l03c01_classifying_images_of_clothing.ipynb

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      "name": "l03c01_classifying_images_of_clothing.ipynb",
      "provenance": [],
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      "toc_visible": true,
      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/azriel-stephen/tensorflow_L/blob/main/l03c01_classifying_images_of_clothing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "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/l03c01_classifying_images_of_clothing.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/l03c01_classifying_images_of_clothing.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": "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": "markdown",
      "metadata": {
        "id": "3b7w6tzQr_9h"
      },
      "source": [
        "Installing tensorflow_datasets library and if it already exists then upgrades it if a newer version is available"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P7mUJVqcINSM",
        "outputId": "db06f078-5446-4663-d474-0c2d79ac0347",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install -U tensorflow_datasets"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow_datasets in /usr/local/lib/python3.7/dist-packages (4.0.1)\n",
            "Collecting tensorflow_datasets\n",
            "  Downloading tensorflow_datasets-4.4.0-py3-none-any.whl (4.0 MB)\n",
            "\u001b[K     |████████████████████████████████| 4.0 MB 8.2 MB/s \n",
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            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.7/dist-packages (from importlib-resources->tensorflow_datasets) (3.5.0)\n",
            "Requirement already satisfied: googleapis-common-protos<2,>=1.52.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow-metadata->tensorflow_datasets) (1.53.0)\n",
            "Installing collected packages: tensorflow-datasets\n",
            "  Attempting uninstall: tensorflow-datasets\n",
            "    Found existing installation: tensorflow-datasets 4.0.1\n",
            "    Uninstalling tensorflow-datasets-4.0.1:\n",
            "      Successfully uninstalled tensorflow-datasets-4.0.1\n",
            "Successfully installed tensorflow-datasets-4.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FxXYSCXGQqQ"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UbK0Uq7GWaO"
      },
      "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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "590z76KRGtKk"
      },
      "source": [
        "import logging\n",
        "logger = tf.get_logger()\n",
        "logger.setLevel(logging.ERROR)"
      ],
      "execution_count": 6,
      "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": "c8506f1e-872f-4d65-e469-7f3e4476f698",
        "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": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset 29.45 MiB (download: 29.45 MiB, generated: 36.42 MiB, total: 65.87 MiB) to /root/tensorflow_datasets/fashion_mnist/3.0.1...\u001b[0m\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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CKbkSqcUwGCu",
        "outputId": "2faae507-1c94-4077-a957-5585be15a82f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "type(dataset)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lFiSvBwswJ1M",
        "outputId": "efbf5b10-b772-431c-f4b2-69b496d89800",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "dataset"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{Split('test'): <PrefetchDataset shapes: ((28, 28, 1), ()), types: (tf.uint8, tf.int64)>,\n",
              " Split('train'): <PrefetchDataset shapes: ((28, 28, 1), ()), types: (tf.uint8, tf.int64)>}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tlKrISKBwnX0",
        "outputId": "a5f8df1d-45cc-415f-8fc9-f680692d90f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "for i in dataset:\n",
        "    print(i)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train\n",
            "test\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYWw-rK2w-Rc",
        "outputId": "ca42331c-b3f4-4498-be81-36cc29279629",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "type(metadata)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensorflow_datasets.core.dataset_info.DatasetInfo"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENswg--dxB5M",
        "outputId": "96f75565-8ced-44fe-8b49-f78c382dc320",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "metadata"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tfds.core.DatasetInfo(\n",
              "    name='fashion_mnist',\n",
              "    full_name='fashion_mnist/3.0.1',\n",
              "    description=\"\"\"\n",
              "    Fashion-MNIST is a dataset of Zalando's article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.\n",
              "    \"\"\",\n",
              "    homepage='https://github.com/zalandoresearch/fashion-mnist',\n",
              "    data_path='/root/tensorflow_datasets/fashion_mnist/3.0.1',\n",
              "    download_size=29.45 MiB,\n",
              "    dataset_size=36.42 MiB,\n",
              "    features=FeaturesDict({\n",
              "        'image': Image(shape=(28, 28, 1), dtype=tf.uint8),\n",
              "        'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10),\n",
              "    }),\n",
              "    supervised_keys=('image', 'label'),\n",
              "    disable_shuffling=False,\n",
              "    splits={\n",
              "        'test': <SplitInfo num_examples=10000, num_shards=1>,\n",
              "        'train': <SplitInfo num_examples=60000, num_shards=1>,\n",
              "    },\n",
              "    citation=\"\"\"@article{DBLP:journals/corr/abs-1708-07747,\n",
              "      author    = {Han Xiao and\n",
              "                   Kashif Rasul and\n",
              "                   Roland Vollgraf},\n",
              "      title     = {Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning\n",
              "                   Algorithms},\n",
              "      journal   = {CoRR},\n",
              "      volume    = {abs/1708.07747},\n",
              "      year      = {2017},\n",
              "      url       = {http://arxiv.org/abs/1708.07747},\n",
              "      archivePrefix = {arXiv},\n",
              "      eprint    = {1708.07747},\n",
              "      timestamp = {Mon, 13 Aug 2018 16:47:27 +0200},\n",
              "      biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1708-07747},\n",
              "      bibsource = {dblp computer science bibliography, https://dblp.org}\n",
              "    }\"\"\",\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ITXGIZ7SxSxH",
        "outputId": "79aa16ac-925d-4026-85ed-0641b104f711",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "metadata.features['label'].names\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t9FDsUlxCaWW"
      },
      "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": "Y3yD_4rQyFgY",
        "outputId": "342c325a-11cf-4fa9-843e-44152773b39d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "metadata.features['label'].names"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T-shirt/top',\n",
              " 'Trouser',\n",
              " 'Pullover',\n",
              " 'Dress',\n",
              " 'Coat',\n",
              " 'Sandal',\n",
              " 'Shirt',\n",
              " 'Sneaker',\n",
              " 'Bag',\n",
              " 'Ankle boot']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IjnLH5S2CaWx",
        "outputId": "8a5e7f12-9bdb-45a2-e648-a53cd52548fe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "class_names = metadata.features['label'].names\n",
        "print(\"Class names: {}\".format(class_names))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class names: ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "1e1cbfbd-4738-4a7f-ebaf-996e6477f75f",
        "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": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of training examples: 60000\n",
            "Number of test examples:     10000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 18,
      "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": "2f246402-d4b4-48f0-d3ea-404e9e875464",
        "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": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "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": "085bd917-f91d-4e6d-dbd6-cb732bb20c83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        }
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i, (image, label) in enumerate(train_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",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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.Flatten(input_shape=(28, 28, 1)),\n",
        "    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
        "    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
        "])"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gut8A_7rCaW6"
      },
      "source": [
        "This network has three layers:\n",
        "\n",
        "* **input** `tf.keras.layers.Flatten` — This layer transforms the images from a 2d-array of 28 $\\times$ 28 pixels, to a 1d-array of 784 pixels (28\\*28). Think of this layer as unstacking rows of pixels in the image and lining them up. This layer has no parameters to learn, as it only reformats the data.\n",
        "\n",
        "* **\"hidden\"** `tf.keras.layers.Dense`— A densely connected layer of 128 neurons. Each neuron (or node) takes input from all 784 nodes in the previous layer, weighting that input according to hidden parameters which will be learned during training, and outputs a single value to the next layer.\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": 22,
      "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": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xvwvpA64CaW_",
        "outputId": "e7f6cab3-0381-4847-d89b-c60707d76019",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model.fit(train_dataset, epochs=5, steps_per_epoch=math.ceil(num_train_examples/BATCH_SIZE))"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 12s 2ms/step - loss: 0.4949 - accuracy: 0.8256\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3750 - accuracy: 0.8637\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3348 - accuracy: 0.8774\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3104 - accuracy: 0.8847\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2948 - accuracy: 0.8924\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff20cbfb0d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "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 0.88 (or 88%) 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": "a0c6ca19-c415-48e1-f963-e3d5eca8218a",
        "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": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.3599 - accuracy: 0.8730\n",
            "Accuracy on test dataset: 0.8730000257492065\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gl91RPhdCaXI",
        "outputId": "01aadfd2-5cc4-4bd4-cd44-7cc8f425d76e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions.shape\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x9Kk1voUCaXJ"
      },
      "source": [
        "Here, the model has predicted the label for each image in the testing set. Let's take a look at the first prediction:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DmJEUinCaXK",
        "outputId": "7802d5d3-964d-44f0-c928-bb0e8381f596",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions[0]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.6994803e-05, 4.1982639e-06, 5.2054550e-02, 1.0018591e-06,\n",
              "       9.2278975e-01, 1.5442716e-07, 2.5072888e-02, 1.5021504e-07,\n",
              "       4.0251460e-05, 1.2404311e-07], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "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": "02df95b0-ed33-450c-ed7e-30ef3338de0f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.argmax(predictions[0])"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E51yS7iCCaXO"
      },
      "source": [
        "So the model is most confident that this image is a shirt, or `class_names[6]`. And we can check the test label to see this is correct:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sd7Pgsu6CaXP",
        "outputId": "9e2e1e47-f766-41c9-9fe3-067be275f5f2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "test_labels[0]"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "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": 31,
      "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": "7f08dc00-b4a9-4f65-c45b-a3000d2cb5d1",
        "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": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko-uzOufSCSe",
        "outputId": "72ae1b2a-2577-4e13-fd93-a4857a818c36",
        "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": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "46f9f82a-ce64-40a8-9a40-5b065c378855",
        "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": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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t4WxvLVq0cLEumaRLLQJ+eoIeMg7Xp4dTdcpTjx49vHaPPfaYi4cPH37wi7D00G39+vVj2xGVJd3XpkyZ4mJdZhCInwkunJFV7yvDZbq8mi6tplOQAH/fqdd33HHHee30rLETJ050cZgKoY8BPvroo9jt0/0wTHls06ZN1mVhiTf9PlF68AwxEREREaUaD4iJiIiIKNUqzfi6rjKhUwQAYObMmVmXLV261Gunh4jCq0j1sI0eBg2vdNXDsTo9IRwu1o/TzxsO9S5cuDDr9obbt3z5cheHV9jr7WXKBJUn/d0OZ4UL+2lGx44dvds6ZUJfjQ74/fKTTz6JXbce/tT9IUxxClOlMnRqRvhcukqMTq0C/KHqcHh25cqVWZ+LqCQ+//xzTJ48GQBw7rnnesu++tWvulinCobVilq2bJm1Xf/+/b12Ou0g/B7rPqD7a9g39O9B06ZNXazTkQC/0pLuawUFBV47vd/TaRvh746eITJcpvvr4sWLXazTLim9eIaYiIiIiFKNB8RERERElGo8ICYiIiKiVKs0Cad6VpmioiJvmZ797dNPP3XxpEmTvHbdu3d3cTgjli4Zo8vFhDPdrFu3zsU6Tzgs76RLOunyMeEsezp3SZeSGTx4sNdO5ziGeZCbNm1ysS4rQ1TW9HdP9w3Az4PXue1hOaV3333Xxb169fKW6Vxh3cfCXHndr3SeYJizH7cdYZ/SZZh0aakwT1g/l55lK1tboiOxatUq3HLLLQCAXbt2ecteeOEFF+tc3jA3Vn/n9fUw+jsO+KXRwn2l7pPTp093sc67Dx+nfwu+9KUvee169+7tYr2fD3OSdQk5/brC16j3vWH5NJ03rPOVw9JtlE48Q0xEREREqcYDYiIiIiJKtUqTMqGHOsJhEJ0modMYwnIxOmVCDxcBfgkaPUtcWP5Ml3TSQzrhkIsu96SHlcOZg/Rz6dm21q9f77XTrzmcgU+XpGPKBJUnPQTbrl07b5n+Puth1rBE4bJly1wcph3ooWH9ON0PAb+/6WHX1atXe+30MKlOnwj7pe5TujRiOIOkfl1hikRY8oroSHTp0gVvvvkmAGDGjBnesmeffdbFetm8efO8dvq7rPuJvh8A5s6d62KdggD4M8bpPhnO1KhLnumUvzBtUKc26v6vS40C/n5Ppzjq0oyA/1sQlk3V5d/0Pjs8VtAz3tLBbr755thlI0aMKMctKV08Q0xEREREqcYDYiIiIiJKtUqTMqFTHMLhjC5durhYD7+EqRV6iDQcctUz7ugZccJhUD08o69E10OsgD+UrIecwlQIvU16Bq/wClt9JW04NB0+N1F50WkBa9eu9ZbpvqO/v2GfSuqXOiVBz7IVpl3o1Ag9FJw0A9XevXtdHPZLXWlGD7uGvyl6e/XzAgdXqCE6UpnvWJ8+fbz7w9sZYcqATmOaM2eOi8NZXXWaX5gOqNOLdBWXcLZHvT9r0KCBi8M0RJ3+oJ/rrbfe8to98MADLm7evDnixM1GCfgpE7qPh/2a0olniImIiIgo1XhATERERESpxgNiIiIiIkq1SpNDrHOawjIrOp9w8uTJLtZ5wYCf0zRt2jRvWefOnV1cUFDgYp3DCAAff/yxi/XMWXrdgJ8nqXOS16xZ47XTucK6rEw4c1CnTp2ytgOAFStWuHjQoEEgKi86hzDM7V+0aJGL27dv7+Jw5iudQxyWNdPfdZ2zr0s/AX7+rl5HOKNX3Exd4Yx2eh061zCcJVOXfNTtgIPLSxGVt3AGVX1bz0aXj4YMGZJ4m6i08QwxEREREaUaD4iJiIiIKNUqTcqEHmYNZ8TRQ646DtMddOpCOEQaNzNPWEpJl4XRqRuzZs3y2h04cMDFuvySLj8D+MPAepg2bKeXhaWkwmFhovIyc+ZMF4el0HR6gp4h7qOPPvLa6RQl/RggvmxaWJZQ9wGdChGmYOj+q8s/hc+rf2N0SbcwDUs/V5gyoUs8ERFRfuMZYiIiIiJKNR4QExEREVGq8YCYiIiIiFKt0uQQ69xgneML+Dl+Oq/3vPPO89rpPOFQ3PSR+nkBf6paLcwXjMs1XrBggddOTzvdt29fF7/55pteuzPPPNPFYV5zWIaOqLzo717YV3Qf0GXXdN4tAMyfP9/FenpmwM/z1VMtL1++PLadjsO+oUvD6dz+cMrohQsXurhNmzZZHxMKc/l3794d25aIiPJLpTkgJiIiCt18882xy0aMGFGOW0JElRlTJoiIiIgo1SrNGeK6deu6OCylpMupHXPMMS6+9tprY9enZ5gCgK1bt7p48+bNLq5Xr57XbvHixS7WQ79hmTQ9bNuqVSsX6+FX4OAyThkTJ070butUjXCbwpnriMqLTiHQKRKAn9qjUxXuvfder92oUaNcrGd4BPy0A11GMUxP0H0iqfSi3iadehWWcYvz9a9/3butX3+4jrj0KiIiyj88Q0xEREREqcYDYiIiIiJKtUqTMqGvKtezwAHAihUrXKyvRE/Srl27xNtx+vTpk1O7IxWmheiZ+kLhMDNReZkxY4aLwwoRehbGDRs25LS+MO0g11SG8hKmPK1bt87FejY+wE/lIiKi/MYzxERERESUapXmDDGlE0sqERERUVnjGWIiIiIiSrVKc4b4K1/5iotXrlzpLdP5tldeeWXsOowxscvCvOSMsJSUpks4heWd4tolrU879thjvdtr1651cTjTl57Fjqg8/fGPf3TxqlWrvGWzZ892cdKZ/qTZ3/SypP6bq7DvZISzP+rbOr7//vu9dieccIKL69ev7y0rr+sNiIgqo3wbAa40B8RE+SDfOjAREREdOR4QExHRYeEfhkRU1UhpDEMe0QaIrAew4pANqbR1MMYUltbK+DlWOH6eVQM/x6qDn2XVwM+xaon9PCv8gJiIiIiIqCKxygQRERERpRoPiImIiIgo1fL6gFgELUXwTxF8LIJpInhFBF1KsJ7GIrg2YfmPRTBXBPNEcIO6/7ciWCiC2SJ4QQSN7f2n2PumiqCzeo7XReLfUxE8J4KONq4vgofUa5sgghMP97XZdf1SxTVF8LYIL5ikslGO/fIntk/OFcEzIqht7xcR/FoEi0SwQATX2/u/Ztu/I4Jm9r5OIvhXwnOICP4rgoZl+dpEUCiCVw93PVT5iOBCERgRdM2x/XIRFGS5f8dhPu9htU9YzxUiaJ2w/Dq7X5wngt/Y+2qI4DERzLF98hZ7f6EI3rV9+EK1jhcP8Rw3iOBydfsoEawXQU5XbIrgVyL4eZb7R4lgWC7rKEn7hPX0EsFX1O2hIrj7SNdLpStvD4hFIABeADDBGHQyBn0B3AKgRQlW1xjIvuMVQXcAVwEYAKAngKEiOMYuHg+guzHoAWCRfX4A+BmArwC4AcDV9r7bANxrDLIWVRXB8QCqG4Ol9q6/A9gEoLN9bVcCB/8o5sgdEBuDvQDeBPCNEq6LKFY59ss2AK4H0M8YdAdQHcCldvEVANoB6GoMugH4p73/OgD9ATwE4Jv2vv9B1DfjfAXALGOwrSxfmzFYD2C1CE4pwbqocrkMwLv2/8roCiD7waoIhgC4AEBPY3A8gN/ZRZcAqGUMTgDQF8APRFCE6D34G6L96w12HecBmGEMPot5jqMAfBfA0+rusxDtgy+x/bSy6QUUHxADeBnAeSKoW0HbQ1nk7QExgCEA9hmDv2XuMAazjME79qzOb+1fnXNEooM/e9b1TRFMt/dfYB86AkAnEcwUwW+D5+kGYLIx2GkM9gOYCOBi+3yv2/sAYBKAtjbeB6Cu/bdPBJ0AtDMGExJez7cAvGi3sxOAEwHcljmANgbLjMHLdvlP7WubK/4Z6zH2rNU8EXzf3jcCQB372p6yTcfY5yMqbeXVL4GoLGQdu4OsC7gd6DUA7lZ9Z529/wsAtVDcLwcBWGMMFie8Htcvy+G1sV9WcSKoD2AggO+h+A84iOA0iUYBn7NnV58KD+xEUEcE40RwVZb13iiCDyUambwr4fn/YPcPb4qg0N7XSwSTpHiks0nc/fZsaD8AT9nvbp3gKa4BMMIY7AG8vmcA1LN9tQ6AvQC2oXhfWQvAAbv8BiA6sxzjdADT1b4XiA6s/wTgEwAnq9e7XAR3qf530Fl5EVxl39c6wf19RTDR7lNfE0GrmO05U6LR4EUiGGofW1sEj9rnnGH/UMh6vwhqArgbwDfse/oNY2AATACi9VGeMMbk5T/AXA+YP8Qs+xpgxgOmOmBaAOYTwLQCzFGAaWjbFABmCWAEMEWAmRuzrm6AWQSYZoCpC5gPAPPnLO3+A5jhNu4FmEmAeQswbQHzT8B0PsTrmQiYE2x8PmBeiGnXFzBzAFMPMPUBMw8wve2ypvb/OoCZC5hm9vaOYB3VAbO+oj9D/qt6/8qrX9q2PwbMDsCsB8xT6v6NgLkVMFMBMy7T9wBzFmCm2b7aCDCvZ/pMwnOsAEyD8nhtgGkDmDkV/RnyX9n9A8y3APOIjd8HTF8bnwaYrXZ/Uc3uZwbaZcvt9+UNwFyu1rXD/n82YB6236tqgHkJMIOzPLcBzLdsfAdg/mLj2YA51cZ3A+aPh7h/AmD6xby+mYC5CzCT7T6tv72/ht0PrgfM54D5vr2/EWBetn31DNvHrjjEe3gXYK5Tt2sD5jO73/s+1P7ZvnfX2fhawPzdxr8CzM8B8yPAvAiYWvb+UYAZZrf3fcAU2vu/AZh/ZNmWUYB51b7vnQGz0m7PzzLtAdPV/h4k3X9F5vMIvisHHWvwX8X9y+czxEkGAnjGGBwwBmsRndXtD0AA3CuC2QDeANAGhxjuNAYLANwP4HUArwKYCcCbx1kEtwLYD0RnYI3BTGNwkjEYAqAjgNWIZnj9lwieFMn6nK0ArM/xtb1gDD43BjsAjAYwyC67XgSzEJ2tbgdE+ctZXtMBAHtF0CCH5yMqLaXWL+1ZrAsAHI1o+LaeCIbbxbUA7DYG/QCMBPAPADAG441BX2Nwnn3sKwC62LNyI2OGJ5sag+3l9NrWIWYomqqMy1CcwvNP+GkTU4zBShONbMwEUKSWvQjgUWPweJZ1nm3/zQAwHUBXZP/t/wJw+fJPAhgogkYAGhuDifb+xwAMjrs/h9d3FICmAE4CcCOAf9sz3QMQ7TdbI+qzPxNBR2Ow1Rh81fbV6QDOA1x/fE6k+GyvEu4rhwJ4yxjsAvA8gAtFUF0tH23/nwb/Pb0cwLkAhhl7Rls5FkB3AONFMBNRWlVbZPdvY/CFiUaaliJ6/wcieo9hDBYiqivcJeH+bPh7kGfy+cKrecBhJ7N/C0AhgL7GYJ8IlgPRhThJjMEjAB4BABHcC2BlZpkIrkDUIc8wBl7RZvtDcBuiobE/A7gJUYe8HsCtwdPsUtsyD0BPEVS3B6+HJILTAJwJ4GRjsFMEEw7x2moB2J3LuokOQ3n1yzMBLDNR7i1EMBrAlxDtbFaieCf4AoBH9QPtge8VAL4M4CVEKVDD7HaMDJ5nvwiq2YOUsn5ttRH9DlAVJIKmiIb7TxCBQZT3bkRwo22iD8oOwN//vgfgHBE8He5nEP3RdZ8xeOgwN6ksJhlYCWC03cYpIvgC0bUv3wTwqjHYB2CdCN5DlHqxVD32dgC/RnGO9XOI+vGXg+fQ+0rY9gNt3wKAZoje5/H2duZ9Dd/TOYhyd9sCWBY8hwCYZ0zWA/JQ+D6W1vvK34M8k89niP8LoJbYXFkAEEEPmxf4DqJ8nOo2T2owgCkAGgFYZ3dMQwB0sA/dDsSfLRVBc/t/e0Q7z6ft7XMQHeSebwx2Znno5QBeMQabEOVJfWH/ZTsTtQCILtYzBh8DmArgrkwemQiKRPBV+9ouFEFdEdQDcJG9rxGAzfZguCuiv9Az9omghno9zQBssD9ORKWpvPrlJwBOsv1AAJyBqA8BUS7uEBufiuhiG+1GAA/Y738dRDuwuH75EaJRnvJ4bV0AzI15vVT5DQPwhDHoYAyKjEE7RAdigw7xOAC4A8BmAA9mWfYagO9KlJ8MEbTJ7LMC1VD8B903AbxrDLYC2Gy/wwDwbQAT4+63cVK/dH1PouorNQFsQNRfT7f310O0f1qYeZBE1Zjamug6m8y+0gAH5SgDal8pUfWXQQDa2/e0CMAPkdsFizMA/ADAWDm4osVHANEjAdsAACAASURBVAozZ6glqpJxfMx6LhFBNYmu/eloH/sO7PUA9n1of4j7+XtQGVR0zkbSP8C0Bsy/AfMxolzal20ejwDmt4jyaOcA5hu2fYHNzZoDmEcBswAwRXbZ07b9b7M8zzuAmQ+YWYA5Q92/BDCf2rypmYD5m1pWF1EOcQ17e5B93mmAOTbLc3wbMP+jbjcEzEj72ubavK1MPtZP7X1zAXODva8WonzJBYAZY9ufZpfdb+9/yt4eBpj/rejPj/+q5r9y7Jd3AWahXf6EygNsbJ9zjl1vz2DbXla3L7Hb+B5svmDwHLcD5v+Vx2tDlNN43eG+3/xXOf7Z/cE5wX3XA+aviHKIX1L3/wU2lxZRHmyB/Y49Cpjf2Pt3qPY/tt+xzHe+U5bn3wGY39vv3H9RnB+bueZltt13NDnE/V8DzEd2n1cneI6agHnSPsd0wJxu768PmGdtn5kPmBuDx/0bxbn+zRHl784DzNeyvI4OgHnbxt8BzD+D5U0R5SrXyrx39v5+gJlg418B5uc2/jJgZtj3eBRghqnX/zai/f48wFyVZVtGAeZviHKgFwFmqL2/tv2s5th1DznE/U0B86F9TzO/HS/BXlfEf/nxj1M3lxN7hetbAE4xOaZJHMFzjQZwszEHnTkjIsVeWf64MTirHJ7rbQAXGIPNZf1cRJWZCF4AcJNJrhBTadnrjJ42BmdU9LZQsXxOmahSTHRBwJ2ILropM7bEyxgeDBMdmjFYDWCkHZotMzbN4vc8GCbKyc1AbBm0qqA9ovkMKI/wDDERERERpRrPEBMRERFRqvGAmIiIiIhSjQfERERERJRqPCAmIiIiolSr8JnqCgoKTFFRUUVvRupMmzZtgzGmsLTWx8+xYvHzrBr4OfpmzQL278++7KijgJ49y3d7DkdV+Sz37fPnd1q2rHjSty+++MLF+4MPSi+rVq1a1hgAqlcvnoX5qKOKD0k6depUwi0uXfnwOSb1AyD/+0I+Sfo8K/yAuKioCFOnTq3ozUgdEVlRmuvj51ix+HlWDfwcfSLxy/bvB/L5pVWVz/Kzzz7zbg8fPtzFu3YVzzy8YcMGr93u3btdXK9ePRfXretPGNmgQfEEbs2aNXPx6NGjkQ/y4XNM6gdA/veFfJL0eVb4ATERERHlpyeeeMK7PXPmTBe3aVNcVl8f2AL+GV59tnjBggVeu4KCAhcvXOhme8aUKVO8dgMGDDiczSY6bMwhJiIiIqJU4wExEREREaUaD4iJiIiIKNWYQ0xERERZbd682bvdpUsXFxtjXLx3716v3ZYtW7LGOmcY8CtL6Jzk8GI+orLGM8RERERElGo8ICYiIiKiVGPKBBEREWW1ceNG77auL9ywYcPYx+kaxbVr184aA35KxqZNm1w8e/Zsr92FF16Y4xYTlQzPEBMRERFRqvGAmIiIiIhSjSkTREQ5Cq+k17NpPfTQQ96yr371qy6+9NJLy3bDiMrInj17vNu6ssT+/fuzxiFdZaJaNf88XOPGjbPGenY7ovLAM8RERERElGo8ICYiIiKiVOMBMRERERGlGnOIy8jy5ctdPG7cOBdfc801OT0+zFXUs/mIiLcsvE1Eh0eXiAKAp556ysXPPvusi3W/BoCioiIXL1q0KHb9zCGmyirMDd63b5+LdQm1sA/pHGDd7sCBA7Hr0yXdtm/fXsItJioZniEmIiIiolTjATERERERpRpTJg6THgbS5WPCYaXBgwe7+NNPP3Xxyy+/7LV76aWXsj5PzZo1S3X7iKqypUuXulgPydavX99rN3HiRBc/+OCDLg5TlPTtVq1aufiUU06J3YY6dep4t3W5qnXr1rm4efPmXrtwCJkon4Qzy+myazrdIdzf6BnudMpfvXr1YtenNWnS5PA3lugI8IiJiIiIiFKNB8RERERElGpMmSgjeiipQ4cOLtYz9gBAt27dXDxkyBAXX3zxxV47PVQbDs1qTJOgqkSnQvz2t791cZh2EGf69Onebd3/CgsLXRwOC2vNmjVzcThrV/Xq1V3ctGlTb9mFF16Y0/bqdRDlm5YtW3q3a9Wq5eI2bdq4eP78+V67M88808ULFixw8bZt27x2OiVJV6ro1KlTCbeYqGR49EREREREqcYDYiIiIiJKNR4QExEREVGqMYe4lOiyMgDQoEEDF+vyM2EZqPfee8/Fq1evdvEzzzzjtdO5j+3bt/eW6dzjyy+/3MWnn356TttOlK90/u53v/tdF4c5uf/5z39cvGbNGhc3btzYa6fzEnUu/o4dO7x2us8m9V9N5wwDwPnnnx/blqiyCHPj9cyoetn69eu9dmeffbaLdWm1sNSovj5Gr0Pn+BOVB54hJiIiIqJU4wExEREREaUaUybKyMcff+ziFi1auLhRo0ZeO31bDwOH5Z300G84s8/YsWNdPGfOHBdPmzbtcDebKK/o/tG/f//Ydj/60Y9crEs3vfrqq167cePGuXj58uUu1ilOgF+GTZeZ0mlNAHD11Ve7+JxzzondPqLKKiy7pkuKauFsrbqcmp6NUc+mCvizQuq4Xbt2h7+xREeAZ4iJiIiIKNV4QExEREREqcYDYiIiIiJKNeYQHyZdciaJzpPSOYgbNmzw2ul8YJ2DFeZZ6eldwxJvOkdZ508SVVU6JxHw+4cup3bRRRd57U499VQXP/jggy7WOf+A3/90nwrLuA0cOPBwNhtA8rYT5Zt+/fp5t/V+SveNMIe4Q4cOLtblCnWeMOBP5VyzZk0Xd+zYsYRbTFQyPENMRERERKnGA2IiIiIiSjWmTBymXFMmdNkmPYS7cuXK2HZ6yCksraaHcJO2YfPmzTltH1FllpRmoFMSwnZ6Zi1dJu3xxx/32q1duzbrutu2bevdTpq5Lmk7iCqLY4891ruty67pGR7DdL3WrVu7WJc13L17t9dO7+vq1avn4ho1apRwi4lKhmeIiYiIiCjVeEBMRERERKnGlIlDCFMX4tIVpkyZ4t3Ww0LdunVz8QcffOC10ykTOi0iHC7SM9fpK3EBP9UinOGOylfLlkDMaDsAoEULYM2a8tuefBFWVtBKO50g11QFPfPdxIkTvWVxaU5169bNeTuYJkFVQbjPa9asmYv1fi5sp/tKmzZtYttpYRUXovLEM8REpSjpYDiX5URERFT+eEBMRERERKnGA2IiIiIiSjXmEFs6V1jnOCXlO+lZsMaMGeMtO+WUU1ys83r1rDwA0LBhw6zrDp9X5yOGy/TMdbrs2pYtW7x2zM+iwxGX91uS3NjyzKctyXOF5dRatmzpYl1Oqnnz5iXfMCvcvqT8aqJ8o2eQW7dunYvDGVS1Tp06ubh9+/beMv3979KlS2lsIlGJ8AwxEREREaUaD4iJiIiIKNVSlTIRlxaR7XbGc889592+7LLLXKzLnV199dVeOz3T1VtvveXiwsJCr52e9UcPHSVtX1gKLm6IeNq0ad7tM844I2s7omwqa9mwpO3W6Q+6tFqtWrW8dosXL3axLoE4c+bMnLdjxYoVLp41a5aLx40b57VjKhNVJi1atHCxTplISv0JU5I0vR8tjZQkopLiGWIiIiIiSjUeEBMRERFRqlXKlIkwZUBLqgqRtGzJkiUuPuuss1y8fPlyr52uHjFgwAAXv/nmm1677du3u7hDhw6x27Bz504X62oUeta68HHhOvRQlR7OGjt2rNeOKROUD8Kh1dJIzch1drpwlseMPn36eLdvvvlmF59++ukuXhNMM/jqq6+6ePr06d4yPXulvjK/devWXrtTTz3VxSNGjIjddqJ8ELcvqlev3mE/BvD7pJ7Rjqi88QwxEREREaUaD4iJiIiIKNV4QExEREREqZZXOcRh3qxWrVrxsXtSLnCS559/3sU/+9nPvGW6RFL//v1dPHToUK/dvHnzXPzUU0+5uEmTJl67+vXru1jnBod0SSf9+sPH7N27N3Ydu3fvdrEuKzV+/PjYxxAdSiYvN2lmtXyZtS7Xdca103n+AHDppZe6eNOmTS7WM9gBwC233OLibt26ect69uzp4jBvWBs8eHDCFhPlF72f0vulsHShljSLnd6fN2jQ4Ai3jqjk8uqAmIgi+qKuEC+8IiIiKl1MmSAiIiKiVMurM8Q6LaKk9AxT1157rbds0qRJLtYl0wDg3HPPdfHHH3/sYp1mAfjDQrrEWZjuodMY9BBzWDIubva8sITN1q1bs64vfG49NLVw4UIQlba4tIOkmapynT0ubKdLMiWtP9fniqP7F+CXTdyyZYuLdfk0APj1r3/tYv06AODOO+90sR5a1mUdiSqbHTt2uFh/r5PSHXSqkU4TDNeRlHZBVNZ4hpiIiIiIUo0HxERERESUanmVMjF37lzv9hNPPOHizz//3MWzZs3y2q1bt87FesglvCJcV4xYtWqVt0zPOKWHaVu1auW1279/v4v1EK6+H/BTIfQQbjicq9MddMqEHqYF/GGl8Ipd/dx628P0jHDWPaKSiKsykWuqQpie0KhRo9i2+ntfkvSJJDql6O677/aW1a5d28W33nqri7t27Zrz+nWlGZ1OoWe+I6psdMqE3n8lpUzUrVvXxeFskeG+k6ii8AwxEREREaUaD4iJiIiIKNV4QExEREREqVbhOcRbtmzB6NGjAQDXX3+9t0yXPtI5SGEOrV6mc/X07HOAX04tXIfOf9Ll33bu3Om10zmNOkc3zNfVuVVxM/sAft6lLkejy7YBfs5krjP6hcaNGxe7jCiU+W6G39kwBzBD5xYC/jUBOp/2pptu8totWLDAxZMnT/aWNW/ePHHbDtd///tfF//ud79z8cCBA712v/zlL7M+Ptf3AvB/UzgDF1UVer+cNAur9sknn7g43Kfu27fPxeG1M0TliWeIiYiIiCjVeEBMRERERKlW4SkTderUQY8ePQAAX/7yl71l8+fPd7Euu6aHWABg8+bNLtapEBs3bvTa6WHWsGyTTlHQ5c9KW5haocs76e1LSq0IUyb0sG3SEG44kxZRLpK+U3//+99dHJZD1P1Pl1a7+uqrvXZjxoxxsZ79EQDefPNNF5ekXJmenRLw0yTOOeccF4fpWpr+rQjfi7gSdED025bB0lJUVTRs2NDFS5YscXG4z9J0mkSYMqFTMEqaCkVUGniGmIiIiIhSjQfERERERJRqFZ4yUatWLRxzzDEAgEceeSS2nR7SHD9+vLfs6KOPdrEecunevbvXTg/V6BntAH9IU6ck5Fo9IqQfp1MwdCUJwK8KkZSqoR8XrkMP2+qUkXD4KUw1ITpS4eySmp4pctOmTS4eNWqU106nSg0aNMhb9oc//MHFvXr1cnHTpk29droP6NSNO++802v3rW99y8WXX3557LZrJR3GLSoqcrFO+SKqzPQ+S+9Tkioc6dTAcD+n973hzLBE5YlniImIiIgo1XhATERERESpxgNiIiIiIkq1Cs8h1rZu3erd1qWaHnjgARf37t3baxeWVoqj84Z1ThPg5/zqZWFeVJhTfKj7AT/XOMzjzXV9Os8qLBmny93o7Q1znNu0aRO7jUTatm3bXK6+nt0N8Mup6b4XzloV5vlm6Jx/AHjppZdcHH63+/Tp42Jdgu2EE07w2r3++usu1nnDf/7zn712w4cPz7pNSZJKq+m+p8usAcwbpqpJlyjV3/927drFPkbnBoezxOp9XdzMlETlgWeIiYiIiCjVeEBMRERERKmWVykTOkUC8Idj9AxRM2bM8NrpIVxd3mnbtm1eu9WrV7tYz44D+MOiOg7TDvSwcFKaRNyypNI0Wvi89evXj12HHqotLCx0cThkrcvQffvb385pOyidGjZsGDsznJ7xsFOnTi7+6KOPvHbr1693sS6FtmzZMq+d7qfhTHUTJkxw8cknn5w1BoB169Zl3Y7SGIJNKrsWpnhoq1atcjFniaSqQpcTfPvtt13csWPH2Mc0a9bMxXXr1vWW6f4Vl2ZFVB54hpiIiIiIUo0HxERERESUajwgJiIiIqJUy6sc4pDOG06i85N0TEQll8ntO+uss2LbnH/++eW1OXlJ5/aH7rvvvnLcEqLyoUse6mtdwvz/OGEOsb4mhvtvqkg8Q0xEREREqcYDYiIiIiJKtbxOmSAiIqL80blzZxfHzfCaJGyn0y5E5Ai3jqjkeIaYiIiIiFKNB8RERERElGpMmSAiIqKchFUiMpJmbdRq1Kjh3eYsjpQveIaYiIiIiFKNB8RERERElGo8ICYiIiKiVGMOMREREeWkUaNGLq5Tp46LmzRpktPjmzZt6t1eu3Zt6WwY0RHiGWIiIiIiSjUeEBMRERFRqomeaaZCNkBkPYAVFboR6dTBGFNYWivj51jh+HlWDfwcqw5+llUDP8eqJfbzrPADYiIiIiKiisSUCSIiIiJKNR4QExEREVGq8YCYiIiIiFKtzA6IRXCrCOaJYLYIZorgxFJe/2kieKkU17XVbudMEdyhlv3Evo65InhGBLXt/U/Z13avanubCC5MeJ7eInjExi1E8JIIZolgvgheKY3XEjzfKBEMO4LHvyGC3IpLUqVSVv1TBBNE0K8kbUTwIxEsEYERQYG6X0TwgF02WwR91LLviGCx/fcde18tEbxq++y1qu3D+rFZnv/CTN8XwbF2O2eKYIEIHj68dyL2OQ75u6XbiGCoCO4ujeem/CSCZmrfs0YEq9TtmnmwfZfY34ovwn4rgltsv/xIBF9W959j71sigpvV/SXeb4rgSvW+7BXBHBuPKO3XnCsRFIrg1Yp6fipdZTIxhwhOBjAUQB9jsMfu3Cq8Y2eI4ChjsD+4+x1jMDRo1wbA9QCOMwa7RPBvAJeKYDqAXcaghwjGi6ARgLoATjQG/5Pw1L8E3PK7AYw3Bn+yz9WjFF5aqRCBABAATwC4FsCvK3aLqDTlcf98D8BLACYE958LoLP9dyKAvwI4UQRNAdwJoB8AA2CaCMYCGATgXQD32nX+nwh6AqhuDKYnPP9NAM638QMA/mAMXgQAEZxwxK+uZF4GcI8IRhiDnRW0DVSGjMFGAL0AQAS/ArDDGPwuszxmf1VmRFDdGBxQd80FcDGAh4J2xwG4FMDxAFoDeEMEXeziBwGcBWAlgA9tvzwKR7DfNAazADxqn3s5gCHGYMMhtr3M2M9lvQhWi+AUY/BeeTwvlZ2yOkPcCsAGY7AHAIzBBmPwGRB9kUVwlwim27/wutr764ngHyKYIoIZIrjA3l8kgnds++ki+FL4ZCLobx/TSQR9RTBRBNNE8JoIWtk2E0TwRxFMBfDjw3gtRwGoI4KjEHXezwDss/dVA1ADwAFEB7h3xq1EBA0A9LCdOvMercwsNwazbbvT7LY+J4KF9i9qscviXttVIvhQorPNz4ugbpbnv0eiM8bVRXCjbT9bBHep9/kjETyO6AewHYCxAC47jPeKKoek/nmH/W7MleiMaua7N0EE99v+uUgEg+z9dUTwT4nOor4AwE1dJYK/imCqRGeX7jrURhmDGcZgeZZFFwB43BgYYzAJQGP73f8yoj8qNxmDzQDGAzgHUf+si6hvil3HPQBuj3tuuyPfo3awYf+cY9tl/T06RL89x943HdGBReY5B4jgA/vb9b4Ijs3ynhhEfyAMDZdR1WV/q/8mgskAfiOCXiKYZH+zXxA7cidqtEUEBfZAESI43vbVmfYxne39w9X9D4mgur1/hwj+VwSzAJyst8UYLDAGH2XZzAsA/NMY7DEGywAsATDA/ltiDJYag70A/mnblsZ+M1sbb9tF8FP7+zVXBDfYNkUimKse83OJ/viACK6XaJR2tgj+ae+LOx65QgRjRfBfAG/a1Y0B8K247aPKo6wOiF8H0M7uOP9PBKcGyzcYgz6IzvT83N53K4D/GoMBAIYA+K0I6gFYB+As2/4biM7cOHaH9DdEHe4TAH8GMMwY9AXwD/hnN2sag37G4H+zbPPJ9oBynAiOBwBjsArA7+x6VwPYagxeNwYLAKwHMB3AfwAcA6DaIc4+9QOKOySiv6AfEcFbEg1ft1bLegO4AcBxADoCOEUENRJe22hj0N8Y9ASwAMD3gvfotwAKAVwJ4AxEZ9oGIDor0VcEg23TzgD+zxgcbwxW2IOMWiJolvC6qPJJ6p9/sd+l7ogObvWB2FG2f96A4p3YNQB2GoNu9r6+qv2txqAfgB4ATpWSj4K0AfCpur3S3hd3/3gARQAmAXhABOcDmJ456I9xCuD13z8A+K/9PfiJCBrb+5N+j7L129oARgI4D9F701K1XwhgkDHoDeAOoHgYOTAV0VlvSpe2AL5kDH4K4HEAvzAGPQDMQcJBpHU1gD8Zg16I9j0rRdAN0Xf2FHv/ARQfyNUDMNkY9DQG7+a4fYfVL0tpv5mN23YAuxDt504EcBKAq0TQ+xCPvxlAb/veXm3vizseAYA+iPbDmd9N9s8qokxSJozBDhH0RfQlGQLgXyK42RiMsk1G2/+nofiMydkAzhdxB8i1AbRHdEb2LyKuA2eGZACgG4CHAZxtDD4TQXcA3QGMl+i8UHVEB7IZ/4rZ5OkAOtjt/gqiv/g627/CLwBwNIAtAJ4VwXBj8KQx0V+eACCC/wD4gQhuBdAT0VmrkcFztEL0Y5B5j14TQUdEZ7TOBTDDbj8ATDEmOjslgpmIdu5bEl5bdxH8D4DGAOoDeE097+2Ifiy+b9d3NqL3eoZdXh/RgfAnAFbYM3DaOkTDYRtj3juqZA7RP4eI4CZEZ1ibApiHaOcF+P22yMaDYQ8KjcFskWikw/q6CL6P6HemFaIDRb28TNjh5W8CgP1D8jUAF4jg94h+Ux43BmODh4X981ERvIaof16AqH/3RHRmK+73KFu/3QFgmTFYbO9/Eoj6IoBGAB6zZ++MXXc2mT5I6fKsMTggUWpBY2Mw0d7/GIBnD/HYDwDcKoK2iE6YLBbBGYj+KPvQ7kPqIPpuAdF3+fnSfgGhI91vxtDbPhDAC8bgc/scoxH9zoX9XZsN4CkRjEG07wfij0dgt3OTejz7ZxVRJgfEAGDzeCYAmCCCOQC+A7gD4j32/wNqGwTA18KhGTussRZRh6kGYLdavBrRF7U3ogNnATDPGH/IR/k8Zlu3qfgVe9asANHBwjJjog5pO9eXADyptu8CRAcI9QF0MgZflyid4akg52+X3Vb9vJsAPA3gaYkuohmM6MBzj2qWeY+SXtsoABcag1kiuALAaWrZh4jOAje1zycA7jPmoHywopj3p7bddqpCsvVPO1z4fwD6GYNPbd/T39ls/TYrERyNaPSnvzHYLIJRwboOxypEKTwZbe19q+B/19vi4PzjaxGdXTsJwFZEZ8j+i4N3kLsQHaA69ozyPwD8ww63dkd0pjfu9yhbv01yD4C3jMFFtv+F257BPphOWfdXgf0oHul1/csYPC1RusVXAbwigh8g+u1/zBjckmU9u0uQexvXL5FwP4Aj22+WcNv1+4RgnV9FtO89D9EfEScg/njkRBz8ubB/VhFlkjIh0RXandVdvXDoqQpfA3CdFOfdZYY5GgFYbQy+APBtRGdGM7Yg+jLfJ4LTAHwEoFCii4YgghqZ9IdDbG9L9bwDEL0vGxGdNT1JBHXt8jMQpSRkHlcD0RDpbxD9tZ2Z9q86Dr5IaQGiIaLMY08Xm+srUZ5UJ/t8cZJeWwMAq+32hLlMrwIYAeBl+zyvAfiuCOrb9bQRQfOY90UQDfEuT9guqmQS+mdmJ7HBfj9yqVDyNorPxnYHXFpEQ0Q7jq0iaIFoFKSkxgK4XKJqEychSl1ajei7fLYImtjRnLOhRkfsfUMRHRDXBfAFoj5aJ3wCHNw/z7H9CSJoCaAZop160u9RNgsBFImgk72tc/IbofhA4YqEdXTBoYeNqYoyBlsBbBZxw/LfBtzZ4uUoTlNy/dWOPi41Bg8AeBFRv3wTwLDM770ImoqgwxFs2lhEF5nXsn8AdwYwBdFJmM4iOFqiKhmXQv0BeiT7zRy8A+BCu8+uB+Aie99aAM0lquhRCzYVTKJ85nbG4C0Av0DUJzOjrNmOR7Jh/6wiyiqHuD6iocD5dgj1OCBKYE9wD6Ihw9kimGdvA9EZq+9IlDDfFcFfZ8ZgLaIv94OIzhQPA3C/bT8TOPgivCyGAZhrH/MAgEvtBTyTATyHKKViDqL3S5df+iGiv7h3Ihp2qWvPtk0zBluC7VwIoJE9KAWiH7Gp9v35AMDfjcGHcRtoL06Ie223A5iM6Ir6hVke+yyiPMaxiH4cngbwgd3W5wC3TaG+ACaZcrzCmcpF1v5pv7MjEf24vwbEfx+VvwKoL4IFiC6QmQYA9iKYGYi+j08Dh74CW6KLW1YiOqM0WwR/t4teAbAU0UU7IxGd9c2MsNxjt/NDAHcHQ5l3APi1PXh9DdHQ6RxE1VNCbwPondkBIjq4zvwmvAbgRmOwBof4PQoZg92IUiReluiiunVq8W8Q/TE/A8lnk4cgqjZB6fUdRHmssxH9AZspxfc7ANfY71CBav91RN/fmYhGNh43BvMB3Abgdbue8YhSEhKJ4CLbL09G9D1+DQCMwTwA/wYwH9GJlx8agwN2f/EjRP1mAYB/27YZR7LfTGTzkUchOjCfjGi/OsMY7EP0nk2xrzuzn6wO4En7/DMAPGC3Ie54JBv2zypCjDGHbkWlQgQ/AbDdGLejz2si+BOAsca4q2mJqiz7ff+PMXijorclw55df9oYnFHR20JUEfJ9vymCtwFcYC9Cp0qMM9WVr7/CzzPMd3N5MEwpci9wcMnCCtYewM8qeiOIKlDe7jdFUAjg9zwYrhp4hpiIiIiIUo1niImIiIgo1XhATERERESpxgNiIiIiIkq1MpuYI1cFBQWmqKio1Nanc6JFJKFlvAMHimt8b9zoT9BWvXpx2dGjjip++8Jc7N27i+v1FxQUZH1MRZo2bdoGY0xhaa0v/BxnzQL2xxRrO+oooGfP0npmAsr+86Tywc+x6uBnWTXwcyw7FXGc43p9mwAAIABJREFUkPR5VvjRWVFREaZOnXpE69AHo/v27XNxzZphje/cbN261cVPPOGXLG3UqHgyK32gq58XAObNKy67+IMf/MDFTZs2LdE2ffHFFy4OD/RLcuAvIoeaKOWwhJ9j0ibt3w8c4UdOgbL+PKl88HP0tWwJrF2bfVmLFsCaNeW7PYeDn2XVwM+x7FTEcULS58mUCSIiyktxB8OHWkZEdLgq/AxxSeiUBsBPY0g6K6zTGJ577jlvmT4T/N57xZNq1a3rlyXdubN4mvUBAwa4eNGiRV67TZuKJ8waMWKEi7/+9a977S67rHgm19NPPz1226tVi//bRZ89TmpHRERERAfj0RMRERERpRoPiImIiIgo1XhATERERESpVilziHXOcOj555938d/+9jdvmc7r3bZtm7dM59527tw5dv16HTpveO/evV67rl27uliXWnvnnXe8dvp2kyZNXBzmQl911VUuHj58eOy2M5+Y6MjpqjE1atSIbbd8+XIXs5QSEVHlxSMmIiIiIko1HhATERERUarldcpErsP/vXr1cvG6detcXK9ePa9d/fr1s8YhPVwazizXrFkzF+/atcvFtWrVit12XSYuLOOmUyN0Wbjt27d77W6//XYXP/vss96yF154wcVMkyDKje6je/bs8ZbVqVPHxStWFNdxHzp0qNdO99NRo0Z5y0477bSsz5tUNpKIiCoGj56IiIiIKNV4QExEREREqZbXKRNxw/933HGHd3utmsOzffv2Lg4rP4RDlZoettTDpzp9AgAKCwtd3Lx589ht3bFjh4tXrlzp4jBlQm+TXkfYrmHDhi6ePXu2t+wHP/iBi0eOHAkiOjTd33SKBADMnTvXxXoGyfA3RKdUXXHFFd6yxYsXu1hXqhCRkm0wERGVGZ4hJiIiIqJU4wExEREREaUaD4iJiIiIKNXyOoc4zujRo73bjRs3drGegS7MC9S5e7rkUvi4Pn36uLhly5Zeu2XLlrlY5wWGJd60vn37uljnEwPAxo0bXdygQQMXG2O8dvq2zicGgKlTp7p4//79Lg5LxhGljc7lB+LLLf7qV7/ybj/66KMurl27dk7r/8tf/uIt078P7JdERPmNZ4iJiIiIKNV4QExEREREqVZpxu7uueceF2/dutVbpmeP27Rpk4vDoU6dMvH55597y84//3wX69JKY8eO9dqdeOKJLtZpEroEGwBMmzbNxS1atHDxGWec4bWbOHGii/XratKkiddOz2KnZ7cLH/f73//exTfddBOIqqIwpSguJSFpRsrrrrvOxQ8++KC3rEOHDi5u166di+fNm+e1GzJkiIsvueSS2OfS2xRuO8uwERFVPJ4hJiIiIqJU4wExEREREaVapUmZePjhh10cpkLoGel09YgwLULP/hauQ1d7mDBhgovD4cxPP/3UxTp1IZzBSs9oN2vWLBc3atTIa9elSxcXv//++7Hbp6tThMPAuu0//vEPFzNlgqoS3bfDmSF1RQftnXfe8W5/4xvfcHHHjh1drPshAKxZs8bFup+fcsopXrvnn38+dnv1b4KeCZMpEkTFrrrqKhfr/ea9994b+xiddpTUn8L9su6HRCGeISYiIiKiVOMBMRERERGlGg+IiYiIiCjV8jqHeNeuXS7WebNhfm1cSbItW7Z47fTjunbt6i1bvHixi88880wX65JLALB9+3YXt2rVysVhrmK/fv1cPHDgQBfrPGHAz3/q0aOHi6dMmeK10zmTYf6kfl06R2rhwoVeu/A1E+WDXPMBw++9tnr1ahffcccdLn7iiSdiH9O2bVsXhznEH330kYsHDRrk4rAMo6ZLvwHxpdb0NQ/AwWUUifLJvn37vNtx+fqhXPv1hg0bXDxmzBgXf+973/PaderUycX6eoIwL1hvb7hM5xTr3xPm9RPAM8RERERElHI8ICYiIiKiVMvrlIm//vWvLtZlkPTMb4A/3KGHKffs2eO108Ms4cxyephFp2ro4RzAL+Wm24XDSHp9y5Yty/oYAOjbt6+LZ8+e7eLevXt77XRKRzg0q1ND9PY999xzXrvbbrsNRPlG99+ktIM5c+a4+IorrvDaLVmyJOu6w98K3S91StGiRYu8ds8++6yLhw0bFrfpnnB4Vvf1OnXquLhWrVo5rY8oH4T7tlxTIeKWhWlMc+fOdXHDhg1dnFQiLWlZrikdGmePJIBniImIiIgo5XhATERERESpxgNiIiIiIkq1vM4hPu+881yspz9ev3691+61115zsc4Fat26tddux44dLp48ebK3TOf7HXfccS7WucuAn+Ooc5TDnCOdk9i5c2cX6xJxgF+GbebMmS7WeccA8OMf/9jF//73v71leopmne/Yv39/EOU73WeTSqvpHPgZM2Z4yzp06OBi3Re3bt3qtdPXEeh8xfA35eijjz7UZh8k/A3QecPan/70J+/20qVLD/u5iCqK7q+59t3rrrvOxXp/DfhlSd977z0X6+sHQknTuI8ePdrFI0aM8Jbpqdt/9rOfuTjsu2FOMaUDzxATERERUarxgJiIiIiIUi2vUyZ0qsFjjz122I8Ph0t0Gbew9JEuXabTJ5o1a+a102kXurxLOLyjZ8RJSq3Qt3UpmZ07d3rt/vWvf2WNiSo73VfCfrRt2zYXT5s2zcXHHHOM10732ZUrV7pYz0YH+P1K9/mwn+uZJks6fPr444+7WP/2TJo0yWuXa1k3ovKSlJKQlBqh3XDDDS4eOXKki7/97W977fr06eNineKk0ycAP90haRuuueYaF4epUHr//cMf/tDF4ey3lE48Q0xEREREqcYDYiIiIiJKtbxOmThSelaqkB4SAoAmTZq4uH379i4OUyt0moQetglnztHr0Mv27t3rtdMzy+l1l2S2HcB/zeHwM2ffoXyUlDLx5JNPulinRTRt2tRrp/vVvffe6+Jbbrkl9nl1H9u+fbu3TFeIGDBggIunTJnitXv00Udd/L3vfc9bpvubnhmzXbt2Xrvbb7/dxeHskkRaJn0n19/yMN0nLv2npGkRjzzyiIt/+ctfest++tOfuviZZ55xcVhpaerUqS7Wffy+++6LbTdkyBAX69QMAKhbt66Lzz77bG+Z/p34xS9+4eKw8gv3lenEM8RERERElGo8ICYiIiKiVOMBMRERERGlWpXIIdZ5szovsHHjxl67pLwondc0b948F+v8xvC2LuESlm3RecP169d3cZi7rEuyHX/88bHbp4W50aWRe0xUUZL65VlnneXihx9+2MVr16712vXo0cPFSXnDmu5H4axy+vZnn33m4k6dOnntNmzY4OJGjRp5ywoLC12sy0mF1xvobSdKksltTcoN1nG4vynJ/uHiiy/2buvv9fz5810cljl95513XKz7rt7nAf6MkS1btnRxONOsLruoZ6MLr/Pp3r27i/U1OoB/jUJ4PQARzxATERERUarxgJiIiIiIUq1KpEzElUjRQzuAPzQbPkYPJekZrJLa6WHVcAhLp1boYdWaNWt67fRwTziUGqekM2cR5aOw1JqmZ6vUfTHslx988IGLTz755Kz3JxkzZox3+6KLLnKxLtUUplDptKww7ULTQ7c/+clPctomolBc2bW4fWC4T9EzNd5///0uHjduXOzjwtJleva3H//4xy5euHCh1+7FF190sU4nCmeF3Lx5s4uXL1/u4pNOOslrp/ejxx13nIvD167XEZY4/OSTT1xcr149EGk8Q0xEREREqcYDYiIiIiJKtSqRMhGXQqBnnwNyn31Hp0WEwzFxV/OG69bDwLqdnkUH8Idg9dBsUloEZ9Ghymb//v0uDq98130nKX1C9wk99AsALVq0cPGkSZNcfOWVV3rt9MxyukrMhRde6LXT6Q833niji6+66iqv3fe//30X66vqAb+6jE6NuuKKK0BUEnG//XrfoaskrVixwmv3xhtvuHjy5MkuDtMLdVrExx9/7C3TKQ+PP/541vsBf+bGJUuWuFj3OwDo2bOni3UFirBCxLZt21ysq7aEFZ50mkT4W6PfP/3ehDPIhqmNlA48Q0xEREREqcYDYiIiIiJKNR4QExEREVGqVYkc4ri8qrAMks4nyrV0WVIOkhauL3xcRpirpMvbxOUnh5hDTPkoLEmmv8NJucG50usLZ7vSt9u3b+/iUaNGee10ObVhw4a5OJz9cc6cOS4+5phjYrfpmWeecXGHDh28Zfo3oE2bNi7Ws3ERHY5MHxs5cqR3/5o1a1ysc/LDa1v0bKinnnqqi8N9pZ4J8vnnn/eW6Xx93Td07jLgl0nTvw1hKTj9XPox69at89qtXLnSxbqvhbnGq1atcnE4i92ZZ57pYl0mTr8XQO7lGqlq4RliIiIiIko1HhATERERUapViZSJOGHaQa4zwWmlnZ6QVMZNL0vaVs5UR+Uh7numv6c6VSEcnoyzaNEi7/bRRx/tYl3yMPTyyy+7OJyBavv27Vm3Q890BwA33HCDixs0aODid99912uXlCah6ecKSypu2rTJxa1atcppfURxduzYgffeew8AMHv2bG9Zp06dXKzTk8JSaKtXr3bxZ5995uKwP+m0g8GDB3vLli1b5mKd7hCWeNMzvoYpTlrbtm2zrkOnNwB+mbjWrVu7eMqUKV676dOnu1inRQFAQUFB1m2YOXNm7LZTevAMMRERERGlGg+IiYiIiCjVqnTKREgP9SalHeSakpDr+uIqToTLcp1Jj1UmqDxkvmfh91dfMZ6UJnH33Xe7+M4773RxWGVBzzo1aNAgb9mPfvQjF5933nlZtwHwU4z0FfPhtu/atcvFt99+u4u7devmtdMz6yVdta/Vq1fPu61n++rSpUvs44hy8fnnn7v0gE8//dRbpqtMaLoyA+CnCenqDGG73r17uzhMp+jfv7+L9XdepzQBfv869thjXTx69Giv3axZs1ysU5rCmWbjDBgwIPG2plNGGjZs6OJevXp57cLZ9CgdeIaYiIiIiFKNB8RERERElGo8ICYiIiKiVKvSOcS1a9f2buv8v6RZtXLN0U16TLj+jFxnvgtnDiIqT8YYN3vbzp07vWWNGjVy8YsvvujiCy+8MLadnt1K3w/4uXwzZszwlp1//vkubtq0qYs3btx40PZm6HzFTz75xGun85dvuukmxClJnn5YMk7PSpmUQ5xUkoooo7CwENdeey2Ag2c81X1Iz8B2yimneO10f9D7qFtuucVrp3Pqc81/v+yyy3Jqd/HFFyfeztD5/oA/m6S+diGcqU7vO8MZMnX5w6Q84fr168cuo6qLZ4iJiIiIKNV4QExEREREqZaqlAktLJOmbyeVUIsrtZY0A50WplLoYVa9LNcSbERlQUTcdzNMcdB0mkQ4C5QeutT9Q5dZA/w0onCoUpeJWrp0adZ1A8C2bdtcrGeIC9MYwtJQGeEQdM2aNbNuXxL9GMDvwzqNI8QyipSLatWqudkQb7zxRm/Z1KlTXazTHc4444yc1h3ur5K+k7t37856/8SJE73bjRs3dnH37t1drMsRAvH7Ot33w9t6+8LfAt2Xw23VaRJ6mZ4FD/B/Tyg9eNRFRERERKnGA2IiIiIiSrUqnTKRVEki15nqck2FyFXS4/VVtOGQjZ6150i3gehQtmzZ4ipI6OFYAHj77bddrCs/hLPWbd++Peuy8PurUxLCK8s3b97s4mHDhrlYV58A/CoW+jFXXnml1+64445zsa7ukDTjXq6Shpn19h3O44iyCVOBTj75ZBe///77Lp4+fbrX7sknn3TxqlWrXBxWiJgwYYKLdT8G/BQFXeEh7EN6f1ZYWOjiMGVKr++cc85x8fjx471269atc/Gpp57q4mXLlnnt9L7zkksu8ZbpWfL0NumUDgAYPny4i3/4wx+C0oFniImIiIgo1XhATERERESpxgNiIiIi+v/snXfYHkW5/z+3kRZCSKMkSIghUUhCEiD0XgQBKaJSpIh68CAiioDiTwSFIwfEgsgRjngQOIJwKNKRJiX0FJIQIEhI6CUhkIQaivP7Y/eZ997Js5s34U3est/Pdb3Xe+/O7Ozs8zyzMzvz3fsWotZ0CQ1xmQYv3e+1ilWulJZEo7ukmmSP1zynuq3WnkuItuD111/nkksuAeD6668vpK2zzjrR9pGgPvzww0I+Hz3L/2a7detWyOe1vKmrRO9Cady4cdFO3ST58j/96U9H+4ILLqCMtB5ltFbjm95TfJ0GDhzYqjKE+LhsueWWpWkbbbRRq8pIo062B7vsskubl7n11lu3eZmi66AZYiGEEEIIUWs0IBZCCCGEELWmS0gmvHsXvwyaLuF6SUK6DOrz+mXg1katq0rzdpVUw6f5ZeQUSSbE0qZv377RZVkagcq7a/LRqNKIU2VtoCpaY+p2zUd/8xHoVl555UI+H3XqS1/6Uno5Ee8mKi2jjNZKntJIdd6dVGvlGUIIIdoHzRALIYQQQohaowGxEEIIIYSoNRoQCyGEEEKIWtMlNMSpdrFBjx49Ctte45fqcFury21t+GdfJ+86KnUr5anSF7eW1uodhahi1VVXZbfddgPg3nvvLaSddtpp0b7wwguj7d2sQfG36H/3XqOfbqc6XF9G3759oz137txCPq8hPvPMMynD63rbmtRVoq/jmmuuWXqc2qkQQrQ/miEWQgghhBC1RgNiIYQQQghRa7q0ZGKttdYqbFfJFXwZ3g1Uipc1VMkT/DJw1ZKoz7fCCis0tVNa6+5NiLZg6NChhe0///nPTe0HHnigkO/888+P9u233x7t559/fonq4SUZ8+fPL6TddtttTY/xUgqovgeU0do2lbp78xHDqu4pcskmhBDtj2aIhRBCCCFErdGAWAghhBBC1JouLZlI33r30ehmzZpVSPMShZdffjna6RvxPp9fSk3zeWmFj6Tn35SHYuQsv3TavXt3yii7XiGWBv73C8Xfn//NbrHFFoV86XaDNILk1KlToz1nzpxC2lNPPRVtL3/46le/Wsi3+uqrR7vMu0Wa1lophM+XepbxaT/96U9bVV7qTUbtWQgh2h/diYUQQgghRK3RgFgIIYQQQtQaDYiFEEIIIUSt6RIa4tZqAU8++eRoz5s3r5D27rvvRnvBggXRTt07+Xxe05hqC33ErVVWWSXaffr0KeTz214HOXjw4JKrkJsmsWypchm2JKR6+9GjR5fm3WmnnRa7/KXplrAt3BpKMyyEEB0P3ZmFEEIIIUSt6RIzxEIIIcTS5oQTTihNO/3005dhTYQQbY2lS/3LvAJms4Fn27US9WSdEMJqbVWYvsd2R99n10DfY9dB32XXQN9j16L0+2z3AbEQQgghhBDtiTTEQgghhBCi1mhALIQQQgghak3lgNiMvmZMyv9eMeNFt718xXGDzJhaknaKGTuXpB1mxoBk3wFm/MSM7c3YsjUX1RrM+LsZc824Idn/aTMeMmO6GZc3rtOMFfLt6Xn6oHz/VmZMMWO8GUPzfb3MuNWs/PM140ozBudlTTLjOTNmu893UCuu4a2S/UeYcWhJ2j5mDEv2bW7G+WaMNmP3RZ23oj6rmfH3JT1eLDvyNvVY/tudZMZmbVTuXWaMWZI8ZhyVt69gRj+338w4O0+bYsZGLu1rZjyV/30t37dC3r6nmnGky/tHf2yT8+9jxkm5/dm8npPMeMKMPy7eJ1F6ju3Te05VHjO+YMYpbXFu0XFYWu3Plb/I39lilLWeGQ+YscCM45K0z5vxZN42T3D7y/rR7+bt8ia3b2szfltx/pXMuNuMbmZ8Ir8XTDXjUTPGmfHptrhOd77DzDjnYxz/KzN2bMs6iWVD5YA4BOaEwOgQGA2cB/y2sR0C7y/JCUPgpBC4Pd1vRjfgMCgOiIHdgL8D20PbDYiBM4FDmuw/g+w6hwBvAN/M938TeCPf/9s8H8CxwO7A94Ej8n0nAqeFwL+andiM4UC3EJgRApvln+9JwOXu831mSS8sBM4LgYubnPeTwD5QHBDT8hmPzq9lSc87G3jZjK2WtAyx9DFjC+ALwEYhMBLYGXi+fWsFwH1kdUlfONkNGJr/fQs4F8CMPsDJwGbApsDJZvQGdgXuBUaSt3EzRpG1uYkV5/8h8IfcPpuW+936wO8/9tUtGTcCe5rRvZ3OL9qYDtz+gNhPeF4HjgZ+leTrBvwXWfscBhzoJlvK+tGDyNrl/cCuZhjwU+DUiip9A7g6BD4C9icbI4wMgQ2ALwJzl+Q6lwb5Z/J7oNwdieiwfGzJhBnDzXg4f8qd0pglBbrls46P5bOlK+X5LzTjy7n9jBlnmDEROBAYA1ySl7VS3lhGkzXII4Bj8rRtzBhkxj/yc95hxkBX/nmWzdj+04wvNKt3CNwBvJlciwE7Alfmuy4iG0AC7J1vk6fvlOf/AOie/31gxrrA2iFwV8XHdhBw7SI+Wl+v/mbck1/7VDO2cWm/MGOyGQ+asUa+72eNJ/l8lussM8YDPwL2As7My1o3L2Yn4HbgFGD/PG1/M/qYcU3+GT9oxkhX/v/mswZPmXG4q+41+fWJjkt/4LUQWAAQAq+FwEsAZpyUz7pMtWxG1fL9d+Vt9eG8XW2T71/JjMssm0X9G2TtPE87N2+Hj5nx80VVKgQeKXkQ3Bu4OARCCDwI9DKjP9nA97YQeD0E3gBuAz5PS5tcDmhE0jiVrONtihmfARaEwGvuM3rB1e3RPN8gM8aaMTH/2zLfv33+GV1pxjQzLnGf3efzfROBfd05N83b0CNm3G/GZ5t8JgG4C5rfx0SnpKr9PWPGz/Pf1qNmrJfvX9mMC/L294gZe+f7m/4ePWZskh+zrhkbWzbbOsGMW/J2lPYT3/PHh8CsEBhH1q48mwLT84md94HLgL0X0Y8aWbvsnpd3MHBzCLxe8Xn5/rI/8HJjsikEXsjbPma8VdIfrmbGVfl9bZzlEzataX9m7JHn6WfGLrk90YwrzOjhvrPGOOYrIfAs0NeMNSuuSXRA2kJDfATwu3yWcwwtnchQ4L9CYDjZE9yXSo6fEwIbhcBfgPHAQfmszLvAhsDkEJhJcYZ6LNlT2EX5E/YlZDM6DQaRNdY9gPPMWLGV19IXmBsCH+bbLwBr5fZa5E/xefq8PP9/AhcDPwbOAX5BNkNcxVbAhFbWCeCrwC35ZzwKmJTvXxl4MARGAfdAYWDqWT4ExoTAL4DrgOPzz/Fpy5amPwiBeRRnqS8Hfg48kn/G/y+/zgYjyW56WwAnWYvUZTy0DNhFh+RWYO18YPsHM7ZzaeeEwCYhMIJscOsHYp8MgU3JVkMaYR+/DbyTz6KeDGzs8v8kBMaQ/Va2azxQLQGx7eU02mXZ/tvI7gEPAmebsRcwsTHoKGErKMwe/xb4hxk3m3GMGb3y/bOAz4XARmSzVf6+syHZZzMMGAxsld97zgf2JPtsfCc5DdgmBDYka3unldRNbaprUdX+IBssb0S2EtKQKPwE+Efe/nYgm9RYmerfI/kA+Tyyh8rnyPrNL4fAxsAFZP1Vg0Y/8etWXkdZ+6vqR88ha5cDyVaEvk42y9wUy2QVg92D8v+RrZhMMuPXZmzospf1h78jGztsQjYO+VO+v7L9mfFFspnexqrpicDO+Wc9HviBy94Yx1yWb08ErZR2NtoiMMcDwE/M+BTZssZTls3JzAwhDtwmQKkm9vKKsj8P3FyStgUtsy3/C/zSpf1f/gT5lBkzgPVoGUS2Kfk1bg5gxrbAy2QRXi8newI+NgReTQ7rD8xejNOMAy4wYzngGve5vg9RJzYB+FzJ8VWf8S5kN+hmbE3+IBMC/7BMU94zT7s2f2h514w7yR5AriG7QaeyF9GBCIG3zNiYbJC1A3C5GSeEwIXADmb8kGwGpw/wGHB9fujV+X/fnrcl74RDYIoZU9yp9jPjW2T3mf5kA0WfvlTIO+KvAuRt5haymavfkHXEF4fAdclhhTYZAn824xaye9DewL9bJrtYDjjHjNHAR8BnXBkPh5BNCJgxiewzeovsXvhUvv8vZLIPgFWBiyxbVQt52c1Qm+pCLKL9QbGdNfq4XYC9rEXDuyLZb/klyn+P6wN/BHYJgZfMGAGMAG7L++huZP1Vg6p+ok0Igf8l66+xTK9/NrCbZe+8PE/WX3qpYT+cJCIEXshncnfM/+4w4yv5im9Zf7gzMMxaoq73zGd3q9rfjmQTfLuEwHzLVpqHAffl5SxPNvZpkH52arOdkMWeITbji9by4teYELiUbBn+XeAmaxGTL3CHfUT54PvtitNVDdaqSJ0rt9bZ8hyy5dhGXT8FvJjbLwJrQ9RYrZrnJ99nZE+Qp5LNlP2QbGbo6CbneRfKZ63N2Mx9xnuFwD1kA48XgQut5YW5D/IlVVjyz7ihH15cyj7jFcmuT3RgQuCjELgrBE4GjgK+lM9m/oFsBmkDst+v/5022nTVbw3IXqohm93aKV9huJGK3/wiiG0vp9Euy/Z7jiRb2dicbFVnfzLdf8pCbTIEXgqBC0Jgb+BDssHEMcCrZCs1Y6DwcnFr73kNTgXuzGfj90zP71Cb6mI0a38uuVk7M+BL7h2TgSHwBNW/x5eB9yDOohrwmCtjgxDYxeWv6ieaUdb+qvrRrCLZiuKmIXANWXvcn2zgu1NyjmbtckEI3BwCx5PN6jbkGGX94SeAzd11rxUCb1Hd/p4GVqHlAcPI5FmNMoaFEHXRsPBnpzbbCVnsAXEI/M39KMabMRiYEQJnk+l8lnRZFDJN7yoAZqxKtkQ7J03LuR84ILcPAsa6tK9Y9jbqumRLl0+25uR5Y7oTMo0z8DVatEvX5dvk6f9wjQ/gUOCmXAvVHfhX/tfsZZgngCEV9XjIfcbXmbEO8GoInE+23FP6pnwr8J+xkX1fk9K0nLHkemAztidbypufp+1txopm9CV74XFcvv8z0NzDiOgYWOZBYajbNZrsRbZGh/BaPoPy5YUOXph7aJmNHUFL++9J1knMy7V8u32MKl8HHGqZt4nmRNVaAAAgAElEQVTNgXkh8DLZzO8uZvS27GW6XfJ95PXpTSb5uJiWNhlwOmdHoU1apvtdLrfXJFsGfpHsQbihYTyEbJatimnAIGvR6x/o0lalZaBwWEUZalNdiIr2V8UtwHetRZfeGORW/R7nkskG/zO/fz8JrGbZS32YsZxlL3gvKeOAoZZ5lFierD++bhH9aINTyWQKkLXHQJP+MtcHd2vIHs3YqCHPs8yL00gW/dndCny3sZHPpkN1+3uW7CHl4vwzepBMAjUkL2Nls8JsfIrabCekLTTE+wFT8yXCEbCwd4PF4EIyze8kslln743ieoiz09uQ/cC/ni/RHkLxRYDngIfJ5BZHhMB76YnMGAtcQfZy3Atm7Jon/Qj4gRnTyTrB/8n3/w+ZUH46mXbIu5jpTtagGlqo3wA3AWeR6bdSbiQbRLaW7YHJZjxC9iT9u8U4NuUy4Pi8rE3JNMKNgf2dZEtLk8zYH/gZsHH+GZ9OywMBZEvfd5LdKE51+swdyK5PdFx6kC0VPp5/t8OAn4XAXLJZ4alkHfC4ijIanAv0MOMJspcyJwCEwGTgEbIB4aVkesFKzDjajBfIZpSmmEWt303ADGB6Xr8j83O8Ttaxjsv/TgnFl3NOAn6RDxZuIVuifpR8yTbhHmDDxoCDbHA91YzJ+bHHh8ArZDPoX8v3r8ciZtXye8+3gBste+lmlkv+Jdlg5RGqZ5PVproWTdvfIo45lWxJf4oZj9HilaHy95jL9b5A1jdtSDZIPSPPP4lWeG4yY828Xf4AODHvL3vm0qSjyNrHE2RSxcfyw8r60TiYDy0eXy4la5db0Xy18lYy+R7A6sD1lrl1nUK2crMoF2lHA2Msezn8cVq8QVW2vxCYRjYhdAXZA/5hwF/z7+wBss97IfIH6SFkOmPRieiwoZvzzvBPIXurfHGOuxC4IYT4hmuHwzKPG3cCW4XMlUx71eNEsreEL1tk5uJxPwPeCqHohidPuwfYO3+yF6LTYMbvgOtDE7eQ7UU+u35pCAstJQtRCyzzHX5MCE3dpHY4LHsZb6MQyr3aiI5JW7xUt1QIgX9r7zosLULgXTNOJnvz9rl2rMd/tGV5ZqwG/EaDYdFJOQ3aNkBCGzCQ5ppnIWpBCEw0404zurXnBNJi8ElotacO0YHosDPEQgghhBBCLAvaQkMshBBCCCFEp0UDYiGEEEIIUWs0IBZCCCGEELVGA2IhhBBCCFFr2t3LRL9+/cKgQYPauxq8+eab0f7ggw+i/f777xfy/etfLVElV1hhhWgvWLCgkO+Tn2z5aFdZpSXWxUorNYsJsOyZMGHCayGE1dqqvI7yPdaVjv59Vr28ay6masozzzwT7YEDB0b7E59Ysmf5V19tiaLevXtLDADfRlN83avq2hZ09O9RtJ6u+l0uy/bQEeiq32NnYfJk+PDD5mmf/CSMGrV45VV9n+0+IB40aBDjx7e//+o777wz2q+88kq0n322GATn3XdbojGuu+660X7qqacK+fr16xftnXZqcSE6YsSIj1/ZNsDMFhXdZ7HoKN9jXWmv79N3jv5hEYqD1o8+KveW5B8eU772tZZYMOec0+J/v2oAW8VZZ50V7dGjR0d7++23Lz3mQ3c37tatGJiurQcEapddh676Xfr24H//adtYEvx9oi3Kawu66vfYWai6xX74ISzuR1n1fbb7gHhp8mHyWOE73nHjikG4pk2bFu2tt9462kOHDi3kmz17drTfe68lAN7w4cUImCuvvHK0TzrppGgfddRRhXw77rhjtP3M9HLLLYcQHR0/CE47MD9Yrhr0zpkzJ9oXXXRRIc3P6K6++urR3njjjQv5Ntlkk2i//XZLsK5bbrmlkO+551rcfv/4xz9ueh6APffcM9p+JlkI0ULVoPWUU06J9muvvVZ6zLe//e1of+YzLdGQ04fojjJAFl0XaYiFEEIIIUSt0YBYCCGEEELUGg2IhRCiJqy5ZqbJa/a35prtXTshhGg/urSGuEq3+Ne//rWwvcUWWzQ9rlevXoV8XsfoXyjwGmQoeq34+te/Hu2//e1vhXxeQ1xVXyE6Il7Xl3qS8O3j3nvvjfall15ayOd1valO0GvzR7nXiR977LFCvgceeCDaK664YrTHjBlTyHfkkUdG278ge8011xTy3X777dHu2bNntH1bhuJLslXX31FIpNKtThP1Jf1dl/VThxxySGF71113jfYJJ5wQbd9WAb73ve9F+9xzz4126omhI75wJ7oWmiEWQgghhBC1RgNiUQu0VCyEEEKIMmq7Ru9dPQH07ds32nPnzo32hAkTCvn8Us3yyy8f7SuvvLKQ77DDDov2+uuvH23vSzWlIy6xdhW0VLx0qFrG9NKIK664Itq9e/cu5POuDVNXS/Pnz4+2d6c2bNiwQj4vbXrnnXea2lBs2z5IzpAhQwr5fNv20oo//OEPhXwHHHBAtLfddttCWt0CGIiuSepf3LfzM844I9p77LFHIZ9vG57tttuusO0lTr/5zW+iffbZZxfyLWkwHiFai35hQgghhBCi1mhALIQQQgghak2tJBNeruDfegd48skno33yySdHe5999ink894onnnmmWjffffdhXw+lPPRRx8d7QULFhTynXbaadE+/PDDo73aasVQ2+mbvh4tx4r2wi+fpnIHLzfykeS8HAGKHlnSCI1+udZHckzP5aUQK6ywQrR9xEiA999/v2nZ3nsMFK/L22ussUYh30MPPRTtLbfcspAmrzGis1IlhfLt9Y033oj2j370o0K+sj7LR3gF2GyzzaJ99dVXRzsNb+w9xkiOJJYGmiEWQgghhBC1RgNiIYQQQghRazQgFkIIIYQQtaZTityqIkI9/PDD0T7uuOMK+bz2qX///oW0p59+Oto/+MEPop1Gptp5552j7SNs/eUvfynku++++6Lt3cWsssoqhXzXX399U/vUU08tPa8QHZHJkycXtr1efu211472Sy+9VMjnNb8ffvhhIc23ba8HTnWNXvPoNcqp27Xtt9++aZ2uvfbaQj6v4ffu2VLt8uzZs6Odah4333xzhOiMVGmIb7755mj7SI0pXq/v23iV+7RNN9002uPGjSukeQ2xf58gfSdBiCVFM8RCCCGEEKLWaEAshBBCCCFqTaeUTFS5WfEu09KlGe8yyS/nQDGaXJUrmd122y3afgn3z3/+cyGfX97x7pfSqD++Tj4SVxqlR5IJ0dGZOHFiYdv/tr3MKZU8+UhVqcTBtx2fzy/BwsJShgbpcq+XOHh5Rtoun3jiiWgPGDAg2j6iJRSv5bHHHiukSTIhOitpu/F4F6V77bVXab6yfrqq/x48eHC0U9eora2fEEuKZoiFEEIIIUSt0YBYCCGEEELUmk4pmUi5/PLLoz1nzpxo9+jRo5DPyySqPFX4t89HjhxZyOffbvXLr3vuuWch31tvvRVtvxybLu36qD0+SpcvG+DBBx+MtpZiRUfk+eefL2z36dMn2t6zxAsvvFDIt95665WW6WVPXj6RLpn6fL5NeZlFeu7p06dHO40M6e8B3ntGz549C/l8nWbOnFlyFUJ0LlorSRg1alRpmm+Tvr+tKnuDDTaI9quvvtqqsoVoK/SrEkIIIYQQtUYDYiGEEEIIUWs0IBZCCCGEELWmS2iI//SnP0XbR4JLdcKpa6WyNO927fXXXy/k8xpCrydOdZHdu3dvWrbXHKbbXsec5rviiiuiLQ2x6Cj46I9euwtFHe7tt98e7blz5xbyee18FT4iVVWkOv/uQKo1XH311aPt26XX/EOx/a666qrRTnWNgwYNiraPdgnF9wBSjbIQ7U3aP3qqXKN5d6NV+D6stRritN/zTJ06Ndo+Ql76Xo5csoklRTPEQgghhBCi1mhALIQQQgghak2XkEz4ZUy/5JJGs1pppZWinS7T+mUWf1zqtqlM1pAu0/h8XlqRLje9++67Ta8jPa+PDiRER8G7WkvlCWVSgzQanW9vaTsqa5dVbpd8BLo0n9/2ZXs5BhTvFf/85z+jnbZLv3Sbunn0keu233770voK0d6kckLfNlIXoKk0qoFvd1AuB0zx7lB9O/RtEIpSRk+V9EOIxUEzxEIIIYQQotZoQCyEEEIIIWqNBsRCCCGEEKLWdEoN8WuvvVbY9hrdNdZYI9qzZs0q5PMav1Sr6MvwmqQFCxYU8nldU5UbN6+f8rbXSwFMmDAh2t41U//+/Qv5Hn744dJzCdFeeHeDqT7euyj0+kIfxhmKusG0faTa3jJ8W6zSF/t8/lxpW/btvEy7CEUXcv7eAwu7bBRiaZG2mwZpW/C/c9/PVbk78++5QNH9mSfVCfu+06dVvSfg8X0ywLx580rrWEaqa/b18O7a0s+p6vMQXRfNEAshhBBCiFqjAbEQQgghhKg1nXJd4Lnnnits+6XJfv36RTuNPtW3b99o+4h2UHSn5JdpU5cufmnFL+mkbtz8kos/Js3nZRL+GB8BLD1u/vz5hTS/NC3EssQvn6YSJd9Oq2RI/redLnGmUagapMuzftm1bFm42XaDdFnYL0H79pvee7xkJF3STe8/QiwtWistWhJ8xElY2B1ag9ZKIVpLKgMpkyhWyRuq0hTRTqRohlgIIYQQQtQaDYiFEEIIIUSt6ZSSifQt9T59+kTbR8fyy5kAjzzySLS32mqrQpqPnlXlPcJHyypbVoXi0qxf9k2Xgfwys1+aSqUVXp6RRq3bZJNNSusrxNJk0003jfbQoUMLaV4ONGbMmGiny5g+2l0q//G/ey+TqIpA59tvupTspRF+yTSVUD399NPRPvjgg6Pt7yFQXD7eaaedCmlql2JZ4aMp3nDDDdH20RKh+Pv3fWUqg/j0pz8d7ZdffrmQ5tvN/fffH+3U+4Tv61IplMdLALfddttoT5kypZBvyJAh0fZ9/gMPPFDI58cHDz30UCHNH7frrrtGe5999inkS2Uioh5ohlgIIYQQQtQaDYiFEEIIIUSt0YBYCCGEEELUmk6pIX777bcL2969kbfTSDc+Ul2qVfRawzTilqd79+7R9hGsvLYYijpJ72Yq1TR6fbHXMZe5tknLE6I92XrrrVuVb/PNN4/2j3/840LatGnTor3lllsW0rz7Qd8mWhtBskpr7DWO/t4Axba92WabRftLX/oSQnQ0vM7da3LTiKde1+/7yldffbWQz2vo00iNXlP81FNPRbvK/WeVizP/ro+/F6y11lqFfFdffXW0vTY4dXf4qU99qrQMH+X2pJNOivY111xTyHfHHXeU1ld0XTRDLIQQQgghao0GxEIIIYQQotZ0SsmEj0wHRRnCgAEDoj1jxoxCPr/M+uKLLxbS3nvvvWh7ycTcuXML+fySk18GevbZZwv5fOQ7L4VorcupNBKXl2Sky1tCtBdlkeSgKEnwkobU9WCvXr2inUaM8+3Fu25K24fH50slE74d+TafRrDzsos0aqSnSg5VVUch2pINNtgg2t41YCpj8G3Du1ZLJUP+t5y6LvO/60GDBkV7xIgRhXy+LZdJNaDYJn1/e/fddxfy+fvE+uuvH+1UXuglI1VjAN9Hp3UX9UQzxEIIIYQQotZoQCyEEEIIIWpNp5RMpJHq/LKof4s09TLh8dIKKC6f+gg+AwcOLOTzS0l++ckfA0U5hV8SSj1k+Hz+7d30LXp5lhAdkaq3x1MJQQO/VAswe/bsaM+ZM6eQ5qNT+bfJ02VSL93wcodUnuGPS+UUHp/ml1ZTvKRDEgnRXqyxxhrR9n1Z6iHC94le0tC7d+/SfGnk1uHDh0fbS/5S2aCXGj333HPRTtuk3/bywjR6pJdM+Git6X3GX3NaJy+H9Pea9dZbDyE0QyyEEEIIIWqNBsRCCCGEEKLWaEAshBBCCCFqTafUEHudEUDfvn2j3adPn2in0eO8fnfmzJmFNO92yWsaUzdQXtfkdYapNni55ZaLttcaV0XZ86S6KK/x8lpjIToqqSuzBqkm1+uQ02O85te7W0x1jWXl+baX4vW/aXm+/VW5lvP1TesuTbFYVnitvf8dpm4+/W/Stw2vO4ZiG/L6Xyi6MvO6Xv/+DhTdn/l6tFa7P3369EKa7yvXXHPNaKfv7/j26vthKH8XZ/XVVy+tk6gPmiEWQgghhBC1RgNiIYQQQghRazqlZCKVMfilFC9J8G6aoCih6N69eyHNL6346D7p8o532+KXplIphF/G8Wlped6F3KOPPkoZXiaSSkaEaC98G0glAmWSgXSJ07tGSiNm+fK9K6hUxlB2rlQK4csoi4IHxftBlfvG1tRBiKXNyJEjo+1/86lkwLcb3x+mfaWXDK299tqFNC+n8G0ojejoz+X77FRa5PP5/rFfv36FfD7anW+vr7zySiGfl3+kskmf5uvev39/hNAMsRBCCCGEqDUaEAshhBBCiFrTKSUT/g1TKHqW8F4gvPQBYNasWdFOo+V4zw3+zdl0KdWX75dw0jdi06WasvL8EpFfcvV1AJg7d25pGUJ0JtL2+8gjj0Q7jU7ll1OrJBN++dO/SZ4uz/p27z1YpDIsj9qb6OiMGTMm2l7ukMp9vNzBSytSr0a+DaWeGXzf5vusKi8r/pi0Tr4tV523TD7l+0YoyhpTiaJvyz4tlYWIeqIZYiGEEEIIUWs0IBZCCCGEELVGA2IhhBBCCFFrOqWGOI3U5t29eLcqVVHmvO4Yinoi7/opjarlNUhexzRw4MBCPh89z7tdS/VTPrqPT3vjjTcK+XxaGhFIiPaiytVYWUSq9BivDUy1jB6vNUzfAfB4PXGqNS6LLJdG9PL6xapIdXK1Jjoa3q1h6qJzjTXWiLb/XaduQ307TNuxb3s+LW1DvnyvV65qk768tK9M3bo1SPtoX0bqdtGfy7telYZYgGaIhRBCCCFEzdGAWAghhBBC1JpOKZlII115F2VeFuHdr0BxucRHt0u3fdSe1H2al1N4mcS0adMK+bwrmKrlV7/t5R7eJRQUo/bIDZToDPjlSu/uKZUDeaokSn4pNG0DXrpQ5soQisu9PopXGtGrzG1iSpksRIj2wre7VApRJmNI24nvi3zbTbd9O0zP5duGP2/qni2VNZSd1/eJZdKntB6pBMvXw8sahQDNEAshhBBCiJqjAbEQQgghhKg1nVIykb456+UJflklfevVe5ZIJRN+aaVqKdUv4/i3Xv0bq1CUcVQtTfl6+OXi9I1af83pEpEQnYnUQ4RvU1W/bd920jfQW9smyiQTqUTC10leJkRnYq+99or2eeedV5rPt5mq33HaXsvaQ9oG00hzZZRJJtLyfH/u61R1P0nTvJwilWcJoRliIYQQQghRazQgFkIIIYQQtUYDYiGEEEIIUWs6pYY4dbm0+uqrR9tH4nn88ccL+bzWeMCAAYU0r9FtbRSs2bNnRzvVJPsoeV5fnOqlfD7vJi7VLvtrTM8lRGcibb9ev9/aKFbeLVRKlR6ytW4Oy9xTCdHR2W233aJ97rnnFtK8rtf3Zaned0ncpFVp7T1peWX6/7QfLot2l7pn8/eXNM2/m/O9732vVfUV9UEzxEIIIYQQotZoQCyEEEIIIWpNp5RMpG7XZs2aFW3vjslHlQPo3bt3tNMIU37Z1i/hVC3HeFdNaT5fXpVbKV+PqmheXk7ho/EJ0VEpi+KWtt8yd0ppGX6ZNF2e9TIJn5a6XvTt0rtgSqNfelL5kqe1rquEWFaMGjUq2l5CCEXJQFX0Uy9PSNPKJA5Vv39fXiq5KIu8mvapZf1yVTTK1LWad4d6+OGHl9ZX1BPNEAshhBBCiFqjAbEQQgghhKg1GhALIYQQQoha0yk1xP379y9sz5gxI9peM5S6VvNu1+bNm1dIK3N5luqlvEbZa5+q3EB5HWOqn/Lba6+9drTnz59fWr80TLQQnYlU/+t1wqnu2LePKi1+a92kpbrEBqkrQ19elZvDsvoJ0REYMmRIYXvSpEnR9n1KqsP1v3/vGhTK+8cqDXGZThhaH7q5tcf7MUD6TsLgwYOjPXDgwFaVL+qDZoiFEEIIIUSt0YBYCCGEEELUmk4pmUhdkvll1tdffz3a3v0MwC9+8Yto77HHHoU0747J22kEK4931eRdOEFxWclHAXryyScL+c4888xo33333dG+4YYbCvn8NXrXOUJ0NtK24vGuDKG4DOvbVNXyrM+XyjN8mnc7lcoifD28TCpFMgnRESiTLuy+++6FfOPGjYu2lxqkv3/f76Wuy8qkDFWR6lL3h2XleWlFKoXw1+WPSeWKvk368QDAjjvuWFoPITRDLIQQQgghao0GxEIIIYQQotZ0SslElUcHv6ySemPwS6QPPfRQ21fsY/LSSy9FO5VF+KWfqqWprsYJJ5xQmnb66acvw5qIxaVM1pB6ePH5nn766UKaly/5Zd3Zs2cX8vk35v2SaSpp8N5bfFrapvy9w7dLIToiZd5ODjzwwEK+448/Ptre61IqC/JlpB6PUq8TDar6pSWJ4lgW6RKK3iP8PQKgZ8+e0U6jYh5yyCFNy0tlIIo6WU80QyyEEEIIIWpNp5whFkIIIUCrSEKItkEzxEIIIYQQotZ0yhniL3/5y4XtCy64INrebUuZXgg6pmboW9/6VrTnzJlTSBs9enS0N9tss2VWJyHamm233baw7dti7969C2ll2shU17jGGmtE22sKU12jP1eqPfR4TWWVlrEqmlZHuKeIelDm/i/df8YZZ0T7Zz/7WbTTiG5VrhF79OgR7aq24dNS120e34Z8ff15oDx6Xqp/9u/fHHnkkYW07bffvmkd1FYFaIZYCCGEEELUHA2IhRBCCCFErbGqJb9lUgGz2cCz7VqJerJOCGG1tipM32O7o++za6Dvseug77JroO+xa1H6fbb7gFgIIYQQQoj2RJIJIYQQQghRazQgFkIIIYQQtUYDYiE6MWb0NWNS/veKGS+67VK/YmYMMmNqSdopZuxcknaYGQOSfQeY8RMztjdjy493RYVyzzBjav63f5P0s814y21/N897U+PazdjajN9WnGMlM+42Y5T73F43Y2Zu396Kem5vxg0laX8yY1hJ2vfN6J7sO8GMg8zYp+y41mDGF8w4ZUmPF50PMz7Kf7OTzZjYVm3RjG3z8j4048tJ2tfMeCr/+5rbv7EZj5oxPW+nlu8/w4wpZlzs8h5sxvcrzt+/0b7ytjYvv84pZtxuxuof49puN6P3onOKOtChB8RmrGnGZWY8bcaEvKP7zBKU08uMIyvSv5d3pI/5hmnGmWZMyxve38zole/fKt833oyh7hy3mpV/pmZcacbg3O5hxn+7a7vLjCVyMGzG/3P28mbcY9Y5fUyLxSME5oTA6BAYDZwH/LaxHQLvL+r4kjJPCmHhgaAZ3YDDoDggBnYD/g5sD23WCe8BbASMBjYDjjOjp0sfAwt1ZAcBI4H7gV3zTvinwKkVp/oGcHUITHaf43XA8fl20weD1hIC/xYCj6f788/y+1AcEAO7ArcC+8CSD4iBG4E90wG36NK8m/9mRwE/Bv6zjcp9jqzdX+p3mtEHOJmsfW4KnOwGl+cChwND87/Pm7EqsFEIjATeN2MDM1YCvg78V8X5fwCc77bH5tc5EhgHfOdjXNv/QvnYQNSLDjsgzjuzvwF3hcC6IbAxWSNfo/rIpvSi5EdvxgiyhrspMAr4ghlD8uTbgBF5w/tnfn6AY4HdyTq0I/J9JwKnhUBLJIHieYYD3UJgRr7rT8DrwND82r4O9FuCawNaBsT5IOgOWHhGTdQTM4ab8bCbVRmaJ3Uz4/z8QfDWvHPCjAsbM0FmPJPP6kwEDgTGAJfkZa2Ut9PRZL/lI4Bj8rRtzBhkxj/yc95hxkBX/nn5A+U/zfhCk2oPA+4JgQ9D4G1gCvD5/PhuwJnAD9NLBZYjG2R+ABwM3BwCr1d8PAcB1y7GZ7mdtcwkP2LGKnlSj/yBd5oZl7gZsbvywTtmvGXGr82YDPyE7MHiTjPuzNN7AsuTDSD2As7Mz7OuGaPNeNA9nPd25f8uzzfVjE0BQiAAd0HTz1Z0fXoCb0CcfLnDslneR83Yu5HJjJ+a8aQZ95rxVzOOSwsKgWdCYAos1LftCtwWAq+HwBtk/eXnzegP9AyBB/Pf4cVkD3j/ApbL20ajjR4H/D4EPqCcL5E9cBfIy1nFXeemZjyQt8v7zfhsvr+7Gf9nxuN523mo0SbJHn4PrPwkRW3osANiYAfggxA4r7Ejn8UZa4bls7dT8wa+P1Q2/NOBdfNO48zkPOsDD4XAOyHwIXA3sG9+vlvzfQAPAp/K7Q/IGnR34AMz1gXWDoG7Kq4ndrx5/s2AExsD6BCYGQI35uk/sJalYj9jfU0+m/yYGd/K950OrJRf2yV51mvy8wkB2UD1d/ns5xjghXz/UOC/QmA4MJes42nGnBDYKAT+AowHDspnaN4FNgQmh8BMijPUY4HfAxflD5SXAGe7MgeRPYTuAZxnRhrKajJZ59rdjH5k94O187SjgOtC4OXkmHPI2ulA4D4WMfNkmaxicAg8U5anCccB38k/y22ARsisDckekIcBg4Gtmhy7Mtm9ZlQInAK8BOwQAjvk6TsDd4TA/RRnqZ8mG1T8KP8sHyWbmWvQPa/PkcAFbv/4vI6iHjT6gWlkEy6NlZH3gC+GwEZk7ejXeR+6CVmbH0W2yjOmWaEVrAU877ZfyPetRcs9Ju4PgTeBm4BHgJeBecBmIXBN2QnM+DTwRggscLu3MWMS2cz1zrT85qcB24TAhsBJwGn5/iPzMoaRrRht3CgoH8ivYEbfxblw0TXpyMvqI4AJJWn7ks1KjSKbVR1nxj3AbLKGPz/vRB804zrgBLKZ3tFNypoK/CJvEO+SzfyOb5LvG8Dluf2fZB3Uu8AhwK/IZoir2Ar4a24PByaFwEdpJrM4W7wZ2YzXQ2bcHQKPAN8IgdfzmbxxZlwVAieYcVRybVOBTRZRH1EfHgB+YsanyOQBT4+fWV8AACAASURBVFkWqXRmCEzK80wgG6Q24/KS/ZDN2t5ckrYF+cMl2dLkL13a/+UPg0+ZMQNYD2JdCIFb8w77frJ2/QDwkWX65a+QyTMKhMD/5ufBjJPIBuC7mXEoWcd9bLKC04/sQWBxuA/4Tf7weXUIvJB/lg+HkA0C8s56EHBvcuxHwFUVZX8e+HO607Kl5l4hcHe+6yLgCpflrwAhcI8ZPc3oFQJzgVksLG8RXZd3G/2AGVsAF1u2AmrAaWZsSzZLuxbZSutWwLUh8B7wnhnXL+0KhsAvye8DZvwJOMmMfwN2AaaEwH8kh/Qna/+esSFkKx9m/Cgv7whgVeAiy1bAAtlqEcDWwO/y8081Y0pSXqOdzPn4Vyg6Mx15hriKrYG/hsBHIfAq2azuJrQ0/CnA7bQ0/FJC4AngDDLd3t/JOuXCQNWMnwAfks1yEQKTQmDzfGZnMNnTrplxuRl/MWt6zmYNu+za/hYCb4fAW8DVtMzyHJ0vtz5INls2tFkB+UD7fbecK2qEGV90y/pjQuBSsiX4d4GbzNgxz+pnXT6i/AH57YrT7ULWdhaX1AH6Qg7RQ+AX+Qzp58ja9j/JZmKHANPNeAbobsZ0f1w+aN40n3k6lkw+NBfYKTnFu7DQzHQBM77jPssBIXA68G/ASsB9ZqyXZ23NZ/les4dgx6bAw1X1KaHss1yRlhlsUSNC4AGyB77VyFYLVwM2zgfMr7KI330reZGWVRvIVlBfzP8+1WR/xIwNydr0k8BXQmA/slXctE9bVBu9Dtg2t08F7gyBEcCeizjOo3YigI49IH4Mt7TRSpao4YfA/4TAxiGwLZke6Z+NNDMOI9PhHZTroXBpRjYzfCrZEuYPycT/Rzc5jW/YjwGjLNNCtgoztidbHtoif2nikUVc2wpkS2WiZoTA39yLdeMte5FzRgicTSbbGfkxin+TTLfXmLn8ZAhxZiWm5dwPHJDbBwFjXdpXzPhELh8aTNYxRszo1ljGNGNkXudbQ+DGEFgzBAaFwCDgnRCi5r/BqWRLppANXAPZzFjhBbN8ubRbE7mGz/Nf7rN8yYx1Q+DREDiD7IWe9cqObQX+sxwOTHMD5pgWAvOAN8zig/EhEGeLgSgZ2xqYl+cH+Aw09yQiujb5g1o3slnPVYFZIfCBGTsA6+TZ7iN78XJFM3qw+HrzW4BdzOhtmaZ9F+CWXMo034zN8z7yUBbW6Z9KJl9YLq8nNGmjZH3xoIo6bA08ndur0jLwPszluQ/YD8Ayzy0bNBLy+q0JiyWbEl2Ujjwg/geZtudbjR1mjMw7hbHA/nmnuRrZE+LDlDf8tKMuYLnbFste+tmX/G1aMz5PNsjdKwTeaXLoocBN+Us73ckadLNGDfAE2cwWuSZwPPBza3n5ZpBlb9aPBfbJtZMrA1/M961KpoN6J7/Zbe7K/sAsLg+RDyReW8SLCqI+7AdMzZfyR0CLy6Ml4EIyze8kslln743ieoiz09sA3wW+nq/YHAJ8z+V9jqzN3gwckS/bepYDxprxOPBH4GCn5y8ln3kiBCbmuy4l09xuRZMXc8hmt7deVLmO7+fa/ilk7xKUyUVawx+Bv1v2Ul3DU0eDy4DjLXtBaF3ga2Qv2U0hk4t5l2rvmfEImYb7m27/DmTeJkQ9aGiIJ5HJnL6WP2BdAowx41GyPmsaQAiMI5thnUL2O34U4sNUxIxNzHiBTKr032Y8lh//OtnAdlz+d0poeYH1SDId83SyAevNrrx9gPEh8FIu7ZmU123FEJjszx2yF2qfNis89G6TX+dksvvKsfn+XwL/mbcFv0LzB2C1/F7yH2QTUo3r3Bh4sDX3FtH16dChm/Olz7PIfrTvkT3FfZ+skf2SrBMJwH+EwOW5bvh6oAfZgHNzYLcQeMaMS8lmmW4OgeOT84wF+pJ1cD8IgTvy/dPJZlobM2APhpB5lbDMndGNwC75AHwbsob3PvDVEBaa8ToE+GwImdbYsjfKfw3sSDZ7/BrZSzTjzPgBmWYZ4E8hcJYZK5C9LDeIbDatF/CzELjLjDPIBicTQ+AgyzwEbBFCvFEI0ebkGsA/hcCDi3nchcANIXDlUqnY4tVlI+CYEDiknetxG3BokxcFF3XcXcBxIRTfe8hlW5eGsJBMRIiIGT1C4K28P7sH+JZ7mOwQmPFFslXfRb2nU3Z8N2C5EHgvf7i8nawvft+M35G9oHtHG1ZZdFI69IC4K2HZi3B3AlstQkfYFue6GjghhBbphxAdhY40IAYw4xtk3jCWartcGlQMiDch89IzqemBQgD5RNEwMvndRSG0me/iNsWMfwuBPy3hsauQ9b3LkemWfxRCNmNtxuEhFHwcixqjAfEyxIxdgSdC4LmleI7lgQNC+FjL4kIIIYQQtUEDYiGEEEIIUWs68kt1QgghhBBCLHU0IBZCCCGEELVGA2IhhBBCCFFr2j10c79+/cKgQYPauxrMndsSwXXOnJYIjssvv3whX8+ePaPdrVtLXI133im6KfZlLLdcdBFM377FkOm9e/dewhp/PCZMmPBaCGG1tiqvo3yPdaWrfJ8ffVR09PDuuy0BpHw7St998Glvv90SWG+FFVYoLf/DD1tcj/7rX/8q5PPtvnv3Zm7Flw5d5XsU+i67CvoeOzaTJ8OHJV6kP/lJGDWquK/q+2z3AfGgQYMYP378ojMuZa69tiWQzoUXXhjttddeu5Dvc5/7XLR79eoV7UmTit6NLr64xclD//79o33wwQcX8u23335LVuGPiZk925bldZTvsa50le9z/vz5he0pU6ZE27ej999/v5DPpz38cEv043XXXbeQ780334z2a6+9Fu30gXbAgAHRHjNmTKvq3hZ0le9R6LvsKuh77NiYlad9+CGkH3XV9ynJhBBCiDZnzTWzzqrZ35prtnfthBCiSLvPEC9NrrrqqsL22WefHe1777239LiBAwdGO32Su/zyy5ses+KKKxa2P/GJlmeNl19uCT51ww03FPIdcMAB0R7l5va/+c1vFvIdddRRpfUVojPz3HMtbrmPPvroQlqfPn2iPWLEiGh7iQTAW2+9Fe1XXnkl2qutVlwZW2WVlgjua621VrT9TDTA7Nmzo+0lE8OGDSu5CpHy6qtLlia6Bl7W5NvTe+8Vo7R7WdQ//9kSS2rBggWFfJ/8ZMtwZfr06dGePLkQ7ZlZs2ZF299boNivHnfccdUXIGqHZoiFEEIIIUSt0YBYCCGEEELUGg2IhRBCCCFErelyGuKRI0dG2+uWoFoL6PVJK6+8crRTN2ne1doHH3wQ7VRD/KoTyfl6jEp8gHg9lXf9dvLJJxfynXPOOdGeNm0aQnQVvPuzX//614U0/7v3WsEDDzywkO/Tn/50tL1rxD/+8Y+FfA899FC0995772inusaNN9442r/61a+ifcEFF5RchRBdk9QVornX+v27MinbbLNN03zPPlt8yd97lvHt0HtxStN8H52+T+Dbv9cnA9x3333RloZYpGiGWAghhBBC1BoNiIUQQgghRK3pNJIJH0kqXabxy6I+StV6661XyOclDqkjfl++LyNdLvKyix49ekQ7lWd4p/9+SefDJKSKl2p4F1E+0ADACy+8EG3vPg4WdlUlREdm7Nixhe3HH3882l/96lcLad/4xjei7V0WnnXWWYV8O+20U7S9S6Y0Ap2XSu24447R9sE8oNjOfRCQ888/v5Dv8MMPR4iujJcJQrEP831xKk/wfae302BXvjwvx0ilED6flyimQXqqIlX6/laIFM0Qiw6NnPsLIYQQYmmjAbHo0Mi5vxBCCCGWNp1m/aDqbdYrr7wy2j4SlSVBrn3km3TpxJfvl3fS5Rif5mUX6VvqfknHSzWqrqNsKQqKb9VeeOGFhTRJJkRnwr8FDkWPLKl0wb+B/tJLL0X74IMPLi3DR6DbdNNNC/l8pMljjz022j66HcDMmTOj7T1abLLJJgghFub1118vbPs+0fe3XtIAxf7RR7dL+0Avf/DyDB+lMj1XKpkYN25c03OlYwVRTzRDLIQQQgghao0GxEIIIYQQotZoQCyEEEIIIWpNp9EQV/Hmm29G22uBvDYJipqkVJ/k9URV+/221xOnGqQyLVR6Xu8Wyh+Tapd9+aneUYjORBqt0W8fc8wxhbR111032r59pJEhjzjiiGi//PLL0U5dvI0YMSLaAwYMiPb1119fyOffRfjKV77S5CqEqCepG7YGL774YmG7zMWZdyEKMHz48Gj7djdlypRCvpVWWinavj9MdcLLL798tL1rVCj2tz4qpqLWCdAMsRBCCCGEqDkaEAshhBBCiFrTJSQTfom0b9++0U6jwvllljQCnV8GKrNT/BKuX6aB4tJMeq6yOnnSqD9+WSh1b+OlIWl0HyE6Gmn0OC8j+tGPflRI88um3/nOd6J91VVXFfKdeuqp0d5jjz2i7aM/Amy33XbRHj9+fLTT5d1UutGg6r4hRB0o67NuvfXWwraXPwwZMiTaAwcOLOTz/egTTzwR7TRa65ouEpOXZ6TyqbTvLKuTjzopyYQAzRALIYQQQoiaowGxEEIIIYSoNV1CMlEWgW7u3LmFfKuvvnq0q7xHVFGWL11K9cvAflm16jz+OtK6++UiH4kLisvKG2+8cWn5QnQEqqJCpdKjGTNmRPuNN96I9tVXX13I9/vf/z7a06dPj/ZPf/rTQr5tt9022vvuu2+002XWz3zmM03rVxVpUoiuSNpnlbXfxx57rLDtI8j5/tH3cwA33nhjtPv16xft1JuSlz9VyRp9VNe07l6u5aWW6f3E3xtEfdDdXQghhBBC1BoNiIUQQgghRK3RgFgIIYQQQtSaLqEh9lHdvMZvzpw5hXxegzhs2LBC2jvvvNO0jCp8vipdpKfKndq0adOinbpP89ro1G2VNMSiq9CnT5/C9jrrrNM0bb/99ivku+GGG6J90EEHRbt3796FfN7tkr8HpLr8sntAa9u5EF2F1mqIvWYYiu3V63rTftmX56PRpa4Q33777Wh7l6qp2zX//k3qetW/o+Bdwf35z38u5JOGuJ5ohlgIIYQQQtQaDYiFEEIIIUSt6ZSSiWeffbaw7eUOntQdS69evaLto7tBUYZQtSxaFpmqyp2aLztdin3zzTejvfvuu0f74YcfLuTzSz9p/ebNm1d6biE6M759HHXUUdH2sgiACy+8MNp33XVXtH3kSoDJkydH+8QTT4y2l10JIVporUvS559/vrDt5YAPPfRQ6XE+eqSXCqZyh9mzZ0fbyy6eeeaZQj4vz+jZs2chzbt/85KMO+64o5Bv6tSppfUVXRfNEAshhBBCiFqjAbEQQgghhKg1GhALIYQQQoha0yk1xN49GcCgQYOi7TVCqdZ2ww03jPbMmTMLaa11tVZWfnour7vydprPu2H77Gc/G+3777+/kM+7i0k1XS+++GKr6i5ER6DKjZN3jQjw9NNPR7tHjx7RTjWJ3/72t6Pt9YVDhw4t5PNhnX2455T58+dH2+sQU5eHCuUsujpVv/GXXnop2j4UMsCAAQOi/eSTT0Y7DYuehmtvkIZ49n2v1yd7G4oa4u7duxfS/PtGXq/sjwG45pprmtZJdG10NxdCCCGEELVGA2IhhBBCCFFrOqVkIl2a8Usp3p1aujTrXTA9/vjjhTQfIae1VEkhyvKlEej8spB3M/Pee+8V8nlpRbps+8ILL7SyxkJ0bFIXihtssEG0d95552jfeeedhXxeNnXrrbdGe8yYMYV8w4cPj/Z///d/R/u4444r5PNRt1LXTUK0F42+ZFlGTPT9K5S7K/NRJaEocdpjjz2inUa08/15lYs3L4149dVXo51GmfQyCR/dDop9p5dFpe4ZU1mmqAeaIRZCCCGEELVGA2IhhBBCCFFrOqVkIo1M45d0vNQgXVYaPHhwtP2yKhSXd9IlotaQLvWULf2kb+z6JWJfv/TNWx+xK+WVV15pdT2F6MisttpqhW2/vHr++edH23tkAbjxxhuj7T1OjB07tpDvtttui/YxxxwT7VQy5d+e92/LC9GeLEupRIMqLxPea0sqhfB9mJf1pTIGL3P0coe0H/YRan3U2dSThC8/jUBZdi1pfz1jxoym+UTXRjPEQgghhBCi1mhALIQQQgghao0GxEIIIYQQotZ0Sg3xrFmzCtteF+Tdk3n3MAC9e/eOdqotWhKqXMR49y5ldlqGdwnl6wrFCF5ePwVFbZUQHZ0qTaKPaAXFSHNeYz9w4MBCPu9G8ec//3m0vSvDdPtXv/pVtCdNmlTI5926eddtVW1eiK6C/51XtdcHHngg2qn+35fh3aT5PhqKuujXXnst2qmG2GuS077ds+KKK5bmK2u/qZtTX4aoD5ohFkIIIYQQtUYDYiGEEEIIUWs6pWTCyweguCzil2PSpZl0+dTT1u5s/NKMr1+6/FTmJi51JeOXnNJod3Pnzv14lRWig5C6Q3z++eejPXHixGjvvvvuhXwvvvhitJ966qlop66gfLvy0e1St2veFZSnW7duZVUXostQ1R9OmDAh2r59enkTFOUPnrT/8pEgfXtdddVVC/nmzZsXbd+PpjJEX/cqN6e+Hun1qp3XE80QCyGEEEKIWqMBsRBCCCGEqDWdUjIxZ86cwna6BNOgypNE1TJLW+PlE609zworrFDY9stFfqkXFo78I0RnZfLkyYXttdZaK9o+Ut1Pf/rTQr5HH3002vfdd1+0Z86cWcg3fvz4aI8cOTLav//97wv5pk2btjjVFqJDknpVaIt+7i9/+UvT8lKJou9jvQQhlQOmfV2DNFprmWeJVApZJndK8TKp9HNZZZVVWlWG6FpohlgIIYQQQtQaDYiFEEIIIUSt0YBYCCGEEELUmk6pIU7djJW5SOnXr19h20d/S90xVUXjaUuqzuM1z6l+ypNqrqrcyQnRmUi1wT4CXZ8+faK97777FvI9/fTT0fYaxzTalb9XeJdRCxYsKORL3bAJ0VlYkndWqrTGaV957bXXRtu/z5K2Ia/l9eWlWuAyV2upe1XfJ/r6plHm3nzzzWin7xetvPLK0fYa59dff72Qb6ONNkLUD80QCyGEEEKIWqMBsRBCCCGEqDWdUjIxf/78wrZf3vRyijFjxhTyeQlFlUu2KvwyS7rM5PFLRGV2WoZf3h0yZEgh39ixY6NdJbvwSz9+iVmIjopvz6eddloh7d///d+jff/990c7dYvmZUN33XVXtPfff/9Cvq222irahx56aLRTN0s+epYQnYnWyiR8X1bVp/zmN78pbPfo0aOpnUam82V66UIqB/R9tnepmvavXvLo5U5pX+7rlMoz/H3CSzVS2WEaJU/UA80QCyGEEEKIWqMBsRBCCCGEqDWdUjLxzjvvFLb9m6M+Ws7QoUML+bwkIY1mszS9TPilqarlLL+E4yN0QXH5KK2rL3/27NnRlmRCdAaee+65aA8bNqyQ5n/rq6++erRTSYOXR/m2ctZZZxXyeenRDjvsEO0XX3yxkM9vv/LKK9Fec801S65CiPbD9wFlEeKg2P9U9XlennTZZZcV0rycz3uCSMtbccUVm5ZdFYHOl5H2lVOnTo325z73uWg//PDDhXy+by+LYpumpXXq27dv6XGi66IZYiGEEEIIUWs0IBZCCCGEELVGA2IhhBBCCFFrOqWGONX/eg2S108NHDiwkM9rkny+pU2VbthrvLwrqXXXXbf0mDT6VmvThOiIeL3ehAkTCmm+TXgNcapr3HnnnaPt3St6F2xQbPd77rlntL1mEuCBBx6IdvrOghAdDa+9XZL3Yf7+978Xtk844YRop7r5t99+O9q+700jxvm25tNSbbF3p+b7rzR6nHeNePXVV0e7f//+hXz+HYLUnVrZ+zz+3SOA7t27I+qHZoiFEEIIIUSt0YBYCCGEEELUmk4pmUglCGWSBB/BDoqulFI3K62N7tNayspLpRreZdwzzzwT7W9/+9ulZVfJPZalFESItuD222+P9uDBgwtpvs0+8sgj0f7Vr35VyOejU62zzjrR3nLLLQv5fvnLX0b7uuuui/a8efMK+byMw0fSEqKj4yPGpS7Jxo0bF+3HHnss2jNnzizk8/KkVHbg5QVe8pfm82leWvHmm28W8nkXitOnT4+2d+kG8N3vfpdmvPrqq4Vt7xYu7YfL5E9pG1ekunqiGWIhhBBCCFFrNCAWQgghhBC1plNKJvxbrlWknhomTpwY7TSCjX+71b+l6u102y/HVOXzpJIGv+29Z/Tq1avp8bDw0pSvR/pmrhAdHb+0OmDAgEKabwd+qXXGjBmFfHvttVe0fTS6m266qZDPyyk22mijaKdeJvxb8X4JWoiOwD/+8Y/C9s9//vNov/DCC9H2kjwoek/w9tprr13I53//CxYsKKR5uWGa5inz9pDKFnx/7o85/PDDC/lSjzENVlhhhcK2v0+k/ai/13g77a/TSJiiHmiGWAghhBBC1BoNiIUQQgghRK3RgFgIIYQQQtSaTqMh9hqfVIdbptddY401Ctte75S6VfFleE1SazXEaZ1aq0n2UYBSbXAZ6bm8FkoaYtHZ8JGmUm3wIYcc0vSYiy66qLD91FNPRdu3hyqXaV5f6N22QdHd20svvVRahhDLivfee48nn3wSgN/+9reFNO9idMSIEdFO3Y75PtBrbWfNmlXI599nqXrvxdtp/+X7Oh9lLm1rDz74YLS9K8TUbeott9xCM9IIef5c6fX7KHleC+3dwsHCYwdRDzRDLIQQQgghao0GxEIIIYQQotZ0GsmElyCklEWF+9SnPlXYnjBhQrRT6YKPiuOXi6okE2Xyiao6pfv9UtWUKVOaHrMo/BJxGrVHiI7OZpttFu3zzz+/kHbFFVdE+7bbbov2LrvsUsi3++67R/vcc8+N9tFHH13I98c//jHaJ554YrRT100+ct2hhx5afQFCLAM++OADXn75ZWDhaG/+95umleFdj1ZFmauSTHjSvs27dfPyh0cffbSQb9999432nnvuWVrfhlxkUXgphL8OKEbZ8zKJVDLhZReiPmiGWAghhBBC1BoNiIUQQgghRK3RgFgIIYQQQtSaTqMhfv/996Odhov0Wt7evXtH22uYAA4++OBoX3DBBYU0H9LVnyvFa628Vik9xuuQvf451Sp6rdJOO+1Uel5Pqmv24W6ffvrpVpUhREfBaxLTUKvPPvtstLfddttoz5kzp5DPh2k/9thjo+3dsQEMGjQo2uuvv360p0+fXsjnNcRDhw6trL8Qy4pGX/LMM88U9nuNrtfNptrYstDFqdbWk2qDfR9WpieGYkhm37622267Qr6rrrqqtIwyfJ+6wQYblNYv1UaXfU7p+0ZV7yyJrotmiIUQQgghRK3pNDPEQnQETjjhhNK0008/fRnWRAghhBBtRacZEHv5w9Zbb11I85GkfAS6fv36FfJ9/etfb2p3Brw7mokTJxbSvPucIUOGLLM6CdEW3HfffdH2UbYA9ttvv2jPnDkz2ulS8Jlnnhlt78at4aaqwQEHHNC0vFTKpEhVoqOxyiqrRFnd7373u0La7bffHm3/u/YRFwHmz58fbS878m5HlxQfBQ6KEem8i8NjjjmmVeWl0kAv3fByh9SNm498mdbJfx5eCpW6dPMyRFEfJJkQQgghhBC1RgNiIYQQQghRazqNZMLzyiuvFLb9G+Fddalz8803j/bYsWMLaSuvvHK0+/Tps8zqJERb4GUS119/fSHt7LPPjvY222wTbR/BDmDgwIHRHj58eLQPO+ywQr5Ro0ZF+4c//GG033rrrUI+v5z8xBNPND2PEO3F3nvvXbm9uKSSCb+dpnkPSr7v8R6eYGHPDYtLWbTXlPRe4CWE6XjASyh83Xv27FnI11XHEaIazRALIYQQQohaowGxEEIIIYSoNRoQCyGEEEKIWtMpNcRppJvnn38+2mkUuzKqXLq0Nf5cS3oe70pqww03LKS999570fZu54ToDHj93uDBgwtp3v2R1wan+Xy0Kx+56nvf+14hn28fW265ZbRnzJhRms/r94XoiqT633S7I/PlL3+5vasgugiaIRZCCCGEELWmU84QC7G4KMKcEK1rB2orQog6Yql0YJlXwGw28Gy7VqKerBNCWK2tCtP32O7o++wa6HvsOui77Broe+xalH6f7T4gFkIIIYQQoj2RhlgIIYQQQtQaDYiFEEIIIUStWWoDYjM+MmOSGVPNuMKM7ovIf5cZY3L7GTP6La26NTn3TmZMzOt7rxlD8v0DzbjTjEfMmGLG7vn+rfLt8WYMzff1MuNWs/LP1IwrzRic28+Y8Whezq1mrLmIOl5oxpdzO35WSwszLmtcm6gnrg0/ZsZkM46t+n238blHmfFA3kauN6Nnvn85My7K9z9hxo/z/avlbXeqGfu4cq41Y0DFeb5vxqG5faEZM/Nr/acZF5vx8eLPVl/jF8w4ZWmVL5YdZuxjRjBjvVbmb9rHmfFWs/wV5SxW/opyDitrJ2Z8Jb8H/Cvtd8z4sRnTzXjSjF3d/s/n+6abcYLbf0ne553m9p3o22yT829oxv8k+64x48FWXtv2ZtxQcs3ntKaMJclfUU4vM45026uZ8fePW674+CzNzu3dEBgdAiOA94EjluK5Wo0Z1qRTPxc4KARGA5cCJ+b7TwT+LwQ2BA4A/pDvPxbYHfg+Ldd1InBaCPyr5LzDgW4h4B2e7hACI4HxwP/7eFfWdpjRjewz+WF710W0K402PBz4HLAbcHKayWypeKv5E3BCCGwA/A04Pt//FWCFfP/GwL+bMQg4EDgP2JSsXWLGnsAjIfBSsxPk9f4GWZtvcHwIjAI+CzwC/MOM5Zsc2+1jXyHcCOy5qMkC0Sk4ELg3/98ZOQxKHxynAvsC9/idZgwj6xeHA58H/mBGt7xt/BfZ/WIYcKAZw8wYSXZPGQlsYsaqZvQHNguBayrq9v+As915e5G1/VUbE0ydjF7QuJGlgwAAC15JREFUMiAOgdnAy2Zs1X5VErDsJBNjgSHpk5oZ55hxWNWBZvwgn/WZahY7utPN+I7L8zMzjsvt480Ylz+F/jzfNyh/Wr2YrHGvnZwmAI3oAKtC7EDL9n8AdM//PjBjXWDtELir4lIOAq4tSbuH7PMZZMZUd13HmfGzijIx48B8tmyqGWfk+44w40yXJz7ZmnGwGQ9bNvP3342O3Yy3zPi1GZOBLci+s52X0mBHdDJCYBbwLeCo/KHyMDOuM+MfwB1mrGzGBflv6xEz9obsQdD93qaYMTTPe2M+EzvVjP2bnPIztHTAtwFfalQFWDn/Xa5E9rA9n5Y2uQLwUZ7+feCXFZe1IzAxBD5scr0hBH4LvELWsS/URpq1pfzvwvy6HjXjmPzYo814PP8MLmucA7gL+MKivwHRUTGjB7A18E2yAWJj//aWreZdaca0fHbUkmNXMuNmMw5vUu5CfVnJ+X9r2QzuHWaslu8bbcaD+bF/M6N32X7LVh7HAJfkv+WVfPkh8EQIPNnk1HsDl4XAghCYCUwneyDdFJgeAjNC4H3gsjzvB8BKlk1ILQd8BJxCk4dsd22rACNDYLLbvS9wfV6u/7wvNONsM+43Y0Z+XWl5m+T3p3WT/auZcVX+eY+rGJyunX+nT5m11LvZOKVi/+nAuvln3einryEbI4h2ZKkPiPOOaTfg0SU4dmPg68BmwObA4WZsCFwO7Oey7gdcbsYuwFCyBjka2NiMbfM8Q4E/hMDwEBZyefJvwE1mvAAcQvaDBfgZcHC+/ybgu/n+/wQuBn4MnAP8gpZZ5TK2AiaUpH2BJft8BgBnkHXso8meuvcBrgK+6LLuD1xmxvq5vVU+G/4RLY1wZeChEBgVAvfmM93TgVGLWy/RNclXN7oBq+e7NgK+HALbAT8B/n97Zx+kVVkF8N9Zc1xQqcQyA2WhYCYGNRwkbMTcIYIRGXQihjUth/5oiqIaZ2JLqT8snaIsP5goHEFZJZCPskFSQ6RkyBWRgYFlamTHBdvEaCGFtVY4/XHO3fe+d+/77lq47PKe38zOvvvcr+c++5x7Pp/7PqPKeKAWWCjC2VgG5R6fb+OAA1g06W8+18ZAbrpwN6ZEwaLCiRO7GjgKtAItwE9U+ScW5Z2BGc93YhGY5aocK3NL5WQyYTt0psE7ZQQ4RL4sfRwYosoYj2Iv9WPrgbEeHUtny7YBE7vpQ9C3mQH8XpW/AIdcbyWMxRyz0cAIKDK0zsEMuxWqLEmfsBtdluZsYJtncTZTMC4fBub7fNtVrl2V1dg8/LxnhNp7eN9DgP2pvw94W267Kk3A65hM/Q74KFClyvYy1xgHhSCRUwes8J9sRP5CzDm5joIeB0CET2JZpBmqvJw57h7gZ6pcgTnfD5Toz3jffinwORHGlbJTytgv9cDLPtZJ5iueA32Ad9MgHiDCDuwf3QLFNUA95CpgnSpHVXkTWAtMVOUl4IMifFiEy4A2VfYDn/GflygosqQO9hXVkjVH3wKuVWUopsDu9vY6YJm3XwssF6FKlR2qTFClFnvItWLfyrxShAYRLsi5xoXYwyDNJh+jQZiR/U65AnhWldc9yvUIcLWnYPaJMEGEwT4OW4BJWKrpBb/uJO8/mEJfkzn/QUqn0YLgaTdGweSu3ufVs0A1cDGwFfiuCPOBYa5sdwGTRfiRCBNVOZJz7jnAV0V4ETgXiwSDKaTj2LwcDtwqwghVjqgyTZVxmOxPB1aLsMQjdFfmXCNPJrOkI3ppGSklS/uAESLcJ8JULHoNsBOLwN0ERRHpkLH+Tx0WrcR/p420RlUOeIBhB1CT2vZbYKkqD+ecs5wuS3MCCxABNABXifBe4H2qbPb2h4CrS7X3+C5PAqp80w3BnwJ3AAtEuE2EVXlRcjIy6rp1JPCcOyAdIoxJ7f8bVU6osgeK9PDHgF8B01VpybnOp4H7XZYfBwZ55D/L06oc8ufYWsxGybVTyrTnEc+BPsC7mQ5v98hJJyK8TbERXv1/nP8xYCbwIQoPBAHuUuWXmevWYFGlLniK6TJVnvemlRQiVl/ColmoslWEauB8bPLi6a/bsbTNfVjNbQ0wD4uYpWmn6/3WqvKPVF/O4eSNz6+xyPleTCjV+/uQqi1EyvCWKsczbdXe7yBArF7vOD7/KZYpAT6bk1ptEuF5YBqWhfmyKs+IcDnmZP5AhI2qxYvLVNmLGQSIMMqPB7gRi8Z1AAdF2IJFkdK1+QuwrE1S17kaU0ZTKCZPJrOMBTb657SMlJQld9KnYJHgWZhxPw0zPqYDt4lwiTuxIWP9GBHOwzJ0l4igWAZFRTojf/9O7X6cYp27BZgqwqNePlN0anJ0WQ/ozS8WeJXi8sOh3kaZdgDESqpexKLkH1FllghPivBIJquTldFZwPuBZjFXdRAm54m+TY932plt9fOMhdw1BVXABFXeyr3TAtnxPVnjHc+BPkBvv3btFWC0CGeJFcZP6mb/PwHXizDQ0683eBuY4TobM4of87YngTmJZyfCEJHO9G4p2rDi/FH+92SgyT+3JH30coNqiiNKXwCe8CjZQMxbP+GfszRhKaJyvIZFvgeLcBbd1xY2Ap8S4XyxWuA66PT+12GpvHT0YiMwMxkTEc4TYViZ84+ia7oqqEDccVwM3J+jvMFk7+vudOGpwcSI3qfKvVhE7FIv9TmmSgOwECu9yF4vmaNVmNO52De1YAYI/kyYgDl9yXEjgaFez5/IpEJxXaRTUibF6qTnYRGqvJKOXFkSe3NAlSprvN+X+z1cpMomYD62HiGJPoWM9W9mYqU5w1SpUeUioJmepb+/h+mfRTnbeqrLqrwPYM7ic55xaRPp7MPNwOZS7f75DSwT8054HJjt+nw4FrltBF4ARoowXGxB6mzfF7+XMynU9w+gYFSeAV0WsGZltA6Y6mNdg2VpZtM9hzGn9C4RrsnZ/hSFkkhEioN5KSa7rA8ArsecmlJ2Sqn2vLGO50AfoFcXTKmyX4RV2D++GUsHldt/uwjLMCEDeMDLJVBlt1jB/auqtHrbU264bnXv8U3gJugS+Uxf421P1awR4QT2gJrjm28FlogtjFHglsQYEFsZfgsexcLKLJ7AUrs35lxqPXAN8IcyfekQew1TI+ZR7y21r+/fKvZKm02YN7xe1RbuqdImQhMwWtXGT5U9ItwOna+H6wDmkvM1kp6aalfl7+X6EJzWJGVPZ2Jp/uUUyomy3AH8HNjpc6sZc+hmATeL0IEtULsTK/VZ6PLWAXwl53x1Ulg4u5ZCLe4iYKkIu7E5v1SVnanjfkghWrQCW6xSjxkfWTb4PaVZKMICzJj+M5bF+U/2wDKy1O79S4IN38EUfYOnrAW4V5XDvr3W9wn6J3XYOo40a7x9Zdfdu/AN4EERfqxaeKtPGV12MHP8UWC8z8WD0LlA9YvAYtdT+7Ba1nLty7y9HbgyXUcswg1YBvQDwHoRdqgyxXXwKmAP9nyYm2RQRPgaZtSfATyoyu5Un+di2ZVjIuwEBoqwCwsuHU7thyp7xd5GcS4wGBgGhdJHVZpFOCLCJ8qOsu37mgjXARtEOnV8wjxgkffnPdiC3rw3YzVi/9+hQIMq2/x+l5Fjp5Rp3yK2gH6D1xHXYjZCcAqJr27uJdyj3IQtwilpoPcV3An4l+r/VPsdBP0CEdYB31blr6fg2hcAj6p2mykLgorFddEbqiUXuvV7RPgjttiv7VT3pZKJb6rrJdzj/j62Arc/cBhbdBEEpzP1WFnEqeBiLAsVBEFpfkFxbfBphZej3R3G8KknIsRBEARBEARBRRMR4iAIgiAIgqCiCYM4CIIgCIIgqGjCIA6CIAiCIAgqmjCIgyAIgiAIgoomDOIgCIIgCIKgogmDOAiCIAiCIKho/gtbtEGUaL1b8QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x720 with 30 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "62f2747b-7d9d-4004-85bf-d67ebfc66ee5",
        "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": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": "4a7ddee3-b49f-4df0-9c9e-f41338b019df",
        "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": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EQ5wLTkcCaXY"
      },
      "source": [
        "Now predict the image:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o_rzNSdrCaXY",
        "outputId": "991f02d7-a930-4a1d-b367-4ce4eaf4bc42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "predictions_single = model.predict(img)\n",
        "\n",
        "print(predictions_single)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3.6994839e-05 4.1982639e-06 5.2054524e-02 1.0018591e-06 9.2278975e-01\n",
            "  1.5442716e-07 2.5072875e-02 1.5021504e-07 4.0251536e-05 1.2404288e-07]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Ai-cpLjO-3A",
        "outputId": "e8ac6c86-9d57-4b53-eff5-fa1a63dc4c38",
        "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": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEbCAYAAADkhF5OAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdmElEQVR4nO3deZxddXnH8c8zCSEBDVuCiSCEJYRNRJAiEhYTwi5BSFiEgmIiWyohKElRNgUMtLQVREEgArHUSFBQVEQsKChWAUWpuOFS1BZBq7SgKPD0j+e5zMntJMzMPef+huH7fr3ymnvv3MzvnHPP+Z7fds41d0dERLqvp/QCiIi8VCmARUQKUQCLiBSiABYRKUQBLCJSiAJYRKSQkQN587hx43zSpEkNLYqIyPB03333Pe7u49tfH1AAT5o0iXvvvbe+pRIReQkws1/09bq6IEREClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYGnEhAlg1uy/CRNKr6VIZxTA0ohHHx0eZYg0SQEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKFKIBFRApRAIuIFKIAFhEpRAEsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoUogEVEClEAi4gUogAWESlEASwiUogCWESkEAWwiEghCmARkUIUwCIihSiARUQKUQCLiBSiABYRKUQBLCJSiAJYRKQQBbCISCEKYBGRQkaWXoDhbtGiRY2XsXjx4sbLEJH6qQYsIlKIAlhEpBAFsIhIIQpgEZFCFMAiIoVoFoRIjTTrRQZCNWARkUIUwCIihSiARUQKUQCLiBRi7t7/N5s9BvyiucVZwTjg8S6VpbKHRvkqW2UP17I3dvfx7S8OKIC7yczudffXqeyXTvkqW2W/FMquUheEiEghCmARkUKGcgB/VGW/5MpX2Sr7pVD284ZsH7CIyHA3lGvAIiLDmgJYRKQQBfAgmJmVXgbpDjN7mT5vATCznvxZ2/6gAB4gMzPPjnMz28fMNiixDMOxrCxv3crjKd0su49lmQwsBbbvUnlD7u6E1c/fzEZ1o5yhyMzWAcbm09r2h2ETwK0P0MzGmNkaTZVTCd83AmcB/9NUWX1pOwEcZGZrd6msaWa2dVNlZRk9wDQzu8TMTgAWmtnYF/p/TXH3HwM/AxaZ2XZNlmVmawE75eMZTW/r/mj7/N8GHNmqBdb19/PnZOCYJgO+BrsR++O5wNLaWkbuPmz+AQcDtwFfB/4aWLOhcg4Ffg0clM9HFVjXA4B/A8Z1oaz5wLeATbq0bt8Gfge8Kp+v1uVta0BP5fli4EZguwbL3AJYCNwM/KCpfXeQy7YLcCvw8jq3cf7cE/gU8E3gMGD10uu7imX+MvB7YPe6/uZwqgFvBSwAzgTOJwL4qPxdR2eqPv7/Z4DfAu8EcPc/m9mITsoY4PLsClwNfMDdHzez1RssaxqxHae6+8/M7LVmtlfNZVSbuasTO/rdwAfMbKS7/6XO8l5oWTw8Z2brAbj7IuD7wLl114Rb6+7uPwI2AP4K+CTwdJ3lDJaZ7QBcATwKPFnX33V3N7PdgMuBJcADwO7A4UOlJtzHcX8J8dkcbWZb1lJGJvuLmpltDlwAjHD3Q/O1XYBlwJHu/rUO/vYKfb7As8CDwGPAd4D73f3Y/P0Id3+2o5V5gWXI52sS6zbR3Xess+w+ypoMLCLO/M8SzeQ/Ate5+yfqLC9Pok+4+6/y+b8AI919tpntSdSOvthpmf1crnnAzsAjwNXu/rCZnQNsQ5z47q+hjOq6vx14I3HymUy0AK5391+b2Tjgt96Fg7X988/X5gLHEhWcb9W1HGZ2OlHTPztPvG8H3gxcBXyqmyfePpat+tkcSnx70JPufouZ/R0wETgVOAT4k7tfO5hyXrQ14Laz08+JZuva2X+2hrvfA/wLcdejQat8CO8igmhfYmBmMrADsK2Z3ZTvbTR8zWwvMzsQWAc4GnjAzG5uhW+ntfC2sk40s0OAp4hQeCWwnOjm+So1fZ1VpbxTiZrWdWZ2RfY1vgMYYWb3A/8I/KSOMl9IBs5hwN/mzwvMbDd3P4fY107ttNVhZmMr6z4VmAmc5O4fI7qWNgRmmdkZxFhDY62cqsoyzTGzM3O/XwrcQLQuX1djP/APgdeb2dbu/rS7fxgYAUwFaqlhDlZlO7wTOB14DXCcmV3j7u8GfkPsk+8msmfQBb3o/tFbc58KHElvX+y7iEsMFwEzgF8STedOy9sc+HQ+Pp/op1s9n68G3EUElDW4zqcBXwE+BNxJ9Mu9DLgyn/fUWNY8os938z5+d1TucFvWWN7RwF35+AKiqbuk8vtZwKQGt+1ORACuntv074AJRBfT7UQf8G3Abvn+jvrdgc2IcB8NrA3cBNwHvL7yngOIwLuHBvueV7J8p+R6TwO+CyzI1xfmvr7DIP5m65jdEZgObJTb+wLgPUTXy5Tcl28C3tvNdV7JMo8GPt06DoBRwD8DC/P5lsD6HZVReiU72Dj7AN/LQPwy0WQhD5pvZTDtm68NKJzagxTYGLgO+CBwSyV8D6kz+FaxPFtU1m8h8AWi9dJDTI25jBywqqGscXnwbZNhdATR1NqTaJJ/GXh1h2W0b9/t8oA8mTi5rQU8TAzOND4AB7yFGAQ6IJ+PypD8YuU9D2dYrFFDeZtk8G5PnNxb+9cCYLPK+3rowmAcKw449hA1OyMqNJ/LoByRv5830H2tEr77AT8C3kv0Kb+OaEUuBL5B1Py3Ik64F7XK7Na/PvbLNYhKzwGV1/YD/qG2Mru5gnXuMESz6PDKa7cCH87HZwOXAm8Y6AFc/RCAw3OHGEXUPO8HNszfzSFqLR2dAfuxPOOBlxMDAEuAz7fWiTgBjG3fcTrc6Ubmui4jRv6vAq4nTmyrAWt3uD7V7bsWMLbymV4H7JfP358H5fgm96PK4/OJgb/Z+XwjYjbC64H9iRPvBjWu+8uzzKuBSUTtbylxspvc5D61iuXbhzgxLCdOwsvJGT7ACcCMDv72NkTLabMs59HcvtMr+/k6+bvvA9t2ed2rn80uRKVnLNHl+DDwhvzdXKKSMLqT4+75skp80IPYOKOAKfl4ErB+BuxhlfdsDFybj1cD/oE4iw6qxkLUxr4HbJrPZxBN0TuImtCDwDYNr/f2wIXAuhnAd5E1JOBtRPNwQk073UyiO2dKljuLnHYGHJ8H48ga1+1dRFPzO0S3xoZEf9plwPsy8Gqp1fdjWeYRte3riW6rN+frJwBfI068HQVCXwcr8Cqi+X157tdb5DY5uc5tvYpl2oHebpWXA9/Ix9MzdFono2OBh1rHQj//9mZEBWFm5bUtgF2Be/P5QuDPwLR8PpYYB2j0uHqB5f4bogV9LdEdsjdxAv4V8GHi5LB1beWVWtEBbJD1iOkpczOMvgOsSUwz+8/Wh0WMIN9N1piImly/a09tYfRqona7QT7fg+gPa/UVzqLSVGxw3XfKg38KMQhwBdEndRnw73XtqMCJxNzp44G/ALu2tgnwVuJE1NFOR/T9/RVRw5pGNPnHEoNcV2Q5OxP9j5/tNPAGsFyb5We9UT4/Mg/AWfl8fTrt56sEFzGn+mPEwNoriC6fs/Iz3ZToknhlF9Z7ZH7u/1r5vFufyejcx38KXJPbp9/7GhG0DwIX5351QuV3c+htqe6ZIVft++7qnO+25Z6c6zqB6H7YK4+/yXkMbk3NlYIiKzqADTKKqA0cSjSLnwbOrvz+FOLMfAlxZmr14XXUL0sE/KVEzeRyoh/oRio17obXe2JrHfKAXZYHzKa5U8ymg4siWPFksz4R6msBxxF9vCMqv/t7Og/ffTPUjiFquocDSyu/35tKDavJg5D/3+UyipgtM5Xerp1zgf8lxxA6LG89YrT/vcQJ6C5i0PFCYj75xHzPYqLV1njNt23Z3kGc8KYT0+uofP6TicHlfp+AMqS+Dbwpnx9NBP32+Xw3Yi7tB4kW3Ov7+ly6tP491bLz+Ppc23vOAeY0tgzdXulBbKSTidHgKUTT/3xiYKh1sOwKbAu8ttMPkuh/+ko+fhPRl7xdPj8buLDpnYWoBV5JzOYYm4F1ITXVuNvC9y1EjWthlnlrZbueRtTQOgoEovXwY2CnymvbE028nSuvLSFnrDS1fdvWfRK9LZyLch9rdbnMzFDasMPyDsxtO41ouX0RODB/Nz4P7puICzDWpTtXNbafgMYCJxG13+eIfvgvE6P9lzPAgTDiRPZc5fl3iUHj7+RnPIKoRJxN9veX+lcJ3o3o7eu+iezKzOfnA+c1tgwlN0B/N1J+gCfm81OAfyL6ZaYS/XSDqvH2sTP2EB3sN7S9/hZqnnrV1zIQfdejiRPKxcTUp5OIq4Q+VHO5s4kum83zQHuQ3gGx2XngTKqhnAXAKfl4ZP5ciziZLib6gt9K9Dl2FHgD2M4LiC6cz2b5Y4im9seJ1sYD9DEFb4DlHZh/p9WVsQFwL3Bl5T3rEVPePjHQoKthG0wnKi+vyOcnEt0BxxEn/UkM8qRPzBT4KTGQd1a+NoqYAXHaypapW/9y3ffKx6fm5/TxPObWJlq7XyROyg+Q40+NLEu3V74fG2c0ec05vf1ymxO1wPWJM/apwEeIydAH1FDmFuR0H+IMvQz4fD7fKp93NPWqH8twYu4EVwJ75GvTiL7uHxNdLOvUVNZORFP45Hy+fh4s12QY3EtNg05EV855rdfobfatA5xBzAK4mi4NvBAtjI8TU8G2JPr8WvM6X5vbu6PWBtGHeAdZ66/sW7sRN/eZV3lvV2q+bcs3j6jxnpn7Vus4OyH3i11rKGM68AwrzjR5e3sAl/hH9PM/R8zs+Wge49vmfngdURE7nhjorr3StcKylN4YfWyc/Yha7jFEv+B4YqDiAnr7ldYgmsetmRGDOovmht44D8K3kTMmiJPAPcAt+bzRuZjE5ZffIy9zJc7ER1d+P4XO+nwnE9OpphG1z4m5490BvCbfsxbRopjROiBrWrdpGe47VrZ5qyY8jzj5NX4zowz/1xA1+yX09nNunGH0wRrLWodovbw696VziNrlMuIqwkeAc5te55Us217EzI41iSu8/oO4zLzV/z6X+uaU7w/8JB9vTkw727vEelf3g/zZuspzaT5fjajcLafSXdb48pTcGCvZQD0ZDP9DNt/y9T2JgZwd6/gA2l6bSvR7HUNvbeU9edB0NPdzJcuwO5X+rzwQ3pOPRxBN8k9Rz6T/A4iR3E8DXwJ+QZztNyQGmy6lwRkHeaCfQ/Sz7lh5/Yhcrsammq3ks56bIfj8HHGiNnwHcVLvfG5nhP1pRDP2l0TLYg7R5G91uzw/Y6fJf+3rQ9S4JxJTy27P15YS08Fq/yyIAdiniC6fjgc1a/psWiF8KFET3qPy++vJaYhdWZ7SG6R9JyFqDccQV+CcT9RQWgfKLKLzvuMaKVHjvRI4j7gS69UZwqcR81C/0NQBQvSxPkbvlXoHECPi21XecxudX3G2L3ExQ3UHO4e4n8FWRNfD2RkQzfVzRf/nWcRskotzm/+gyeBvK38WMdrfqu0fn9u3OvOh1tkHxFWEu9B2i0WiiTujjqDvxzJUj6spwFaV5++ltwtqbh5vHfV7r2I5pncz1PqzXSohfBQx9fI9GcgP0cULYYpvjLYNswW9N37pIZpslxC1qD2I6+cvZYDzJCsbu/VzLtHPeTjRn/y9PFi2Ju4jcT0NXH9PzIXdIx8fQgxU7EtMgj+P6H54EzEKf38nJwCipvMcvaPuoyu/O5fo+xtDnHzeTQcXdPRzecZk4J2TAbhFg2WtUXk8n+jXPJuo5R6fr88hLn3dpYv792yiu6uRoFtFuQvyuLqVmAK2Lr39n5cSLcuJXViOEgNufZaZ+dLKg9l5rHyMGrvf+rV83d4gbRthAjEg1EP0Ef0c+MfK71+WO8xVxA3Q96FSmxtAOZPa/uZ8YP/KawcTsx9a3Q+N3N8hD4S76b36aDYx+r87Mep8AlHzvoGsrXVY3gF5clkvn1drYnfSO3VvyN4Ee5Dr/E9ErXtnYFm+/q48qX20EsLHdOOAI5r884lmeLcvsZ1B74DyeeT9LTKEjyS6RBodYC64L1RbAPsRrZEpwJjW7yshvB8NtgJXuoyFN9Cctg1yHtEE2KjynjFEf11rIvcKtdl+lHEgMf1ldWJK1weIK69uqrznFUStt6P7HPRzRziZ6IvdPZ8fliHcuiZ+DDX0/bbteA+TMyjobXLfTJfvstWF/ak19evgyrbcIEP5TuJilnOIKXfHd3G5xuQydLXmm2XvQEwtO4/ok259/l2r+RdY5/Z+7wVEv/9iotWzV/W9/c2SRpZ1CGys9YlR6Tfm8wtzI/XZzTCQjUXvjT2m5IF4WyXA7ySaHCOIAaGv0fyNdVpfsXNCWwgfSoxENzJC3EcIH5PbuNH17fJ+1D71a0yedDckar9n5OvHEbXgYbPulW3QHjxGdDF9k7iJU2vfn0O0xNYpGT4Nbodx+bPVsv5EPp9PdMP0ELMeiq9717+FtfIVLJ4/f2NmvyS+8O8v7r7QzC4AvmRme3t+O0JL6//1o5y9iQGPu4mBvBOJ6WtbEaF8IDEz4DriQ3q7u/+mjnVcyfJsB5xuZsvd/fLcDmea2fvc/UYz+wvRJ1w7d/9CfsPDV83sw8Rc10bXt4CnicGUP5nZaOIKtKnEXNR1iRuJb0509xw4zNYdWOEm4qcQwftzYgzlEuKkf5KZvYLocjvS3f+70KI2Io+p8cDPzOxId/+Mmf0OeMzMridOOAd6fN3UYcQJ+9GCi9zdADaz1d396Xz8BmJj3ePuZ+Wd94/LL2U4w8xWI/pFf7Xyv7jScqYT9444lagZ7UA0TTcBdjazJ9z9l8CM/HqfEe7+RA2r2Cp/hZNMPv6umX0b2MfMnnP3j5iZAxeb2Xx3/0xd5fclQ3gEMb3tte7+702WV8DviSb23xPzqW8nLip5iJiPeg0xHeoCd+/KN2t0S34DzFP5eCox6+MKIoSvIVo8vyGmH44gpnf+qMzSNisrdMcBHzOzt7r7Z83sSeIim3e4+zNmdizRKrqr6MLSxe+Es/j69JvIS4eJaVc/ISaC35JnqwXEoNxH3P2rHZS1E9HX9fX8nrEjiO8xW5u4/PNu4E53/0Un67SK8ke6+zP5eB+iubs0n7+TuBfCJ9391txZbnf3/2hiWfpYtucP1uHGzF5GTCd8FXBz5WR/LfAZd7+x5PI1wcwOIAbaLiJq9ycBF3l8d9l44vaK2wLzu7WPDQVmti8xgD+TOAmfQ3RDPkYMzh42FCohXf1STjNbSO9t7s529x+Y2fHEQXNbhvDpwK3u/t0ayuvJ5sYU4n4OTxJXfG1GnACWec3f42ZmM4h+xgeIwT+IaW9L3P2GfM8SonZyprt/oc7yZUVmNpuYWniYuz9cennqlN8PeD5xv4WbzexVxHzer7v7Cfme9YjumE2IY+BZd3+u1DI3oXWc5+MjifGji81sFjG+tL+7352t7vWAB4bKyagrXRCtL3t09wvN7HFi7uG1xGT8GwAHZuaXS15UV7mtD8Xdf5h9QIcT/YQPAXc0EL77EhdxLCUGF/clLm1cAvx1bodPEvdgHU3MCZUGmNlE4vOeS3xzynAL3wnERUNz3P1bZramuz9iZicBS81snrt/yN1/a2aLiamVxb5luClm9hpgsZnNcvcniVru4wDuvtzMngNuNrM57v7pksval8YDuBW+2Tf1B3e/2uJrti80s0fd/T4zW070TTXWL5UhvBw4iLgj1W/r/Ptmti4x0jwz+502IpqFqxMhDPA+M5tJ3JPgzcNxIGgI+T1xscnM4dbnm9oHHd9tZnsSg0qPAAvNbLy7n+3uvyu4nI1y9wfM7BlgmZm9mRhoe7zy+0/lmMwlZvYl4Kmh1ALoSheEme1H3PH/WHe/K187iWiqn+zu/1btN214WVZrqiaQ/XEXEXMsnzCzfwa+6u5X5O+3Ie6IdfswDQXpkgyVBcTN7FuDjncTrbs3ESF0MHGif6zUcjYl17+n1Yo1sxuJ+1k8nD8fIk5SEFNMn3L3P5ZY1lVpPIDNbAOiZjjX3b+ZTYaxRO3kEGLQ4A3A/w6lM9Ng5cnmEmJE/pXAUe7+x1ZLoOzSyXCyikHH64husNuH4z5XPZbMbIPWVFUzu4LocrqCmPEylpgPfsZQ6fNtV3sAm9nWxKWNy/L5WsR9R58ipp1tTlx3fYO7X2Vmm7j7z2pdiMLMbC/ioo8JOS1mtLv/qfRyyfBXGXQ8fDi2strCdx5xM537iO+Z+76ZXUZc8HRQvmeUu/+53BKvWk+df8zMtiC+ymTN1mvu/gfiwodRxOW/M4i5qDvm74dV+AK4++3Epad3mNn6Cl9pmplNNLP5xHSrY4dj+MIKF5scTNxreh5xxd/xZraLu58M9JhZq++38W7NTtQ2CJdTvW4Blrv7knxtTPa7LCW+J+pZM9uZuCptUV1lD0V54cMo4FYze128NPyagzJkDPdBx+eZ2ZbEbKPrcxD/p8SXOByRM6kONLNX5vE2pI+5WmrA2e3wceLSxz+Y2a4A2fe5KXHf3QkWl+POJ+Yt3ppnqGHL3W8m7vfwnMJXmuTuf3T3zw3H8LW4fLrqCaIVfVTWev+buM/0n4GDssvv191ezsHouA/YzMYQg2xXETXg08juBuKeuzcTE8PPz/dPcPf/0qCUiLyQrO1+n/ga+4fc/aP5+mjiqtrpxOXl9+Sg5Gh3f3ylf3CIqWUQrhWq+XgK0TE+kgjgH7n7g9WrVURE+sPMNiTu6fFZImwfJS4x/ld3f9LMTiYuuDnd3b9RbkkHp5YuiEr49rj7D4k+32eICw7WzvcofEVkQDxumvVN4oZa+xOXWs8FPp9jKw8QN94a8E27hoJaZ0FULv39MRHCo4k+mXXqLEdEhr/KGNEiYjBtHPBfxM2FfkB8j9sRxLd8PFJkITvU6IUYZjYZng9kEZEByRBejbiWYFNi+uoid78pp70+5i/i+xp39W5oIiKDkWNLXwEuc/f3l16eutTaBSEi0oQcW1oEjDCzNUovT10UwCLyYvENYjBu2FAXhIi8aNgw+0YXBbCISCHqghARKUQBLCJSiAJYRKQQBbCISCEKYBGRQhTAIiKF/B+ZvYWoH6lUQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "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": "5a069720-cc94-4628-b256-5d29c39e746e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "np.argmax(predictions_single[0])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "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"
      ]
    }
  ]
}