AZYiGEEEII0eBoQCyEEEIIIRqaDiGZ8EukG264YW6nWeH8Mkuagc4vA5XZKX4J1y/TQHFpJr1WWZs8adYfvyyUhrfx0pA0u48QbY00e5yXEX33u98tlPll069//eu5fcMNNxTqnX322bm933775bbP/giwyy675PaECRNyO13eTaUbNaruG0I0AmV91p133lnY9vKHwYMH53b//v0L9Xw/+tRTT+V2mq11o402ym0vz0jlU2nfWdYmn3VSkgkBmiEWQgghhBANjgbEQgghhBCioekQkomyDHSLFi0q1Ovdu3duV0WPqKKsXrqU6peB/bJq1XX860jb7peLfCYuKC4rjx49uvT8QrQFqrJCpdKjOXPm5Pbrr7+e2zfeeGOh3q9+9avcnjVrVm6ffvrphXo777xzbh988MG5nS6zfvKTn6zbvqpMk0J0RNI+q8x/p0+fXtj2GeR8/+j7OYDbbrstt3v27JnbaTQlL3+qkjX6rK5p271cy0st0/uJvzeIxkF3dyGEEEII0dBoQCyEEEIIIRoaDYiFEEIIIURD0yE0xD6rm9f4LVy4sFDPaxCHDh1aKHv77bfrnqMKX69KF+mpCqc2Y8aM3E7Dp3ltdBq2Shpi0VHo0aNHYfvjH/943bLDDjusUO/WW2/N7aOOOiq3u3fvXqjnwy75e0Cqyy+7BzTXz4XoKDRXQ+w1w1D0V6/rTftlfz6fjS4NhfjWW2/ltg+pmoZd88/fpKFX/TMKPhTc7373u0I9aYgbE80QCyGEEEKIhkYDYiGEEEII0dC0S8nEs88+W9j2cgdPGo6lW7duue2zu0FRhlC1LFqWmaoqnJo/d7oU+8Ybb+T2vvvum9uPPfZYoZ5f+knbt3jx4tJrC9Ge8f5xwgkn5LaXRQBcccUVuX3//ffnts9cCTBlypTc/sEPfpDbXnYlhGiiuSFJn3/++cK2lwM++uijpcf57JFeKpjKHRYsWJDbXnbxzDPPFOp5eUbXrl0LZT78m5dk3HPPPYV606ZNK22v6LhohlgIIYQQQjQ0GhALIYQQQoiGRgNiIYQQQgjR0LRLDbEPTwYwYMCA3PYaoVRru+WWW+b23LlzC2XNDbVWdv70Wl535e20ng/D9qlPfSq3H3rooUI9Hy4m1XS9+OKLzWq7EG2BqjBOPjQiwOzZs3O7S5cuuZ1qEr/2ta/lttcXDhkypFDPp3X26Z5TlixZktteh5iGPFQqZ9HRqfqOz5s3L7d9KmSAfv365fbTTz+d22la9DRde400xbPve70+2dtQ1BB37ty5UOafN/J6ZX8MwE033VS3TaJjo7u5EEIIIYRoaDQgFkIIIYQQDU27lEykSzN+KcWHU0uXZn0IpieffLJQ5jPkNJcqKURZvTQDnV8W8mFm3n333UI9L61Il21feOGFZrZYiLZNGkJxiy22yO0999wzt++7775CPS+buvPOO3N7zJgxhXrDhg3L7f/5n//J7VNOOaVQz2fdSkM3CdFa1PqS1Zkx0fevUB6uzGeVhKLEab/99svtNKOd78+rQrx5acQrr7yS22mWSS+T8NntoNh3ellUGp4xlWWKxkAzxEIIIYQQoqHRgFgIIYQQQjQ07VIykWam8Us6XmqQLisNHDgwt/2yKhSXd9IlouaQLvWULf2kT+z6JWLfvvTJW5+xK+Xll19udjuFaMv06tWrsO2XVy+77LLc9hFZAG677bbc9hEnxo0bV6h311135fbJJ5+c26lkyj8975+WF6I1WZ1SiRpVUSZ81JZUCuH7MC/rS2UMXubo5Q5pP+wz1Pqss2kkCX/+NANl2WtJ++s5c+bUrSc6NpohFkIIIYQQDY0GxEIIIYQQoqHRgFgIIYQQQjQ07VJDPH/+/MK21wX58GQ+PAxA9+7dczvVFq0MVSFifHiXMjs9hw8J5dsKxQxeXj8FRW2VEG2dKk2iz2gFxUxzXmPfv3//Qj0fRvFHP/pRbvtQhun2z372s9yePHlyoZ4P6+ZDt1X5vBAdBf89r/LXhx9+OLdT/b8/hw+T5vtoKOqiX3311dxONcRek5z27Z511lmntF6Z/6ZhTv05ROOgGWIhhBBCCNHQaEAshBBCCCEamnYpmfDyASgui/jlmHRpJl0+9bR0OBu/NOPbly4/lYWJS0PJ+CWnNNvdokWLPlpjhWgjpOEQn3/++dyeNGlSbu+7776Fei+++GJuz5w5M7fTUFDer3x2uzTsmg8F5enUqVNZ04XoMFT1hxMnTsxt759e3gRF+YMn7b98JkjvrxtssEGh3uLFi3Pb96OpDNG3vSrMqW9H+nrl542JZoiFEEIIIURDowGxEEIIIYRoaNqlZGLhwoWF7XQJpkZVJImqZZaWxssnmnudtddeu7Dtl4v8Ui8sm/lHiPbKlClTCtsbb7xxbvtMdaeffnqh3hNPPJHbDz74YG7PnTu3UG/ChAm5PWLEiNz+1a9+Vag3Y8aMFWm2EG2SNKpCS/Rzf/jDH+qeL5Uo+j7WSxBSOWDa19VIs7WWRZZIpZBlcqcUL5NK35f111+/WecQHQvNEAshhBBCiIZGA2IhhBBCCNHQaEAshBBCCCEamnapIU7DjJWFSOnZs2dh22d/S8MxVWXjaUmqruM1z6l+ypNqrqrCyQnRnki1wT4DXY8ePXL74IMPLtSbPXt2bnuNY5rtyt8rfMiopUuXFuqlYdiEaC+szDMrVVrjtK/8y1/+ktv+eZbUh7yW158v1QKXhVpLw6v6PtG3N80y98Ybb+R2+nzReuutl9te4/zaa68V6m211VaIxkMzxEIIIYQQoqHRgFgIIYQQQjQ07VIysWTJksK2X970cooxY8YU6nkJRVVItir8Mku6zOTxS0RldnoOv7w7ePDgQr1x48bldpXswi/9+CVmIdoq3p/POeecQtl//ud/5vZDDz2U22lYNC8buv/++3P78MMPL9TbYYcdcvsLX/hCbqdhlnz2LCHaE82VSfi+rKpP+cUvflHY7tKlS107zUznz+mlC6kc0PfZPqRq2r96yaOXO6V9uW9TKs/w9wkv1Uhlh2mWPNEYaIZYCCGEEEI0NBoQCyGEEEKIhqZdSibefvvtwrZ/ctRnyxkyZEihnpckpNlsVmWUCb80VbWc5ZdwfIYuKC4fpW3151+wYEFuSzIh2gPPPfdcbg8dOrRQ5r/rvXv3zu1U0uDlUd5Xzj///EI9Lz3abbfdcvvFF18s1PPbL7/8cm5vtNFGJa9CiNbD9wFlGeKg2P9U9XlennTNNdcUyrycz0eCSM+3zjrr1D13VQY6f460r5w2bVpuf/rTn87txx57rFDP9+1lWWzTsrRNG264YelxouOiGWIhhBBCCNHQaEAshBBCCCEaGg2IhRBCCCFEQ9MuNcSp/tdrkLx+qn///oV6XpPk661qqnTDXuPlQ0kNGjSo9Jg0+1Zzy4Roi3i93sSJEwtl3ie8hjjVNe6555657cMr+hBsUPT7/fffP7e9ZhLg4Ycfzu30mQUh2hpee7syz8P87W9/K2yfdtppuZ3q5t96663c9n1vmjHO+5ovS7XFPpya77/S7HE+NOKNN96Y23379i3U888QpOHUyp7n8c8eAXTu3BnReGiGWAghhBBCNDQaEAshhBBCiIamXUomUglCmSTBZ7CDYiilNMxKc7P7NJey86VSDR8y7plnnsntr33ta6XnrpJ7rE4piBAtwd13353bAwcOLJR5n3388cdz+2c/+1mhns9O9fGPfzy3t99++0K9n/70p7l988035/bixYsL9byMw2fSEqKt4zPGpSHJxo8fn9vTp0/P7blz5xbqeXlSKjvw8gIv+Uvr+TIvrXjjjTcK9XwIxVmzZuW2D+kG8I1vfIN6vPLKK4VtHxYu7YfL5E+pjytTXWOiGWIhhBBCCNHQaEAshBBCCCEamnYpmfBPuVaRRmqYNGlSbqcZbPzTrf4pVW+n2345pqqeJ5U0+G0fPaNbt251j4dll6Z8O9Inc4Vo6/il1X79+hXKvB/4pdY5c+YU6h1wwAG57bPR/fWvfy3U83KKrbbaKrfTKBP+qXi/BC1EW+Dee+8tbP/oRz/K7RdeeCG3vSQPitETvL3pppsW6vnv/9KlSwtlXm6YlnnKoj2ksgXfn/tjjjvuuEK9NGJMjbXXXruw7e8TaT/q7zXeTvvrNBOmaAw0QyyEEEIIIRoaDYiFEEIIIURDowGxEEIIIYRoaNqNhthrfFIdbplet0+fPoVtr3dKw6r4c3hNUnM1xGmbmqtJ9lmAUm1wGem1vBZKGmLR3vCZplJt8DHHHFP3mCuvvLKwPXPmzNz2/lAVMs3rC33YNiiGe5s3b17pOYRYXbz77rs8/fTTAPzyl78slPkQo8OHD8/tNOyY7wO91nb+/PmFev55lqrnXryd9l++r/NZ5lJfe+SRR3Lbh0JMw6becccd1CPNkOevlb5+nyXPa6F9WDhYduwgGgPNEAshhBBCiIZGA2IhhBBCCNHQtBvJhJcgpJRlhdtkk00K2xMnTsztVLrgs+L45aIqyUSZfKKqTel+v1Q1derUuscsD79EnGbtEaKts8022+T2ZZddVii77rrrcvuuu+7K7b322qtQb999983tiy++OLdPPPHEQr1LL700t3/wgx/kdhq6yWeu+8IXvlD9AoRYDbz//vu89NJLwLLZ3vz3Ny0rw4cercoyVyWZ8KR9mw/r5uUPTzzxRKHewQcfnNv7779/aXtrcpHl4aUQ/nVAMcuel0mkkgkvuxCNg2aIhRBCCCFEQ6MBsRBCCCGEaGg0IBZCCCGEEA1Nu9EQv/fee7mdpov0Wt7u3bvnttcwARx99NG5ffnllxfKfEpXf60Ur7XyWqX0GK9D9vrnVKvotUp77LFH6XU9qa7Zp7udPXt2s84hRFvBaxLTVKvPPvtsbu+88865vXDhwkI9n6b929/+dm77cGwAAwYMyO3NN988t2fNmlWo5zXEQ4YMqWy/EKuLWl/yzDPPFPZ7ja7Xzaba2LLUxanW1pNqg30fVqYnhmJKZu9fu+yyS6HeDTfcUHqOMnyfusUWW5S2L9VGl71P6fNGVc8siY6LZoiFEEIIIURDowGxEEIIIYRoaNqNZMLLH3bcccdCmc8k5TPQ9ezZs1DvS1/6Ul27PeDD0UyaNKlQ5sPnDB48eLW1SYiW4MEHH8xtn2UL4LDDDsvtuXPn5na6FHzeeefltg/jVgtTVeOII46oe75UyqRMVaKtsf766+eyugsuuKBQdvfdd+e2/177jIsAS5YsyW0vO/JhR1cWnwUOihnpfIjDk08+uVnnS6WBXrrh5Q5pGDef+TJtk38/vBQqDenmZYiicdAMsRBCCCGEaGg0IBZCCCGEEA1Nu5FMeF5++eXCtn8ivKMudW677ba5PW7cuELZeuutl9s9evRYbW0SoiXwMolbbrmlUHbhhRfm9k477ZTbPoMdQP/+/XN72LBhuX3ssccW6o0cOTK3v/Od7+T2m2++Wajnl5OfeuqputcRorU48MADK7dXlFQy4bfTMh9Byfc9PsITLBu5YUUpy/aakt4LvIQwHQ94CYVve9euXQv1Ouo4QlSjGWIhhBBCCNHQaEAshBBCCCEaGg2IhRBCCCFEQ9MuNcRpppvnn38+t9MsdmVUhXRpafy1VvY6PpTUlltuWSh79913c9uHnROiPeD1ewMHDiyU+fBHXhuc1vPZrnzmqpNOOqlQz/vH9ttvn9tz5swpref1+0J0RFL9b7rdljnkkENauwmig6AZYiGEEEII0dBoQCyEEEIIIRoaS6UDq70BZguAZ1u1EY3Jx0MIvVrqZPocWx19nh0DfY4dB32WHQN9jh2L0s+z1QfEQgghhBBCtCaSTAghhBBCiIZGA2IhhBBCCNHQtOiA2IwPzZhsxjQzrjOj83Lq32/GmGg/Y0bPlmzPcq59ghmzzAj+umaYGRfGsqlmbOXKvmjGzPj3xbhvbTP+Fl/z8a7upf7YOtc/yIwzov1DM150790By2n7rmbcGu1jzbho5d+J5WPGZ804a1VeQ7QNnA9PN2OKGd82Wz0/nM04NF73X7X7giv7XvTJp83Y2+3/TNw3y4zT3P4/Rv89x+37gRkHVVx/SzP+N9rHmrHAjMejv99hxvZlx35UzOhlxt9W1fnFqiXez4MZmzWzft3+zow369WvOM8K1a84z7Fm9CspazN+6fbdZMYjzXxteX9Z5zU3u+9sqb7WjG7JWEG+30Zo6Y7unRAYFQLDgfeAr7bw+VeKOMhNX+uDwJ4sK27fBxgS/74CXBzP0QM4E9gGGAucaUZ3YG/gH8AI4JhYdyTQKQQmVTTrO8Bv3PYvQ2AUcChw+eoahCwPM9YAbgP2X94PHNEhqPnwMODTZP5wZlopfi9ammnAwcDfk2sNBY4AhgGfAX5jRiczOgG/jm0cChxpxlAzRsTXMQLY2owNzOgLbBMCN1Vc//8BF7rta0NgyxAYAvwEuNGMzdODWuK9CIEFwEtm7PBRzyVahSPJ+oEjW7shK8mxUH9ATBvzSzO6AaOBDcwYWHpU26UbNA2I5ftth1U56BoHDE5/nZlxkRnHVh1oxrfiTOk0M74Z9/3EjK+7Oj8045Ron2rG+PjL80dx34D4C/UqMofe1F8jBB4PgWfqXP5A4KoQCCHwCNAtOu3ewF0h8FoIvA7cRXYTeB/oDKwJ1LJunA2cXvH6PgksDYFX07IQeAr4AOhpxRn0nmZ12+vPO8CMe+P7cI8Z/eNN59naANuM9cx43ow1zRhk2ez2RDPG1WY3zLjCjEvMeBT4aQgE4H7gs1XXFx2LEJhP9qPwhPij8lgzbjbjXuCe+F263IzH4kzqgQBmDIv7Jsfv4pBY9zbLZp2nmXF4nes9FQJP12nKgcA1IbA0BOYCs8h+lI4FZoXAnBB4D7gm1n0fWDd+59cEPgTOos7AvoYZ6wMjQmBKyXtxH3BpfD9qq1vnmzEBOMmM0WY8EH3pjnjPwIwTzXgyvg/XxH27xPdmcnzf1o+XuQk4qvQDEW0SM7oAOwJfJhsg1vbvGr8n15sxw7LZUUuOXdeM2804rs55l+nXSq7/S8tmcO8xo1fcN8qMR+Kxf7Zs8qbufjMOAcYAf4zfyXX9+dugXx4M3BLP69/vKyxb3X3IjDnxdaXn2zr63KBkfy8zbojv93grH5xuGj/TmWZN7bY6Y5aK/T8BBsX3+ry4T77fBlglA2LLZkz2AZ5YiWNHA18im4ndFjjOjC2Ba4HDXNXDgGvN2ItsNncsMAoYbcbOsc4Q4DchMCyEZoc52Rh43m2/EPeV7b8LFCwKOQAACGZJREFUGAA8AlxomdxhUgjMq7jGDlB/9tiMbYB/AQua2V7Pr4Ar4y/wPwIXhsBiYDJQS+/3WeCOEHifrIP/RgiMBk6hOGO9CbB9CHwrbk8AdlqJNol2TAjMAToBveOurYBDQmAX4PvAvSEwFtgNOM+M9chWhi6IKx5jyHzlM8C8EBgZV5BWZIlwhXwy/qhcQOZjtwCDgY8tZ8VmDNkP5yomQWFJfK0QGEM2e/UrsvdlNHA58ONY5zRgy+iTtRWzU4Cvx/dnJ+CduF8+1j45EPhbCPwTWBj7sBpbAt8kmykdCIWBVhey7+efQuAyf8Ll9Gue9YAJcUXnAZoGl1cB343fuyeq9ofA9WTfvaPi6tA7NI/W8ssjgT/Fv3RGvi/Zj5PPkg08cyyTPF0CHBgCs5PjLiBbpd0a+Bzw25L2jI3lI4BDzRhTNmapGMucBsyO7/Wp8bzy/TZASy97rmvG5GiPA/4XVlh3tyPw5xB4C8CMG4GdQuBCM3pbpnPqBbweAs+bcRKwF/B4PL4L2Y3kOeDZOMu7ygiBD4DPx7auCdwBHGjGL4D+ZLPNNyeH9WXZAe/JZhwNvAEcHgLBVjzL83Zkv54Bfg/8NNrXAocD95H9ov5NnNXYHrjOXWdtd67rQuBDtz2f8iU10TjcFQKvRXsv4ACLKzXAOmTf+YeB75uxCXBjCMw04wng5/+/vbMNkaqM4vjvqJmZaRaSkOaKVFRaSBQKUdqLShopmbmZBRJJmVYERRh9yNIssZJCoyjDVdIozJcVtSS1LbAXwlq1CLcgsYxyN2u3Mvf04Twzc+funZ2xdXdmnfP7srtn7stzn7nnPuc55//cFWEhsEGVne3ZSNWsLM16YKYIc4HLwjW8GtslySfjxD1ydfh5ITAU2Bp8qStwMHy2G8u8rYV0WbgGWCzCSqx/fgx297HOSSUWUIFlLSuBz8Pfu1LfbxgbKzBpBcB7WAVuZcIxx5A8ru2IbddM5j6swmQ9fYAzVdke7G9iz/lE+/Fdattoq1+KcA7WDx+FMfKoCENV00HzWlWagT1h2xQXYQmgMTmSVdcDF0fGwt4i9FJtodHeqsqvoS3vYvGKkhCzYM+LJHs8HgD3/ZLgRAfETSHrkUaEf8nORPdow/HfBiYD/ck8BARYoMorsfNWgN2Ix8kBsuUVA4LtADAqZv8wtu992Ax8BNCABaHbaOkATUCfmO15VRbFbNG+a0u/rQPmi+mgLw9tOh2oj39fEeJ91wMKzhw4JwliGr1j2AMbsu8LAW5JKKfuFZPbjAeqRZipyjaxRaY3Ak+J8IFqwQs1c/kkrdhT7b8ZC056AUNUmSImaVipSmNk0yby+9hwYG/k71RfCFCrysiEfcYDVwM3YZOEYao8I8JGrC9qRBiryj7cxzod4Zl6LTBMBMUmQyqSzvz9Hdn8GNljbg0wToRVQZaWdWgSxrUC6Mh/LFAMv5wC9AXqQvDaG5uAzA2fR/s7OoE9GI4zHBID4i7ACFX+SrzSDPH+PVH97b5fAnTEwq0fsJnXqWJi+OvybL8TmChCz1B+nRRsYEHwVCwoTs1sNwMzQsYTEc4VSZd3/w/rgDvFNJMjgAZVDobzjAmaq77Y7H1zaqdgm4AFxD2xmbtCth4rsBcrF+Xje0iX31rooRL4mIymahqh38Is91Msi7FBlWOq/I49VG4N7RexxYC5uID8JWXnJEJMj7gMeClhwAa7/2dL0EWGcmAqiN6vyhIsC3ZpqOw0qlIFPAe538CSwDpganiGDMYyRLuwe/p8EQaL0B2799OTz1CxeRCrlJxGZvDqCnSPnaNVnxThGkw/HM9gAXwD9BOxgFhMn3+JmFZyYNAfP4pNgnuJMESVr1RZGK4hJcNwH+t8TAZWqDJIlQpVBgJ1FFb+fgI4jC1Ai1PouNaFzNhwO5Y5bQAOi6TbMB3Ynssefj8CaS17oRTDLyuBcaGvK7DxcSr5qccmpwtEshJbKbYAsyNtzJUoukGEs8R01hOxSU2umCWXPamv3fdLgPZYKZ5FkDWswb7sOjIloFzbfyHCcsyxAF5TtX1UqRUT2R8IQSqqbBFb+f1JmDH+AdwBWeX+FogwB3vTQ39gtwjVqtwNVGOZm++ARkwDhCq/iTAPc3aAJyOlY7CH29OqNIuwGZiFabSWJZx+B1Y+lhyBRopFwBoR7sHe9JCP2cAbITvxS6rtgdXYJGJUxDYNWCrC49gih7cgeVERphF9rIA2OJ2blOzpFKxCsQJYnGPbecALmP90wfx7ApbFmS7CUeAnYD5wBaYxbsYW19wbP5gIkzAtbj9gowhfqjI2+P0aYE9o06yUnEeE+7HgoSvwuiq1kUPOwjT1jSLsBnoG6Ua1KvXRc6uyT2wB6hmqHAnm20S4Cpvg1mHZ8GiGOLXvP2ILeJaEsnS30C/fAlXBJpimv16EeSKMxibNtcCmcKjRFObnTulQCSyM2d4J9tUtN2/BA9hbhZ5V5ZGUsZVx7VBs/z+BK8Mz/BCkF6veBSwTezPQfjJjQS778mBvAkZGdcSl4pfA2cAgyMggVakToUFs7U2rqPKzCBOATSLMiH08B3g5tKcbNkYnvSVrF/b9DgCqVPksXO9yEmKWVuw1InwNbAo6Yvf9EsD/dXOREOFFYL0q7xe7LfkQ02KtUs2b3XecTosIDwFHVHMuqGnv8+/AFvwcLsb5HacUKbZfdgTu+6VBSbzrtkyZD53mvb7nAQ8XuxGO084sJVuD2GEEecpiHxAdpwVF88uOwH2/dPAMseM4juM4jlPWeIbYcRzHcRzHKWs8IHYcx3Ecx3HKGg+IHcdxHMdxnLLGA2LHcRzHcRynrPGA2HEcx3EcxylrPCB2HMdxHMdxypr/AKJEK4a22cL3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R32zteKHCaXT"
      },
      "source": [
        "Finally, use the trained model to make a prediction about a single image. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yRJ7JU7JCaXT",
        "outputId": "7fb9795e-0e6b-4ed3-b9fa-2889903add7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Grab an image from the test dataset\n",
        "img = test_images[0]\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vz3bVp21CaXV"
      },
      "source": [
        "`tf.keras` models are optimized to make predictions on a *batch*, or collection, of examples at once. So even though we're using a single image, we need to add it to a list:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDFh5yF_CaXW",
        "outputId": "9b18613e-6d95-4db2-9ad5-0fd19a17dc0a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Add the image to a batch where it's the only member.\n",
        "img = np.array([img])\n",
        "\n",
        "print(img.shape)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 28, 28, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQ5wLTkcCaXY"
      },
      "source": [
        "Now predict the image:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_rzNSdrCaXY",
        "outputId": "68f553bc-af28-48db-8c69-c9a5b9467a2d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.8473925e-09 3.5318640e-12 1.1473051e-05 9.4112955e-13 9.5932966e-01\n",
            "  6.9410139e-10 4.0658925e-02 1.4194466e-17 1.4645473e-12 1.6268186e-11]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ai-cpLjO-3A",
        "outputId": "e6349a03-56c7-45d6-c8f2-153b2c5d0285",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "plot_value_array(0, predictions_single, test_labels)\n",
        "_ = plt.xticks(range(10), class_names, rotation=45)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cU1Y2OAMCaXb"
      },
      "source": [
        "`model.predict` returns a list of lists, one for each image in the batch of data. Grab the predictions for our (only) image in the batch:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tRmdq_8CaXb",
        "outputId": "50c64dbf-67e1-426a-dfd0-7dac12f7ebe0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YFc2HbEVCaXd"
      },
      "source": [
        "And, as before, the model predicts a label of 6 (shirt)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-KtnHECKZni_"
      },
      "source": [
        "# Exercises\n",
        "\n",
        "Experiment with different models and see how the accuracy results differ. In particular change the following parameters:\n",
        "*   Set training epochs set to 1\n",
        "*   Number of neurons in the Dense layer following the Flatten one. For example, go really low (e.g. 10) in ranges up to 512 and see how accuracy changes\n",
        "*   Add additional Dense layers between the Flatten and the final Dense(10), experiment with different units in these layers\n",
        "*   Don't normalize the pixel values, and see the effect that has\n",
        "\n",
        "\n",
        "Remember to enable GPU to make everything run faster (Runtime -> Change runtime type -> Hardware accelerator -> GPU).\n",
        "Also, if you run into trouble, simply reset the entire environment and start from the beginning:\n",
        "*   Edit -> Clear all outputs\n",
        "*   Runtime -> Reset all runtimes"
      ]
    }
  ]
}