project_notebook_cnn.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "TDT4173_project_grp10_cnn_implementation.ipynb",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tomassams/tdt4173-machine-learning-project/blob/main/project_notebook_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsU1QHVNqj_I"
      },
      "source": [
        "\n",
        "----\n",
        "\n",
        "<div align=\"center\">\n",
        "\n",
        "<p align=\"center\">\n",
        "\n",
        "<h1> TDT4173 Project Assignment </h1>\n",
        "\n",
        "\n",
        "<h1> A comparison of KNN and CNN for image classification in the gastrointestinal tract</h1>\n",
        "\n",
        "<h2> CNN Implementation </h2>\n",
        "\n",
        "</p>\n",
        "\n",
        "</div>\n",
        "\n",
        "----\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P09dqkvnrG8e"
      },
      "source": [
        "*TDT4173 Machine Learning - NTNU, Fall 2020*\n",
        "\n",
        "This notebook is one of two notebooks containing code used in the group project assignment. It is intended for use in Google Colab, and should run without issues there. However, most cells and commands can likely also run locally with minor adjustments. Things like CLI commands in the beginning (wget, unzip, etc) might be different based on your operating system.\n",
        "\n",
        "The dataset used is the [Kvasir](https://datasets.simula.no/kvasir/) v2 dataset, containing classified images from the gastrointestinal tract.\n",
        "\n",
        "Note that some parts are computation heavy - e.g. model training in this notebook, and is preferably ran with GPU support. Colab environments are provided with GPU resources, this can be set in \"Runtime => Change runtime type\" in the top menu."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4yhdyBawJmSx"
      },
      "source": [
        "## 1 Setup and prerequisites"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2hO4Ntlsr1j"
      },
      "source": [
        "If any of these imports return an error, you might have to install them with pip. It can be done in the command line, or by executing it in a notebook code cell. An export of the environment requirements can be found in **requirements.txt**."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQPjrnUEePW7"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    If the notebook is running in Colab, we want to download models and results\n",
        "    when they are ready so they don't get lost if Colab times out.\n",
        "\n",
        "    This global variable keeps track of that.\n",
        "\n",
        "\"\"\"\n",
        "RunningInCOLAB = 'google.colab' in str(get_ipython())\n",
        "\n",
        "if RunningInCOLAB:\n",
        "    from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y9yv5011yCUy"
      },
      "source": [
        "%matplotlib inline\n",
        "from matplotlib import pyplot as plt\n",
        "import os\n",
        "import shutil\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EeEEA_YxvP3m"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Verify we are running the correct Tensorflow version\n",
        "\n",
        "\"\"\"\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4SgSfFivZg5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12407588-2102-4b7e-ee1a-3d60bcb3a48e"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Verify that we are using a GPU runtime\n",
        "\n",
        "    If there is no GPU found, you can activate this in the Colab settings at \"Runtime => Change runtime type\".\n",
        "    If you are running this locally, GPU support might require setup of CUDA/CUDNN beforehand.\n",
        "\n",
        "    It is highly recommended to use a GPU while training the CNN models.\n",
        "\n",
        "\"\"\"\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "af3jUE8dgScS"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Set up our project folder structure\n",
        "\n",
        "\"\"\"\n",
        "def create_folder_unless_exists(path):\n",
        "    if not os.path.exists(path):\n",
        "        os.makedirs(path)\n",
        "\n",
        "create_folder_unless_exists('data/raw')\n",
        "create_folder_unless_exists('data/zip')\n",
        "create_folder_unless_exists('models')\n",
        "create_folder_unless_exists('results/cnn_simple')\n",
        "create_folder_unless_exists('results/cnn_resnet_tl')\n",
        "create_folder_unless_exists('results/cnn_resnet_tl_tuned')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I45YsjIQgr4q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f89f6a81-0837-4e4d-8416-10c0d0bfa5ea"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Download and unzip the dataset\n",
        "\n",
        "    1. Downloads the zip-file with wget\n",
        "    2. Unzips it into the root folder\n",
        "    3. Moves it into the ./data/raw/ folder\n",
        "    4. Moves the original zip file to the ./data/zip/ folder\n",
        "\n",
        "    If you are not running in a Colab environment, you might need to do this manually.\n",
        "\n",
        "    Folder structure should look like this after:\n",
        "\n",
        "    |-- project root\n",
        "    |   |--> ...\n",
        "    |   |--> ...\n",
        "    |   |--> data\n",
        "    |        |--> zip\n",
        "    |             |--> kvasir-dataset-v2.zip\n",
        "    |        |--> raw\n",
        "    |             |--> dyed-lifted-polyps\n",
        "    |             |--> dyed-resection-margins\n",
        "    |             |--> esophagitis\n",
        "    |             |--> normal-cecum\n",
        "    |             |--> normal-pylorus\n",
        "    |             |--> normal-z-line\n",
        "    |             |--> polyps\n",
        "    |             |--> ulcerative-colitis\n",
        "\n",
        "\"\"\"\n",
        "!wget \"https://datasets.simula.no/kvasir/data/kvasir-dataset-v2.zip\"\n",
        "!unzip -qq kvasir-dataset-v2.zip\n",
        "!cp kvasir-dataset-v2/* data/raw -r\n",
        "!cp kvasir-dataset-v2.zip data/zip -r\n",
        "!rm kvasir-dataset-v2 -rf\n",
        "!rm kvasir-dataset-v2.zip"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-14 13:33:59--  https://datasets.simula.no/kvasir/data/kvasir-dataset-v2.zip\n",
            "Resolving datasets.simula.no (datasets.simula.no)... 128.39.36.14\n",
            "Connecting to datasets.simula.no (datasets.simula.no)|128.39.36.14|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2489312085 (2.3G) [application/zip]\n",
            "Saving to: ‘kvasir-dataset-v2.zip’\n",
            "\n",
            "kvasir-dataset-v2.z 100%[===================>]   2.32G  13.3MB/s    in 2m 42s  \n",
            "\n",
            "2020-11-14 13:36:42 (14.7 MB/s) - ‘kvasir-dataset-v2.zip’ saved [2489312085/2489312085]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "woWgZDDlZc_u"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    A helper utility to generate a pretty confusion matrix\n",
        "\n",
        "    Citiation\n",
        "    ---------\n",
        "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "\n",
        "\"\"\"\n",
        "def plot_confusion_matrix(cm,\n",
        "                          target_names,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=None,\n",
        "                          normalize=True,\n",
        "                          save_path=None):\n",
        "    \"\"\"\n",
        "    given a sklearn confusion matrix (cm), make a nice plot\n",
        "\n",
        "    Arguments\n",
        "    ---------\n",
        "    cm:           confusion matrix from sklearn.metrics.confusion_matrix\n",
        "\n",
        "    target_names: given classification classes such as [0, 1, 2]\n",
        "                  the class names, for example: ['high', 'medium', 'low']\n",
        "\n",
        "    title:        the text to display at the top of the matrix\n",
        "\n",
        "    cmap:         the gradient of the values displayed from matplotlib.pyplot.cm\n",
        "                  see http://matplotlib.org/examples/color/colormaps_reference.html\n",
        "                  plt.get_cmap('jet') or plt.cm.Blues\n",
        "\n",
        "    normalize:    If False, plot the raw numbers\n",
        "                  If True, plot the proportions\n",
        "\n",
        "    Usage\n",
        "    -----\n",
        "    plot_confusion_matrix(cm           = cm,                  # confusion matrix created by\n",
        "                                                              # sklearn.metrics.confusion_matrix\n",
        "                          normalize    = True,                # show proportions\n",
        "                          target_names = y_labels_vals,       # list of names of the classes\n",
        "                          title        = best_estimator_name) # title of graph\n",
        "\n",
        "    \"\"\"\n",
        "    import matplotlib.pyplot as plt\n",
        "    import numpy as np\n",
        "    import itertools\n",
        "\n",
        "    accuracy = np.trace(cm) / np.sum(cm).astype('float')\n",
        "    misclass = 1 - accuracy\n",
        "\n",
        "    if cmap is None:\n",
        "        cmap = plt.get_cmap('Blues')\n",
        "\n",
        "    plt.figure(figsize=(10, 8))\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    # plt.title(title)\n",
        "    plt.colorbar()\n",
        "    plt.rcParams.update({'font.size': 18})\n",
        "\n",
        "\n",
        "    \n",
        "    if target_names is not None:\n",
        "        tick_marks = np.arange(len(target_names))\n",
        "        plt.xticks(tick_marks, target_names, rotation=45, fontsize=18, ha=\"right\")\n",
        "        plt.yticks(tick_marks, target_names, fontsize=18)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "    thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        if normalize:\n",
        "            plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "        else:\n",
        "            plt.text(j, i, \"{:,}\".format(cm[i, j]),\n",
        "                     horizontalalignment=\"center\",\n",
        "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.autoscale()\n",
        "    plt.ylabel('True label', fontsize=16)\n",
        "    plt.xlabel('Predicted label\\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass), fontsize=16)\n",
        "\n",
        "    if save_path:\n",
        "        plt.savefig(save_path, bbox_inches='tight')\n",
        "\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qbswis9FBHG-"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Utility function to plot, save and download the confusion matrix\n",
        "\n",
        "\"\"\"\n",
        "def display_confusion_matrix(y_pred, y_true, target_names, save_path=None):\n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "    plot_confusion_matrix(cm, target_names, normalize=False, save_path=save_path)\n",
        "\n",
        "    if RunningInCOLAB and save_path:\n",
        "        files.download(save_path)\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "    Utility function to print, save and download the classification report\n",
        "\n",
        "\"\"\"\n",
        "def display_classification_report(y_pred, y_true, target_names, save_path=None):\n",
        "    cr = classification_report(y_true, y_pred, target_names=target_names, output_dict=True)\n",
        "    \n",
        "    df = pd.DataFrame(cr).transpose()\n",
        "    df = df.round(2)\n",
        "\n",
        "    print(df)\n",
        "    \n",
        "    if save_path:\n",
        "        df.to_csv(save_path, sep=';')\n",
        "    \n",
        "    if RunningInCOLAB and save_path:\n",
        "        files.download(save_path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FD2lR9QywgA"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Simple helper utility to plot training history metrics against validation\n",
        "\n",
        "\"\"\"\n",
        "def plot_history(history):\n",
        "\n",
        "    # loss vs val_loss\n",
        "    plt.plot(history.history['loss'], label='loss')\n",
        "    plt.plot(history.history['val_loss'], label = 'val_loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.ylim([0, 1])\n",
        "    plt.legend(loc='right')\n",
        "    plt.show()\n",
        "\n",
        "    # acc vs val_acc\n",
        "    plt.plot(history.history['accuracy'], label='accuracy')\n",
        "    plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.ylim([0, 1])\n",
        "    plt.legend(loc='right')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEMxkkm3ghiv"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Even though Keras has provided an ImageDataGenerator which contains a \n",
        "    train/validation splitter, we actually want a train/validation/test split, \n",
        "    so lets do this manually.\n",
        "    \n",
        "\"\"\"\n",
        "def get_files_from_folder(path):\n",
        "    files = os.listdir(path)\n",
        "    return np.asarray(files)\n",
        "\n",
        "def split_image_dataset(raw_path, processed_path, split_ratio):\n",
        "    # make a copy from raw to processed\n",
        "    shutil.copytree(raw_path, processed_path)\n",
        "\n",
        "    # split files into train/test inside processed\n",
        "    train_path = os.path.join(processed_path, 'train')\n",
        "    test_path = os.path.join(processed_path,'test')\n",
        "\n",
        "    # get dirs\n",
        "    _, dirs, _ = next(os.walk(processed_path))\n",
        "\n",
        "    # calculates how many train data per class\n",
        "    data_counter_per_class = np.zeros((len(dirs)))\n",
        "    for i in range(len(dirs)):\n",
        "        path = os.path.join(processed_path, dirs[i])\n",
        "        files = get_files_from_folder(path)\n",
        "        data_counter_per_class[i] = len(files)\n",
        "    test_counter = np.round(data_counter_per_class * (1 - split_ratio))\n",
        "\n",
        "    print(f\"Total data per class: {data_counter_per_class}\")\n",
        "    print(f\"Test instances per class: {test_counter}\")\n",
        "\n",
        "    # transfers files\n",
        "    for i in range(len(dirs)):\n",
        "        files = get_files_from_folder(os.path.join(processed_path, dirs[i]))\n",
        "        train_path = os.path.join(processed_path, 'train' ,dirs[i])\n",
        "        test_path = os.path.join(processed_path, 'test' ,dirs[i])\n",
        "\n",
        "        if not os.path.exists(train_path):\n",
        "            os.makedirs(train_path)\n",
        "        if not os.path.exists(test_path):\n",
        "            os.makedirs(test_path)\n",
        "        \n",
        "        # moves data\n",
        "        for j in range(int(data_counter_per_class[i])):\n",
        "            src = os.path.join(processed_path, dirs[i], files[j])\n",
        "            if j < test_counter[i]:\n",
        "                dst = os.path.join(processed_path, 'test', dirs[i], files[j])\n",
        "            else:\n",
        "                dst = os.path.join(processed_path, 'train', dirs[i], files[j])\n",
        "            shutil.move(src, dst)\n",
        "\n",
        "        os.removedirs(os.path.join(processed_path, dirs[i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vDdEMlpmo0_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "643f9a40-7ccd-494f-f76a-da8eea2a0323"
      },
      "source": [
        "\"\"\"\n",
        "    Split the dataset into 80/20 train/test and put it into 'data/processed'\n",
        "\"\"\"\n",
        "split_image_dataset('data/raw', 'data/processed', split_ratio=0.80)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total data per class: [1000. 1000. 1000. 1000. 1000. 1000. 1000. 1000.]\n",
            "Test instances per class: [200. 200. 200. 200. 200. 200. 200. 200.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5OYeaSKeupJ"
      },
      "source": [
        "\"\"\"\n",
        "    Parameters that apply for all models and datagenerators\n",
        "\"\"\"\n",
        "train_path = 'data/processed/train'\n",
        "test_path = 'data/processed/test'\n",
        "batch_size = 32\n",
        "\n",
        "target_w = 256\n",
        "target_h = 256\n",
        "target_size = (target_w, target_h) \n",
        "target_dims = 3 # (RGB channels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-PhNz-VZvMvQ"
      },
      "source": [
        "## 2 Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yu-Q2Ii26w3p"
      },
      "source": [
        "### 2.1 Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x2JRpOsS3r9u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d22daf29-f4ed-4a21-915e-20581244fbae"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Set up datagenerators for CNN model\n",
        "\n",
        "    Datagenerators handle image augmentation (shear, zoom, horizontal flips) as well as rescaling.\n",
        "    The datagenerator also has a built-in training/validation splitter, which creates a new 80/20 split.\n",
        "\n",
        "    In addition to that, we want a separate generator with our test set.\n",
        "\n",
        "    The dataset will be split as follows:\n",
        "    - Training set (64% - 80% of 80)\n",
        "    - Validation set (16% - 20% of 80)\n",
        "    - Test set (20%)\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "\"\"\"\n",
        "    Datagenerator for training set\n",
        "\"\"\"\n",
        "cnn_train_idg = keras.preprocessing.image.ImageDataGenerator(rescale=1./255,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    validation_split=0.2) # If defined, will split based on 'subset' in flow_from_directory()\n",
        "\n",
        "cnn_train_datagen = cnn_train_idg.flow_from_directory(\n",
        "    train_path,\n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='sparse',\n",
        "    shuffle=True,\n",
        "    subset='training') # Marks this as the training data\n",
        "\n",
        "cnn_val_datagen = cnn_train_idg.flow_from_directory(\n",
        "    train_path, \n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='sparse',\n",
        "    shuffle=False,\n",
        "    subset='validation') # Marks this as the validation data\n",
        "\n",
        "\"\"\"\n",
        "    Datagenerator for test set\n",
        "\"\"\"\n",
        "cnn_test_idg = keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "cnn_test_datagen = cnn_test_idg.flow_from_directory(\n",
        "    test_path, \n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='sparse',\n",
        "    shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5120 images belonging to 8 classes.\n",
            "Found 1280 images belonging to 8 classes.\n",
            "Found 1600 images belonging to 8 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSHfIPQg6yXs"
      },
      "source": [
        "### 2.2 Build model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PSIhSXjXwCId"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Function to build a simple CNN model layer by layer\n",
        "\n",
        "\"\"\"\n",
        "def build_simple_cnn():\n",
        "    model = keras.models.Sequential([\n",
        "        keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(target_w, target_h, target_dims)),\n",
        "        keras.layers.MaxPooling2D((2, 2)),\n",
        "        keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "        keras.layers.MaxPooling2D((2, 2)),\n",
        "        keras.layers.Conv2D(64, (3, 3), activation='relu')\n",
        "    ])\n",
        "\n",
        "    model.add(keras.layers.Flatten())\n",
        "    model.add(keras.layers.Dense(64, activation='relu'))\n",
        "    model.add(keras.layers.Dense(8, activation='softmax'))\n",
        "\n",
        "    model.summary()\n",
        "\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ufTUvzwxwC-r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c1929d1-909e-4a33-824c-a2c041ee4e82"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Actually build and compile the model\n",
        "\n",
        "\"\"\"\n",
        "simple_cnn = build_simple_cnn()\n",
        "\n",
        "simple_cnn.compile(\n",
        "    optimizer=keras.optimizers.Adam(0.001),\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=[ 'accuracy' ]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 254, 254, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 127, 127, 32)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 125, 125, 64)      18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 62, 62, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 60, 60, 64)        36928     \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 230400)            0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                14745664  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 8)                 520       \n",
            "=================================================================\n",
            "Total params: 14,802,504\n",
            "Trainable params: 14,802,504\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPLcJZfI6z9y"
      },
      "source": [
        "### 2.3 Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHaV8x1TwJzJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "117f4913-ec44-4d48-e7cf-f972c828e5a3"
      },
      "source": [
        "\"\"\"\n",
        "    Start fitting the model\n",
        "\"\"\"\n",
        "cnn_history = simple_cnn.fit(cnn_train_datagen, \n",
        "                        epochs=20,\n",
        "                        verbose=1, \n",
        "                        validation_data=cnn_val_datagen)\n",
        "\n",
        "simple_cnn.save('models/cnn_simple.hdf5')\n",
        "\n",
        "if RunningInCOLAB:\n",
        "    files.download('models/cnn_simple.hdf5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "160/160 [==============================] - 165s 1s/step - loss: 1.1104 - accuracy: 0.5387 - val_loss: 0.8176 - val_accuracy: 0.6297\n",
            "Epoch 2/20\n",
            "160/160 [==============================] - 164s 1s/step - loss: 0.7543 - accuracy: 0.6541 - val_loss: 0.8155 - val_accuracy: 0.6266\n",
            "Epoch 3/20\n",
            "160/160 [==============================] - 164s 1s/step - loss: 0.7017 - accuracy: 0.6814 - val_loss: 0.7468 - val_accuracy: 0.6539\n",
            "Epoch 4/20\n",
            "160/160 [==============================] - 163s 1s/step - loss: 0.6653 - accuracy: 0.6982 - val_loss: 0.6840 - val_accuracy: 0.7125\n",
            "Epoch 5/20\n",
            "160/160 [==============================] - 163s 1s/step - loss: 0.6573 - accuracy: 0.6939 - val_loss: 0.7322 - val_accuracy: 0.6812\n",
            "Epoch 6/20\n",
            "160/160 [==============================] - 164s 1s/step - loss: 0.6811 - accuracy: 0.6920 - val_loss: 0.7155 - val_accuracy: 0.6953\n",
            "Epoch 7/20\n",
            "160/160 [==============================] - 165s 1s/step - loss: 0.6391 - accuracy: 0.7072 - val_loss: 0.6629 - val_accuracy: 0.7141\n",
            "Epoch 8/20\n",
            "160/160 [==============================] - 163s 1s/step - loss: 0.6210 - accuracy: 0.7234 - val_loss: 0.6552 - val_accuracy: 0.6945\n",
            "Epoch 9/20\n",
            "160/160 [==============================] - 163s 1s/step - loss: 0.5835 - accuracy: 0.7346 - val_loss: 0.6674 - val_accuracy: 0.7008\n",
            "Epoch 10/20\n",
            "160/160 [==============================] - 163s 1s/step - loss: 0.5758 - accuracy: 0.7381 - val_loss: 0.6742 - val_accuracy: 0.6984\n",
            "Epoch 11/20\n",
            "160/160 [==============================] - 164s 1s/step - loss: 0.5646 - accuracy: 0.7457 - val_loss: 0.6788 - val_accuracy: 0.7227\n",
            "Epoch 12/20\n",
            "160/160 [==============================] - 170s 1s/step - loss: 0.5552 - accuracy: 0.7467 - val_loss: 0.7842 - val_accuracy: 0.6391\n",
            "Epoch 13/20\n",
            "160/160 [==============================] - 174s 1s/step - loss: 0.5524 - accuracy: 0.7531 - val_loss: 0.6673 - val_accuracy: 0.7258\n",
            "Epoch 14/20\n",
            "160/160 [==============================] - 173s 1s/step - loss: 0.5591 - accuracy: 0.7502 - val_loss: 0.6232 - val_accuracy: 0.7297\n",
            "Epoch 15/20\n",
            "160/160 [==============================] - 167s 1s/step - loss: 0.5129 - accuracy: 0.7723 - val_loss: 0.6494 - val_accuracy: 0.7203\n",
            "Epoch 16/20\n",
            "160/160 [==============================] - 165s 1s/step - loss: 0.5388 - accuracy: 0.7611 - val_loss: 0.7643 - val_accuracy: 0.7141\n",
            "Epoch 17/20\n",
            "160/160 [==============================] - 167s 1s/step - loss: 0.5151 - accuracy: 0.7709 - val_loss: 0.6958 - val_accuracy: 0.7117\n",
            "Epoch 18/20\n",
            "160/160 [==============================] - 173s 1s/step - loss: 0.4899 - accuracy: 0.7840 - val_loss: 0.6359 - val_accuracy: 0.7320\n",
            "Epoch 19/20\n",
            "160/160 [==============================] - 175s 1s/step - loss: 0.4853 - accuracy: 0.7867 - val_loss: 0.6309 - val_accuracy: 0.7391\n",
            "Epoch 20/20\n",
            "160/160 [==============================] - 172s 1s/step - loss: 0.5109 - accuracy: 0.7695 - val_loss: 0.6786 - val_accuracy: 0.7367\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_712e4293-4f7b-4e4d-a26e-afa9f115f68e\", \"simple_cnn.hdf5\", 177677736)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cv9zSMXn61fS"
      },
      "source": [
        "### 2.4 Evaluate model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H9d3P5lWHxaV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16dd64dc-f6e3-4798-bcc0-e99d37040258"
      },
      "source": [
        "\"\"\"\n",
        "    Use the model to predict on the unseen test set.\n",
        "\"\"\"\n",
        "cnn_predictions = simple_cnn.predict(cnn_test_datagen, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 23s 451ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqGeYo_Vc8K4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "5c6b5220-c8fe-451d-9d41-d4656a0b751a"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Evaluate the prediction results.\n",
        "\n",
        "    Displays a Confusion Matrix and a Classification Report.\n",
        "    These are also saved to the 'results' folder.\n",
        "\n",
        "\"\"\"\n",
        "unique_labels = list(cnn_test_datagen.class_indices.keys())\n",
        "\n",
        "y_pred = np.argmax(cnn_predictions, axis=-1)\n",
        "y_true = cnn_test_datagen.classes[cnn_test_datagen.index_array]\n",
        "\n",
        "display_confusion_matrix(\n",
        "    y_pred=y_pred, \n",
        "    y_true=y_true, \n",
        "    target_names=unique_labels, \n",
        "    save_path='results/cnn_simple/cnn_simple_confusion_matrix.png'\n",
        ")\n",
        "\n",
        "display_classification_report(\n",
        "    y_pred=y_pred, \n",
        "    y_true=y_true, \n",
        "    target_names=unique_labels, \n",
        "    save_path='results/cnn_simple/cnn_simple_classification_report.csv'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fAnH5vG/yPZeqbsJ9QHrdq08eAwwUkWdVNcZuKm+MMSZ65KhYraG47AgUz6e40/VX+7UPDbHPa7ir//+Du5Dq7QBlB//FrVAQcObXm1n0jaEcMMSv241AVd8GVV2iqlP9HpkhYi2K3KS2ZoBt/8W9154ItGOA1wNwj1+fi/ApeQBQ1ZUBXo//MgJXiMgxPuPEA3cCB4HPveZ5wFrgbyJSz6dvBS8O9YkrKFVdBMzCrfpwnTfm10G6t/Bek6/c980E7/mTRCTfbYO898oi79tAx9oYY4wJzu7wZjO/xfQU7lT+a+LW1V2MW4LqFNxSYAWo6lIR+QG4EpdUBboi6QXcBWJPi8jpuDrPPcCxwBlAJu70PriLrAYDD4lIE+An3EoGlwAriNzPeAluebCbRCQdt0rBVlX9VlU/EpG3cKfrO+CSzlTgGNyxOo5Dda6TRWQicI2I1MTNijcDbsCVRrQpYlx/AL+IyCtefJfjloV7TFXXec95UERuwa0mMVtERnt9B+A+oDyeu9JDGEZzqIzlkRAz978C74rIa7iZ8dNwJQ3f4eqHwc1+fyci43GvfSduxvhG3IWI3/sPaowxxoRkM78281sc3ixjd9xM3dW42dwkXCKTFmLX3OW+pqlqgdPpqrofV+N5O6784RHcsmUDcKfEn/Dpm41LlN/09nkGlzSdBaw//FdXNKqagVsXdw/uwq8PgId8tl+LO0Y5uOXhXsSVB+yj4Nq4A3BLvXXBrbnbHegHzD2M0F7E/Vxyfz41gDtU9SHfTt4KDWcAy3CzvU8CicDfC1nj19+HuGOQg/tgEsw83FrGXXGvsQduPecLfBLmdbifa3vgQW/7RbizB91UNb0IcRljjDEGm/ktNlVdi5ux85cSYrfcBVH9L3TzHfcA7uK4EWHEsAN3mt3/bmS9CtvXZ4zpuAvTfNtW+7d57SlBxvgC+CLEc4zB1asWFksGrozDv5Tja2BQYfsHGC/c4/gdbua1sH6DQsRxAFdSMVVV1xQyzlTczUiCbd+OK9Ewxhhjii+CZQsich/uhk4dcRfXB7zvgM+yqqFcqarvhdF/saoWeobYkt/ouBl32v+TaAdiStwVuNnl0YV1NMYYYyIucmUPjwM7cGc6CywC4GMb7n4HgYzELdE6OcC28RTMowItM1uAJb8RIiJ1cKfVu+NOcd+nqlmh9zKxwrs9cmPcDTuW4F20ZowxxpRRzXJLO0XkNwquyw+AqqZRcCUqROQU3J1XP1LVQNdRLVLVAvuFw5LfyGmNu6vYLuAVXJ2nOXq8CDTA1SX/PQI3DjHGGGOKLkJlD4GuaSqiv3tfg5aIikgiEFfUa2As+Y2QQDW15siJ9PEO5xbSPn3tfWCMMSYKYmOdX+8OrpcCa4ApQboNwV1YLyKyHneR+b/DOatuya8xxhhjjCmKZBGZ4/P9aFUtyWtdBuDKJJ4JsGRoDm4J2Am45Lg2LlH+P+AUEeld2NlXS36NMcYYY8qKkil7SFXVTiUxUBB/J8iSod4qW2f4Nb/hrdF/PW7Z1fdCDV76576NMcYYY0zxCa7sobiPIxmiSGvcDaameIluuP7tfT2/sI4282uMMcYYUybERM1v7j0Lgl7oFsQ63Dr7yYV1LPVHwBhjjDHGHP1EJB635u824NMi7t4UKAdsKayjJb/GGGOMMWVF7l3eivM4cvrgLmAbo6r7A4cvtQK0xQH/8r6dWNiTWNmDMcYYY0xZEaGyBxG5CnfzJ3AJbbyIPOh9v0ZVxwTYLZySh9dEpCowE1fqkAxcjLuN8qfAR4XFZsmvMcYYY4wpadcBPf3aHvO+fgfkS35FpBFwNjBTVZeGGHcSrjRiMFATyAIWAzcDrwRYGq0AS36NMcYYY8qKyN3hrVcR+6/D1ewW1u8N4I3DDAuw5NcYY4wxpmyQmFjt4Yiz5NeYo1TdKgncfXqzaIdRZLe/My/aIRyWa7ukRDuEIpMIzQCZ2GbvE3O0seTXGGOMMaassA8zlvwaY4wxxpQVNpNvya8xxhhjTJkgWPILdpMLY4wxxhhThtjMrzHGGGNMWSDeo4yz5NcYY4wxpkwQK3vAyh6MMcYYY0wZYjO/xhhjjDFlhM38WvJrjDHGGFNmWPJrZQ/GGGOMMaYMseTXGJPPeW3qBnz069ykQN+lC2bzyC1XcdUZ7bmoY2Ou7d2FFx6+i03rVh+R2G44rSkvXNmeb4b24I+nevPtvT3D3vfuc5vzx1O9mf/YmQW2tW5YlaHnt2D87acwe9gZzB52Bh/fegqXn9KI8nGRnyXJyclhxPDnademJdUrJ3Jck0YMvWcIaWlpEY+lKGIx7liMGSzuSIvVuAMRkWI/Yp2VPRhjCjih4184t/9V+drKlc//62LOD98y7KYrqNcohQsGXkvV6jVZu+J3vvxoDD9OmcSo8dNJrlu/ROMacm5zdqZls2TDHqpUDP/XV6v6VfhbjxT2ZR0IuMrP9T2b0PX4WkxdvIVxv6ynXJzQq1VthvU9gTNa1+W6N+aU3IsIwz1D7mTUyBH0uagvt98xhGXLljJq5AgWLpjPF5OnEhdXOuctYjHuWIwZLO5Ii9W4C7ClzgBLfo0xAdQ/pjGnX9A/ZJ8J/32VuLhyPPvu51SrUSuv/dhmLRgxbAg/fP0ZF111Q4nGdcaT37FuRwYAn9/VjaT4wn+FxQn8q38bZvyeSuWE8rQ5pmqBPmN+XMPQcb+SfSAnr+3dmWt5+rITubBDA3q1qs30pdtK7oWEsGTxYl5+6UUu7NuPD8d9nNeektKEIXfexrixH3LZwMsjEktRxGLcsRgzWNyRFqtxByK21BlgZQ/GmCD2788mIz34Kb30tL1USEigctXq+dpr1akHQGLFpBKPKTfxLYqrT21Ms7qVeGzCkqB95q3ZlS/xzfXFwk0ANK9bucjPe7jGjf0AVeWW2+7I137t368nKSmJD99/N2KxFEUsxh2LMYPFHWmxGrcJzpJfY0wBP3w9kb6dUri4S1MG9mjNy4/fR9rePfn6dOh6Ghlp+3ju/ltZuWwxqVs2MffHabz+9DAaNW1Oj3P7Rin6QxpUT+T2s49n5JQVbNyVWeT961VLBCB1X3ZJhxbU3DmziYuLo3PnLvnaExMTObFde+bOmR2xWIoiFuOOxZjB4o60WI07GKv5tbIHY4yf5m1PovvZfah/bAoZ+/Yx+/upTHz/TX6d/RPPvjeJikmVABhw/W3s3pHK1+M/YNqkQ6cCO3c/k6FPv0JSpcjNlgbzSL8TWLcjg7e+X13kfZPiy/H3nk3Yk7GfbxZvLfnggti0cSPJyckkJCQU2NagYUN+/mkm2dnZxMfHRyymcMRi3LEYM1jckRarcQdzNCSvxVVqkl8RWQ2sVtVeUYxhOpCiqinRiqG0EJG3gWtU1f4vOUwiMgx4GGiiqqujG034hn/wVb7vz7jwUpo0b807I57g0zGjueyGOwGIiytHrTr1aP+X7nQ94zyqVKvBkvmz+Oz9N3jynht4aMQ7lK9QIRovAYDz29ene/NkBr78CwdztEj7xgk8c9mJNKqVxJ3vL2R3xv4jFGVB6RnpxAf4IwuQmOBmotPT00vdH9pYjDsWYwaLO9JiNW4TnJU9lGEiMkhE7ii8pynrLv7bzZSvEM/sGVPz2p574Da+Hv8+9z/3OudcfAVdzzyPv98zjBvu/Rdzvv+GqZ+OjVq81SpW4IELWvLR7PXMX7OrSPuKwBOXtOXMNnV57ss/mLRg0xGKMrCkiklkZ2UF3JaZ5Uo3kpJKvp66uGIx7liMGSzuSIvVuIOxsgdLfsu6QUCw5Pd6oGLkQjkq/Qt3DNdEO5DiKl+hArXq1GX3rh0AbN20nmmTPqZzjzNJSMz/Nul+zgUA/Drnp4jHmeuWs5pRMb4c42at59haSXmPxApxiMCxtZLy6nl9icC/+7ehb6eGvDjlT16ZtjLisddv0IDU1FSyAvyx3bhhA8nJyaVyhikW447FmMHijrRYjTsgKaFHjLPktwSJSDkRiZ2PfyGo6n5VLfoVQkcpEalS1H1U9YCqZqpq0c65l0LZWZmkbtlEjVq1Adi+xc2G5hwsuELCwQMHvG0HIhegn4bVK1IpoTwf3XoKU4f2yHu0O7Y6SfHlmTq0B69f1zHfPrmJb//Ox/DS1D95ccqfUYm9Y6fO5OTkMHv2rHztmZmZLFq4gA4dO0UlrsLEYtyxGDNY3JEWq3Gb4CKe/IpIIxEZJyK7RWSPiEwUkWZ+feJFZJuI/BhkjHtEREWkh09bgojcLyKLRSRTRHZ5Y58UYP8aIvKaiKSKSJqITBeRjv79Cnkdg7wYzhSR/xORFUAmcKm3XUTkRhGZKyLpIrJPRKaJyGkBxrpaRGZ5MaeJyEoReU9Eavv1O15ExojIJhHJFpHVIvK0iFQKMGY9ERnhjZUlIltFZIqInOVtXw30BBp7ryP30cvb/raIFEjaROREERkvItu947xERP4pIuX8+r3tjVdNRF72nj9TRH4UkZOLcJyne68zxXveXSKy0xu/sojEeT/3Vd7480Skm98YcSLygIjMEJHN3rFb68VVy69vihf3MBEZ4P38MoAXve3lvJ/3Gu/5Fnn9hnn7pfiMFaqthYg8LiLrvZ/PQhE5L8DrD+u9UVL2eDO7/sa8+B8OHjhAl15nA3BMk+OIK1eOn779kn17dufrm1vucHybAv/rRczo6au4dcz8Ao/lm/eSuf8gt46Zz+MTl+Xb518Xu8T35W9W8MLX0Ul8AfpfMgARYeSI4fna33z9NdLT07ls4BVRiiy0WIw7FmMGizvSYjXuYMTKHiJ7wZuIVAdmAI2AV4AluARsGj6n2FU1W0TeAYaISAtV/d1vqGuBP1R1hjduBeAroCswBhgJVMOduv9RRHqo6hyfvpOBzl7fn4H2wFRg+2G8rGeACsBrwB4gN9YxwEDgI+AtIAG4ApgiIv1U9TMvnquAd4DvgYeADO/4nAfUAbZ5/ToC3wK7gFeBDUA74Dagm4j0VNX9Xt8U4EegLvBfYA5QCfgLcCYwBVfu8ASQDNzp83qWBnuhItIJ+A7YD7wEbAYuAP7jxRLoN8Bk7zU8CtQC7gImiUgTVd0b7Ln8VPJe+3fAvbif3bVAIu5ndjIuOa0A3A1MFJHGPuPHA/cAHwOfAmneGNcBp4pIR1X1X8vqItyxfRn3Xs1d52sk8A/ce/YZoDYwClgV5mvJ9Q7uOD7jxXcHMEFEmudeHBfue6Mkffjq8yxbNJcTO3ejdv1jyExPY/b3U1k060danNiBPpdfB0CVajW46MrBfPLOy9x6yRn0vvhKKnsXvE2f9DH1G6XQ++KS/4NwYYcGNKjuyhVqVoqnQrk4bjy9KQAbd2Xy6byNACxYG7jO98qujWlQQ5n865Z87UPPb8ElXY5h6cY9rNi6jz4n5b8z3drtGUHHLGlt2rblhhtv5pVRIxlwST969z4v725S3Xv0ZEApXUw/FuOOxZjB4o60WI07ELvJhRPp1R7+CaQA16rqW17bKBEZDtzu13c0MASXoPwzt9Gb1WsJDPXpewvQC+itqpN9+o4CfsMlGL285r/hEp9HVfVhn75LgOcpen1mReAkVU33GasvLhG8QVVH+7S/gEu2XxCRid7p8L7AXuB0VfU9T/yQ3/O8CWwCOvsmjSLyDfCJ93xve82jgAb4HQ+vfxyAqk4Qd7FbRVUNd4XuF3BJ/CmqusgbbyQwFrhcRN5U1W/89pmnqjf5PP8SYBxwOS6JD0cy8JSqPu19/4qI1MDNss/z4slN/JfiElzf8bOA+qrqe4eEV0RkJvA6LtEd5/ecJwAnqmrehwEROQGX+E4GzlPVHK/9f8CCMF9LrlTggtySCBGZBswCbgDu8/qE+97IIyKDgcEAdeofU8SQoG3nrqxd8QfffDaOPbt2ElcujobHNuWa2+6j7zX/ID7hUJ3sdXc/zDFNmvHVx+8x9rUX2L8/m1p16nPegEFcedPdJFUucqVIofp3PoaTm9XM13Zn7+YA/LJiR17yW1RtjqkGQKsGVXlmYLsC2z+ZsyFiyS/AM88Np3FKCm++PpqvvphEreRkbrz5Vh4a9mipvo1qLMYdizGDxR1psRp3IJb8gkSyHNFLfGoCDVX1oOYnLy8AACAASURBVE97fWAj8J3vUmfilh5rCRyT+8dfRN4ErvLatnhtc4EkoHuAp/0PcA1QRVUzROQL4GygpqrmrdovIgnAVmBnOEudicgg3Izunao63G/bx8BZwHGAf1HkzcAwoIWq/iEib3mv52Lgs0D1oSLSFliEWzZrlP9m3KzjZ6p6uYjUxCVWk1X13EJew3SCLO0mfkudiUgdYAswXlX7+fVth0v+XlLVW3z3B5qr6nKfvrW8+J5V1btDxecT46lAZd8aZBG5E3gOuF5VX/dprwHsCDa+uPKMKrgPflWAlcBzqjrE256CO56fqupFfvsOBZ7EJb5f+m37AjgXDi1rJgGWOvNpO1tVp/iNsRf3c+vvfV/oeyOU409oryPGfV2UXUqF29+ZF+0QDsuix3tHOwRjTIzrdnIn5s6dc8Sy0wrJzbRGnyeKPc62twbMVdWYLXaO9MxvU2C2b+ILoKqbRCTQtMpo4D3gr7hTwlVws32f5ya+nla4GdhQp4GTgXVeDJt8E18vhiwRWQnUyG0TkWoUXPFgm1/8fwR4rla4xGpLgG256nr7Pg70ACYA20XkO+BLYKzPDG8r7+sj3iPYeOASbgHmh3juw9HE+7o4wLaluCS/aYBt+S6XV9Xt3qfOvFpbEannt0+2qvoWn24KcPHdTu9rvnIDVd3pP773HJfiziSchCuP8FWDggL9XHOPgX8ZTm5byA8bfgItI7Cd/HGH894wxhhjwmcTv6XnJhdBfAyMwJU+TAAG4Oo/X/frJ8CvuHrSYA6nPvIF3OylrybAap/v0ylIvOcLVQj0G4CqLheR1sAZ3qMnrn74Ea9WeQWH3qrP4mqbA9kZpD2q/D/o+PD9389/IdXvOFSmAhBsjFDb8sYXkX640oxZuPKadbiLE8vhjmegc1aBfq4lqdC4w3xvGGOMMeERK3uAyCe/K4HjRaRcgLKH6v6dvdnY/wK3iUgDXBK8gYIJ4HLchUff5tZhFhLD2SJSNUDZQ1PyJ5FPAf71sJsLGT83nubAz6q6r7DOqpoFfOE98K76n4RL5m/2xgM4qKpTAw5yyJ+A4i7iK/Spw+iTK3eG9YQA21riEsjDXRT1LL/vSzqRvwqX7J7mV5vdsojjrPa+tqDga21x2NGFEMZ7wxhjjDFFEOkq7U9xp+ev9msfGqBvrtdwM3T/wa1W8HaA2cT/AvUIMvMrInV9vv3UG2+IX7cbgaq+Daq6RFWn+j3CWfv2v7hjG7CwxjceEUkO0CW36DH3yp75uJnif4hIgdICESnv1frilQt8CZwrImcG6Ov7kW8fUEPC+BioqluBmcAFItLGb7zcC7TGFzZOkLH9j/HcwxknhIO4RD/v/e7F/WARx5nofb0998JBb6y2wDnFDdJfmO8NY4wxJmyHu7yZ2FJnh+0pXCnAa97SXYtxp7dPwV0EVYCqLhWRH4ArcQnMmwG6vYCbPXxaRE7HLYu1BzgWd7o4E8hdX/ct3NXwD4lIE+AnXB3oJcAKSuCYqOpH3sVKt4hIB+Bz7/Ud473W4zhUH/u1V+/8Pe50fHXcndcUt1waqqreslffAou8i/4W4y7yOw7oh0tA3/bGvAWXqH4pbsm4ubja5ZNxs5e5HzZ+xtVTj/RWPjiImz3fGuSl3Y4rSfheRHKXOvsrLvF7P8BKD6XFR7iLxr71ziRUwK3wUKQbkqjqYhEZjXv/TBWR8bgzDjfjPqB0pGiz6YUp9L1hjDHGFMXRkLwWV0STX+9ipO64q/RzZ3+/wyWmoRKn0bgr/qepaoFT66q6X0TOB27CneLOvShsI67O8x2fvtnibvTwNC4BuhiYjUuen8EtxVZsqnqtt3zVYFxiGo9LFudxaKYU3Dqyl+KWuKqJu+hpPnCrqk7zGW+BuBt23Af0wS25tReXzL6Nz/FT1VXi1uT9P9yasFfjSgkW4o5lrudxSXh/b7w43M8iYPKrqnNEpCvu+N6Eq79eiUumny3C4YkoVf3Qu1jyTtzPeCduFvdeir62802499V13li/484adMElvxnBdy2ysN4bxhhjjAlfRJc6O1zelfpjgctV9YNox2OMPxGZCJwOVA1xkV9E2VJnkWVLnRljiutIL3UWX/s4Te73VLHH2TT64phe6ixWVma+GVc28Em0AzFlm4j4L32HiJyIW+bs29KS+BpjjDEBSQk8wnkakftE5H8islJEVERWh+j7ttcn0KN/gP4JIvKoiKwSkSwRWSEiD4q7i2+hSu1SZ95NFc7A3biiB3Cfd+W7MdF0jYhcjVtxYRtupYvBQDYh7rxmjDHGRF1klzp7HHfTqXkEWNEriKsCtM0K0DYWuBB3HdhPuOupHsNdBzWosCcptckv0Bp4H9gFvEIprik1Zco83G2Hb8PV4e7FXYj4iKqW9I1FjDHGmFjVLPc6LRH5Dahc2A6q6r+8bAHekp8X4nN3VuB17wLxu0RktKrODDVGqU1+VXU6dh8SU8qo6iyOwLJmxhhjTCREauY30AIFhfGWIa0C7Atx34bcG4gN92sfjlvy9krcildBxUrNrzHGGGOMKaZSvs7vbu+RISJTROTkAH06AxtUdZ1vo/f9Rm97SKV25tcYY4wxxpRKySIyx+f70ao6Omjvwm3GLb86F0gD2gF34O4rcJ7f3W0bAEuCjLMBd0+FkCz5NcYYY4wpK0pm4ja1JJc6U9V7/ZomiMj7wALcmvfH+2xLAoItgJBJGDewsrIHY4wxxpgyopSXPeRR1eXAOOA4EWnusykdSAiyW6K3PSRLfo0xxhhjTGm02vua7NO2EWgYpH9DXOlDSJb8GmOMMcaUASUx6xvBdYLhULnDFp+22UBDEWnk29H7vgHgW4sckCW/xhhjjDFlRGlLfkWkkogkBmg/CbgEWKqqK3w2feB9vcNvl9zv3yvsOe2CN2OMMcYYU6JE5CqgsfdtbSBeRB70vl+jqmO8fx8PfCkiE4DlHFrt4VrgIO4uqnlUdZKIfI67oUU1Dt3h7TrgXVX9obDYLPk1xhhjjCkjIli2cB3Q06/tMe/rd0Bu8rsZmAqcBlwBVAQ24W5h/ISqLgsw9iXAg7gbWlyFq/N9CHgynMAs+TXGGGOMKSsilPuqaq8w+23GJbBFGTsTl/w+WFjfQCz5NeYoVTWxPKe1qBPtMIps0eO9ox3CYRk8dmG0Qyiy0QPaRTuEw7Iv80C0Qzgsb8xeE+0QDsvt3ZtFOwRTgiJ8wVqpZBe8GWOMMcaYMsNmfo0xxhhjygKxmV+w5NcYY4wxpkwQwHJfK3swxhhjjDFliM38GmOMMcaUCRG/Q1upZMmvMcYYY0wZYbmvlT0YY4wxxpgyxGZ+jTHGGGPKCCt7sOTXGGOMMaZsECt7AEt+jTHGGGPKBAHi4iz7teTXGFOonJwcRo54gTdef5U1q1eTXLs2F/e/lIeGPUqlSpWiHV5QpSXuv55Qh5QaFUmpWZE6VRLYti+bIZ8uDdq/aa0kLmlXj6bJSQAs35bGuAWbWLszM1+/U5vWYPApxwYcY8rvqYyZs6HkXkQYSsvxDmT4M/9h0cL5LFowjzWrV9Ho2MbMW/xngX6ZmZn874N3+fqrL1j82yK2bd1C3Xr16dCpC3cPfYDmLVtFNO6sjDRmfvIOC779nJ2bN1C+QjzJx6TQ5a+X0fGcfnmnsL8c/RSrFs1m+8Y1ZKbtpXL1WtRv1orul15Hs/Z/iWjMoZTm90gosRq3CcySX2NMoe4ZciejRo6gz0V9uf2OISxbtpRRI0ewcMF8vpg8lbi40nntbGmJ+9L29dmXdYDVOzJIii8Xsm+zWkncd1Yzdqbv55NFmwE4s3kyD5x1HI99/Sfrd2UW2Oez37awcXf+9k17skruBYSptBzvQP79yIPUqFGTE9ufxO7du4L2W7dmNXfddiMnn9KNK67+G3Xr1WfN6lW8/carTPpsPGPHT+LUHr0iEnNOTg5v3XsdaxbPo8PZ/eja92r2Z2aw8NvP+eipoWxbu4JzB/8TgLVLF1C/WUva9DiHilWqsW/HNuZP/ZTX7rqSS+99mg5n941IzIUpze+RUGI17kCs7MGSX2NMIZYsXszLL73IhX378eG4j/PaU1KaMOTO2xg39kMuG3h5FCMMrDTFPeTTpWzblw3A4+c3J6F88AT4yk4NOZijPD7lT3ZmHABg1ppdPHlBSwZ2aMDT364ssM9vm/aybGvakQk+TKXpeAcye9HvpDRpCkD3Lu1JS9sXsF+t5Np8++Ns2p7YPl97/wEDOb1bZ4Y9OJSpM3454vECrFu6gNW/zqHbxYO44OYH89r/cuEVPDfoHH6Z+EFe8nvD8+8X2L9rv2t4+srTmf7BK6Ui+S3t75FgYjXuYOyCN1vqzBhTiHFjP0BVueW2O/K1X/v360lKSuLD99+NUmShlaa4cxPfwtSpHE+z5CRmrdmVl/gC7Mw4wKw1uzihXmWqJQaes0gsH0e5KNbylabjHUhu4luYmrVqFUh8AVq0bE3L1iewbMnikg4tqKx0l6BXrVU3X3v5CvEkVa1BfGJSyP0TKlYiqWp1MvbuOWIxFkVpf48EE6txm+Bs5tcYE9LcObOJi4ujc+cu+doTExM5sV175s6ZHaXIQovFuJvWcsnMn6npBbatSE2n53G1SKlZkYUb9+bbdmevJlSsUI4cVdbvyuSLJVuZuTr4qf0jIRaPd1Hk5OSwZfNmatepW3jnEnJMy3YkVq7KjLGjqVGvIce2ak92VgbzJn/ChuW/0feORwvsk7Z7B5qj7Nmxldmfj2XrmhV0Ord/xGIOJVbfI7Ead0C22gNgya8xphCbNm4kOTmZhISEAtsaNGzIzz/NJDs7m/j4+ChEF1wsxl29YgUAdmbsL7Bth9dWI6lCXlv2gRxmrtrJki372JN5gNqV4zmzeS3+0a0xdaokMOHXLZEJnNg83kXx9huvsmXzJoYMfSBiz5lUpRrX/OtVPn7mft5/9La89oSkSlw57CVOOPWsfP2zMtJ4rO+hBK1CQiJd/noZf73x/ojFHEqsvkdiNe5ABCt7AEt+zVFGRAYBbwGnqer0WIlDRHoB04C/qerbRzK2okrPSCc+wC99gMSERNcnPb3U/eKPxbgTyrs/SvsPaoFtuW0J5Q5Vq81au5tZa3fn6zdt+XYeOfd4+rSpyw8rd5CaVjCRPhJi8XiHa9bPM3novns4oe2J3HH3vRF97viKSdRt0pzWXc/g2BM6kLF3Fz9NeJcP/30nVz/2Csd3OjWvb4X4RK57+h1yDh5g15aNzJ/6GdkZ6WRnZRBfMXSJRCTE6nskVuM2wVnNrzERIiLtRWSYiKREO5aiSKqYRHZW4JUDMrPcCgNJSdH/w+ovFuPOOuAS3ArlCs7M5LZlHcwJOcaBHOXLJdsoHye0qV+l5IMMIhaPdzgWzp/L5ZdcSL36Dfjgo89ITEyM2HNvXvk7L996Kcd37MZ5/7iXNt3PpvN5l/KPEWOpUrM2Hz/7ADkHD+b1jytXjuM7dqNFl56cfMFABj/3Lru2buS1IVdx8EBkPgSFEqvvkViNOzBBpPiPWGfJrzFHxhigIjDDp6098DCQEqD/DK//mCMeWRHVb9CA1NRUsgL88t+4YQPJycmlcsYjFuPelVvaULFCgW01c0si0gtPYraluQvsqiRE7uReLB7vwixcMI/+F55L1arVGD9pCvUbNIzo83//0VscyM6ibc9z87XHJ1akxcm92LVlAzs3rw+6f1y5crQ/ow9bVv3BqkXRr0uN1fdIrMYdjEjxH7HOkl9jjgBVPaiqmaoaepruUP8cr//BwntHVsdOncnJyWH27Fn52jMzM1m0cAEdOnaKUmShxWLcK7e7C92OSy44i9QsOYkcVVbvyCh0nHpV3Cna3ZkHCulZcmLxeIeycME8+vfpTeXKVRg/aQqNjm0c8Rj2pLp1njWn4K+R3Blf35nfQPZ7M5PpeyJ7AWQgsfoeidW4g7GZX0t+TZhEJEFE7heRxSKSKSK7RGSiiJzk1y9ORO4QkUUisldE9ojI7yLyhohU8Ot7kYj8KCJpIrLP+/eFAZ57tYhMF5EOIvKt13eHiLwjInWChBwnIneLyAoRyRKRP0TkmgBjDxCRz0RkrdcvVUQmiMiJQY7Djd7ryRKR5SJyi4gMEhH16nZz++VrE5FhuBpggGneNhWRt73tvbzvBx3OsTyS+l8yABFh5Ijh+drffP010tPTuWzgFZEKpUhiMe6t+7JZuT2dLo2rU73ioVnb6hXL06VxdZZu3pcvoa0c4IYZFSvEcX7rOuw/mMOvfqtCHEmxeLyDWbRwPpf0OZdKlSozftIUGqc0iUocdRofB8DcyR/na8/Yt4clM6dSsUo1ajVsTPre3RzYX3A5veyMdOZ8+T8kLo5GLdtFJOZQYvU9Eqtxm+DsgjdTKC/R+groijstPxKoBlwP/CgiPVR1jtf9AeBRYCLwCnAQaAL0ARKA/d6YNwEvAcu8/gCDgAkicoOqjvYL4xjgG+Bj4COgA3At0ElEOquq/9pQj+PKCF4FsoAbgbdF5E9V/dGn3y3AdmA0sBloBgz2XlcHVV3ucxyGAk8C84D7gCTgHmBb4UeRT4D63tiPA7n3tl0RYp+wjuWR1qZtW2648WZeGTWSAZf0o3fv8/LubtS9R08GlNLF3UtT3F2b1CC5kvu8UiWhPOXjhD5t3Oe21LT9zFy1M6/vu3M2cN+ZzXjgrOOY+nsqAGe2SEaA9+dtzDfuv89vwbKt+1i/K5M9mQdIrhxPj6Y1qZFUgffnbgi4asSRUpqOdyDjPniXdevWArA9dRvZ+7N59qnHAWjU6FguHXglAOvWruGSPueya9dOrr/xFmb/8hOzf/kp31jnXXBRRG5pe+rFf2P+lAl89drTbF75O43bdCRj7y5mTRrH3u1bufD2YcSVK8eqhbMY//yDtOl+DrUaNiahYiV2bF7P/CkT2L1tM2dcfSs16kW2ZCOQ0v4eCSZW4w7oKClbKC5RLXhVsTG+RORO4Dmgt6pO9mmvCvwGrFTVXl7bPCBRVVuHGK8GsA6XbHZQ1T0+480H6gCNVHWX174aaAzcqarDfcbJjes+VX3SaxuEm2FdAJysqtlee0NgJfCJqg70GaOSqua7NZaItPL2f0NVb/LaagIbgOVAF1XN9NrrAb8DVfFZ2UECrPYQqM3nOXvht9pDOMcywLEdjEuwaXTssR3/WLEm3F1DOnjwIC+OGM6br49mzerV1EpOpv8lA3ho2KNUrly5RJ7jSIhk3IPHLgy67b4zm9GqbuDnW7plH09Mzf8Z6LjkJC5uV49mtZJQ4M9t6YxbsIk1O/OXPAzsUJ+WdSpTu3I8iRXKkZF9kJXb0/n691R+3VT4rO/oASU7Gxip473vMMo5Ljz3DGb+MCPgtq6n9uDTL78B4Mfvv+Oi884MOdbc35ZzbOOUIsfwxuyi//+4fcMavhkzkj/nzWTfzu1USEigfrPWnHrxINr0OCevz7QPXmHt4nnsTt3C/sxMkqpW55gWbflLn8tp+ZfTivy8vm7v3qxY+/uy3yWhdTu5E3Pnzjli6Wmlhi205T9eKfY48x46fa6qxla9hw9Lfk2hRGQubpaze4DN/wGuAaqoaoaITMPNyp6vqj8EGe9SYCxwu6qO8Nt2OzAcuERVP/LaVgM1gNq5yazXngBsBZap6sle2yBcgjlYVV/zG/tXICvQ/7DiipiqALlXLXwL7FfVjt72AcCHwE2q+rLfvqNwM8slnfwWeixD6dixk/74y5zCO5oSESr5La1KOvmNlMNJfkuDw0l+S4OSTH5NaJb8RobV/JpwtAJa4k7v+z+uBcoByV7f+4FM4HsR2SAi74nI5SLieylsbgFdoPuE5rb534t0pW/iC6CqWbjZ3ED3LV0ZoG07UMu3QUROEpHPgb3Abp/X1RaXcPvH/HuAcQO1lYRwjqUxxhgTNlvtwWp+TXgE+BW4K0SfbQCq+pOINAPOAU7zHpcDD4rIqaq640gH6wl2CXTe/7YicixuibE9wGO4JDYNUNzsc1TPwZWiY2mMMeYoIUdD9lpMlvyacCwHagPfhrN0l6ruw12Y9jHku7jtOuBpDs3KnoC7iM1Xbn2r/8xtUxGJD1D20BR30dzh6ItLcPuo6jTfDSJSC3ehXK7V3tcWuJIIXy3CfL4i1xiFcSyNMcYYUwRW9mDC8V+gHkFmfkWkrs+/kwN0med9rel9nYKbYb1VRPJuQeX9+1Zgn9fHV1XgJr+2m7z2CWG9ioJyZ4fzfQwWketxr9fXFLxVI0Qk0advPSDcdW72eV9rhux1aOxwjqUxxhgTNit7sJlfE54XgLOAp0XkdNzM5x7gWOAMXF1q7uXES0XkZ+AXYCOHlvfKxl0whqruEpF/4mYwf8ld6xa31NlxwA2qutsvhhXAwyLSBpgLdMTVGy8DRnB4vgTSgTEiMhLYCXQDzvOeL+//D1XdLiKP4JYp+1FE3sVdBDgY+APoROEzu7OBHOABb8WLNGCVqv4SpH+hx9IYY4wJm1jZA1jya8KgqvtF5HzcTOtVwCPepo3ALOAdn+7P4pLH23BrAW8FfgaeUNWFPmOOEpFNuHVyH/aaFwJ9VTXQTO564FLgGWAgLgF8D7jbf6myIryuFSJyLi6hvR83E/wj0BO3lnGKX/8nRGQPcDtuvd+1uNIDwSW/IW+9paprReRaYCjwMlABd+yCJb9hHUtjjDEmHMLRMXNbXJb8mrCo6gHcDGvIWVZvvd0nwxxzPDC+CDHMA04vpM/bwNtBtvUK0DYDODVA9wJ9vf4v4Was84jIi94/1xYWh6q+Q/4PC7nt0/ErvyjKsTTGGGNMeCz5NSZMIpKYe3MLn7b6wNXAb6q6OTqRGWOMMeEQK3vALngzpih6icivIvKIiFwvIv/GlWpUBu6NcmzGGGNMoSJ1wZuI3Cci/xORlSKi3g2rAvVL9P6mfioiq0Ukw9vnA++Oq/79U7zxAj1+Cyc2m/k1Jnx/4i6Eux53s4xMYA6uBndqNAMzxhhjSpnHgR24VYqqh+iXAowGfgDewF1P1BR359R+ItLbfzlSz3jgE7+2XeEEZsmvKfVUNSXaMQCo6p/ARdGOwxhjjDlcESx7aKaqK73n/I3gN47aBpykqgt8G0XkPWA+7sLyQLdSXqSq7x5OYJb8GmOMMcaUBRFcpzc38Q2j33Zge4D2JV7S3CbYvt66+3Gqml6U2Kzm1xhjjDHGlCoiEodb335LkC5DcGv1p4nIOhF51Lvza6Fs5tcYY4wxpgxw6/zGzGoP/8Alv4/5tefgbrY1AVgD1MbdB+D/gFO8GuGDhGDJrzHGGGNMGVFCyW+yiMzx+X60qo4uiYEBRKQr8BxuRaXHfbep6lrc3WV9vSEio3EXpF+GuwlWUJb8GmOMMcaUESU08ZuqqoEuQis2EekITMKt+nC+//r6Ifwbl/yeTyHJr9X8GmOMMcaYqBORDsAUYDdwmqpuKMLu64CDQHJhHW3m1xhjjDGmjCitNb9e4jsV2ItLfNcUcYimQDmCXyCXx5JfY4wxxpiyIIJLnRWFiJyEm/Hdh0t8V4XoW8tbHs23LQ74l/ftxMKez5JfY4wxxhhTokTkKqCx921tIF5EHvS+X6OqY7x+jXGJbw1gBNDVu+DN13hVTfP+/ZqIVAVm4kodkoGLgY7Ap8BHhcVmya8xxhhjTBkgSCTLHq4Devq15S5b9h0wxvt3E6CW9+9hQcZqAuQmv5OAq4DBQE0gC1gM3Ay8oqo5hQVmya8xxpSA0QPaRTuEIqvR475oh3BYds54ItohHJbbuzeLdgiHJSM75JKppVaFcqXw/H4hNALPEcE7vPUKs9903BLE4Y77BvDG4UXl2GoPxhhjjDGmzLCZX2OMMcaYMiKuNF7xFmGW/BpjjDHGlBGW+1rya4wxxhhTJoiU3nV+I8lqfo0xxhhjTJlhM7/GGGOMMWVEnE38WvJrjDHGGFNWWNmDlT0YY4wxxpgyxGZ+jTHGGGPKCJv4DTHzKyI5InIwzMeBSAZtjImsnJwcRgx/nnZtWlK9ciLHNWnE0HuGkJaWVvjOUWRxF8/dV/XkvX9dzpL/3UPGzCdY9vE/Q/Y/+y/N+WLEdayaeD+p3zzCwg/u4olbzqVOjcr5+k0eeT0ZM58I+vh8+LVH8mXlU1qOdVHFatxbt2zhrttuok3zFOpWr0jbFk249+472b1rV7RDC1t6ejptWx5HlcRyDLnj1miHUySCd4vjYv4X60LN/D5KZO60Z4wp5e4ZciejRo6gz0V9uf2OISxbtpRRI0ewcMF8vpg8lbi40llBZXEXz2M39mb77nQW/L6BalUSQ/b9W5/OjLq3H3OXrue5d78jLTObjq2O4ZZLu3FhzzZ0umo46Zn7AfjPO9N4a+LsAmP0P+NEzj+1FV/8uOyIvJ5ASsuxLqpYjHvb1q2c1asrmzdtZNB1g2nV+gSWLlnMW6+/wk8/fs+X38wgKSkp2mEW6l+PPkxq6rZoh2GKIWjyq6rDIhiHMaaUWrJ4MS+/9CIX9u3Hh+M+zmtPSWnCkDtvY9zYD7ls4OVRjDAwi7v4WvV/itUbdwIw593bqVwxIWjfOwZ2Z9O2PZxx46tkZbuTgW9+OputO/Zx76DTOaPL8UycsQSAb2f/GXCMewedRmbWfj74an4Jv5LAStOxLopYjfu5p59k3do1vPbWu1x86WV57V1OPoXr/3Ylo158nruHPhDFCAu3YP48Rr34Ao89/h/uH3p3tMM5LLbaw2Fc8CYilUWksYhUOBIBGWNKl3FjP0BVueW2O/K1X/v360lKSuLD99+NUmShWdzFl5v4hqNKpQR27s3IS3xzbUrdC0BaRnbI/bu1S6FF4zp8NmMJO/dmFD3Yw1CajnVRxGrcP8yYTsWKFel3yYB87X37X0piYiLvj3knSpGF5+DBg9x60w2cefY59Lmwb7TDOTwizvKgpAAAIABJREFUSAk8Yl3Yya+I/FVE5gG7gZVAW6/9dREpfR8xjTElYu6c2cTFxdG5c5d87YmJiZzYrj1z5xQ8fV0aWNyRNfWX5bRuWpcnbz2PFo1rc0ydalzY8wTuHXQaM+atZPrcFSH3v+avnQB467PIvb5YPdaxGnd2dhYJiYkFkqe4uDgSK1Zk9aqVbE9NjVJ0hRs5Yjh//L6MZ59/MdqhFIu7y1vxHrEurORXRC4CPgVSgaGQr9p5FXBNyYdmjCkNNm3cSHJyMgkJBU95N2jYkNTUVLKzQ8/qRYPFHVl3D5/Ix98s4uZLurLgg7tYPuFePnziSr7++Q/Ov/0NcnKCX0JSJSmBfqe3ZdWGHYUmySUpVo91rMbdolVrdu3cya8LF+Rr/3XhAnbtdGcZ1q9bG43QCrV61Soef2wYQ+//PxqnpEQ7HFNM4c78Pgy8papnA8P9tv0GtCnRqIzxISIpIqIiMizasZRF6RnpxAf4IwuQmOAugkpPT49kSGGxuCNr/4Ec1m3ZxWczlnDdo+O4/IH3ePOzWVx5bgdeuOfCkPteelY7KlWM551JcyIUrROrxzpW477x5tuIi4vj2qsHMuWrL1i/bi1TJn/JdddcToUKrpIyI6P0xQ1wx603ktKkKbfefme0QykWAeJEiv2IdeGu89sKyF3jxv/j+06gVolFZIwpVZIqJrFt39aA2zKzMl2fUniFtsUdOSLCZ8//jfLl4jjthlfy2sdP+40du9O5+6pefDR1EdPmBJ7VHXRBJw4cOMiYz+dGKmQgNo81xG7cp3TrzuvvvM99d9/BgIv7AFCuXDmuGnQdLVu15vPPJlClStUoR1nQh++/y7ffTOWrqdPzkvRYdhTkrsUW7szvHiA5yLYUwNb8MOYoVb9BA1JTU8nKyiqwbeOGDSQnJxMfHx+FyEKzuCOna7vGnNq+CROm/1Zg2yff/gpA95OaBtz3hKZ16dS6EV//spyNqXuOaJz+YvFYQ+zGDXBRv/78tnwN382cw6Svp7Hkz3U8N2IUGzesp3z58jRpdly0Q8wnKyuL+4bezdm9z6VuvXqsWPEnK1b8ybq1awDYs3s3K1b8ya4YWqfYhJ/8TgHuE5HqPm0qIgnALcCXJR6ZKXVEpEq0YzCR17FTZ3Jycv6fvfuOj6LaAjj+OyRACEWE2OgoigpIVyxUEcGCqCDNgohYEIWn2Av2roiIiIgo0sGGDUVFFJQmRalSgkgPAhpCgiTn/TETSDa7m02y2ZKc7/vsZ1/u3Jk5O4xwcvfOuSxatDBbe2pqKiuWL6NJ02Zhisw/izt0qh53DAAxXmrLxsbEuO/e/7m5sXNzAMZ5qftb2KLxWkP0xp0pJiaGBg0bce75LTnu+OPZuWMHK5Yv4/wLWkXciPXBgwdJ2r2bWV9+QaN6dY+8OnVoB8DkSRNoVK8u7737TpgjDZxVewg8+X0IOBFYC4zBmfpwP7AMqAYMLYzgTO5EpI87H7adiNwjIhtEJE1E1olIjgcRRaSfiPwqIgdFZL+IfC0iF3jppyIyTkQuFJGfRCQZmOluSxSROSLSUERmi0iyiOwSkZdFJFZE4kTkJRHZKiKpIjJXRM7wOH55EXlKRBaISJIb83oReU5ECvy3n4icKCLDRWSje+xdIvKNiFzk0e9UERkvIttF5JD72V4UkbL5OWbmtfGybxv3mvbJ0pb5Z3ehiDwqIpvdP5cFItLC7dPavf4H3BgfKei1yauu3bojIowYnn26/9gxb5OSkkKPnr1DHVJALO7QWb1pJwDdOzTKkeRee0kTAJas/ivHfqVKxtDj4sbs2PNvSBe2yBSN1xqiN25vMjIyuH/IINLT0/nfvQ+EO5wcypYty/iJU3K8Xh0+AoCLOlzM+IlTuOSyy8McaWCCUemhCOS+gc35VdVEEWkCPA5cDKQDrYCvgEdVdVvhhWgC9AxQBngLSANuA8aJyHpVnQcgIs/jzN1eCDwIlAf6A9+LyBWq+oXHMZsBVwNvA54FGKvhfCMwBZgOdAD+BxwG6rmxPIczXeYe4GMROUNVM9z9qwL9gBnARHe/1m58jXHus3wRkVrAPOAE4H1gMVAWaAG0d+NGRJoC3wH7cK7bVqAhcCdwvoi0VtX/8nLMfHoOiAFeA0oBdwNfi8j1wDvAaGACcA3whIhsUtWQFfKs36ABt9w2gFEjR9C921V07HjJkdWkWrZqTfcILKYPFncw9OzYmBonOl/4JVQsS6nYWO7r0xaAP3fsO7IYxW/rd/DR979xZdsGzBt7B5NnLSUl9T/an3Mql7U8kwW//8nMH1flOH7nVmdS+Zh4Xv7gB9LTM3JsL2yRdK3zIlrjTk5Opn3rc7ns8iuoUas2/+zfz4fTprBs6RIeHvokLVu3DXeIOZQsWZIuV3XN0b45MRGA2ief4nW7iWyBPvCGqv4F3FSIsZiCKQ00V9VDACIyHace8x3APBGpCwzBSeDaZek3BlgFjBSRU1Q1Pcsx6wEXqepsL+c7BbhGVae5P48SkSXuOWYC7VVV3XPswUnsLgJmuf03AtUzk0vXGyLyJPCwiJytqtm/0wvcSKAK0FFVZ2XdICJZh6XGAttxrtu/Wfp8C3wI9AbG5fGY+REDtMjyZ7IKp7TgNOBcVV3str8DbAYGACGtYv/SK8OoWasWY8eM5qsvPqdyQgK3DRjIo0OfiMhlVDNZ3AXT57JmtGqSfa7u0P4dAJj768ZsK7Hd8NgUFq/6i+4dGvHIzRdRQoQ/d+zjhfe+5/n3vvda6uyGyzOnPIS2ykNWkXKt8yoa4y5VqhT165/F9KmT2bljO2Xi42ncpBnTPv6cCy/K93iHyaOiUK2hoMTNTwLfQaQKzqjdVhvxDT/3a/R3gf6q+rbHtt+ANFVtJiL3As8DV6jqpx79XgUG4SSBmYmWAstVtZGXcyYCsapazaP9NZxR02wJs4g0Bn4FBqrqCC/Hi8UZhY7BSbjnAHeq6uvu9lo49aQfz23ZbRGphFOPepaqdvLTrwGwAqeM30jPze75PlXVXoEe0z1uIpCoqm082tsA3wM3quo4t60PXv7sRORY4G9gnqpe4HGcT4DzVdXrA6gi0h9nNJ/qNWo0Xbdhs79wTTF3bKvI+5o5EHvnPhvuEIqVg4fSc+8UgUrGRF+S1+q8s/l1yeJCC7xS7TO1w9AJBT7OlD5NlqhqZE8u9yMvK7xdLyKbgC3AL8AWEdkkItcWWnQmLzZ6advD0TJ0td33lV76ZbZ5Po69zs/5Nnlp2+tjW2Z7tpJ4InK7iKzAmabxN07VkDnu5mP9nBsROc6dg5v5Os7dVAcneV3qZ3dwyveBM5Vnt8drF86UhhPyeMz8yvZnp6q+riPkUlpQVUerajNVbXZcwnG+uhljjCmmxB54C2zag4jcAQwHZgNPAjtxEoOewHsicoyqvlFoUZpA+PrVvCB3qb9q4/6GAnKNRUT+B7wMfI1zb20DDuF8qzCO3H8xWwTUzPLzZpyye4HKjOVlnLnr3uz10e6Pr69S/P235ut6RedwizHGGBPBAp3zezcwTlX7erSPFZFxOA80WfIb2TJHF+sBnpXmz/ToEwrXAYlApywPwSEiHQPcvzfOQ3WZDrrv63ES0BzTNTz84b6n+5jTnFWgxwRnBLuSl3bvRU6NMcaYEHFWeAt3FOEX6LSHE4HJPrZN5OjXwyZyfYqTwA0RkSNL1IjIScCNOCOnhfW1vjfpbjxZR4NjcUro5UpV56nq7CyveW773zh1pzuJSHvP/eTo9zVLcZbmvlVEciSm4pRsq5THY4IzVeR0EamaZXtpnIfUjDHGmPAJwpSHYjPtAfgN5+l+b07FSSJMBFPVtSLyIk4psbkiMoWjpc7KAb09Kj0UtunAs8CXIvIhUAHoBfznd6/A3AHMd4/9HrAEZ5T4HJzR5vtUVUXkOpxSZytEZCzO3Od4nDm+VwEPcLTaQ67HdPuNAHoAs0VkFE7psuvwP4XEGGOMMSESaPJ7FzBZRJKAD1U1XURicGrADsH5x95EOFW9T0TWA7fj1JY9BCwAeqnqjyEO50WcUd+bcMqg7cCpGfwuTum1fFPVTSLSDHgEuAS4Hmf+7nKcmrmZ/Za5lSgeADoDtwL/4iSz44Bv83HMeW4Vhwfdz7gVeBOnLvCR4xljjDHhUAQGbgvMZ6kzEdlC9od3jsEZIUzH+Uf/WJzSVMnAPlWtmeMgxpiwadq0mc5bEL76qSbyWakzEwgrdRY6hV3qrPLJ9fTSpyYV+DjjezeM6lJn/kZ+v8X3k+vGGGOMMcZEHZ/Jr6r2CWEcxhhjjDGmEFm1B0fAyxsbY4wxxpjoVhSqNRRUnpJfEWkI1AXiPLep6vvBCsoYY4wxxgSfpb6Br/BWEfgcaJHZ5L5nnRNsya8xxhhjjEFEHgCaAE2B2sBmVa3lp/85wNM4JUQVp7zo/aq6zEvfKjhVqzrhFGNYCTyvqtMCiS3QRS6eASoDrXAS3yuBdsAEnFXBzg7wOMYYY4wxJgxEoIRIgV8BegYnV9yAUyXMT1zSAvgBJ0l+FHgMZx2JH0WkgUffSsBPOPX438Qpx5sMTBWRGwMJLNBpDxcDjwO/uD//papLgDkiknni6wM8ljHGGGOMCYMQTvk9RVU3OueU33FGaH0ZjrP2QCtV3eruMxVYDbwMdMjS936cJLmzqs50+74D/Ay8JCLTVDXZX2CBjvyeBGx0VwBLxVkZLNOHwKUBHscYY4wxxhRxmYlvbkSkDtAcmJaZ+Lr7bwWmAe1F5MQsu/QCNmQmvm7fdOB1oBLOQlR+BZr87gAquv9/M3Bulm11AjyGMcYYY4wJIxEp8CvImrvvP3vZ9gvOdNumbuwnAVU5OhPBs2/W4/kU6LSHn3AedvsMGA88JiK1gMPADcCnAR7HGGOMMcaESZBy1wQRybqE6GhVHZ3PY1Vx37d62ZbZVjUffX0KNPl9PMsJX8R5+K07EI+T+A4M8DjGGGOMMSa6JQVxeeN49z3Ny7ZUjz556etTQMmvqm7AeVoPVf0PuNt9GWOMMcaYKCDkqVpDqKS476W9bIvz6JOXvj7ZCm/GGGOMMcWBhLTaQ6C2ue/epitktm3NR1+ffCa/IvJobjtnoar6ZB76G2OMMcYYs8h9PxcY47GtBc6CF0sAVHW7iGzl6KJrnn0BFnvZlo2/kd+hue2chQKW/BpjTBTZO/fZcIeQL8c2vyPcIeTL3kUjwh1CvsSVDLQwVGQ5nK65dyqGCqFaQ4Go6nr34bluIvKIqm6DI6u4dQO+U9UdWXaZBNwjIpdnqfMbg/P82T7gi9zO6TP5VdXovNuNMcYYY4xXoUruROQ6oKb743FAKRF52P15s6qOz9L9LuB7nBXdXnfbBuKE6/mM2XM4SfFEEXkFZ5pDT5wSZ/1U9d/cYrM5v8YYY4wxxYAQ0pHfm4DWHm2ZswR+wCmdC4CqzheRNsBT7kuB+UA3VV2e9QCqukdEzsdJggfgrBy3CuihqlMCCcySX2OMMcYYE1Sq2iaP/X8GLgyw71bgunyEBVjya4wxxhhTbJSIrCm/YWHJrzHGGGNMMWHJb+jmPRtjjDHGGBN2NvJrjDHGGFMMiEReqbNwyFPyKyJnAa2AysBbqrpDROoAOwMpLWGMMcYYY8LHpj0EmPyKSGngA+AqnEoZCswEdgAvAOuA+wspRmOMMcYYY4Ii0Dm/TwPtccpKnICTAGf6Erg4yHEZY4wxxpggc6Y+FOwV7QJNfnsCD6vqROBvj22bgFrBDMoYE1kyMjIYPuxVGtY/nYrl4qhTuzr3DbmbAwcOhDs0vyzu0IqUuO/p24EJL/Rl1cyhHFw6gjWfP+63f7+uF/DzpPv4++dX2D73BT4ZcTtnN6iV63lOTKjAth9e4ODSEQy6LqDypEETKdc6r158/ll697iGM+ueQnypEpx+au1whxSQCmVivL5OSqgQ7tDyRIASIgV+RbtA5/xWBlb72FYCKB2ccIwxkWjI3YMZOWI4nbtcyV2D7mbNmtWMHDGc5cuW8sWs2ZQoEZmFYyzu0IqUuJ8c2Jk9+w6wbM0Wjilfxm/f1x7sTv9uLflh0Toeeu0T4uNK0veq8/l6zF1cfvtIflzyh899X7mvG7Ex4fmziJRrnVePPfIQlSpVolHjJuzfty/c4eTJeee3pM9N/bK1lYwtGaZo8i8y74zQCjT53QScC3znZdvZwNqgRWSMiSirVq7kzTde54orr2Ly1BlH2mvVqs3dg+9k6pTJ9OjZK4wRemdxh1YkxX3GZY+RuHUPAIunPUi5eO/jM2edVpX+3Voya95Kutzx5pH2MdPnseyjR3jjkZ40vPJJVDXHvpe2bkDntg15ZPgnPDP4ysL5ID5E0rXOq5Vr1lP75JMBaNaoAckHksMcUeBq1a5Nj57XhjsMEwSB/gLwPnC/iPQGMn/NURFpCwwGxhZGcMaY8Js6ZRKqyh13DsrW3rffzcTHxzN54gdhisw/izu0IinuzMQ3N62bnwbAhJkLs7XvTz7IZ3NWcGrN4zmv0ck59isXX5ph91/D29N+ZPHKPwsecB5F0rXOq8zEN1odOnSI5OToSdi9sTm/gSe/LwCfA+OBvW7bT8Bs4CtVfb0QYjPGRIAlixdRokQJmjc/O1t7XFwcZzVsxJLFi8IUmX8Wd2hFY9ylSzlffqakHsqx7aDb5m3u75MDOxMTU4LH3phZqPH5Eo3Xuij45KMZnFCpHFWOO4aTa5zIPYPvZP/+/eEOK08kCPN9i8Kc34CSX1VNV9UeQGvgZWAMMBxop6q9CzE+Y0yYbd+2jYSEBEqXzvnVcZWqVUlKSuLQoZzJQ7hZ3KEVjXGv2rAdgDbuCHBWFzQ9FYBqJx6brf3sBrW4uVtL7n1pBv8kpxZ+kF5E47WOdk2bnc0DDz3G+IlTGTXmXVq1bsvoUW/QsX3rqB8JLo7ytMiFqv4I/FhIsZhiTERq4cwtf1xVh4Yxjj7Au0BbVZ0TrjgiScrBFEp5+UcWIK50nNMnJYVSpUqFMqxcWdyhFY1xz5q3ilUbttP/mpZs372fj79bRnxcKe68th316pwEQJm4o/HGxpbgjUd68e0va5j+9a/hCjsqr3W0+/7Hn7P93Kv39dRvcBZPPPYwb74xnCH3PRimyPKuCAzcFpg99GeM8Su+TDyH0tK8bktNc0a+4uPjQxlSQCzu0IrGuNPTM+hyx0h+XraRpwd1YeWnQ1k09UEanl6NR4Z/CsC/B46O7t7d5yJOqZ7AoOemhitkIDqvdVF01+B7KFWqFLO+/CLcoeRJCSn4K9oFusJbBs6qbj6pakxQIjLGRJSTqlRh9epVpKWl5fiaddvWrSQkJETkCJPFHVrRGveWHXvp2H841U88lhpVKvH3vgOs3riD/t1aArB2007Aqel7300X88FnCxGBk6snAFD1+GMAqFSxLCdXT2DH7n+8ziEOpmi91kVNyZIlOemkKuzZkxTuUEweBTry+4SX1xvAH8Cf7s+mCBCR8uGOoagRkRgRidphmKbNmpORkcGiRdmfiE9NTWXF8mU0adosTJH5Z3GHVrTGnWnLjr3M+3UDqzfuAODiC+qRnp7B7J9XAXB8pfKUiSvFzV0vYOWnQ4+83n2mDwBD+nZg5adDufiCMws91mi/1kVFamoqW7f+xfHHnxDuUAJmi1w4An3gbaiqPu7xuhOoh7P4RXQ97hhlRKSPiKiItBORe0Rkg4ikicg6EbnBS/9+IvKriBwUkf0i8rWIXOCln4rIOBG5UER+EpFkYKa7LVFE5ohIQxGZLSLJIrJLRF4WkVgRiRORl0Rkq4ikishcETnD4/jlReQpEVkgIkluzOtF5LmCJoNu3Coix4nI+yKyR0QOiMi3ItIkS7/jReSQiEzwcZw3RCTDnXPs73wJbt8t7vG2uD9X9uiX+WfVXkQeEZENQCpwjbtdRWScl+Nn7tcmS1slEXnV/fNOdT/jEhEZEviVKriu3bojIowYPixb+9gxb5OSkkKPnpH5zKvFHVrRGrc3l7ZuwCWt6jPx84X8ud0pcJS4bQ+9hozJ8Xryzc8B+GDmAnoNGcOC5ZsKPb6idK2jwZ493kvnPfX4oxw+fJhOl1wW4ogKxkqd5fGBN0+qmi4iI4ERwLDc+psCewYoA7wFpAG3AeNEZL2qzgMQkeeBe4GFwINAeaA/8L2IXKGqnpOTmgFXA28D73lsqwZ8A0wBpgMdgP8Bh3F+8SkDPAckAPcAH4vIGaqa4e5fFegHzAAmuvu1duNrDFxcwOsB8BXOkttDgROBO4AfRORcVf1dVXeJyKfAVSJSUVWPLCkkInFAL2C2qib6OoGIHAPMB+rg1LT+1Y3/NqCdiJytqv967PYSTk3st4F/yN9CMNOAVsAoYAXO9T4DaAO8mI/j5Uv9Bg245bYBjBo5gu7drqJjx0uOrCbVslVrukdoMX2LO7QiKe6elzanxkmVAEg4thylSsZyXz/nr5s/t//NpM+PlgJ787FeCMKKdX9xMPU/zmt8Cj06NWPx74nc8+L0I/3+SU7lo9nLcpwrqamznPDKP7Z53V4YIula59XED8bz55+bAUhK2s2hQ4d47pmnAKhRoya9rr0unOF59eJzT7No4QJatW5DterVOZB8gK9nfcncH76nWfNzuOX2O8IdYuCKyJzdgipQ8usqDVQKwnFM7koDzVX1EICITAc24iR880SkLjAEmIdThi6z3xhgFTBSRE5R1fQsx6wHXKSqs72c7xTgGlWd5v48SkSWuOeYCbRXd+kjEdkDvAZcBMxy+28Eqqvqf1mO+YaIPAk87CaN2b+3y7vNwNVZ4vgQWISTfHZ0+4zGSfB7ASOz7Hs1UBGndJ8/9wKnAgNU9cj+IrIM5xe/e4FHPPYpAzRW1ZR8fKbMhLsd8KaqDszDfv1xftmheo0a+Tm1Vy+9MoyatWoxdsxovvricyonJHDbgIE8OvSJiF1GFSzuUIuUuPt0OY9WzU7N1jZ0wOUAzF38R7bkd/Hvm+l71fl0ubARpUrGsGFLEk+8+TmvT/ie1LT/iFSRcq3z6r1xY/lx7g/Z2p4Y+igALVu1jsjkt2Wr1qxZs4qJH7zP33/vISYmhlPqnMqjjz/FHXcOJi4uLtwhmjwSb8s25ugk4u1f0VJAfZwRqC2q2i7IsRlXlvJb/VX1bY9tvwFpqtpMRO4FngeuUNVPPfq9CgzCSZ4Xu20KLFfVRl7OmQjEqmo1j/bXgDvxSJhFpDHOiOhAVR3h5XixOKPQMTgJ9xzgzswFUvJa6sydOnADcK6q/uKxbRZwIVBRVZNFRIANwF5VbZql33fAWUCVLL8o9MGj1JmIrMIZ3a6iqoc9PtM2YJeq1vfYf7Cq5vg2xL3m76lqH4/2bOcVkVI4I8YrcZL7xNyuiaemTZvpvAWL87qbMRHv2OZRNNKWxd5FOf5qjAqB5AmR6HB69MXd+vyz+XXJ4kIbm61at4EOGPlxgY/zUPs6S1Q1aieXB/rrYSJOYpL1tRb40N0+IOiRGW82emnbA2TOO63tvq/00i+zzXNtyXV+zudt8tpeH9sy2z3nwN4uIitwpmn8DezGSXwBsleP9+DO5z0xy+s4L91We2lbhZNk1wRwR4XHAE1EpJF77JNxpg+Mz0x8/agNrM2a+LrHPYxz/byt1+nvuubKjWkQzi+Ym0RkpYi8LiIXFuS4xhhjii/ngTcrdRbotIcbvbSl4nzlvMjja3RTeHxd54Lciv6+lvf355prLCLyP5wVAb/GWRFwG3AIZy7wOHL/5WsRbgLr2gzUymUfX8YCjwM3AQOBvm6suU15yK+8TnfI8d+iqo4SkU+AS3HmSncF7hCRKe6Ki8YYY4zJo1yTXxGJAZYB21R1d+GHZAogc2S4Hs7X/Fmd6dEnFK7D+dagU5aH4BCRjj73yK43ztzZTAe99DkD+MWj7Uyc5HxzZoOq7hCRmUBvEbkf6AMsUFVvo+SeNgJ1RSTWy7SH08jbNf0b73PkvY0eo6rbcRL0Me5/i+OBniLysqou8raPMcYY40tRGLktqECmPSiwGOfpdhPZPsX58xoiIiUzG0XkJJzR+83A0hDGk+7Gk3U0OBa4P5CdVXWeqs7O8prnpdu97pzezOM3AdoD36qq54Lrb+NMtRiFM/oc6Kjvx8BxOJUrsrrZbf8owOOAMx3i3Kyl3kTkWDy+XRGReM9ycO43LCvcH+0hU2OMMXkmIgV+RbtcR35VNUNEtgBlQxCPKQBVXSsiL+JUH5grIlM4WuqsHNA7xFNUpgPPAl+6VRgq4FRcCOYj1DWBWW45s5NwKl8cxKlI4WkWzi8A1wLJwOQAz/EC0A2nUkUTnF8gGuNMoVjrbg/UCOAD4DsRGY9TbeJmN64Ts/Q7Dadk20fA7zhzqs/AKa+2CfgxD+c0xhhjjCvQOb9vAYNE5PMAHg4yYaSq94nIeuB2nBq8h4AFQC9VDXXC9CLOqO9NOGXQduDUDH4X56G0YOgIvIIzn7cMzhSIIaq6wrOj+4vcOzgrEk71MjLslaruF5Hz3XN0xhml3YkzgvyYlxq//o41QUSq4CTpr+BMmXgCyADOydJ1C8485bZAF5wyd1txRq+fz28JNWOMMcVX5gNvxV2gyW95nJqvG0XkK2A7ztfZmVRVHwt2cMahquNwHhDztq2Nl7a3cZKk3I7r8z8BVa3lo30ozoISnu2JeDx4544yP+u+PHn2zbF/INx56HkpDJnmvnud8uDrWrvnud19+YvH6/4efV7E+yIV47L02QMM9nccY4wxJk+KyAptBeUz+RWRjcCVqrocZ6WwTH29dFfAkl8T0dz5xrcAv6nqz+GOxxhjjAm1Epb9+h35rYXzVSuqGrnLxRiTCxGpDZwLXIFTVaFneCMyxhhjTLgEY3ljYyJda5x5xknAE6oa6INuxhhjTJFhc34duSW/0bc2oCkW3OWB+wTYdxy5zMMJTxMnAAAgAElEQVQ1xhhjigOb9ZB78vu4iCQFcBxV1RuCEZAxxhhjjDGFJbfktxFHn473x0aIjTHGGGMimlAi74WV8ncmkaH4L4ZwWFVLBtB3iKq+FMzYckt+u6jqwmCe0BhjjDHGhJ4Q0mkPHwLrvbSfhbMQ1Uwv2wbjPJ+T1ZIgx2UPvBljjDHGmOByF5vKseCUiLzl/t93vOz2sVv3v1BZ8muMMcYYUxxIeKs9iEhZoAfwF/CVjz4VgBRVPVxYcVj9XmOMMcaYYqKESIFfBdANqACMc1eB9bQC2A+kish8EelUkJP54nPk1xa2MMYYY4wpOoI45zdBRBZn+Xm0qo4OYL+bcIokjPVo3weMBuYDe4G6wCDgcxHp65YsDRqb9mCMMcYYY/IiSVWb5WUHEakLXAB8q6qbsm5T1WFe+o8FfgdeFZHpqppckICzstFdY4wxxphiIozTHm5y38cE0llV9wCjgIrAefk9qTc28muMMcYYU0yEY4U3EYkFrgf2AB/lYddE9z0hmPFY8muMMUHwd/KhcIeQZ5XKlQp3CPny1485viGNCsdePSrcIeTL3hm3hjuEfCkZG33r+EZfxAG7HDgBeE1VA1k8LdOp7vvOYAZjya8xxhhjTDEghG2+a+aUhxy1fd1R4bKqut+jvTpwG85o8fxgBmPJrzHGGGNMcSAgIZ73ICJVgI7AQlX9zUuXcsAmEfkYWM3Rag/93G09VfVgMGOy5NcYY4wxxhSWPkAMvh90OwjMAM4BuuAkvEnAbOAFVV0Y7IAs+TXGGGOMKSZCPa9YVZ8BnvGzPQ1nlDdkLPk1xhhjjCkGBAq6QluRYMmvMcYYY0wxYamvLXJhjDHGGGOKERv5NcYYY4wpJmzWgyW/xhhjjDHFhIS81FkksmkPxhhjjDGm2LDk1xiTq4yMDIYPe5WG9U+nYrk46tSuzn1D7ubAgQPhDs2vSI/79VdeoH+fnpzbqC5Vjy3NOWed5rXflj8TqXpsab+vD6dOCnH0OUX69R720vP0va4HTRucRkL5kjSuV8dn3znfzebuu26nfesWVE0oR0L5kvz04w+FFts9Vzdmwr0XseqtXhz85FbWjO7ttV+N48tz8JNb/b56tD71SP9GJyfwbJ8WzH/larZNuJFtE27kp5euon+nesTGhD4FiPR7xJdojdtT5gpvBX1FO5v2YIzJ1ZC7BzNyxHA6d7mSuwbdzZo1qxk5YjjLly3li1mzKVEiMv86jPS4n3vyESoeW4kGDRvxz/79PvtVrnwcw0e963Xbw/cOIjX1IG0uvKiwwgxYpF/vpx5/mGOPrcRZjRqzf/8+v32nT5nIjGmTOf3MepxW93R+W7G8UGN78vpz2PNPKss2JnFM2VI++yXtP8iNr3zrddur/S+gTOlYvvl1y5G2/13ViLYNqzJzQSLvfr2amBJCp+Y1ee3Wllx2Ti06D/086J/Fn0i/R3yJ1ri9sWkPlvwaY3KxauVK3nzjda648iomT51xpL1WrdrcPfhOpk6ZTI+evcIYoXfREPf8paupWetkANqd29jnKFJ82bJc3T1nrIsX/sI//+zn0iuuolLlhEKNNTfRcL0Xr1hLrdrO9b7g7EYcOJDss+9Djz3Jy8PfpHTp0ox47ZVCT37P6D+BxJ3/OnEOv4ZycSW99ktJO8zkH/7I0X5O3ROoWK40H87bwJ5/U4+0v/nZ79z82vek/Zd+pG3UFysZO7gdPducRqdmNfhy8Z9B/jTeRcM94k20xm18i55fVYwxYTF1yiRUlTvuHJStvW+/m4mPj2fyxA/CFJl/0RB3ZuKbX5PGO6PBva67MRjhFEg0XO/MxDcQJ1WpSunSpQsxmuwyE9/86nPR6QC8+83qbO0/r9mRLfHNNP2nDQDUq1mpQOfNi2i4R7yJ1rh9kSC8op2N/Bpj/FqyeBElSpSgefOzs7XHxcVxVsNGLFm8KEyR+RetcQfqQHIyMz+eTrXqNWnVtn24wyny1zuSlY2L5eoLTmHzzn/5dtlfAe1TtXJZAHbuO1iYoWUTrfdItMbtldi0B7CRX2NMLrZv20ZCQoLXUbAqVauSlJTEoUOHwhCZf9Ead6A+/WgaB5KT6d77+oiYb1jUr3ck63pBHcqXKcX7365BNff+ZeNiGXxlI/Ylp/HZgsRCjy9TtN4j0Rq3N/bAm6MofAZjjhCRWiKiIjI0ROcbJyLq0TbUjaFWKGIobCkHUyjl4+vfuNJxTp+UlFCGFJBojTtQE8e/S4kSJeje6/pwhwIU/esdyW686HTS0zMY/+3aXPuWKCG8O/hCap9YgbtG/cje5LQQROiI1nskWuM2vlnya4zxK75MPIfSvP8DmZrmPFgTHx8fypACEq1xB2LdmtX8umgBrdpeSNXqNcIdDlC0r3ckO736sZxz+ol8u/wvtiT5foAPnJW93hrYhstb1ObR8QuY+uP6EEXpiNZ7JFrj9kVECvyKdpb8GhN8TwFlgM3hDiQYTqpShaSkJNK8/OW/betWEhISKFXKd2mmcInWuAMx6QPnQbee1/UNcyRHFeXrHcn6tHcedBv3zRq//UTgzTvacG27ujw1aTEvTl8aivCyidZ7JFrj9sUeeLPk1xQSESkf7hjCRVUPq2qqaiCz7yJf02bNycjIYNGihdnaU1NTWbF8GU2aNgtTZP5Fa9y5OXToEDOmTKRywnFcfMnl4Q7niKJ6vSNZydgS9GxzGrv2HWSmn7m7mYnvDe1P59kpS3h68uLQBZlFtN4j0Rq38c2S32JERPq4c1Hbicg9IrJBRNJEZJ2I3OClfz8R+VVEDorIfhH5WkQu8NJP3bmvF4rITyKSDMx0tyWKyBwRaSgis0UkWUR2icjLIhIrInEi8pKIbBWRVBGZKyJneBy/vIg8JSILRCTJjXm9iDwnIvn+rklE2rix+3r1yedxc8z5zdJWV0SeEZG/3M+xXEQu8XGc7u71/FdEUtzP3zVfH7YAunbrjogwYviwbO1jx7xNSkoKPXp6X4kq3KI17tx8/eVn7EnazdXde1GypPdasOFQVK93JLvs7FocX7EMk+as43B6hs9+Iwe05ob2p/P8tF95YmL4KhNE6z0SrXH7IlLwV7SzUmfF0zM4X8u/BaQBtwHjRGS9qs4DEJHngXuBhcCDQHmgP/C9iFyhql94HLMZcDXwNvCex7ZqwDfAFGA60AH4H3AYqOfG8hyQANwDfCwiZ6hq5t/mVYF+wAxgortfaze+xsDF+bwOq4HrvLTfAzQEdubzuP68B/wHvASUAgbhfN7TVDUxs5OIPAU8BHwFPAJkAFcC00TkDlV9oxBi86p+gwbcctsARo0cQfduV9Gx4yVHVjdq2ao13SO0uHs0xD198gT++stZYGDPniT+O3SIYS89C0C1ajXo2iPnP6qTI6i2b1bRcL2nTvqALVvc6520m0P/HeLlF54BoHr1GlzT89ojfVf+voKvvvgMgIW/zAdg2qQPWPDzPABuvmUAFY45Jmix9WxzKjWOc74wSzgmjlKxMdzXrQkAf+7+l0lzci5scUN777V9s3q2Twv6XHQGyzcmsWbL3mxLHwNs2vEPC9YWxl91OUXDPeJNtMbtjVPtoQhkrwUkReSbWRMAdyTzXWAZcI6qHnLbqwIbgQ9VtaeI1MVJDOcD7bL0qwKsAvYBp6hqutueeRNdpKqzPc6ZCNQErlHVaVnal+AkrjOBLplTBETkTuA1oKOqznLbSgGqqv95HPtJ4GH3syx022oBm4DHVXVoPq7RbcBI4DVVHRRA/3HADaoqWdqGAo8BtTMT2ixtnwOXZ/m8zXF+wXhOVR9w25oAS4BnVfVBj/N9DLQDqqpqjqr4ItIf55cUqteo0XTdhuBMO05PT+f14cMYO2Y0mxMTqZyQQNdu3Xl06BOUK1cuKOcoDKGM++/kvJc66nrZRfw8b67Xbeee34rpn32TrW3rX1to0fA0mjQ7h09mzclPmNlUKhfceYqhut4HUg/na7/OnS5k/k/er/d5F7Ti0y+PLhs86YP3GHhbP5/H+vX3P6hRs1aezl+t9xif22Y91ZlWDap43Tb3t21c/PCn2Y+VUJY1o3uzcN0u2t3/cb6OCzD+27X0H/6937j3zrjV7/a8sL9L/Dv/nGYsWbK40LLTU+s11Fcmf13g43Q+68Qlqhq18z0s+S1GsiS//VX1bY9tvwFpqtpMRO4FngeuUNVPPfq9ijNa2VxVF7ttCixX1UZezpkIxKpqNY/214A78UiYRaQx8CswUFVHeDleLM4odAzOqPEc4E5Vfd3dXot8Jr8i0hH4DPjS/ey+v0c8us848pb8dlDVbzyO8S8wS1W7uj+/DAwGzgD2eJyyM/AOcLGq+v0brGnTZjpvQXjm9hVH+Ul+wy3YyW+o5Df5DTd/yW8kC2bya/wLRfL76pSCJ7+XN4ju5NemPRRPG7207cEZoQWo7b6v9NIvs+1kIGtmtc7P+TZ5advrY1tme+WsjSJyO3ArTsLrOVf9WD/nRkSOw0mWM6Wr6m6PPg1wpmX8DvTMmviKyIkehzykqn/7O6cfvq591s97Bs63U/4e3z4hn+c3xhhTbAli0x4s+S2mci707ijIfxH+Knz7Ol9AsYjI/4CXga+B4cA24BDOXOBx5P7g5iKOJvbglCCrleX4J+KM+B4ALlNVz2KZ2z1+/gFok8s5fQnk2gugQCc//b39YmKMMcb4VRQeWCsoS36NN5mjk/WADR7bzvToEwrXAYlAJ48R2Y4B7t8b56G6TEcWs3erRczEediular+5WX/izx+3uulTzD9AXQE/lRV30+yGGOMMSbPLPk13nyKM+d3iIh8mfmgmYicBNyIM3Iaygrp6TgjoVlHg2OB+wPZObOChSdxlqn5AGgKXK2qS3zsP9tbeyEaDwwEnhGRrpkPFmYSkRNUNTSPZxtjjCkyrNqDw5Jfk4OqrhWRF3FKic0VkSkcLXVWDujtmZAVsunAs8CXIvIhUAHohVMyrCBuxSkf9gNQVkSu9dg+X1VDOcINgKouch+QGwosE5FpOFM9TsJJ1C/BKZNmjDHGBK6I1OktKEt+jVeqep+IrAdux6nBewhYAPRS1R9DHM6LOL+w3oRTBm0HzsNp7+KUXsuvzIfGWrsvTzcS2ukdR6jq4yKyGKcixiCgLLAL54G8O8MRkzHGGFMUWKkzY4ooK3UWWlbqLHSs1FloWamz0CnsUmen1W+kI6Z9k3vHXFx85vFW6swYY4wxxkQ+K3WWe4koY4wxxhhjigwb+TXGGGOMKQYEKGEDv5b8GmOMMcYUFzbtwaY9GGOMMcaYYsRGfo0xxhhjigmr82sjv8YYY4wxxYYE4X8Bn0tEfbySvfStKyIfi8heETkgIj+KSLugfniXjfwaY4wxxhQDYXrg7UdgtEdbthVaReQUYD5wGHgB2A/cDMwSkU6qOjuYAVnya4wxxhhjCstGVf0glz7PAhWBpqq6DEBE3gdWAm+IyOkaxFXZbNqDMcYYY0yxEIxJD3kfOhaRUiJSzse2skBnYE5m4gugqsnAGOA0oHn+Pq93lvwaY4wxxhQH4jzwVtBXHnUFUoB/RWSXiLwuIsdk2X4WUBr42cu+v7jvQU1+bdqDMcYYY4zJiwQRWZzl59Gq6jmvF2AhMA1YD1QALgHuAFqLyHnu6G4Vt+9WL/tntlUNTtgOS36NMcYYY4qJID3vlqSqzXLrpKrneDS9LyIrgKeBu9z3eHdbmpdDpLrv8V625ZtNezDGGGOMKQacag9S4FcBvQgcAi51f05x30t76Rvn0ScobOTXmCJKgfSMoD0cGzIxUbrwfKVypcIdQp4l7j4Q7hDypVqlMuEOIV/2zrg13CHkS8vn54Q7hHwZ3zeo00RDIu1wRrhDKHSq+p+IbAMS3KZt7ru3qQ2Zbd6mROSbjfwaY4wxxhQTEoRXgc4vEgdUA3a6Tb/hTHk410v3Fu77Yi/b8s2SX2OMMcaY4iJE2a+IVPax6UmcmQcz4UhJs5lAGxFpmGX/ckA/4A+cB+eCxqY9GGOMMcYUE/mp05tPD4tIC+B74E+gHE61h7bAAuD1LH0fAC4EvhaRV4F/cFZ4qwpcGswFLsCSX2OMMcYYE3xzgDOBG4DKQDrOKO5DwCuqmlnJAVVdLyLnA88B9wOlgF+BjsFe2hgs+TXGGGOMKTYKXqwhMKr6CfBJHvqvBq4ovIiOsuTXGGOMMaaYiM56OsFlD7wZY4wxxphiw0Z+jTHGGGOKCxv6teTXGGOMMaY4cCqVWfZr0x6MMcYYY0yxYcmvMcavdWvX0veGa2ly1plUOa4ix1UsS+MGZ3D/kP+xY/v2cIfnV0ZGBsOHvUrD+qdTsVwcdWpX574hd3PgQGQv6xvJcW9av44hA/pyaasmNK9bhcYnH8clLRvz3ND72bVzR7a+K5Yu5umH76FX5/Y0rXMCZ1Qpx0dTPghT5IFJSUmhwel1KB8Xw92DBoY7HJ8i5R7pc14Nnr3qTD6+/RwWPdSGTwa08Nn3jrYn884NjZk16Dzm3deKzwa24JVrGtCkRsUcfcuUjKFfy5q83K0+nw08l0UPtWHUtY0K86MU+XsbAHGqPRT0Fe1s2oMxxq+tW/9ix47tdL6iC1WqViM2NpaVv//Gu++8zfRpU5i/cCnHH398uMP0asjdgxk5Yjidu1zJXYPuZs2a1YwcMZzly5byxazZlCgRmb//R3LcO7ZvZfeuHbTv1JkTTqpCbGws61avZNoH7/LFJ9P56Jv5VE5w7oe5385i4rjR1K5zGnXPbMDSxb+ELe5APfXEYyQl7Q53GLmKlHtkQNuT2ZfyH2t3/Eu5OP8pRf2qFfhj1wG+W7Obf1IPU7lsKTrVP4G3rmvEo5+s5svfdx7pWzG+JLe0qs2e5EOs3vEvlcuWLOyPUuTv7UxFIHctMEt+jTF+tW13IW3bXZij/fyWrbi+V3cmvD+OwffcG4bI/Fu1ciVvvvE6V1x5FZOnzjjSXqtWbe4efCdTp0ymR89eYYzQu0iP+9yWbTm3Zdsc7c1anM/gW67noykT6DdgMAA9buhH39sHER9fllmffRTxCcKypb8y8vXXePKZ53nwvnvCHY5PkXSPdHnjF7buc9YqmHxzc8qUivHZ99YPluVom7JoKx/dfg59zquRLflNSk7j0uE/s+vfNAB+GNIyyJHnVJTv7Wws+7VpD8aY/KlRoyYA+/btDXMk3k2dMglV5Y47B2Vr79vvZuLj45k8MTK/oozWuKtUqwHAP/v3HWlLOO4E4uPLhiukPElPT2fg7bfQvsPFdL7iynCH41ck3SOZiW9+Hfwvnf0H/6NCmexjcf+l65HEN9yi/d42OdnIrzEmIKmpqSQnJ5OWmsqa1at45KH7AejQ8ZIwR+bdksWLKFGiBM2bn52tPS4ujrMaNmLJ4kVhisy/aIk7LTWVlAPJpKWlsWHdGl5++hEAWl3YIcyR5c+I4cNYt3YNH0yaFu5QchUt94gvx5QpSQmBhHKl6NK4CicfV5ZPlkXO8wNF7d7OTqzaA5b8GmMCNG7sGO4ZfOeRn2vWrMU748Zz/gWF/3Vkfmzfto2EhARKly6dY1uVqlX55ef5HDp0iFKlSoUhOt+iJe7pE8fx1MNHpwZUrV6TF0a8Q7Nzzg9jVPmTuGkTzzw5lPsefISatWqxOTEx3CH5FS33iDdlSsYw+39H75HU/9L58NdtvDp7fRijyq4o3dveFIUH1grKkl9j8kBE5gC1VLVWmEMJucs7d6Fu3dNJTk5m+fKlfPHZTJKSksIdlk8pB1Mo5SU5AIgrHef0SUmJuAQhWuK+sOPl1K5Tl5QDyaz+fTnff/0Fe/+O3PvBn0EDb6NW7ZMZeNfgcIcSkGi5R7xJO5zOgAnLiSkhnHhMaTrWP4H4UjHElYwh9b+McIcHFK1723hnya8xJiBVq1WjarVqAFx+RRe6XHk1rc47m4MHU7jn3gfCHF1O8WXi2Z28y+u21DRnnmJ8fHwoQwpItMR9YpWqnFilKgDtO11Oh0u70O2SVqQePEj/gZH7sJinyRM/4LtvZ/PV7DmULFn4FQWCIVruEW8yFBYmHn1O4JNl2xl1bSPe7N2Qa99ZQnqGhjE6R1G5t70R7Hk3sAfejDH5VL/BWTRs1Ji333oz3KF4dVKVKiQlJZGWlvOhmW1bt5KQkBCRI2PRGnfdM+tzRv2GTBr3drhDCVhaWhoP3HcPHTp24oQTT2TDhvVs2LCeLX9uBuCf/fvZsGE9+/bty+VIoRWt94g3GQpfrdxFnePL0aTGMeEOx6tovLf9kiC8opwlv8aYfDt48CB7//473GF41bRZczIyMli0aGG29tTUVFYsX0aTps3CFJl/0Ro3QFrqQfZHaPUPbw4ePEjS7t3M+vILGtWre+TVqUM7ACZPmkCjenV57913whxpdtF8j3hTOtZJRSrERe7Ie7Td28Y/S35NkSQifURERaS9iAwVkc0ikiYiK0Skh5f+XURknogcEJFk9/9fEcB5PhGRFBGp4GVbczeGR92fa7k/DxWRnm4sqSLyp9sW67F/dREZmyX2XSIyX0RuKMi1yaudO3Z4bf9hzvesWvk7zc/2vaJTOHXt1h0RYcTwYdnax455m5SUFHr07B2myPyL9Lh379rptX3BvB/4Y80qGjZtHuKI8q9s2bKMnzglx+vV4SMAuKjDxYyfOIVLLrs8zJFmF+n3iDfl42KJLZFzyDCuZAmuaHgS6RnKym3/hCGyo4rSve2PBOF/0c7m/Jqi7nmgLDDS/flGYJKIxKnqOAARuR14A1gDPOH26wN8LCK3qOpoP8d/G+gM9ATe8th2E5ABjPVo7wyc7J5zh/vzY0BNNz7cRPgboKob+zrgGOAsoCXwXiAfPhjuGng7O3dsp3WbtlSvUZPU1FSWLf2V6VMnU758eZ594aVQhZIn9Rs04JbbBjBq5Ai6d7uKjh0vObIKVstWrekegQtcQOTH/fj9d7F7105anN+aKtWqk5aWysoVy/jyk+mULVeeex999kjfrX/9yafTJwGwfu1qAL7/5gt2bN8KQOeuPanq1lANh5IlS9Llqq452jOrPdQ++RSv28Mtku6RTvVP4KRjnIfsKsaXpGSM0Pd8pwb49v2pRxauaFKjIg90Oo3v1+5my98HSTmUTpWKcVzS4AROqBDH6LmJ7Pgn+zSObs2qUr60k6bEug/IZR77j13J/PjHnqB+lqJ0b/tj1R5AVMM/udyYYBORPsC7wJ/AWaq6320/BlgBlMdJLOOALThJaBNV/cftVwFYChwPVFfVfW77HLJUexCRGGATsENVjxTdFJF4YDswT1UvcdtquX0zgOaq+qvbLsCHQBfgXFX9RUTOApYD96nqC3n43P2B/gDVa9RouvqPxEB39WnG9KlM+mA8v/22nKTduxERqteoSbsL2zPof0OoXiO4f8HHeBkdyq/09HReHz6MsWNGszkxkcoJCXTt1p1Hhz5BuXLlgnaeYAtV3Im7D+R5ny8/ncEn0yexduVv/P13EiJClarVOa9VO/reNogq1aof6btw/lxu6Oq7DvR707/g7PNa5TmGapXK5HmfvNicmEj900+h/6238/Kw14N23NiY4H3ZGsp7u+Xzc3xuG3VtI5rWrOh125LN+46s6la1Yhx9zq/JWVUrcHz50sSVLMH+g4dZtf0fZvy6jXnrc06f+mRAC6pUjPN67M+W7+Dxz9b4jXt837yN1EbCvd21Y0t+X/5roaWn9c5qolO+mFvg4zSoXn6JqkbX/JosLPk1RVKW5PcBVX3OY9sDwDPAJThJ8BTgLlUd7tHvLmAY0E1Vp7ttc/AodSYiQ3FGbs9S1d/ctutxRme7quoMt60WTvI7S1U7epzrXGA+8IKq3iciNYFE4CvgBlX1/mi3H02aNtMff47sYvfeBDP5Nf7lJ/mNBIWd/BaWYCa/oeQv+Y1keU1+I4Elv6ERnf8lGhO41V7aVrnvJwO13f+/0ku/lVn6+fMOkI4zzSHTTcAu4NN8xISqbgaeBjoA20VkiYi8ICLR97e5McaYyBCMSg9FYHzCkl9jCkhVt+CM0F4rIqVE5FSgFfC+qv5XgOM+DJwKDAI2AP2AhSLyfBDCNsYYUwzZA2+W/Jqi7wwvbWe67xvdF0C9XPrlZjRQGWfebl+3zVd9pNxiOkJVN6rq66p6DVAFmAvcKyLHBxCTMcYYYzxY8muKutvch9yAIw+83QrsA37AqahwABgoIuWz9CsPDASS3T65+RzYBtwC3IDzoJuvpzEuEpEmWc4lwL3ujx9nxiki2YpeqmoqR6dMHBtATMYYY8wRglPtoaCvaGelzkxRlwQsEJF33Z9vBGoA/VQ1BUgRkXtxyo4tEJFxbr8+QB3glsxKEf6oarqIjAUedpse9NN9OfCdiLyBUxHiCqA9MF5Vf3b7tAVGi8gMYC1OEt4UZ+rDAlVdm+snN8YYYzwUgdy1wCz5NUXdfTh1cQcAJ+DUy+2tqhMzO6jqSBHZDgzBqdoAToJ6pap+nIdzjcFJeg8A0/z0+xQnoX0AqIvzYNyT7ivTcpzyZ22A3kAMTtm2Z4CX8xCTMcYYY7Kw5NcUdYdV9TGOJrVeqepHwEe5HUxV2/jZfAhQYJKq+q0hpaqTgEl+tm/CmZ5hjDHGBI8N/Vrya0wQ3YYzQutvRThjjDEmbIpCtYaCsuTXmAISkR4484iH4CxgsSTMIRljjDFeFYUH1grKkl9jCm4SkAr8SPaFLowxxhgTYSz5NUWSqo4DxoXoXAH9Hq2qidhsK2OMMWFk/whZ8muMMcYYU3xY9muLXBhjjDHGmOLDRn6NMcYYY4oBwao9gI38GmOMMcYUD0FY2jiQahEicpqIPCEiv4jIbhH5V0SWichDIlLWo+9QEVEfr3sK4zLYyK8xxhhjjAmmvjgrq34KTAD+A9oCTwHXiEgLVT3osc9gIMmjrVBKh2IosroAACAASURBVFrya4wxxhhTTIRo0sN04FlV3Z+lbZSI/AE8hFMWdITHPh+7VZEKnU17MMYYY4wpLiQIr1yo6mKPxDfTFPe9vtfQRCqISKEPzFrya4wxxhhTLEhQ/lcA1dz3nV62rQD2A6kiMl9EOhXkRP7YtAdjjDHGGJMXCSKyOMvPo1V1tL8dRCQGeAQ4DEzMsmkfMBqYD+wF6gKDgM9FpK+7aFVQWfJrjDHGGFNMBFKtIQBJqtosj/sMA84FHlTVtZmNqjrMs6OIjAV+B14VkemqmlygaD1Y8mtMESVATAmr52h8K1MqJtwh5EuJIP3rbQIz4aazwx1CvnR47rtwh5Bn23f8W6jHD3DKbvDPK/IkcAfOCPGzufVX1T0iMgoYCpwHfB3MeGzOrzHGGGOMKRQiMhR4GHgXuDUPuya67wlBDslGfo0xxhhjio0QDv26ie9jwHtAP1XVPOx+qvvu7eG4ArHk1xhjjDGmmAjV8sYi8ihO4jse6KuqGV76xAJlPcuiiUh14DZgD86DcEFlya8xxhhjjAkaERkAPA78CcwGekn2ufo7VfUboBywSUQ+BlZztNpDP3dbTy8rwRWYJb/GGGOMMcVEiJ4Xbe6+18CZ8uDpB+Ab4CAwAzgH6IKT8CbhJMwvqOrCwgjOkl9jjDHGmGIiFLmvqvYB+gTQLw1nlDekLPk1xhhjjCkOJGQjvxHNSp0ZY4wxxphiw0Z+jTHGGGOKDRv6teTXGGOMMaYYEGzaA9i0B2NMADIyMhg+7FUa1j+diuXiqFO7OvcNuZsDBw6EOzS/LO7g27j+D1559gm6XNyKJnWrU6/mcXRqcw4jXnmelCzxqSofTZ3EHf2uo3XzepxevRLnNTyVfr27snRJoTzAnS9/rFvHk48/SpuW51Kz6vGcULkCLZo35oXnno6I6+1LJN8jmzb8wWsvPMk1l7ahRb2aNK5zAle0b8Gbw14gJSVnfF/O/JAel7ej0cnH0bjOCfS64iJ++ParQo3x9van8EafJsx9uC2Jwy7jp0fbBbzv/ZefTuKwy1j5fMcc217q1ZDEYZf5fH3/UNtgfgyTTzbya4zJ1ZC7BzNyxHA6d7mSuwbdzZo1qxk5YjjLly3li1mzKVEiMn+PtriDb+rE9xj/zlu073gpXbr2IDa2JD//9AMvPTOUzz6Zwcdf/UBcmTKkpaUx+Pa+nNmgIZdf2Y3qNWqxa+cOJox7m6s6tuGVN97hymt6hu1zZHr/vbGMHjWSSy7rTPcevShZsiRzf5jD4489wozp05jz48+UKVMm3GHmEMn3yIzJ7zPh3dG063AJl1/VndjYkiyYP5dhzz/OlzNnMPWzOcS513T0iJd5+elHObN+Q+669xEAPp0xmVuu68oLr4+h89U9CiXGey87g70HDvH7X/upUKZkwPudWbUCN7U5meTUw15HUCfO38xPa5NytJ93WmWuOacG364M+mJleWYDvyB5W2nOGBMtmjZtpvMWLC7wcVatXEmzxg3o3OVKJk+dcaR95IjXuXvwnbz7/gR69OxV4PMEm8Wdu537U/O8z4qlS6h1Sh0qVDgmW/tLzwxlxCvP8/hzr3DD/9k773ApiqyN/87lEgVRCQpIMIIZAbOIOe+as66uOef0rTmLiglzWnPOoquYc8S4aw4giAEMGMjM+f44p5mavnMvoEz3XKj3Pue5M1U9PadraqbfOnXC3gcwdepU3nr9VVZdo3/JcWN++J4N1+xLkyZNeON/X/0pktahTfNZfk19eHvYWyy2+BK0bVt6PaedciLnnXs2gy66lP0PPHi2vFdNzeyhHVnP7a/Hjp+l4z949216LLoYbVJz5KJzT+OqS87jpLMGseue+zN2zPes3a8Xiyy6OPcPfYWmTY2ETpkyha03XJ3vv/uWZ974kNZt5v1Tem947jP19nVt14qRP9p1PXHcWszTvJY1T6//eIAagQeOWJMffp1ImxZNWa5rW5Y5buYs1DfvvzJr9erIBuc+x2ff/V7vcd/ecQSTvv+sYvx0hRX76uPPvfqXz9N5vubDVLXfbFApF1Sn2SMiIqJqcPddd6CqHHzo4SXte+69D61ateLO22/NSbOGEfWuDJZfsW8d4guw+ZbbAvDJRx8CUFtbW4f4AnTouCCrrL4mY8f8wNgxP1RW2ZlAn7796hBfgG222wGAD//336xVmiGqfY4s17tPHeILsOkW2wDw6cc2R95583WmTJ7M37beYTrxBWjatCmbb7U94375maceH1IRHRPiOyv451qLsMSCrTn1vv/N0uu6zN+SNZbswNvDf26Q+EZkh0h+IyIiGsSwt96kpqaGlVZauaS9RYsWLL9Cb4a99WZOmjWMqHe2+Hb0NwC079Bxpo5t1qwZ87adr9Jq/Wl8M2oUAB0XXDBnTeqisc6R71JzZPLkSQC0aNmqzrFJ23tvV8e1dJm/JUdu2pNLnviMb36etWq7263SlSY1wp2vfl0h7WYNMhv+Gjsi+Y2IiGgQ344eTfv27WnevO5Wc+cuXRg7diyTJ0/OQbOGEfXODtOmTWPwoHOora1li212aPDYZ598nPfefovNt9yWFi1aZKThrGHatGkMPOdMamtr2X6H6nONaaxz5MqLB1JbW8vmW20PwOI9lwLgtZeer3P86y+/AMC3o0dlp2QDOHO75fj6x/Fc99yXs/Q6Edh25YX5feJUhrwzukLazSJkNkgjRyS/VQwRGS4iz+Wtx+yAiPQQERWRU/PWZXaj3OckIs+JyPBZOMeNIlKVDvjjJ4ynWZmbLECL5kZexo+f9S3ESiPqnR1OP+Fo3n7zdY44/mQWW2LJeo/76ovPOeLAvVioU2dOOP3cDDWcNRx71OG8/tqrnHjKaSzZs2fe6tRBY5wjZ598LO+89TqHHnMiiy5uc6TnUsuyxlrr8vQTQzjvjBP44tOP+eLTjzn/zBN54dmhAEyckP91/L1PZwb06sAJd3/AtMKs/Uz379mBhRdoxZB3RjN+8rQKaThriNw3kt+I2QgnuKeKSO+8dalGiMgeInL4jI+sLrRq2YrJkyaV7Zs4yQKmWrWqu22ZN6Le2WDQOadx03VXsdM/9uKgw4+p97iRI4azy9abICLceNdDtGvfIUMtZx6nn3oSV115OXvutQ/HHPt/eatTFo1tjlw88HRuveEqdth1T/Y7tHSOXHT1zWy46RbccOUlbDqgL5sO6Mt/Hr6fU86+CIDWrf9csNvsQttWTTlpq2W46/WRvD3851l+/fardAXgrteqw+UhwhBTnUXMTvQATgGGA++m+kYALYGp2aqUGzak7gJ5D2yMLi5z/D7A/pVV6c+hU+fOfPTRh0yaNKnONuvob76hffv2NGvWLCft6kfUu/K4aOCZDB50Ltvt/A/OHjS43uNGfj2CHbfciD/++IPb7n+MXksvm6GWM4+zzjiVgeecxW6778Gll1+Vtzr1ojHNkcEXnMWVFw9k6x1347TzLq3T33a++Rl8/e2MHfM9w7/4nFbzzEOvZZbnRbf8JlbivHD4RkvSqlkT7nz1a7q3Ly4oWjStQYDu7VsxeWqBb3+pmzllvlZN2WC5Bfl49K+8M+KXDLWuHyKxyAVEy29EPRCRNrPzfGqYqKpzBflV1cmqWt40U/74Kao663mnMkDffitRKBR4883SwgQTJ07k/ffepU/f6sx2E/WuLC4aeCaXnH8W2+y4KwMvvhKp54468usR7LjFhvz266/ceu8Qll2+OjeGzjrjVM4+83R22W13rrjqunqvpxrQWObI4AvO4rJBZ7PV9rtw1qArGhzT9h0WpN+qa7D0cr2pqanh+aeN/K613kZZqVsWXRZoyTzNa3noyDV5/sR1p0vv7vPTqnktz5+4Ljfut3LZ126z0sI0r23C3a+PzFjrhhED3iL5zRzuFqAi0qNM30z5+IrIiiJyj4h8LyKTRGSkiNwhIouljltfRIaKyC8iMlFE3heROtbF5H39vE+IyDjgfe9rIyJnisjrIjLW3+9zETlXRFoF59gDeNaf/tuvUZPrSfv8ish8rtP99VzjOX5876CtrYgM9PefJCJj/LoXndGY/cnx21tE3haRCSIyzsdyzZl8jxKfX388AOgejI2KyNreX8fnV0S6isgNIjLC9fxBRF4Rkd1n5Xr/KrbdbgdEhMsuLTVY33DdtYwfP54dd9olS3VmGlHvyuGS88/mkvPPYuvtd+b8S6+uN1fvqJEj2GnLjfh13DhuuecRluvdJ2NNZw7nnHU6Z595OjvtshtXXXN91RY/SdAY5shlF57DZYPOZottd+Lsi66apTH94N23uef2G1l5tf70W2X1Cmo5Y1z19Occ8O9hdeTTb39j4uRpHPDvYZzxwIdlX7v9ql2ZNHUa979ZHUF7EUVEt4dGBhHZHLgP+AO4DvgcWAjYCFgW+MKP2xe4CngNOMuP3wC4UkQWU9W0c1434BngHj9/a2/vAuztbbdjbgsDgGOBFf19AV4Azgb+BVwDvOjtZcvZqOovIvIwsIWILKCqPwXXWAPsAryvqu96W1vgFdfzBuB/QCfgQOB1EemnqiNm4/gN9Gt8w6+pDbAv8KyIbKGqj83ovVI4HDgHaA8cEbR/VI+etcCT2PhfAXwKtAWWB/oDN83i+/9pLLvccux3wEFcdcVl7LDd1my88abTq0n1X2sAO1RhoQiIelcKN19/FRcNPIMuC3dljQHr8tC9d5X0t+/Ykf5rr8fvv/3GTltuzKivR7DHPgfw5eef8eXnn5Ucu+ba69KhY76pxK6+8nLOPP1Uunbrxjrrrsddd95e0t+x44Kst/4GOWlXHtU+R27799UMPv9MOnfpyur91+GR+1NzpENH1hiwHmD+wCO++oLle/el9bxt+fCDd7n/zltYcKHOnDf4uorpuFW/LnSZ36rMLdC6Oc2aCAdvsDgA3/w8gQfesrRsbw//BajrsrB7/x50WaAl/3nv27Ln7919Pnp2mpch74zml/FTKnMRfxaN33D7lxHJbyOCW1r/DYwDVlTVb4Lu0500IiKdgEuBO1U1/BW8QkQuAY4UkStVNczZsgiwj6qmf22+BLqqavjtvVxEzgBOFJGVVfUNVf1SRJ7EiOKrqjozWdZvArYDdsQIXoJ1gK6U+saeDiwKrKqq7wVjciPwAXAa5lNbL2Zh/HoCxwAvA+uq6mRvvw74EBvHxVR1pkN3VfVBD3ZrOZNjszTQEzhOVc+b2ffxRc++AF27dZvZl80QF1x4Md179OCG667h8ccepV379hxw0CGcfOrpVW0li3rPfrz3zjAAvhk1kqMO2rtO/yqr96f/2uvx888/MXLEcABuvPZK4Mo6x97x4BO5k99hw6wK4sivv2bfvfao099/rQFVR36huufIB+/aHBn9zUiOO2zfOv0rr9Z/OvldZrnevPbSc7z8/NNMmDCezl26sute+7PfIUdXNA/0Dqt2Y9XF25W0Hb1ZLwBe+/zH6eT3zyIJdKuW3L4hIveN5Y0zh2/7nwIsoqrDU33DgeGqunY9z7cC7geOV9WBDbzHIRj5XR94L9XdG7Mo7qeq1wTv0wbo2BChc2tkG6AJsAzwHHCoqg72/rUx14d/quqNqdf2AL4CTlPVU4PzjfJrXDU49iZgZ2BhVf1ezFFsDDAMswincTuwrKp2rk93P+/Mjt+xwEBgC1V9ONV3EWbFXUlV3/K24QSfk7c9B/RQ1R4NtQV9NwK7q6r48+5Y4ODj3j7LpbBmV3njiDkXf6a8cTVgdpY3zhKzq7xx1pjV8sbVgobKG1crKl3euHefvvrUC6//5fN0aNO0UZc3jpbfxoUl/P87MzhuKf//VAPHpM0tX9RHfEXkQCwTwTLU9ROffwa61AtVnSoit2GW6CVV9VMRmQfYGhiqqonLRAegHZZBYUw9pyu4rk38+BATVHUcMz9+i/j/cjUsk7ZFgYoxS1UdISJnAf8HfCsi7wJPA/eoanWUPIqIiIiIaHSo4ljOzBDJb/ZoyNQ+uz6PZGr/AyjvkGTuDCHKLu1F5EhgEDAUsyaPBiZjvqg38teDJm8GjnRdT8SIb2tKfVqT63kKs8g2hK6YhTnETczAJaIaoaonisgNwGaYn+/ewDEicp6qHpevdhERERERjQ9zRraGv4pIfrNHEti1ALatDYCItMACuD5v4LWf+v/eGBmtD0lUyVhVbcj6OzPYDdNzE1UtJI0isnGZY2fZh0ZV3xOR94BdReQkjAT/AoTuBmO8bd6ZuJ7vsMC+EElNyZkdv2RhsAweABdg6dQxs4I/Mz5fAoOBwT5HngCOFZFBf8YVIiIiIiIiYm5H9UZ8zLlICNj6qfYjmPHnMRQYCxzlQW0lcN9YgLuBScBpItKyzHFtRWRmneamYaRt+lLRfXWPL3Ps7/5/gZk8d4KbgO6Yn++6wF1hzlsn3bcBK4vItuVOICId/diJqvpUSpI8NDM7fg9j13yMiDQN+jsB/8QKdszIdaIcfgfmD96nXvhn1DRs8zFJskP8aXeTiIiIiIi5E0Kx0MVfkcaOaPnNHk8Bn2DZBdphW/RrAqtixKxeqOp4EdkLuBf4r2cf+Bzzcd0IuBB4SFVHicgBWCqvj0TkFoywdQCWA7bELJjDZ0Lfe7EUXf8Ry8k7L0ZSy+Vu+RD4DThQRMZj1tofVHVGUQe3AedhGR9qKJ/G6wRgDeBuEbkbS+E2GSPNm2LBcHs09CazMH6fiMj5WKqzF0TkLoqpzloDu8xKpocArwGbA5eJyCvYwuKZeiy46wDXiMh92Hz5HeiLuT68rqqf/In3j4iIiIiImOsRyW/GUNVpIvJ3zH/2EIzADcVy5748E69/2Ast/AvYCyNl32N5dT8Ijvu3iHwKHA3sB8yHketPgJMw94CZwfnYYnEv4BJ/3V1YyrCSzN6qOkFEdgTOxNKUNQeex/IHN3RNP4jI4xgx/ExVXy1zzDgRWQM4Ctge2ALLOTwKeAkj+jPELIzfcSLyOZZH+Fzsc3od2FlVX6xz4pnDRVig3LZYAGENRnLLkd/3sMwUa2MZLpoAX2O5lAf9yfePiIiIiJjLMSdYbv8qYqqziIg5FDHVWcSMEFOdZYuY6ixbxFRndbFin3767Mt/PdXZ/K1qY6qziIiIiIiIiIiI6kfM9hDJb0RERERERETE3IE5JGDtryJme4iIiIiIiIiIiJhrEC2/ERERERERERFzAQSi0wOR/EZEREREREREzD2I7De6PURERERERERERMw9iJbfiIiIiIiIiIi5BDHbQyS/ERERERERERFzDWK2h0h+IyIiIiIiIiLmGkTuG31+IyIiIiIiIiIiZjNEpEZEjhCRj0VkooiMFJFBIjJP3rpF8hsRERERERERMbdAZoPMHC4CLgQ+BA4B7gEOBR4RkVz5Z3R7iIiIiIiIiIiYS5BFwJuILIMR3vtVdZug/SvgUmBH4PaKK1IPouU3IiIiIiIiIiJidmInzEZ8car9WmA8sGvmGgWIlt+IiIiIiIiIiLkAQmbZHlYCCsAbYaOqThSRd70/N4iq5vn+ERERFYKIjAFGVOj07YGxFTp3JdEY9W6MOkPUO2s0Rr0bo85QWb27q2qHCp0bEXkc0/+vogUwMXh+japeE7zPB0BHVV2wjA53A9sBzVV18mzQZZYRLb8REXMoKvwD+paq9qvU+SuFxqh3Y9QZot5ZozHq3Rh1hsarN4CqbpzRW7UCJtXTNzE4JhfyG31+IyIiIiIiIiIiZifGA83r6WsRHJMLIvmNiIiIiIiIiIiYnRgNtBeRcgS4CzA2L5cHiOQ3IiLiz+GaGR9SlWiMejdGnSHqnTUao96NUWdovHpniTcxjrly2CgiLYDewFt5KDVdjxjwFhERERERERERMbsgIssB7wEPpPL8HoLl+d1NVW/NTb9IfiMiIiIiIiIiImYnRGQwcDDwAPAYsBRW4e1lYF1VLeSmWyS/ERERERERERERsxMi0gQ4HNgX6IGlh7sLOFlVf89RtUh+IyIiIiIiIiIi5h7EgLeIiIiIiIiIiIi5BpH8RkREREREREREzDWI5DciYi6GiNSknmdT9f0vwP3IGh0aq94RERERcxoi+Y2ImEshIk2SaFsRWQpAqzwIwHWe5o+3EpGl89ZpZpDSe18R2SJvnWYFjWFRlEZj1DlBY9O93MKuMVxDWse0MSBizkUMeIuImAshIrWqOtUfXwKsCjymqqflq1n9SBHIm4GNgJewfJG5lcmcEVJ63w70B34E1lHVn3NVbgYQkZpy6Yjqa68WpMa8u6qOyFunmUF6XJPnIiLVujBNjfXW2Br6gZzVmiESvUWkA7A0MCzvDAQR2aE2bwUiIiKyhd9QE+L7MLACcCtwW+q4qrnhui7JDfZhoA9wKvCfKie+od4PAn2Bk4ChCfGtpnEOEZCDbsA2wO/Al6r6tBOyqiTAqYXd+cBKInKbql6bs2oNIjXeWwDLAeNF5H4sL+q0XBUsgzIL0tWAYSLyoqqOzVe7+hGMdU/gBqAJcC1wfb6aRWSFSH4jIuYyBK4O1wErYXkYH1fVcanjqoaQJbqIyBmYlfogjPhOt9SISFtgQp714kWkdahToPcxwJpYgvchqvprQNJ6isgIVZ2Qj9Z1kZB2d4d5FmiHEYQJInKTqh5YjQQ4tbB7EFgRuBd4Lk+9ZgTXe5qILAM8jrkkTsPG/EDgEhEZrKpf56lniNTCbgi2iB4E3J0mvtW0wAvGemlsbv8PuElVb0odVzU6R8x+RP+WiIi5ECKyPLAecDXwkKqOE5G2IrK6iFwgIpeJSB8RaZqzqtMhIm2A1YG3gQdV9XcRWVBE/ikiLwD/Ba4XkT456bc7cK2ILFmmux8wErjDie9CwEEi8ibwITBERLbNUN0GoaoqIq2x+TEMs/z2A54B9heR+/y4QjX5SQYLu5uBVYDjgDNU9bNcFZsBfBy7AY9i82F3Ve2GLfSGA7tgC5CqQbCwOwXoDRwN3KCqo0WkRgyLhsfmhfB3zMd6IWyn633g2JD4ikhTEWmat84RlUXV/GhFRERkigWA7sAPqjpRRBYDrgDux6yqBwD3AeWIXF5QYD6gPVAQkZUxfc8AWgPvYCRhz6xJu4jUAssDOwCLp/paAM2BDsAqIjIAs+4dA4wHLsKI5Z5+ntyQCgBqjs2Ty1T1YVV9G7NCXglsJSL3QvURYBFZE1gbuBB4RFV/EZE2ItJbRE4SkeNEpHu1BGQFemwHTAHOxyySYDsFCwOnAJ8Hr6ma8cYWpF9gOzHJwm4PbKH0kogMEZEukH0QnIic4S4OU1JdfYBuwI2q+pYfu6yI7I2V4X3If1+qbawjZhdUNUqUKHOZYATtF8waeS8wDrOCnINZmHYACsB5Oekn6edAU+Bs12ssMBV4FTg4OO5N4C2gRQ46zwcMCJ4vGDzeF5joOhcwYrBn0H8z8BPQPsc50cT/twLmBboCTwPNvL2p/++ELZQKwL3B62vy0j11HVu6btv580WB64DRwfi/AnTKW9eU3vcC7wbPz8PI8D7APMEcWyZvXQMd2wBfAc8DywAr+9iOxnZohvh435eDbtv7ez/tz2uCvkO972/+fE/gDWAC8ImP+3dAj7zHOEplJPr8RkTMwQgDUkKo6ucisj1wGWYFuRHbkn/NXzcW+BXzh8sUqYCleVT1D7U71BQRGQSMwW6072EuG1/7sUsCzTBCnLnfr6r+gpEAPEhpmoj8n6p+rqrXiMiPmLXpByyzRhLwtghm3XseG/PMEfhB9sJcHRbzro5Y4NUwYKof9637XitwgIgMVdUNNQff33CuBEgyaBwjIutiFtXvgbuBy4HNMd/UbYHBWelaH9waWuvyk7ddAByGWdpv1aI/+GBgVRHpo6q/Za2nfw+nZ6FQ1d9EZCC2GHoNmAf7/p2hqlf6sc8AfZLvcoYqP4EFlz4NRZcYxyPAv4Bb/Hu5CL6LpKqPiMi+wFXABlggXMQchkh+IyLmUKQisbcBFsLI4TPAV6o6VERWxyx+3wev64L5A/8MfJqDzgnxvRDo5+TgTuBRVR0uIhcnN+HgdQtjlp5OwJNZEzH3EZzijwWzfO0P/CQig1T1U1W9r8zrugO7Yj6TB2tOwXpqrguLYAT8e8x63hLoDBwvIsf42CfEJyHAbYBtRGRhVR2Vg97JXDkSuF9Vh6vq8yJyCOYOszjmvnOrqiYLk6+ASUBVBI8FC7tngYvcneRvmPvR7QnxFZENset5lIwXd2UW0S0wlx1U9SonkGthrkdDVPUHf11XjGe8iGWuyCSIzOfoOBE5x+f20ph70aZ+HSOArTGf8CbAUcBbwRxWbJH6QaV1jcgJeZueo0SJMvuFwG0As2j8gUWPF7Acs9cBnb0/3A5cBrgAIwdH5qj/ENf5PWwbsoBZ7nqW0XkAcA1GCI7LUMcm2GKiSdC2G+YrOw9wmut9DbBYmddvhFncxwPHl/vssriG5D0xAvA8FrVf49d2k1/D9UD34Ngaf7wgsFBe88R12Mp1vBPoGrR3T3QO2jpj7gSfACvmoGsy3k1wN5Kgr6/P92nA4FTfSpif+MfAInno7I9Pw8j3MGyHYD2glfc1S72uO3Ay5l61fQ5jndQxEOB4nyMvpq6npp7PYSjmvtEha72jZDQ/8lYgSpQos1dSxPBqzAp5HFZcYTMsmKaABXaEfqmHYUE1owmIbxZkDKgNHu/o5GQbYH5vuwojt0MpEmDBAmu+xSzUB5Ubgwrp2wTYCziWoi/sM5jlawl/3hazPiYEeNHg9Rt5+3vAvlnpXc+19HRycB1weZn+G13XGyhDgPMWzDXjOMxf8y6gWz3H9QLOxRZ2h+WgZ0J8F8fcjR7Dgh7XCI7ZF/PDn4r5t+/sOg/D/NyXy3GcH/Gx+6/LJGx36BygdTIv/P9GmIvJeLJdkCbv38L/t8cIbhssL3gBc8+YvghJvX5L/x6PoYp8q6NUYK7kY4MjLAAAIABJREFUrUCUKFFmn1BKfBfCrKVH49aZoO9uvxFcjG1h1gK7Y8Uutih3voz0Xw3YG7MutU71nU9dAtwJ2x5eLUudsewSiTXpfL9hfo8VJ2geHDcv9RPgvxNYH/MgkxiJP8v1Gw+cHvQ1DR7fGFxDppbHlL7hjkY419v75zEJI8ALp153BObK8T1wdLnzZaT/Uk6sfsEWbQUnkrsFx+yIEc2Cy2jgYaBX1nMjeLwT8CXFnY0WWKzAe67j6cm8x/xkf8MW0lkuSLtjwYEL+PM+GDlPAh9bu55pAlzrfTf78e8Tie8cL7krECVKlNkvwAOYZekXYD1vq6HUwvoalkO0S9A2T/C4IjcrjGTvX6b9//zG9DUwMGgPdU5cMv4DLJvWs5JkBvPLbRs87+D6TPOb/S4UrcChTiEBvgon7qlzZ+nqkM6ksRhwJpaN4kVKXQdCAnydX8Pg8DPJUO+QjDUr0xYS4OkuEE7WTsEI5bbB8ZksNii6iLQEHvTv5RqYW8nfMTekUZTuALQGlsVStnUntRDMeNzXw9Lb/QdoE84hLDPM+xiR7+tt3bFF4MpZjHUwvltiC4Wh2CL6R8yNZ+lA3/oI8AJYzMARpBZOUeZMyV2BKFGi/HVJ31yAF/wH/ndg/fCYgKDt48fsnKGeHbB0ZAVg+VTfGsAdTl6Ghjd8SgnwQH/9q8D8WRBHLJdpYikPLbs3UvSlviSlc2ilbEtx2/V2AhKd8TxJSMBCwOJB+6KYL+w0v44Fgr6QAF9OzlYx4B7XtaU/DwlwB4zoFjCyHhL5+YPHWRHfZLw7OVEcSpDizvvWxVwdRgF75zm2ZfS/gKJ7zkneVhuOoetfAM5paAwqpF/42c+D5XYej+0QPYn5eNeEx1JKgF8PrqfEfz/KnC25KxAlSpS/JimS1Sd4fDPFII/EChbeLPbEfAs3yFjfdaiHcGNVue5xvY9I9YW6XwEckqHOS2JZA/4V6oNtUW+PWUMLWER5SBzDz6al92fub5q6lgWwSPb7gSWD9u5OHhKSX5YA56BvQlqaYlvUXzu5OaEeAtyToktBHR/gCpOxo3DraGq8v8VcLt7H/ez9WhJyvC5GfkcBe+Q41rWp592Al3wshwHt0mPo1/c98EClxzel261YLuFwcdYbW8BNAV4G5k3mjv9PiHBCgCdi8QKR9M5lkrsCUaJEmT3iZOZJgqhrzMqYBCstHLR3wqyWP+DblRnoV+em6DoflGrrBzzkeh+a6quz3Z7hzXa+4PEuBIU0gC6YxbTgBLJd0Dc/tg3bInW+TP1Ng/edBwummuhzoGfQVy8BzknX0H3keMyS19yJ2BQsj2tCgMPdgduwRV8B2CwjXbejaNkPyeH8TrS+cJ23DOcARQK8NuZXOwHYNedxP5xiNpguFINkT0y+BwGR7In5MV+WXFMG+rUEnsKCAMMF3KZYPMMdPo5PBPqmg9taY5btn4jFLOY6yV2BKFGizB7BrKG/kvIppWhJfRuzUh6G+fBNBI7KUd9ewLtOCHZP9fXDgnzKEeBcSGPw/ge5XqdR6gPc3QljQoDnx7a6j8Gslf3zniOBrq2dyExrgABPxFwH5s9Lz0Cn+7BgpM39eUsss8YUv47QV31JPIgMWDpDHbtghRNW8echAU78kX/C/FDDxVFIgDd03ZfIcaz/6XM4DMbsgrkI/O7zPrFe98D8xacBW2es57x4CkGsGmHiB16LLZAGOwEeSsrNCK+kiFUzzK2qYpT8JHcFokSJMnsE2NgJy5WY/1q4HXib39B+Br7BKlxlmmKL8lbbNfzmNI3Udm9AgKeScoHIeFzTAWK9sKwHUzCLXmgR7o65NkzBfJIfdf1PyUn3tLUrtI62aYAAd/Nr/JEgHV4eemN5V4c5KWsVtLfEFk+TcF9sJ2MnYFbW5YJjs/LxTfxHl8LcjtoEfe2wlGzjMatliXsMRQLcKgtdG7iGpbEdobupu7vxmv+OfIFZV9/0YzNJZwbsVKatM+ZW8iylu14tsZRyiQU4SX/WCwsIPjzPcY6Sr+SuQJQoUf6aUGph+g+WYijJuxneDBICfB3QKWjP1N+NIC2ZP1/DyUA5AtwXi44vAL1zGNtw2z0kAothZU+TNE8hAV4Yixr/BrOWHVjufBlew+IEFn7qEuCTfeyvJbCUYta0zIlvSveTsS34D1JjnJDMllj52gKW2WS0Pz4+I/0eBa4JnjfFfMGPcz3uoTQIcoGGCHD4P8cxF8zSPgZPr0bR77pLMN7PYbsgmaTroxjoekaqfV7gXswqfX/qN68FRoAnAm9g5aIf9/OskOc4R8lXclcgSpQoMyd+Uy1LVCnm2NzEf9hPCvpCsnO/91+BE+BK32xTBPJkf/8tU8c0RIBXBf6Ww3iHi4p7MJIbujksTj0E2PvbUVpEJOucyeJk7HnXMQzWC+dER6yiXpLGbNmsx7oe/ddynaYA/6NuJa4wSv9wzHp9O7BLuc+wAvqFRPC8VF8nn+tTMCtjfQT4cQIXiErPh3AeUmpdT/qSMV3ByeQFZY5JLMA/YEU56vhcV0j/RbDF5C/Amam+tsC/sV2ANAFujhUK+Q5zC/uIHIuFRKkOyV2BKFGi1C/A/sDFZdrvxHLHrp1qX9iJwhsEvmwpspP4AA8myPFbIf3D910KKwjxPTCOFKGllADvXs/5Mt2+9sfdMSvXRMzS1RABLpvCrJIkbEbvhW1jv+Wkob5F0eHen+Qizi27Q6BTayyw8L9OIrdIf/7UdesI09Bl4crTi2LBmPNTfQti/rH1EeBj/HUPZKRrGBgWfvZ1smFgPspPAV+FrwuO6RLMqYNJVXirgO6J5bmb/7b9xqwR4FqM0A/AA/mizN2SuwJRokQpL8B8FLfozgra+2OWl6mY9ehWLFAmiWre1QnkdqnzhTe8O/28g9IEYjbqH1p8H/Cb1jfAKxQrim2des0awTXvl9O4hxaxc7DMEx+4TpNIpbOiSIAnYdXScsnhG+qOWZ37AB39+RKYf2yaACfWvIswy+n+wFI56F1f5bZ5fT6PxgI2G3R9qRT5msF79qK4oJwVAtweCz6teOU2rFR4Adgn1X6Ht19JUNnR+7b3vj3KjS1GgBOf32MIgg4rdA1/iQBHiRJK7gpEiRKlfsGqPCWWpXODdnGieK0Tg4l+I/oblu7nDcxa2TF1vpAA30gGW9zA9Vig3U4UXS32dEI5kboEuD+2vXlopXWbgd4PYWT9bKzIxbEYcZ/iN/uQAC/mN90CsGpO+ibkoCdW5OQFYI+gPSTAZwSvWx4rz3xcTuQxXGy0ALr74yR6vy1GgL/3eVF1W9bYrsafIcBZ7WRshsUC/Aj8M2jfF/OJ/QNbTD/sc6YVVhHyeczyXnaHCCPAn2GVIiueFYRZJ8D3EAlwlDKSuwJRokRpWLBt68RXN+1b2BxLqXUuFhE/DSPBP2PptZbz40JrWmalaZ2IDceC7dJ5bjcDPsQswJun+jplpWM9eu/sZOWIFMldyQnMFOBISl0glsRLSeegb2LB7YUFKj1DaTaPhDQsjhH4pALWFVjKsLEEFd8y1Dskvuf7HE7S8g2maLlOCPAPNG4CPB7zE66olbQe/ZI0auNIVZIDVsbStH3tc/tjYC+f69/huZIps0uE+TcvmvWcmQkCnJTjviXvuRGl+iR3BaJEiTJjSRHg0AKcTu2zN/CIk4QCcFvGejZJ/V8aSzV0bvoYf3wIxTLMW5Y5X16FII7HLGHd/XmYNm5VilbrQ/AqUqnX55HVoR1W1eoVoF9al+B/V2yb+ytskfQGOQS5Uerq8KgT8CGYz/E7Pi8+xK2OAQFOXCBWzFrnmbimhghwR4zgjyEoOJPxOG8UEOC9yhzbFvPhfQqzBg/3a3kwp/FMfkcE89tNBz12xxb75QjwfJhVO3M3nijVL7krECVKlJmTBghw+obQHnOXeNOJQmL9zTKrw5mYlbSTE6wnKbWgJlHl4kTyCyeTA7LQtYFrSCyoJ5CqDpa6vnO8fzJerjgvnQOdVnIScHT6ehL9g+tr4eRgSVJZKnLQ+xRsO35XirlYBfNDLgDvUSxK0Br4B2ad3DFPvRu4noYIcAdyKKqQmgeJBfhXYM+gvVnqNTthVQt/8mvJugx6QnwXxSqxPYvtaAzG3C0St5iGCHCu38ko1Su5KxAlSpRSoQGroZPacgS4SUBskv8rOKHMJAF9oMv9wEhge39+AWZF3S/Uzx+vjvlyHoqlIBpBKvo8p89gNYo5kcNqXMkN+WDMynqPX1umxKAenfdznZf356G1OiTumVdsw/yNm9fT9xTmylCSmxpL03Y5xUwaieV6HjIIEpvF60t/95YJCPDAvPVzncIdlw2xReevBBbg8HckaOuDWYEHZahr8lkvjRWwGI25xHztY/pf4O+4+4gT4DewRdSFeY91lOqXGiIiIqoGIlKrqgV/vISI9BORfkm/qv4Xs5Q9CBwrIud6+zTMsoeqqh/+DTAK8+erqM7B457AQpjldIg3349tY18pIgdg1i9EZGHsJvwJlrHiFmxLfoVK6hvo2qSB7mHY9vuewOEi0hFsnEWkM0aOX8cqi/0AnCsi81ZY5RlhpP9fD0BVp4iI+ONkTv0fsG/4mVUaIrIJ9hnvKiLNgvYmIrIARq7GqurvItJUVSeLSI2qTgGOxgK1VkuuQVX/UNWP/RyZ3MOSuVJuzoiIqKqKSF/gPL+G/2F5fu8GjhGR07PQM6VXia7+G5E8HooFbY4ALhKRvYJjJDhHraq+jfn+7ioi3bLQXVULIrIgtoD4GLPy98Xmyv7YAuhCf46qjsAyWvwEbCciHbLQM6LxIrMfwIiIiIYhIk1Udao/vhojMYvYU3kYCxq7X1U/EJFT/GXHisg0VT0hvLk55sduZH84mShUQu9A59OwbelFgKdUdbz3vyIip2KldC8H9hORz7CAlZWAE1T1JxG5BXOX6IX5LVcMflNP9N4Us0z+Anyoqm86AbsBc9v4F9BTRB7B/DW3ArbGUkC9LCJD/XlbzJJWUSRkK/0Ys4pNAHYXkTdU9eWgDxFZE9gO86/NEj8CL2Jp9aaJyO2qOtnn608i8jywlogsqqpfOmFX/z5MEJERQC8RaQVMCK+pUnM6hOsxTUQWAzYRkbtUdYz3JcR3Zcy151HsO/Czqn4kImdiuy93VVrPlM7h/N4aCzxthbmQ/McXEI/7WJ+HEWBU9XonnjWqWkjOgS2ka7GA2krrnszp1bDfiPNV9QUAVR3rvxM/Y/68ZwBre99IEVkH2/EYU2k9Ixo58jY9R4kSpVSwdENjsBRhSeWqsdjW30EUt97DNGiXps7R0l8/kaBkbQV17ud6/AS8FrSHW+99MIvwl5jF6VXggKB/f+zm+vcK6xpu/96FBf8UXIYDx6R0viDoL2AE97jgmFuwrdmKZ6gIPvs2WIBbb8w9IPGhPsjH8HFgo+B1A7DS118Ci+Uwp/sAQ7Ht8z0oDdQ8wsd1CO7yQnHbuxsW3HYvtpDL1IeTuoGbBVIZPSi67jwIdC1zjkxTbVHq4nIf5gs7JZi/DwHrB8dsjLlA/EgqD7D3L4mlTfyADP2VMVeoAh7YmLquNljQZgEv9EOF8pVHmTMldwWiRIlSFKz2/K/A7hT92ZpjuW9HY9a9TYPjl3FScXSZc60OLJOh7ptgJL1AqR9hbeq4jliw1UJBW18soOVjKliBKSRPTgy+w1KW9XIdPsaC2E5NvW45LNBqe2DNoH01f80QoFWFxzchYr0wcvstRnSHERTeoBis9wfwmI/rl36ty2c8n8Px7kspAW4R9CX+sU/6nK5x0nUWtoDbOUu9U+O9nBPIT3xuHJrMaydhP7nemWVwmEn9b/XP/BCgM7AK5js9HiuSs05w7AZ+fQUf//BzW5OAhFZ6ngRte/v7HlTP59KfMqXSo0SZGcldgShRohQFuMZJTbsyfetgxPjmVPsCZY6tmIUsdWOsCduwVEq/YQEpG6ePq+d8x2JBTz9lRc6wVGZfOqFNFhm9nRh8iwWxnTiDc2yIbXP/SIXTKQXj28vf72Us48SOTmQKWHaEhBhs5kTzK6ywxeXkk8c3vfDpRykBnifou92v4zesRPdXWAq847OY1yk9w92VXzFr6brY4u701LHLVCHxXQnbPTqb0iwr82D5e8cDd6Zesxmwaz3nq1hGkGCsO2AEvbM/b+/z4H94HuHUb8+hfh0r5T3eURqf5K5AlChRTDBr1wNYAFUS8T6dXGK+hI87SVwsTQSyIAakthYJqlUFbZs5uXkT2LA+/fya/oZFab9KRlZqzPL8KHATxSIKa7nO/wbWx7axC8C/ws/H/zfFfA1/xIKxsiLsbTEr7rOU5vEdiLlu7Aw0o0iUm6fnUV4SkirqEuCw4tleWKDh21gBjq2DvkyvAQu8/Bmzni+NuZkMB65I9EnrlMV3cCZ139rnb39/HlZ2nBdLYVbHhSM91sFcqsh1Ubqb8Sq2i7KDtzXF3L4mYAu4PhRT4a3s34N3ySF1XJTGL7krECVKlKIAl/pNaXdSBSP88RVOzBbKQbdQj5OcFAzHLJCrpo7dnCIBrjcNmBO6ZYEOGV/LaQmBxIKBxmFb71297Vhvm0jdqnpNMbJ8LLBIhjr3wBZGhwVtgzB/zn3wanM+pmmLa26kDMvYUADWCNrSLhDzpF6T1j9r4tsGK7DxIoHPPBYw9kAyD4L2qvA3DUjr9j7mRwW/I+lcv9Pdk/KYH5TuZowFniDlc+yfw8nePw4rtzyEYqnmqqv0F6VxSEx1FhGRMWaQYusizLf3cCwCvql6FgcR6YpZoD7CCE9m8OjvRI8hrl9H4FOM3FwhIjsnx6vqEGAHLIvC+SKyWbnzquo4Vf2vVig6O0n1FTxv4u97iqq+JSItMD/IEVgKuVF+6ALYzfVTiinEEp2nqEWfX6CqX1VC73qwBLYV/BiAiJyHbf0eCNyqquP8uFMx6+p0qKpmp2YdfOL/108aVHUY8H+Y+8blWHqqFsFrSrI4aAZZHVKYhKW521NVPwzaf8cWF6ilYkNElgS28NRcmaJMOrNknF7E5u/fsRy4qKoGqeGSLA7jkr7Ka1sK12c+4Grse3aCql4L09Pgiar+hi3w/oEtlhYBFsaqGK6uqh9krXfEHIK82XeUKHOTULr9uCn2o/5PLKVWU2/fHXNteB8LVmmDkZmzMdK7d47634ARxN3wYgmYdang+u6QOv7v3pd5Na7UWM9HabR4Yg1rhS0mHgj6umLBcPtSxq0jI93rWDp9DkwGDsAyUExxHVsGx+yNkZ71s9J1Jq/nPp8386XaEwvwL5jLQ9lCGHkLxWwaTwBvBO1LAU9jC6Ssdy/C+b0VcBi2EFrd247Adi7uwXY3EktrFyxN2PdAn5zHdSlsQXFi0BZaqNNuVl2xTDYtstAvypwruSsQJcrcIqkf9XuxYI0k/dAPmOWxC+YLuzPmUpD0T8SsNMeVO19G+u+IBbIdTHGLfT0sIOhpJ2afAjulXpeJawAWlZ8Er4VE90LMUvQMlrM38fOtwSL238F8TDsBK2IuHb8A2+Ux1hSJ+UJYAOFyQd8bPhcSEhzOqVWwwMGnyKeEbpqoTPeJxRZ4BeA4n9/h59MH898sUOHAwb9wbQlxvMXneBtsF2aIfy8zJZGpz/1BzJIbpuM7H1tcn+e/M8Mw3/D9MTI8BTiqCsZ1C9c3Ieyha1U4RyqWASbK3Cm5KxAlytwgqZvVVZhrwylOWA7ALEoF4FqK0c4dML/SCzE3g3WDc2QewOS6fI9HtmMR5b9jUfpNMIv1NKzy2W5lXl8xnbFMGAUsi0OYQWAI5lf6FvCZH/MERd/eJpj19EcnCWOwAJtMS0IH+ibEdyknLG/7Z5+0D8B8UcdiuwatvH1TzIL6HTmU/k3N7yXK9LfEFk4vp6/VH68CbJWRrnsxi5kvKJLfi32M+zvp/A1YIcexHoQVoDgaC4LdCiPo07CAzg18fo/yuT/N58/BwTlyC4ak6Ht8BqW5n8NrPBuzYleFX3WUOUNyVyBKlDldKLVg9MCsXEfhW+pOwDpgFdym+Y2sXktjzjerXv6/u99QHwJ6eNuyTgwKTibrkKAK6rUMVkZ5Ala2tRVmVfoIi3xPLMKXYpbqNygWVGjnJOFGzFK2TR5jHRCsnhgJf5JUflssVdUOwBeYte8zJzOjvS3TPL5lruERzGp+FmaJD0lMkrd1j/Q1p85RyUXSfq7DJcm8ncFn0Ru3Svrz07BF0nvYwq93xuMbjmcHzMp/OqXpzDo5YZyGp2XDqj2ugVnZu2Ux1jN5PbU+lh9ii+l0Rph+2M7MpWRcLCTKnC25KxAlytwimIX0OicpS3tb6LfXCQsAGon70+akZx0LC0V/5Bps2/oAzPq4EUWr5PqY+8N2BJalDPXuBdyJ+0Vji4ingXlTx52JWezeALo3cL48rOvzYpXY3gRWLveZYIulHlig2MPYAuTwkNTkOHcOw9LxTcBcee7FXEnmxQIkf/K25jmNbxOKlcEuLUeAA+K7shPc63FfZGAbf+0Ecsw0gC30HnXi2Mfbwt+ShX1uFKjHMp0mmjlcQ+ISszuWUu4t/z1JjALr+Vz6mhxyVEeZsyV3BaJEmRsEs7z8h6JPXlgAYnouTSxSvwBskpOe6a3o9ZyoNE0ddy5meUwCgToDg4EXKLV0Z52iaims5PNUbJExMOgLyUFCgF+pBtIY6NXVCeJ5QVudoiLVIPXpQrHs8j3YlvxUzN96bSxV3yQqWC1sZvTGXI/qEOAU8f3JSWbXoH8xnztL5qh/SyxDwh9+DQfWM1f28v4t0n3VIMFYt8EWTd/63PgQc/f53udPrrsZUeZMianOIiIygKr+jPneXelNu4hIN+9TTyWmWMotxW5smcJTCyXpzO7H0iU9iflqHiIi7byvBktGXwPcKiI7YKR9b+AODdJSacYpqlT1I9flFiwt0gAR6eJ9U0Wk1h+fiKWV6wsMFZHWWerZAHpimSleARCRZj4vks+n4I+XCl+UTulWaYhIbaBLRxHpJSILAKjqj6r6LrALtoA6G1gQI8Db4sULRKRlljoncL0PxAjkwcBRItLD+1REFnddn8dKGY8MXvsFcJqqfpq13oEOEzD/+wswsrixiCzifRqkPxvr/wtJX9a61gefyyoivbAFxWAsM8y9WADhr5jFvb+qvp+fphFzLPJm31GizE2CWfZuwqxhZ1FqdeoE3IFZQPLcUh2MuV6chhH2FzH/wYuBBf2YBTGC+TvFbBVHBefIqgxtaOkKLbvLYz68UzGi0Kqe4y4ksJzlLZjvcsF1D3cEwus8BrNYtslJx3B34HpsW7qA+YDfQRmXHWwhshVWivk7LJNJpyznShmdaii6QAym6LsumMtM1zz0mgX922IZHKZh7lSLB31dsLSEv5Cjlb0evZN5vQrFLDfhnGqBuadUlaU6ypwlySSMiIj4i3Dr7QwtnW6JPB8LXHoUuBWzhq2OVeo6TlUvqqSuKX1E/YfALaAPYBbfi1V1slt6H8Sqtl0GnKOq34rI/JifbRvgBzVr30yPw2zQu4m6pbqe58sBJ2B+mv+HlaUd7321qjo1db7p45AX3Br6ElbQ4kDgLvViCt6/EvYZvA8coqoTc1HUdLkXy7LxFOZ7uhmwGrZT8A9VfdeLtIT6LwBsAlwDDFbV4zPUN7E2JouJgltJL8MC4S4HLlXVz7LS6a9CROYFTgSOxBapz2KL57WxeX+Cqg7KWKd6v//J905EVsZ++14H9lfVUdXw/YuYexDJb0TEX4CI7A+0U9Wz/HnZH/70D7uIdMa2g//hTR9iUc3Pq+p15V5TIf3ThLEzRkwOUdWvfNt9svc9TJEAn62q35U5X+bEV0ROBFbAMlA8Brykqk95X0iAjweuDAhwVd1sk7ETkb4UU7QNxix447GMFEdhfs3rasZb7+GCwd0uHsOsdver6u9erWt/zDI9CsuS8EdwXQn5bI35hk/ASl+Pr7DeJe9fpj9NgC9Ur9xXbXOkHJwA/wur9tcCG9svgGdU9TY/JtPvpVej3BDLLvEZ8LGqPu7H9MJ+7x7CfmdG1XvCiIhKIW/Tc5QojVWwUrMvY9umRwbtNanjkkVmM8yNoJ0/70ox8OZkSgNrKp7TktKtxrOwAKUHsa3ITYO+psHjJIL8CmChKvgMHsGI4ZdYWrMCttV7WHDMslgWiMRXcp6MdfwzeWU3x9KXFYCvsBLB32EuBnmnMzsf8+d9nmJkfpINpA1WJKQAXNXAOS7za6lo8QKKmUh6YC4CD2NBbAPwQi3JcZRxgWgsgrlAnI75AF9GUG0ui98Sf58ke8MyPmd/8Dn7B5b6cKD3t/bvYfe8xy3K3Cu5KxAlSmMWLCr8Mb9pHhu0JzeCkPg+6zeB0DdvYSwF2kQnwJmWSHUdHgoIZOK/eQmB72aKACdZKwbkoGtI2A/GLIy7YzlPm2Jp1ka4fmEi/6WwErsFMqzGxZ/LK7uqP+6M+VkPwVI+nZQ3KcP8dgtYhbl3neym53o7zPXhbVJZQrx/EeADzP+3Yin9Ar2W9nnyDbaIGOnz/VRKc94mBHgK8G+qKAvITF5vW2xhkixO86jy18PHdyiwkbcti7liZPrdixKlIcldgShRGqMkN3p/3M9/7AuUlh9OrE7NsHyzXwHrUtcy3BkrcDEZ801dsMK6hwFff8O2SHfGCiisTrFC1GkEOXJTBPhvOY//mq7fjUALb0vIzgZ+s/0WWCN4zbJknEKOWc8r+wcWRNYyz/Ft4HpaYG4kX2EW9pVTY5/8vwXL3do+9fomPue+poKBWMGYLoot6p70eVGLkfNPMcvkuXjFwkC/W/zaKvo9rNB1z4sVapmGLbgyWUwH430G5tKwVtB3KhYY+0/q5tyumtR9UeYuyV2BKFEaq6TI4EpOcAtYeqSkvdZvsN9jwUFlI5idAN/srz86i5sC5m+8H5bitIaLAAAgAElEQVQXN8yGsDgWPV5oiAD78zwKFVyNWcq/Bc5I9KJ0QbK7679/PefIsnLbn84rmzpPtRQlaI75mP6GWVI7pY7rihXpeCmcV0F/hyxIGbaYuxlLWzYgaB+IuQe8jWUDOZe6FuDcXXr+wnXP69dUcCKc5Vx/GhgaPD8Ps6TvQ7HK4rzA2nmPU5S5W2qJiIiYZXhgxxR/PBBL/bWsd1/sASYXq0U2P4u5NnygqmWDZ1R1tIj8C7spP6YVDk4RkeOxgLsfgTtVdbyItFDViar6uYic44eeBEwTkYtV9VcNIvdd70zz+DoeBDbGSNZirscUEanxcZ8GPIHlCt0AuCod8JOl3mrBVgdi+ZsPxuKoBqnqcNWSvLJPYgunsgFA9c2drODXUaOqk0RkEEauTgGeF5GDsQwUC2G5fPsAB2iZYDZVHZORyvO4Hk+r6vMAInIWcARWofBxLLPJ3sBUEblWVUf4/KkTzNlYoKq/+vd3Mqm827MT6WBZR0vMoo7rcDiWteRWtfzEYK4Z84vI26r6ayV0i4iYIfJm31GiNGbBiNg32HbfDljEdRKoVMcFYibOl1VwyqKYr+8k4NWgPbRmL4ZZWRMLUh3/zYzHenq+W6A/5seZtrQn1sk+2Nb1mXnPkVA3qjSvLJaibNFZfE1LzAI8DvOh/QJLt/UJpQGguVms/bqSObNvMl8oWiGvway/BWx7PpPvX1bzLYP3WALYMHk/zNXiO8y/fiqwJ6U7R+th/t5n5f17EmXultwViBKlsQqwB7b9fhiBjybmj/q831APD9qrwr8tIIjdnLwXgGuD/pAAL45lSjgsSx39vRskIljE/iisktURQfvCQGL52iLHcQ6LVCRjns4qsEQVzIc1XJ8raSACP7iepkBvf9zCCfBwJ8B98QwQ4VzLaqzrm0OYW9ELWG7ZzkH/zVilv4uBpfP+LBqTAK2wPL0jKPrdr+S/iQXg8tTxq2A7Mh/RyLJpRJnzJLo9RET8eSyM/cg/oaoTkjyoqvqS5559BLhQRKap6mDNx0WgDrS4ff21iByCkbN/uJ77q7kQNFXVKWouEPup6rgsdUzllN0Fcylpg5VVvgMYp6rPi8g/MAIzSETWw26882AWv5NU9aEs9XZ90y4Wirk8oJYD9WDvOsiPzzuv7GtYdoN9gCmuz/DwgCBHbzPMXaCtiGygqj+JyIXYHDoBy0k8wF8zPUd0JRHklm2HlYauUS9UocVt+WmYlfIRVR3tr1sJm1dXq+rVldZzDsQE4FVsHm8G3Keqb4rIrtiCeRsRGYsFA68PbIotpgek51dERObIm31HidLYhKIF7AKM/K7lz5ukjjsFC/YoAKfmrXf6GoLrWATLfzqZIDcrdYPbsipZXBM8fgCLFP8NIzAFLJBqa6CZH7MWZgGeim25b4cVgahzvgx0b5R5ZZm5oLxmmOVuJB68GfQ1x4qITAD+h291p78TlZorWDqzj7FsGVOxVF8rBMclQXjfYT7Ju2CkbGTeY98YJfjcO2BuX/el+jfF3GCS37+x2KIpWtejVIXkrkCUKI1Ngh/+NTBLY+gyUBvckM/2G8ArwFF5611G/1qgoz/uTjHf73V56+g63YhlydgPKyiyBOav+YOT3L8HZHMAlj5rBLBrcI7M/App5HllqccnORlHLChvlI91mFkjue5mwHFYoOG3pNJaVVDvLv49ewkLproSW8g9B6wfHLcHRX/83/01uRYMqXYJfiukTFuN/4YMwhYcf0/1d8UqL26NxRjMl/f1RImSSO4KRIlSzUJda25oleyEVUUrAOemjlsI244/Hq/oVg1CqRVvCEFOWcwHOCnYsW5eOrouyzlRuQhoE7Q3d9L7A5ZWqVnQt44TzW+Avcp9ZhmMa6POK1sfAcasvFdST7q+FAE+DbOwzlJVuz+ha7Lw2RLzPV0luIbtMSv0a3hAlvetCewG7AR0yXu8q1UI0tcFn237cr9lwKpOfq9MPpdycyRKlGqS3BWIEqVahdJiEIdh1rmHgcvxoBmsItcrThZu95vu37At5PHATsE5Kn5DoMw2MynrjROUoZh1bi1Kt697kHMBC9djAx/TxJoUVnZrSbGE7o6pa0sswMMJKrxlpHOjzSvrhDG06IUEOKxIWO9CIkWAK7bgC0hv8v9Q4PYyemwZEOCN8hzfxiRY3u8nUp97N6xoyUc+roumXnOlj/VyeesfJcrMSA0RERF14EFLScDVEGzLekXM2rsj8JaIbKyq72J5LG/G/NzuxHw8dwZOUdU7knOqakUDmTxIbJo/XllENhKR1hgZQXV6wNLLwJJ+HS+qw695uKo+koxBJfUN9JYyj5P/a4hI8+C6RC1f6CPev3DwUlHL57or5ot4kIjMV/krmI4kr+wHWjev7IHYouhtLK/sviLSHSwoS1UzzSsrIk3C56paSOanWrDewViau4OAw0Rk0aQv/LzS5/A5NFlVf6yU3mrBbYsCA0XkbGBDYB4RqfX+guvzIGbhXQE4SUQ2rYROcyAmUcwAkmAslhlmMpbG7CEROVpE2vnvxB3Y78w+ItIia4UjImYZebPvKFGqWYBrMb/TnfBqVVhqpwJwMsUUP/Nhvp5HYKSyf3COLLbdQ3eMezErTAHzgf0XvqUOdMQCfzakCrYmKbXotggfY1am9ykNXEqseqtjVtQ9UucL/bGXzOF6qj6vLKU7GrtigZmXAstTWumvqoLyAr2WwlxIfsF8dwuYm0VZqyPmJlPAXFHqVJyLUnbMuvn/TuH3CLMA74cFNRb8O3oeMD8W0PYxqZLWUaJUoyQ/0hERESmISE/M2nEvcJ6q/iYia2JbgncCp6vqiBmcoyTtVaUhIv/Gqp/dgG3/b4sllr8G80se7paZyVnqVQ5hhSgROQOz0B2kqiO9bQ+MdA0DjgL+q1ZdbGHgSCyAaTNVfTV13oqnC6vvPQLLZGdsjvwG7KPF9Fo3Y1X1BLhGVT+spJ716eeP7wPWxoLZajAyMxC4RVW/To4HLsPSoF0LXKCqX2Sps+tRo2ZZboLtrDTHcjn/FzgEq9g2BnOT+bLM6zcDvlDVjzNUu1FDRBbEyOyTwMnh2PmuxXJYertVMF/7Udji72xVPTF7jSMiZgF5s+8oUapFSFlCgXUxK916/nwdLJXSbZQGhOxF0QKcqTWVugF5L2Fb1mEg2O0YsbmGHKuI1ac3RmY+B/5DUPQBCxQ7Aasg9gXmc/1/WKGCycAxeeru+i1GmUIVWLnrbzGCm7SthLk97JeT3mHE/l2YtfRQoBcWmf8CtstxDnWD8q71ObR2jnOmO+bT/SiwW9DeBiPnP2BkeJYq1UUpGePa1POr/TfwesrspGCLkK2woM2CfwZL5X0dUaLMSHJXIEqUPKQM0S0XKLYR5v+2ArYlPB64NUV81wV+ArbL+XoexKx2z+JR7JRub4cEOOmvBreHOzGr0S6USYWEbaduSzFYbCJmCT4gOCbLPL6NPq8sZjX/GFu0Jfl418as1KOx1GvnEiyUsKwVuWUAwfypv8Gsi6PwBQfFXM8tUwR4kTzHuDGJf8fCHNRLAycGz8/z344SAlyGKG9KAxUCo0SpJsldgShR8hK/Ya5EabnTe/GcvFi6sk+wNFV/YJbH7sGxXYHrgDeAZXK8jhUwF4cpwGeYhawG21oPCfBtfhO7gZxzyro+W2CWxkMS4uskpyu2jb0OvtDw61kBs1LWScOUsd6NNq8s0BoLFnwW6OBta/j8vgXLpfwkFtl/Zrl5ktWYU7Sw9/Lv6gGuVwHYpcxxCQH+xqVHnmPdGARYwMntmZj7Sy9sEXQ3ngPcj6uPAE8v3R0lSmOS3BWIEiVrwaLtB2DW3Hewbd52mGXuB2AT/1EX75vqN9NewTkWwwKWfsd8OvO+ps2wbetCok9wDaGLwd1+zHpVoPOBThoX9+ddgQuxPLkFzOp+G8HiJPX6XFxMaOR5ZbE0cn398ZJY4Ni9uKsD5mpSwKzsg/FgvZx07YktkC7x5/u5bsMpTSeXfDYtsMXUZ0T3h5kZ31bYjlEBKyrzK5bru04ltuC464GeeeseJcpfkdwViBIlS8GsRwUsgKoLxWj2L534bkZQFQzb7k1cBoYDJ2Jbws872Tk+ODaLPL4N5VndDHMPKADbJDqVIcAb5/wZJIG2+zjBuhPLozwKs47eiG3DJ59Nv5z1bZR5ZSnNAFKuIltLzA3mM6Bv0H4QFrn/CHBEXnpjlsgHsQDTNYP+A7FF5zC8tHjq82lBrCY2q2N+O7bI/4JgYUzdmIKEAF9TjiBHidJYJHcFokTJSoCVnWANpFikoo23TcOsX4sExzf1/7VYUYU3/ab7E2Yd+UdwbBbpzEIXhkWwvMM9KU0RtilmzS5HgNM+elltX5d9H6AtFrz0q+t7B7Bp0L+bt6+W45xJCNWiwAVYyeohWCno2jLkICHAL4XXkoPe4VyZl/KVuZoALwIvBG1dsUIuF+Wkd7Iw6oL5078C7F7muEP8u/h2OQIcZZbGfF5sJ+MH/x0cRKlrUTo+4hyK6e8yKx8eJcrslFoiIuYedMTcG15VTz2F+exOwPw1twI+EZErVXWUqk4RkaaqOgU4Q0QuBBbHEr7/rqrjIJt0Zp6iKim6cQVGDBbDLKePiMg9qvqAqj7mNQjOAu4RkW1V9X5PzTU1PGeldXZdawO9e2OkfQwwRlU/EZEdvK1WVd8JXtcJy+X7PyxoLBeopS1bCrP0N8MIbyuKUe0fpI5/0K/pQeAIEXlOVcdnqXOqQMu/MZ/e+UTkOuBWLaZXa41l0lhdRAZgKdh2wFyCbgvOV/HUcSn958N87b/BrLjPenstUFAryDHY5/k5wPki8i9VfVo9jVvELGEqcAwW0LsblqtcROQiVR2Z/uxV9f9EZBq2+zEle3UjImYD8mbfUaJUWihak9bBiOvt2Jbvk9g236JYcNtlmOXjXFL+mUDzes6Ztd/pI34NN2HFCW7Dbl7fAnsHx22CWaoLBCWWM9Y1dLW4HSOxBdd3dH16Ydbs07Gb8f456Z5svTfBrLyPY6SwHebr/T0NpNXCXFB6ZaVvPTrchRHaIdhOxRSf8+sGx+yC7WRMxXx/JwDH5am363Wsz5UCQSETUgFWmAtEAVuctMxb78YglNmJCeb7AljBkwLmfx+W4V6cINAwSpTGLLkrECVKJYVSX8dmTnAnYlalJLgtSZfUEfMzTQhw4hrRFvODPClDvWvLtB2GRbvvSl1Xh6lYGq1NgvZNMF/mzP02U3o/4OT8OCe2f8P8TAvAyqljD3Sy9lNIwsh4keHv2Z1GlleW4qJsReBdJ7fNXHahfFDeJsBpLqHbSR6ZNMIF014+Rz6htGJimgDvQ84LjcYiFN14OgBrYZbenpS6yXQALvHfwQuwDCC9sEXg18BCeV9HlCh/VXJXIEqUSgmWX3UvUn5pmDUsSU01PZWW/+8QEODzMQve8X4TPjIDnUtu8qm+m7FyxR2T/kDvv7mOl6dek2tKMyzd10jgnxRzyq6Jpda6ntJcsvNg/srPEViFcyJhjSqvLHV9j9fCsn+0CecSMxmUl9WYz+h9sEVnwefEakF7TLH1J+cIVh76TczlpYClNjuToDgF0B64iGIw8MfYzkDvvK8jSpTZIbkrECVKJQSYz3/cpwDLelsNVv1sDBZIM9kJZXvvTwhCe8zyUcC23scD/8pA59X9PR8t01eD+T5+Frb5/yYuQzF3gk7UJc65FLTAgmI+xa1FWKnlpFhIl+C47v6/FaXVxbIsYNEo88pSai3dHUvldyEWdNeizPFhUF5uWSmCcezq38trXe9tKc24cmh9BDgv3RubBL8VSeq4V4DDscX9FVhawauT30o/trV/Li/69zVa16PMMZK7AlGiVEqAPsBBqbau2DZeSyy7wCT/YW/n/aGbxDaYq8HGQVvFyBjQA7gPOCXVnlgbr3ISsGvQFxKfm5381onsz2Hsk4XEUOBJf5wQ31sItk4x6/yzBEn1059Fhno32ryy2Lb0ZIo5kn8DVq3n2C2w3Y2PyKHkdUDGlvGx/RWzLCZ+vjcQFATxMS5gPsv9s9Z3ThDMresF4Ck8R7W3n+tjOw2LJVg29bpWxKwOUeYwyV2BKFFmp+BVq8q0X49ZxELftiZYjtnJKQJcNl1ShYlvQhbbBG2Hp47pg/krv0GqSAWWGuoF4GmsXGlVWMWwoLxxmJXpdye+ocW3l+t8K0GJ1Yx1bJR5ZSld+JyB+WMejOXsvQDb4WgoKG974OAc58bCTnyfBDb3tv6YJXIythDskfocCljQZx2LdpQZjveOPie2D9oGYrtj+wAXOwG+Nk2Ao0SZ0yR3BaJEmV0C/MMJyupBmwDdMIvjCCzop2mqvxwBrhNwloH+odU58XW8OXXMXtiW9TvA/k7g+zn5mQLsmffnkNJ3MYp5fP+TusYurvdoYOuc9GuUeWVT47iYk5izgFbeNktBeWS4WArG/BBsYfR3SgPYOmM+qAXg9NRr9yHHUuKNSSguzpLS4UsD1wb9SSzD/piPe2+KFvh7CMoYR4kyp0nuCkSJMjvESeyu2HbvM6S2e4Flgc8x69iu9RDgP7D0UGWtx7Nb39TzFqnn3bAqSgXglqC9DUbyf/e+313GkXN2hAaudU3X9XMsl3I7YH2/vinAUTnPm/l8DD/BFkjdvK82RcoSAvw6VVAe2nW6k2Lmku28LXGTaUGZoLxqmRtYSq1f8RRllO7KLI4tRH7Hsm7E4LZZG9tkgbGcf+/WCscYs7CPAc4D5g9e95aP+6/UU1Y8SpQ5QWqI+P/2zjvMivL6458vXSyxxF6CUYMVNfbYsJtgNLHHihossf7Ehi2IBVCDIlEkMYKx1yhqFOxiwRIitmDUiIK9oIjI0s7vj/MOOwx3YVl3791yPs8zz907887MuffO3vud854SNAPMzHDheigeHnCppC1z21/HE31m4l26DpDUNrfv7/Barvul/RvcXkntJW2dnk8DkHSnpL3N7APcK/o34GBJN6Vx35rZ3/FudecAd+A1cfczs/7pGK3Sa2oUmNkzePWBH+NT2e/g4QU74cL3T+B2V8A2M7Ov8fdwLTwmfMe0bSZgmV1mNgivP7sZcL6kRcptb2r0gKS2kjrgSWCL4u/t8snO6akpyjR8NuMcvH7rw5LWqvS1Ial1+rM1nlS1RdaERalzhZm9g8cwd8RDgRq8IUtzIWtKImlZ/OZoAh7SAx7WAD5b0BH4h5lNktRG0oFp3T74DeBHxWMHQXMhOrwFzQbzjmz/wL15Q3ABfIaZjU7bX5e0J96+9RIASbeb2Yz0Y7Efnv0+okwmbwP0l/RvM+sh6UFcEF6fxMsESX3S2KMkYWaHpNfyJvBm8YDl6DZXF8zsGUld8Ne3ER63/B8zGwOVszu9z7PM7FJJX+Lxjr0kvWtmo9J1Mcc+M7tG0gxglJl9X257rbpL3z+A78zsAEmz8WTI8yW9ZmZPmXema21m30u6EReag4At8OS8spG9x7n3MBNgt+Ge6UPxigLF7mxL49U2JpfP2qZN9h5LWhyfJfoCT6B9ujC0DZ70uzbwPB469bs0flq6IQyCZks2NRIEzYbk0d0bF8CvAnMEcNq+Pi6A2wC9gDus0KazTC2LV8aTe36FN4FohwuB58zsu5wHZ1U8cexI4GYzOzR7nUW7myLlbJ+7oM9V0vG4SHwa6GVmz2c24t+XFbmxyNst6QL8WjgfuNXMpkk6DvgTfkN0aiZ2csJzEWCNNANSTruz8/802fxjfFr9eTN7W9IN+DV/NS7Svkr7/RwX9N8Be5rZt+W0uykjaXW8o98kPPl1QzObXhjTBfesr4DfDC2Je313sELL7iBojoT4DZoltRTA9+Dd284Gri/ndHBhavJ1PA72MbysmmXCtiCAz8cT3u4wswPLZWt9U06xWzhvJsRWxUt9bYiXA3sOuC+7kZB0Ep75Po8ArpDdc84raS3gdLxRS28zq8qNOxHoi9dVPqUogHPjyuJlz1276+LhGUukTe3wuOkeeFm5v+Cfx0v4ZyG8s14nvOrGGw1ta3NC0kZ43oPwa2HL9DnM9blL2gYv5bcingvRz8z+Wwmbg6DsWCMIPI4lloZY8Di3A/Ds5aeZNwluA9w7clSZ7cpKa7UCdse9vi/gFSeuzY1rjf+AZTepqwDD8OSxijUnaIoLzaCuLD5b8Wa6Xn6b1tWUlPci0LWCtmbX7GJ4LP0DeMhLW+BcPNlqPNAFvwHthXfSm5EeRwDrVvo9bypL7v3OrvNNqG4h3js3rlU2Nj3PqoO0r/RriCWWci4VNyCWWBZmobp8T61KTNVCAJe1IQRz12ZdOT22xxNQ7k4C+K/F8dmPE56QtXOlP4emuNDE68omgZiJ9ZNz6+dq9ZvsngG8S66mchntzITYUngS4b/S/2D+2j8G9/qOBzZL65bFkwl/QmqFHcsC3+saq2Dgibtvp+++k0vtkxfCscTSkpYIewiaDJKOwMt8dTOzqcXp3Ny4uaanJbXDS2wNwevjnmNmzxX2KUeM7xx7JV2Bl197DBhgnqG/Gt5WuRswzMyOTmOXwZNRvjSzW8tpc3MgN/1+Il4/9lDgAauOoV0JF4xnAxeZ2fm5fXvgMdgVm3ovxPsehSflfYS3XH4qrZ8rJlnSacD3ZnZ1hWxeFvdSv5vs2iKtb2cp/lTSsUBvvHHLoWY2qhK2NlVyYTwr4K3RV8NLHt6Nf/YzJG0O3IKHnFxkZlfl962U7UFQcSqtvmOJpTYLPr17I+71upfq6brWhXHZDV17XEhm3tW2eEer74FXyLXXLZP9eW/LA3j5oauorr2aTVeugsciT8dDHDbAp4ln44lMFf8smupCE6krW7ymS2w/Ll0PTwFb5darlN1UwLuHVxr4My7GZuN1nbP/zaIH+IP0uWxRbjub6pL7vlgXr0/9KZ4cOBtvxHIUsGgasyleXvAzKtjRL5ZYGtMSpc6CJoF5DdDjcHFyBHCHpP0t5wHOefja4ZnMO+K96j+06jJoHfAfhU/KbH/mkbsSLzd1IvCgmX2b7J6dPHwTk4eyCvdy742Lml5mNqCcNjcXcl6ufF3Zp9I1JXPekXQfsCUVrCsrqY2lcmaSdsLDBmYDb5vZEwBmNjgldF4JXCLpbDN7Pl378xzTzMo+vZeu63PwmPoz8da6Y4HPrboM2ywzG5IqURyLJ/EFtSB9X6yBzxy9gXdrewlYHb8pOhR4RtLbZvayvIbvjcBASTPMbEilbA+CxkCI36BJkH4sp6Tp3Na4MLxT0n5JALdJYqYdniyzBrAL7uUF5tQBvjETA+XO3k/Tk7/EyxANN7Opya7Mhiwj+0NJJwMP4pnYb5nZ8HSMCHVYAE21rmyyOxO+t+PJkIvntl8L/NnM3jSzqyQZPrtxoaTzzey5SgjdmjCzbyRdjs/C9ASmSeptZl8UBPCVkoZZ1JatNfJGIScBX+El4p5N67N47xuBibmb6pclHYl745+olN1B0FgI8Rs0FbLOT99JOhPvZtUVuEXS78yL+bcBTsNDBw4Hni6KgfzzCgiF1XFP3sV5wQ5zCXGTtJiZfYZ355pDCN8FU6wrKylfV3aUpFvxmYOpkv5oc9eV3QEvizepErZbdTz4bXipr4vwEJ+f4Z7To4EVJJ1lZv81s0GSZuGCZilJ3co9o7EgkgC+CP//PRWgBgEcwnfhaA1sDbyeE76XAv+Hh8Xclr4rF0ljp5jZ85K2ttRNMghaMiF+g0ZPwSM2FJ+63gH3cOyJC+CDkgC+D+9+Na4xecESmahaG+aEcmTT7pk3ejdgK0n9rdBBLITv/Env5awSdWWPBl5IyWunpfXHA5tLKlVXtmINFSTtAuyKN6wYbGZTgLcljQXew5PyxpOEpHnHuY7+Z+MSvhlmNlnShenpqcAsSReZ2ecWSVd1ZTE8hOsTAEmXAafgiZs357477sfDIk5Oz6sIgoBWlTYgCBZEziN2Nz4V/ALwGzwedhReIP9OSR3Ns/LfrqTwVS7wUlKr3LqpeGLKAZK2A1csuTGr4QlAa+CNAIJakov3XgwYgNe57YaHDZyPJ7Q9gIeRdAfOAVYGTgD2xZOBtrbKN1RYDe+29c8U5tMGwMwmAn/DQ2FOkbRxtoOZXW5mf4K5r73GhJlNBi4ELsPj3U/Prvtg/uS+H9pkn2+asfgY2CnlEZyM39DNEb6S9sA76n2awiQqEv8dBI2R+PIJmgSSugG74a1nrzGzx83LPHUDbsZbBN8saZHkUW1dITtbF35glsk2mdkHuOdudeAMSTvAnOSV1fD2r12BkWb2TRnNbtLkhO9SuLhdFo95fNLMZpjZRXjFjEXwRhE/M7O+wMZ4iaitgf3M7M3KvIJqgYMLX4DO6XXNzMaY2fvA7enpCmm/ucRuYxY3SQD3xcM5hsZMxoJJ3yezJXUC+gDdUygP+Hfh8njs75nADVkeQbo5Ogkw4KbwsAfB3ETYQ9BUWBbvPf9K7gu+TfKOHYPHRe6Fh0AcnGJqy53Qlq/jezGwHbCOpH8Bd0m61cyGytvr9gY2kXQ/Hg6xBbAVnrxyYzpGRdrpNjWS8M3XlZ1pZrdDdV1Z86oCwt/3OyVldWU/r4TNKtRZzQnBh3FxuBfVJe/y8d5T07iqtF+Tuj5SDHDvEL4LJn3mWRjPA3iY171mNjQNeQ6/8T8anwn7QNK/gT3wWbEueHfCD8pvfRA0bsLzGzQVZqTHJaC6JJSktkkMDwC+wOMlHy7hgW1QsnjT9Pc/gd/jmdjX4d6ZwcBVSYz1wTtefQUcTIrfBI4xs37pGK2amrCpMNNwr+g6wGaSdk6fyfTclO+1wB/x770HJW1RCUMLN0kHSjpZ0o6SVkthF8Pw6+OyFM+bzQ6shM90fIg3uWiShPCtHTmP7wj8pu5IMzsT5nw/fAH0By7FQ6XuxOv5XojXNd+2EYTxBEGjJDy/QaOi6JGscAYAACAASURBVO3MebzG4I0h+kh63Mw+S8I3E8XL4N6w54BHyz3Nl0tYuwzYEJ9yfCBlXD8KjMTFeUdgupndKelxXIi1AyZniVaKqg4LjTWRurKFm6ThuJcuY5S8VNV5wEp4bOwakh7BY5J3x2+WTjOzceW1PGhoirMBaabiD3hC5iVWXdWhVRLGMrOPJfXDW3Tvgv+mvwH8L8UFB0FQgmhvHDQaSnz5LwZMSx7eVkA/PFv/aWB/83JgSFoR9+hNA3rmxEW5wx4WSba9jndjm5Sy9+/BY017mdkH8gYFs4s/dGn6PkIdfgCSfoQns/XEve29k4es6HFd0spcXiv/2SbB8jvgcuBxXKyfgIv33wAT8cYFx+BJe7Pwm7+BZjaweLyg6SJvWX2TmVUVrtHWeEKvmdnWaV3+GooWxUFQR8LzGzQKCl/6F+AxsJ2BV1Os7G2SeuEesYOAlyQNxEXBpsB+wMmVEr6JVYBNgD8l4bsjXqf1H8DpZvZxGncgPnX9eLZjZmuImR+GNdK6sgXRsiQenjEMT/yaApwn6UM8Jnk4sI+ZnSHpGmBz3PP7qZn9Jx0jZgeaAZJ649VIukg6PQvTSd9jHYAfAV+rumFLvkFP9l3XDRhtZtEhLwhqSYjfoOJkiR3p7weBzfDObE8CP8eT2FY3s76SDgfexstTXZ4O8RlwtuVadja0iKxBXE/GQxs6y+v13oOL3znCV9JWeFvac8iJ36D+sEZYVzYnWu7Bw1yWw2cCpuSS8q71mW56A3fnkvLG54+Vrr0Qvs2DQXi1kaPwj/a0XJz6VDy2e1O87fZzMI+j4Fg8v6A70R46CGpNJLwFFSf7IZd0Be45PQEvPXUEPnUNsFqaqp5lZhcAO+MxkFsDu5rZgHSMBr+m88l0kpbOvY5P8ZCHk6gOdTgpJ3xXAfbHa/2ObWg7WzLWCOvKprCYz4EdcUHTBaCGpLzWwB2SuhaPE7MDzYOUtPsl/p0wGhexl6eboVnpc74M/50+T1LngvDdGG/y8z2p2UUQBLUjxG/QKJDXrtwBuAsv8P+NpO3xTlfDgL5m9nVKAsHMPjWzkWb2vJm9mo7R4B6xwo/PpcDtkrZRdV3hM/AfotbAo6SSVJLWxuM3jwWuNrPnG9LOoPHVlTVvPnAGPmMxHfi1pOXTtlk5ATwEt3spvCZ00AxJuQxtzGwSHrY1GuhBEsBp2FhgIP7deB1wvKS1JJ2MzyBtARybxbUHQVA7IuEtaBRI2gCv6HCAmd0jaSfcczpXvKykE/F+9k9UwMY5cZbyNsob4J3EzjWzd9L6RfCEpX54vN4Y4APco70q0M+qy5lFwlIZaGzxsQuRlLe+mb1eOUuDhiT7rCUtal4VZhm8XNlWwF/x770qeV3wffHrZaW0+wx8lunwuEaCYOEJ8RuUncIPfFblYCXgv3gHtCeB5/F42dNywndbYCjuORtSKeEo6Ua8E9vpwENW6MaWPHjr4R2Z1saz9Ufipc/uTmMalSALyoukJfCSZqcyrwBuY7nObnGtNF8k/Qz/PrvYzF7ICeAt8XbWpyUB3A7/HtkV71Q4Dm/jXpEmLUHQ1ImEt6Ds5ITvYLw+5Wt4eMCzeObzJXj4w5l4fCxJHO+DlzMbU0Hh2xX/AboCGJHCMxbH2+rug3t5XzezscBv5GXN2qeM/uwYIWZaODUk5V2YqlLMLIyNa6X5sjde63lRSWeY2b8k7YcL4N8DSOppZtPxhLZbK2dqEDQfQvwGFUHeXesYfPruJDP7UtJf8ULt3wGPm9knaexawOF4G8/TzOzFCpkNnqW/LO7xnSRpNaAX/gO2chrzoqQjzexN8yYcM/IhDiFmAphLAM/CY4GXkHScmU2rsGlBmTCzfpLa498hAySdWkIAW74KRKUqlgRBcyLCHoKKkLylI4AlgZ3N7KO0vgcwBBcEdwMz8Xq/6wAXmln/NK7cDSyy8Iyd8UoOb+IJbcfgjQkexacv98c91781s/vKZV/QdEkhEH2BcWY2qNL2BA1D8TsrH94ir21+JvAC3iDnX7kQiE3w78Jjkwc4CIIfSIjfoOyouj1nN+B+4HgzG5zbvidwCF4Oqh0eA3yvmd2V37+BbSzpYUkJbf2AvfAEthuAm83ssbR9Z1zUdzOzhxvSxqD5kNX6TX9HImQzI/edt7Tl2g5r3uY+vfB8h1PM7N+plOIjeFjVxqmcYhAEP5AQv0GDkPOUFpN38l/2q+LidyYuJj/KfvTTVGBrvBzftJyHpBzCN++R2R5YE6+l+V8zeznF8S4FLGVmb+X2WwlPgtsD2MvM3mxIO4MgaDrI27C/gZc6PC+3Pv+deBle1WEU1R7gpYAlzOz9StgdBM2RqPMb1CuSuknaOAlf5UTkYZJ+lPemmtkE4Ba8i9vamVhOm2eY2dSUKDY7t085PL6ZzXfi041/xWsNPyvpbKCjmX1WEL7rAscDxwEDQ/gGQVBgFl4asZe8VTswd41nvATe63iY198kbWRmk0L4BkH9EuI3qDck/Rz35A6WtEHOi3slLh7HSjpX0qa53f4GvI93MOqQCc+8yC1ngljOA3MnsA3eJWwVYHN8OvIivAzRHOQtRq/FG1icb2Z/TutVLruDIGjcmNlneOLuPcDFeQFM7gYfTwKeiIc6TCqfhUHQcgjxG9QbZjYGj4ddH/izpC5pU1+8d/1rQG9glKRBkrqZt/ccnvbZCMrTonh+SPoNLnD7AMNSMt4SeAzyDcB/cmPbA7sBX+EJKZem9a0ibjMIgjwpZvdEPJFtjgDOfVdsD3ydHjuHxzcIGoYodRbUC1ncmpmdLWkmXrppUKpR+TLenGJoSmbbBTgCOFrSg/hU4NKkHveNoBTYRnii3f2pju+OuEf7XuCcXGWKzmb2lqQD8Zi8z9P6qOMbBEFJzOxTSSelpxdLWgG4A698cwJeTrFdcgwEQdAARMJbUG9Iam9mVenvs/BubWPwzOVXCmPXAg5Ky6pAB3yK75flrOObJbelWOMVzGyipL8AW5nZBkn4PoBPVebbLB+Oe4a3y3tnIlM/CILaIGlZ4Fw8XKot3uhnMl768bVK2hYEzZ0IewjqheT5zYTvwcC3eBWHrYGBktZP25QE59u4eFwfL+9zG15B4RfZuDLYPCchDxe3x0jqgGdaryepN+7tvQc4Kyd81wJ+A4zFq0DMIYRvEAS1Ic0UnQHsDPTHvwe3CuEbBA1PeH6DH0ze2ynpfmBjYDTwDbAWnjg2CjjBzF7LCVtl4QGpTNgQPORgM0vd3RrQ5la5c1+AV2o4BZ9+XBOv8tAZGGVm2+f2WwXoAfwBONHMbmtIO4MgCIIgqF/C8xv8YHLC92JgB9ybcbCZHWVm2wFXANviSXDrZ+NT0Xelvz8CngCWB1ZvSHuTWM+E7zK44B4KDDez6alMWX/ce725pOMlrSfp13j3trOByzLhG1UdgiAIgqDpEAlvQX2yCV627CEzq8riac2sZ6rgcDJwtaQTzezVEvGx44HpwAoNaWROrN8BdALaA/3NbLKktmY2w8yGpcS9M4FBeI3OWcB7wP/lyplFclsQBEEQNCEi7CH4wSTPZzvgVeBrM9siiV0DWqUi7u3xUIjVgTfxEIgxuWMsj5f/WRPY3MwmNrDNi+Me6uPwShNnAgOygvO5er9rAGsAG+LF58eb2X/SthC+QRAEQdDEiLCH4AdjThXwMLCppN3NbHbysGYhDlV4Ldxv8Rq6xdCG1rjXeI+GFr7Jnm9xj+7FeGzyr3CRO1fHJTN718xGmtllZvZQTvgqhG8QBEEQND3C8xvUG5LWxr2j44BjzOzZ3LZOeFzt+cBkMxtbYv92Zja9PNbOOedyQHe88sT9eEjDPOI7SpgFQRAEQfMgPL9BvWFm44AdgXXxhhZ7S1pM0gbA74ENgFmZ8C12cqsv4Zt5bbPHBdj8GXA98EdgT2BAquhQHBfCNwiCIAiaAeH5DeodSdvgIRAdgU/xAu5LAmdn7X8b8NxHAIcB3cxsaj5+tzBuLk9uKjh/JO4Bvgc4w8wmNKStQRAEQRCUn6j2ENQ7ZvaMpC7AfsBOeILbM2Z2FzRcoljq0rYjsD1wi6SDSgngTPimJLzL8EoPH0r6GzAbL3P2I0n7m9mU+rYzCIIgCILKEZ7foEEp4WFt0AoJkhbDBe0RwKPA/nkBnBO+7YDhuFjeMqs8IenHwAnAl2Y2qKHsDIIgCIKgMoT4DRqUciaK5QTuonhjjcOAx4D9kgBuY2Yzk/AdgVd3OBTv4jY7d5wOZjat3PYHQRAEQdDwRMJb0KCUWThm3eK+w+v2jsBbK98iaZEkfNsApwGrAIcDTxc90ZnwTX+H8A2CIAiCZkR4foNmQaExxVBgMWA3YAawFHAfcJCZfS9pPTy2d1yI2yAIgiBoWYT4DZoVku4GfgH8CRiDtyTuA2wL/JPqGOA2ZjazcpYGQRAEQVAJQvwGzQZJ3YDbgUuAK81salq/GDAYOJi5PcAly6AFQRAEQdB8iZjfoDmxLF5b+JWc8G2TypUdA7wE7IXHAHfMqj9UztwgCIIgCMpNiN+gOTEjPS4Bc4TvTEltkxgeAHwB7Ao8nDy/MfURBEEQBC2IEL9Bk6Porc21SR4DTAD6SFouJ3wzUbwMUAU8AtwRIQ9BEARB0PII8Rs0KYre2hTPm13Hb+Exv2sCdyQBPCONWxHoAtwN7GNmf07rI+whCIIgCFoQ0d44aDIUypldAGwBdAZelXSrmd0mqRewEnAQ8JKkgXjFh03xdssn544RDSyCIAiCoIUR1R6CJkG+LbKkB4HNgFeAD4GfAxsA55hZX0mtgXOBfYH10iE+Ay41swFlNz4IgiAIgkZDeH6DJkFO+F4BbAKcAIwws28kHQtcA6wmaUkz+xq4QNK1wIbAt8B3ZvZqOsYcIR0EQRAEQcsiPL9Bk0HSj4FHgWeAs8xsiqTt8eYVtwO9zeyD+YUzRKhDEARBELRsIuEtaEqsiIcxPJ6E70648P0HHvLwQRp3gqQdSh0ghG8QBEEQtGxC/AaNkhS3m/2dVWT4Ei9VtoqkLsBw4F7gdDP7OI3dFjgZ6ByVHIIgCIIgKBLiN2iU5CoyDAbWT6urgGeB84HngLuAnsAnaexKwD7ANGBMeHmDIAiCICgS4jdotEjaAm9L3APAzL4E/gosDRge/vCJmZmktYA/AEcD15jZixUyOwiCIAiCRkwkvAWNFkmLAyOAJYGdzeyjtL4HMASv33s3MBOv97sOcKGZ9U/jIrktCIIgCIK5CPEbNEqycmSSugH3A8eb2eDc9j2BQ/DmFe2AJ4F7zeyu/P7ltzwIgiAIgsZMiN+gYmSeWUltzGxmbn2+k9uquPidCewFfJR5cyW1B1rj4TvTsmOE8A2CIAiCoCYi5jcoO5K6Sdo4CV/lROthkn6UCV8AM5sA3IJ3cVs7E8tp8wwzm2pmU4DZuX1C+AZBEARBUJLw/AZlRdLPgZeBF4EeZvZaWn8lcBLwAXAd8LCZvZy2LZP2eR/Y3cymVcL2IAiCIAiaPuH5DcqKmY0B+uHly/6c6vUC9AWOAl4DegOjJA2S1C1VeRie9tkIPLSh3LYHQRAEQdD0Cc9vUDYKsbx9gDOAF4CemZc3bdsT2AU4AmgLPIh7ii8BrjSzU8ttexAEQRAEzYMQv0FZkdTezKrS32cBZwNjgFPM7JXC2LWAg9KyKtABmAT8Mur4BkEQBEFQF2LqOCgbyfObCd+DgW/xKg5bAwMlrZ+2KVWAeBvog4c79AJuA5YCfpGNK/+rCIIgCIKgKROe36As5BtOSLof2BgYDXwDrAVsA4wCTjCz13LCVln1htS+eAge97uZmX1S5pcRBEEQBEETJzy/QVnICd+LgR3weN+DzewoM9sOuALYFk+CWz8bnxpdKP39EfAEsDywegVeRhAEQRAETZwQv0G52QQvWfaQmVVlNXvNrCcwEBfAV0vqkqsDnJ+eGA9MB1Yos91BEARBEDQDQvwGZSHF8bbHPbZTzGxSKlc2S1LrNOwsYCywIXCtpJ/nha+k5YFTgMnAS+V9BUEQBEEQNAdC/AZlwZwq4GFgU0m7m9nsJG6zEIcq4Cs8EW5L5g1taI17jfcws4nlsz4IgiAIguZCJLwFZUXS2sDrwDjgGDN7NretEzAUOB+YbGZjS+zfzsyml8faIAiCIAiaG+H5DcqKmY0DdgTWBYZK2lvSYpI2AH4PbADMyoRvsZNbCN8gCIIgCH4I4fkNKoKkbfAQiI7Ap3gntyWBs83s0kraFgRBEARB8yXEb1AxJP0U2A/YCXgTeMbM7krbWmX1fYMgCIIgCOqLEL9BxSmWMwvhGwRBEARBQxExv0GjI4RvEARBEAQNRXh+gyAIgiAIghZDeH6DIAiCIAiCFkOI3yAIgiAIgqDFEOI3CIIgCIIgaDGE+A2CIAiCIAhaDCF+gyAIgiAIghZDiN8gCIJmgKTukiy3fCtprKQTJLVp4HN3Sufsnls3TNL4hTxOV0m9i23N68G+3pIWWNpI0nhJw+p6/Pp6n3OfZaf6OF4QBHMT4jcIgqB5sR+wFbAP8CIwCDi/AnZcCPx2IffpCvyR+G0KgqABaVBvQBAEQVB2XjGzd9LfIyWtCZxMDQJYUltgptVz0Xcze7c+jxcEQVBfxN11EARB8+YlYAlJy+XCE/4g6VJJHwFVwJIAkvaWNFrSVElfS7pT0mr5g0nqKOkaSV9KmiJpOLBK8aSlwh4kLSqpn6R3JVVJ+kTS3ZKWl9Qb9/oCzMjCNwrn7S/pPUnT0+M5xRAJSRtLGiVpmqQPJZ0HqC5vnKRlJQ2R9N/0nkyQdIuklWvYZR1JT6SxH0vqU8K+ZSVdm2yrkjRO0tF1sS8IgroRnt8gCILmzerALGAK0DGtOwcXxUcDrYFpko4FBgNDgT7A4kBv4ClJXczs27TvEOAA4IJ0jF2AWxZkhKR2wCPAhkA/YDTwI2A3YCngOlxEHwVsk2zO9m0DjADWxcMpXgO2BM4DlgZ6pnE/Bh4HPgEOx4X96cBcAn4hWBqYBvQCPgdWSud6VtLaZjatMP5e4Hqgb3pd5wGz8fcRSUsAzwCLpHXvpXGDJbU3s0F1tDMIgoUgxG8QBEHzonUSi4sD+wN7A/eb2VRpjgP0U+C3WaiDpMWA/sBQMzsyGyTpReAtXJBeKakzcBBwjpn1S8NGpv2PXYBdh+CxyHuZ2fDc+rty55uY/nzBzGbmxvwOF8Tbm9nTad1j6fX8UVJ/M/sM+D9gUWBXM5uQjvkI8P4CbCuJmb2Fh4xk9rUGngU+AH4J/KOwy18L78sSQE9JV5rZ1+lYPwE2MLO307hHJS2ZXsfgwusOgqABiLCHIAiC5sU4YAbwFXANcDNwZGHMvYUY362AJYCbJbXJFmBCOt52adwW+O/GHYXj3VYLu3YFPikI39qyOy5gnyvYNxJoi3uBs9cxOhO+AGb2HXB/Hc4JgKTjUtWMKcBMXPgCdC4xvNT7shiwfu51vAC8V3gdI4BlcM92EAQNTHh+gyAImhe/BSYC3wLvl5iaB/i48Hy59PhoDceclB5XTI+fFrYXn5diGeDDWowrxXK4x3TGfI4Nbt/rJbbXxr55kHQicBUwAA+fmISL/9FAh1qcJ3uexQgvB6zJgl9HEAQNSIjfIAiC5sXruWoPNVGs7PBleuwOvFFifBbvm4nm5YH/5bYvXwu7vqDaA7qwfInHx+5fw/bx6fHjGmypjX2lOBB4zMx6ZiskrT6f8TW9L5no/xL4jFwoRYG36mhnEAQLQYjfIAiC4Dlc4K5pZjfMZ9wLeALX/njSWsaBtTjHSOBASb82s5rCEKrS4yJUC26Ah/G6xVPMbNx8zvE8cLqkVXMxv4sCv66FfaXoCEwurDtiPuNLvS9T8AQ98NdxIvBBilEOgqAChPgNgiBo4ZjZZEmnA1dLWhZ4CPgGn67fHnjSzG4xs7ck3QJkJbxewmN5f1WL09wE9ABuldQXF9KL49UOrkyi9s00tqekh4BZZvYyHrd8BJ7k9idgLNAOWAPYE/iNmU0FrgD+gCeb9aa62sP3dXxrHgbOlHQ23jBkR2Df+YzvkXtfdgN+D/Q2s2/S9ivwShmjJF2Be3oXBdYGtjWzvepoZxAEC0GI3yAIggAzGyJpAi4WD8J/Hz4ERgGv5IYeg3szT8MF6ONp/DMLOP4MSbvitXyPTo9f4tUTvkrDHsCT9P6AN+UQoLTvbsBZad/Vge+Ad4EHgenpHF9I2gkYCNyQjn9tei116XLXB6+B/H94jO9TuKj9Xw3j98I76p2H3zxchJdmy96DbyT9ItlyJn5z8TUugu+ug31BENQB1XNTnyAIgiAIgiBotESpsyAIgiAIgqDFEOI3CIIgCIIgaDGE+A2CIAiCIAhaDCF+gyAIgiAIghZDiN8gCIIgCIKgxRDiNwiCoBkhaVVJd0n6RtJkSfdIWq0W+/WWZDUspVokZ/sdmMZMLLGto6QrJH0oqUrSa5IOXoAdP5U0NR1zzdq96vpF0jBJ4+v5mJ3Sa+pen8dtaCRtI+k5Sd9L+kTSAEmL1GK/fSXdLen9tO9bkvpKWrwwLntfSi1LFsZ2kHSZpI/TMZ+XtF0N519Z0vXJ5ipJ76X60kEQdX6DIAiaC5I64nV3q4DD8TbGFwFPSOpiZt/NZ/fr8KYOeRZN64bXcL4lgSuBT2o45j3AVsC5eC3bvYGbJMnMbqphn2vwGrkLFFgNyIV4reAWjaQuwCPACGAPvL7yZXh94gMWsPtpwAfA2cBEYGOgN7CDpF+Y2ezC+L7Me519W3j+N6AbXov6f8DxwAhJW5nZnFrUkjrh9aPfA04CPgU6ARW5mQoaHyF+gyAIakCSgLZmNr3SttSSHsBPgc5m9g6ApFeBt/HmFANq2tHMJuIiZQ6SDsV/J2pqeXwp3m3tY2Dnwr7b4A0hjjCzYWn1SEmrAJdKutXMZhX2OQgXSX3xbmgVwczerdS5GxkX4NfEfmY2A0DSdOAGSf3NbMx89v21mX2ee/6UpK/wa6krfpOW539mNrqmg0naEG+mcqSZDU3rngLewJuR7Jkbfi3eoGWHzG68QUkQABH2EARBBZC0pqQb01Tk95L+J2mwpKVKjN1e0iNpGv87SWMlHVUY00PSmHSsSZKeSp20kNQ1TaF2LezTPa3vlFs3XtJNko6UNA7vHNYtbbsgnWOypC8kPS5pyxL2LivpGkkT0nTrhPRa20vaJ51zwxL7PSmpxh//WrInMDoTvgBm9h7uBatL69zDca/ZiOIGSVsDh+Det1Jk781DhfUPAyvmtmfHWwoX56fhXc/qTO4z/42kIZK+kvS1pCsltZa0maRn0vX0hrx7XH7/ucIeJLWRdKGkdyVNS5//M0ng5/er8Tqswc7N5CEqE1UdGnCJCmEFknaThx58I2lKGnd+bvvPJP1D0mfJvg8k3Smpzg4uSW2B3YE7cgIS4A78/2K+11NB+Ga8lB5XroNJewIzgNtz55gJ3AbsJql9snsN/KZrUMHuIJhDiN8gCCrBSsAE4BT8h6oPsBPwz/wgSXsBj+FtdI/Bf3CvB36SG3M58BdgDLA/LsieBhYY51oDOwCn4l6v3YFX0/qVcW/kXkB34DPgaUkb5GxZCngOnxIeAPwKOANom17DfcBH6bXkX+fawPa4xypb1zqJrgUtyh1qPeD1Eq/pDWDdhXkTJK2a3oubk8jIb2uLv+eX5YV2gcyrW/SaV6XH9QvrLwXGmdmN87Epu2HpWouXAB6S8R3+eQwCTk7r/o5fR3vjrZXvkfTj+RznTLzF8VUkbzZ+XS6ds60u1+FqeOvoY/FrbSBwJDA0d9yf4uEA76XXsSd+bS2aO86D+PV5XLLvLPx9bpU7Tm2upbxYXgNv6TzX9WRm0/C20gt1PSW2T4//KbGtr6SZSeAPz/9fJdYD3jOzqYX1b+D/W1lIw9bp8Xv5TXNVuhH5u6Rl6mBz0Bwxs1hiiSWWii741Po2eIzqxmmdgPHAy0CrGvZbExdZA+Zz7K7puF0L67un9Z1y68YDU4EVFmBv62TzW8DA3Po+yZ6N57NvbzymddHcugHAJGCR3Lonk30LWrrm9pkO9CtxzouAmQv5mfRKx+9SYtu5wDtAh/R8GDCxMOZXaf9fFtZfn9b3yq3bFhdr6xY+mzUL+x4GzAS2X4Dt2Wd+fWH9mLR+m9y6Lmnd4bl1w4DxuecPAPfM53y1uQ47pfN0r2G70jV1CDAbWCat3zftt0QN+/04bd9zAe9Jba4ly43/RVq3e4ljPQM8tpDX08r4DeMjhfUr4jd9e6froAcu9L8F1smNG4nPahSPu3Oyc9v0/Kz0fDJwNbAjcDTwJfP5LomlZS0R8xsEQdmR1A6f3j4M9+J2yG3uDPw7Pf4EF3PF5JiMnXHv1l/q0bzRZjZPApeknYFzcLG0dG7Te7m/dwVeMrN/z+f4f0nH+R1wnaQOeHjB383s+9y4Y4DFS+xf5K1ajKkLhwH/NrNX8yvlFRjOAX5r7gWsiZG4h+8qSYcB43CB87u0fXY6XjtgCHCFmb05P4PM7O+417a2FEMuxgE/M7NnCusAVp3PcV4Cekm6OB3zRZs7DrxO16GkJfD3ct90/ra5zWvhgu0VfLr/NknXA0+b2We5cV/iyV/9JC0PPGlmb5c43WYLY1t9ImkxfNZjJu41n4OZfYx7vjNGSXoY9+ieg98MLAyZt/tJM8tCch6X9A0pRIJ5r4ughRHiNwiCStAXOBH3lD6He3lWwasDZEI4m6Kcp4RWjtqMWVg+Lq6Q9HM8JGMEcFQaMwuvkJAX7svgCWA1YmYfSboP/8G/DtgPF9NDCkPfwb2BCyKfNDYJmCduOh1/Ui2OBYCkzYG18bCUIlfhyUqjVV2Kqp3vpiWBKjP73sxmStoXuAX/jMHjh3vh4SPZ+3xKsvmq3PE6psfFJS1uZsWs/9pSfM3TKcQTklcrNQAABg1JREFUm9n0FDnSgZq5BJiGC7GzgSmS7gJON7MvqPt1OBQXzufjIvc7YHPcY9kh2fdOikk+E7gRaC/pReBMM3vKzEzSLviMQl9gGUnv4SEpg3PneoWFI3vvarqe3qjNQVL88v14Iub25omV88XMJkh6hrkF+yRy4U4FW8DDV8BvBsCrVOQZmR43JsRviyfEbxAEleBA3NN5UbYieYfyfJEe55cckx9Tkwc08062K6yvKf7PSqzbB/da7W25JJoU45sXU18swN6Ma4DHJG2Ce3hHlfB6PkZ1jOT82AEPkQAXJOuVGLMuMF+vaoHDcW/jLTUc6yeUFtOT8LjVUwDSa9pInlS4KPBf3PsLnoSXHW8FPDu/yBj8ZmKjhbC93kmfeX+gv6QV8LJfA3CRfgC1uw7nInn89wJ6m9nA3PpirCtm9gRerq49HtPaB3hQUicz+8LM/gccluK/NwROAK6RNN7MMqFX2+Sv7IbrXTwUZa7rKdn9U+DOWrzGtsBdwKbALmb2Wi1tyMj/L74B/FZSR5s77ndd/Kbmndy4+VHTLFLQggjxGwRBJejIvD/GRxSe/xePwf29pL+YWSlR+ij+Y3Y00LOGc72fHten2vsDqYrDQtg7i9yPsaQd8YSlfNjDSOBcSRuaWY0eYDN7XF5NYgAuZko1fqhL2MNw4HJJP02CKKt5ujUeC7lAUhjCgcBDVjpj/0Dm9ZKeBWyCe7Hn8eyZ2fh07La4MBtp1eXE+uExtnl2xz2dh9BwYR11IoXEXCfpV1Qn7dXmOizSHo8dL/4fdJ/PuavwKfwsjGB1qoU36X/kFUmn4jMU61Pt5VyosIfkEX8Y2F9Sb6tOetw32V6y9nOGpFbAzXjM7R42nzJmJfZdDc8BuDe3+n48CXU/Uum9lKB3AH49ZYmUo/G607vhSY4Zu6fHlwhaPCF+gyCoBA8Dh0t6DffY7I0n2MwhTeeegodCPC7pWuBzYB1gOTP7o5m9K+kK4FR556jhuEjdHK8ccLuZfSyvB9pL0hd40s0huPdqYew9BRgmaSjwM+A85vVWXoHXIn1U0kXAa3hC0l7AsYXp+8G4l/QL4O7iCc2sLqLvr7i4vE/SubhYvxCvrDEnrELST3DPXh8z61M4xh74VHLJ2r6lRIy8a1mVmT1ZWN8Lv/n4CL9ROD49Zhn5mNk4quNus/06pT9fsFw1iXSeoXj91rnO1ZCkMJWxuCd6Ej51vjvpPa3NdVg8ppl9Iy9t11PSx/h1cCSFmQNJxwLb4WE3E/DrqRf+nr4ub0QxEC8B9g4uqLvjMxVzauma2ct1eOm9cTF5h6Sr8aS9y4C7zOxfORsPwxMZdzKzrJ7u1bhQvRj4TnOXBZyYhT9I+hMep/s8/v/dOb2+2WnfzP5/S7oduDLdRL2HV7dYndzNYwq3OQv/X70W//5YMx3rSeatLxy0RCqdcRdLLLG0vAX/Ab8NFxKTcA/RZpTIhsc9R08AU9IyFm+ckB9zLF6SrAqP/XsS2Cq3fRXcc/Q17hW6BPg9pas93FSDzSfiP7jf496jndN5niyMWw5PfPoYn46dgAvJ9oVxK6bzX1bP7+1quJiejMdS35t/jWlMp3Tu3iX2vw+Pm2y3EOccRqHaQ1p/UXrPqvB43xuAVWtxvO6UrvZwfFq/zgL275rG7VxLOw24qDBufO55T1wEfpk+/7dwYdi2ttchJao9pHUPpc/pM+DP+IzEnCoeeIe8+9J1VJWuqzvxRibZ9XYDPlMyNZ33KWC3erqetsOF6bT0GV4JdKzh8+pa+F+qqapE79y4I/H/p0m4F/wTPNymcwlbFsFnSz5J9rxAoYpLbuyheJm27D0bBCxWn/9rsTTdRWalZhKDIAiChkRSD9xz+DOruVZukEPSLcCSZvarStsSBEHTJcIegiAIyoikdfEGAhcA94bwXSi2wxtIBEEQ1Jnw/AZBEJQRSU/i8c3PAQeZ2UeVtSgIgqBlEeI3CIIgCIIgaDG0WvCQIAiCIAiCIGgehPgNgiAIgiAIWgwhfoMgCIIgCIIWQ4jfIAiCIAiCoMUQ4jcIgiAIgiBoMfw/dA1P/lxo3K8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_1edc70e0-f11b-44de-ab2d-37d4a9204cd3\", \"cnn_simple_confusion_matrix.png\", 84127)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "                        precision  recall  f1-score  support\n",
            "dyed-lifted-polyps           0.66    0.60      0.63   200.00\n",
            "dyed-resection-margins       0.65    0.71      0.68   200.00\n",
            "esophagitis                  0.86    0.52      0.65   200.00\n",
            "normal-cecum                 0.78    0.94      0.85   200.00\n",
            "normal-pylorus               0.87    0.97      0.92   200.00\n",
            "normal-z-line                0.67    0.86      0.76   200.00\n",
            "polyps                       0.77    0.66      0.71   200.00\n",
            "ulcerative-colitis           0.79    0.74      0.76   200.00\n",
            "accuracy                     0.75    0.75      0.75     0.75\n",
            "macro avg                    0.76    0.75      0.74  1600.00\n",
            "weighted avg                 0.76    0.75      0.74  1600.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_b277929e-119f-40f5-8795-2292175b04fd\", \"cnn_simple_classification_report.csv\", 420)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHDlpspb6pPT"
      },
      "source": [
        "## 3 ResNet"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CRMp-xYP67FX"
      },
      "source": [
        "### 3.1 Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcZGN4CBfDYr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "82454d17-f9b3-4b31-ad2a-48fd1bd9639c"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Set up datagenerators for ResNet50 model\n",
        "\n",
        "    Datagenerators handle image augmentation (shear, zoom, horizontal flips) as well as rescaling.\n",
        "    They also have built-in utility to replicate ResNet's original preprocessing steps.\n",
        "    The datagenerator also has a built-in training/validation splitter, which creates a new 80/20 split.\n",
        "\n",
        "    In addition to that, we want a separate generator with our test set.\n",
        "\n",
        "    The dataset will be split as follows:\n",
        "    - Training set (64% - 80% of 80)\n",
        "    - Validation set (16% - 20% of 80)\n",
        "    - Test set (20%)\n",
        "\n",
        "\"\"\"\n",
        "\n",
        "\"\"\"\n",
        "    Training set\n",
        "\"\"\"\n",
        "rn_train_idg = keras.preprocessing.image.ImageDataGenerator(preprocessing_function=keras.applications.resnet50.preprocess_input,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    validation_split=0.2) # If defined, will split based on 'subset' in flow_from_directory()\n",
        "\n",
        "rn_train_datagen = rn_train_idg.flow_from_directory(\n",
        "    train_path,\n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='sparse',\n",
        "    shuffle=True,\n",
        "    subset='training') # Marks this as the training data\n",
        "\n",
        "rn_val_datagen = rn_train_idg.flow_from_directory(\n",
        "    train_path, \n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='sparse',\n",
        "    shuffle=False,\n",
        "    subset='validation') # Marks this as the validation data\n",
        "\n",
        "\"\"\"\n",
        "    Test set\n",
        "\"\"\"\n",
        "rn_test_idg = keras.preprocessing.image.ImageDataGenerator(preprocessing_function=keras.applications.resnet50.preprocess_input)\n",
        "\n",
        "rn_test_datagen = rn_test_idg.flow_from_directory(\n",
        "    test_path, \n",
        "    target_size=target_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='sparse',\n",
        "    shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 5120 images belonging to 8 classes.\n",
            "Found 1280 images belonging to 8 classes.\n",
            "Found 1600 images belonging to 8 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AICO664i68Ss"
      },
      "source": [
        "### 3.2 Build model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKFCDlBUJUQY"
      },
      "source": [
        "\"\"\"\n",
        "    Function to build the ResNet50 model\n",
        "\"\"\"\n",
        "def build_transfer_learning_model():\n",
        "  \n",
        "  # The ResNet50 is used as a base model\n",
        "  base_model = keras.applications.resnet.ResNet50(include_top=False, \n",
        "                                                  weights='imagenet', \n",
        "                                                  input_shape=(target_w, target_h, target_dims))\n",
        "  \n",
        "  # We need our BatchNormalization layers to be trainable, the rest should be frozen\n",
        "  for layer in base_model.layers:\n",
        "    if isinstance(layer, keras.layers.BatchNormalization):\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False\n",
        "\n",
        "  # Assemble our final model\n",
        "  model = keras.models.Sequential([\n",
        "                       base_model,\n",
        "                       keras.layers.GlobalAveragePooling2D(),\n",
        "                       keras.layers.Dropout(0.5),\n",
        "                       keras.layers.Dense(8, activation=\"softmax\")\n",
        "  ])\n",
        "  \n",
        "  model.summary()\n",
        "\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rxAveAwA5Cx_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3933dd0-c9b9-4da2-bec8-e6bb64b2c23d"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Build and compile our ResNet50 model\n",
        "\n",
        "    This model consists of frozen layers where only the BatchNormalization and output Dense layers are trainable.\n",
        "    We will unfreeze and fine-tune the entire model later after the first fitting.\n",
        "    \n",
        "\"\"\"\n",
        "rn_model = build_transfer_learning_model()\n",
        "\n",
        "rn_model.compile(\n",
        "    optimizer=keras.optimizers.Adam(0.0001),\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=[ 'accuracy' ]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 0s 0us/step\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resnet50 (Functional)        (None, 8, 8, 2048)        23587712  \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 8)                 16392     \n",
            "=================================================================\n",
            "Total params: 23,604,104\n",
            "Trainable params: 69,512\n",
            "Non-trainable params: 23,534,592\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yxdEj7QR69vS"
      },
      "source": [
        "### 3.3 Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lWceWlIx5FLk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "be9639c8-e4c4-40c4-e6d2-02ccd87597f2"
      },
      "source": [
        "\"\"\"\n",
        "    Start training our ResNet50 model\n",
        "\n",
        "    This will also save the trained model file to our 'models' directory.\n",
        "\"\"\"\n",
        "rn_history = rn_model.fit(rn_train_datagen, \n",
        "                        epochs=30,\n",
        "                        verbose=1, \n",
        "                        validation_data=rn_val_datagen)\n",
        "\n",
        "rn_model.save('models/cnn_resnet_tl.hdf5')\n",
        "\n",
        "if RunningInCOLAB:\n",
        "    files.download('models/cnn_resnet_tl.hdf5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "160/160 [==============================] - 183s 1s/step - loss: 2.0441 - accuracy: 0.2658 - val_loss: 1.2939 - val_accuracy: 0.5312\n",
            "Epoch 2/30\n",
            "160/160 [==============================] - 182s 1s/step - loss: 1.3266 - accuracy: 0.5033 - val_loss: 0.8722 - val_accuracy: 0.7273\n",
            "Epoch 3/30\n",
            "160/160 [==============================] - 184s 1s/step - loss: 1.0007 - accuracy: 0.6268 - val_loss: 0.6915 - val_accuracy: 0.7617\n",
            "Epoch 4/30\n",
            "160/160 [==============================] - 187s 1s/step - loss: 0.8341 - accuracy: 0.6834 - val_loss: 0.5851 - val_accuracy: 0.7977\n",
            "Epoch 5/30\n",
            "160/160 [==============================] - 189s 1s/step - loss: 0.7268 - accuracy: 0.7135 - val_loss: 0.5146 - val_accuracy: 0.8125\n",
            "Epoch 6/30\n",
            "160/160 [==============================] - 192s 1s/step - loss: 0.6338 - accuracy: 0.7533 - val_loss: 0.4806 - val_accuracy: 0.8195\n",
            "Epoch 7/30\n",
            "160/160 [==============================] - 191s 1s/step - loss: 0.5828 - accuracy: 0.7711 - val_loss: 0.4614 - val_accuracy: 0.8250\n",
            "Epoch 8/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.5426 - accuracy: 0.7855 - val_loss: 0.4203 - val_accuracy: 0.8383\n",
            "Epoch 9/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.5027 - accuracy: 0.8074 - val_loss: 0.4040 - val_accuracy: 0.8453\n",
            "Epoch 10/30\n",
            "160/160 [==============================] - 193s 1s/step - loss: 0.4696 - accuracy: 0.8166 - val_loss: 0.3828 - val_accuracy: 0.8445\n",
            "Epoch 11/30\n",
            "160/160 [==============================] - 191s 1s/step - loss: 0.4523 - accuracy: 0.8236 - val_loss: 0.3667 - val_accuracy: 0.8555\n",
            "Epoch 12/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.4367 - accuracy: 0.8232 - val_loss: 0.3640 - val_accuracy: 0.8562\n",
            "Epoch 13/30\n",
            "160/160 [==============================] - 189s 1s/step - loss: 0.4136 - accuracy: 0.8266 - val_loss: 0.3545 - val_accuracy: 0.8531\n",
            "Epoch 14/30\n",
            "160/160 [==============================] - 193s 1s/step - loss: 0.4216 - accuracy: 0.8326 - val_loss: 0.3432 - val_accuracy: 0.8594\n",
            "Epoch 15/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.3735 - accuracy: 0.8551 - val_loss: 0.3323 - val_accuracy: 0.8703\n",
            "Epoch 16/30\n",
            "160/160 [==============================] - 192s 1s/step - loss: 0.3733 - accuracy: 0.8553 - val_loss: 0.3267 - val_accuracy: 0.8734\n",
            "Epoch 17/30\n",
            "160/160 [==============================] - 192s 1s/step - loss: 0.3680 - accuracy: 0.8553 - val_loss: 0.3193 - val_accuracy: 0.8687\n",
            "Epoch 18/30\n",
            "160/160 [==============================] - 188s 1s/step - loss: 0.3492 - accuracy: 0.8609 - val_loss: 0.3215 - val_accuracy: 0.8719\n",
            "Epoch 19/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.3535 - accuracy: 0.8557 - val_loss: 0.3214 - val_accuracy: 0.8695\n",
            "Epoch 20/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.3452 - accuracy: 0.8641 - val_loss: 0.3057 - val_accuracy: 0.8789\n",
            "Epoch 21/30\n",
            "160/160 [==============================] - 181s 1s/step - loss: 0.3327 - accuracy: 0.8656 - val_loss: 0.2917 - val_accuracy: 0.8758\n",
            "Epoch 22/30\n",
            "160/160 [==============================] - 180s 1s/step - loss: 0.3145 - accuracy: 0.8717 - val_loss: 0.2987 - val_accuracy: 0.8750\n",
            "Epoch 23/30\n",
            "160/160 [==============================] - 181s 1s/step - loss: 0.3161 - accuracy: 0.8758 - val_loss: 0.2824 - val_accuracy: 0.8852\n",
            "Epoch 24/30\n",
            "160/160 [==============================] - 181s 1s/step - loss: 0.3299 - accuracy: 0.8703 - val_loss: 0.2976 - val_accuracy: 0.8805\n",
            "Epoch 25/30\n",
            "160/160 [==============================] - 181s 1s/step - loss: 0.3027 - accuracy: 0.8818 - val_loss: 0.2914 - val_accuracy: 0.8891\n",
            "Epoch 26/30\n",
            "160/160 [==============================] - 180s 1s/step - loss: 0.3041 - accuracy: 0.8818 - val_loss: 0.2835 - val_accuracy: 0.8836\n",
            "Epoch 27/30\n",
            "160/160 [==============================] - 180s 1s/step - loss: 0.2845 - accuracy: 0.8842 - val_loss: 0.2759 - val_accuracy: 0.8922\n",
            "Epoch 28/30\n",
            "160/160 [==============================] - 179s 1s/step - loss: 0.2824 - accuracy: 0.8861 - val_loss: 0.2891 - val_accuracy: 0.8852\n",
            "Epoch 29/30\n",
            "160/160 [==============================] - 180s 1s/step - loss: 0.2796 - accuracy: 0.8924 - val_loss: 0.2655 - val_accuracy: 0.8938\n",
            "Epoch 30/30\n",
            "160/160 [==============================] - 180s 1s/step - loss: 0.2790 - accuracy: 0.8867 - val_loss: 0.2715 - val_accuracy: 0.8992\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_70adc81b-654d-445e-a44a-f1d3614dc346\", \"cnn_resnet_tl.hdf5\", 95529912)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_c6R6N1l4Gef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "outputId": "9673ad2f-ce78-4b31-d7ca-c85b5db2cffa"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Examine the training process\n",
        "\n",
        "\"\"\"\n",
        "plot_history(rn_history)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "awTNrEYR6_NO"
      },
      "source": [
        "### 3.4 Evaluate model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lfDf5H2j5LNi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4097ce75-cffb-41bc-faff-1017324efb7d"
      },
      "source": [
        "\"\"\"\n",
        "    Use the model to predict on the unseen test set.\n",
        "\"\"\"\n",
        "rn_predictions = rn_model.predict(rn_test_datagen, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 25s 508ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pSKz_A1nbQHU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "696279f0-f9b9-4d7e-b0f2-5ab0e9e07cfd"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Evaluate the prediction results.\n",
        "\n",
        "    Displays a Confusion Matrix and a Classification Report.\n",
        "    These are also saved to the 'results' folder.\n",
        "\n",
        "\"\"\"\n",
        "unique_labels = list(rn_test_datagen.class_indices.keys())\n",
        "\n",
        "y_pred = np.argmax(rn_predictions, axis=-1)\n",
        "y_true = rn_test_datagen.classes[rn_test_datagen.index_array]\n",
        "\n",
        "display_confusion_matrix(\n",
        "    y_pred=y_pred, \n",
        "    y_true=y_true, \n",
        "    target_names=unique_labels, \n",
        "    save_path='results/cnn_resnet_tl/cnn_resnet_tl_confusion_matrix.png'\n",
        ")\n",
        "\n",
        "display_classification_report(\n",
        "    y_pred=y_pred, \n",
        "    y_true=y_true, \n",
        "    target_names=unique_labels, \n",
        "    save_path='results/cnn_resnet_tl/cnn_resnet_tl_classification_report.csv'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_ffd03b69-196d-4c9d-b06f-d4c72a3fe602\", \"cnn_resnet_tl_confusion_matrix.png\", 83824)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "                        precision  recall  f1-score  support\n",
            "dyed-lifted-polyps           0.88    0.94      0.91   200.00\n",
            "dyed-resection-margins       0.93    0.87      0.90   200.00\n",
            "esophagitis                  0.88    0.77      0.82   200.00\n",
            "normal-cecum                 0.92    0.97      0.94   200.00\n",
            "normal-pylorus               0.94    1.00      0.97   200.00\n",
            "normal-z-line                0.81    0.88      0.84   200.00\n",
            "polyps                       0.97    0.92      0.94   200.00\n",
            "ulcerative-colitis           0.97    0.96      0.96   200.00\n",
            "accuracy                     0.91    0.91      0.91     0.91\n",
            "macro avg                    0.91    0.91      0.91  1600.00\n",
            "weighted avg                 0.91    0.91      0.91  1600.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_10f5621d-cf28-466d-8f12-9a8d4734b10f\", \"cnn_resnet_tl_classification_report.csv\", 419)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-oE9A4s7AcZ"
      },
      "source": [
        "### 3.5 Unfreeze and re-train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuWToEG4wdVa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "917c6451-3d05-475d-a4f4-c5a9d526361c"
      },
      "source": [
        "\"\"\"\n",
        "    Unfreeze all layers\n",
        "\"\"\"\n",
        "rn_model.trainable = True\n",
        "\n",
        "\"\"\"\n",
        "    Compile model with a lower learning rate\n",
        "\"\"\"\n",
        "rn_model.compile(\n",
        "    optimizer=keras.optimizers.Adam(0.000001),  # Low learning rate\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=[ 'accuracy' ],\n",
        ")\n",
        "\n",
        "\"\"\"\n",
        "    Resume training\n",
        "\"\"\"\n",
        "rn_history_ft = rn_model.fit(rn_train_datagen, \n",
        "                        epochs=30,\n",
        "                        verbose=1, \n",
        "                        validation_data=rn_val_datagen)\n",
        "\n",
        "\n",
        "rn_model.save('models/cnn_resnet_tl_tuned.hdf5')\n",
        "\n",
        "if RunningInCOLAB:\n",
        "    files.download('models/cnn_resnet_tl_tuned.hdf5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "160/160 [==============================] - 216s 1s/step - loss: 0.2675 - accuracy: 0.8998 - val_loss: 0.2713 - val_accuracy: 0.8914\n",
            "Epoch 2/30\n",
            "160/160 [==============================] - 212s 1s/step - loss: 0.2590 - accuracy: 0.8982 - val_loss: 0.2665 - val_accuracy: 0.8938\n",
            "Epoch 3/30\n",
            "160/160 [==============================] - 211s 1s/step - loss: 0.2532 - accuracy: 0.8992 - val_loss: 0.2552 - val_accuracy: 0.8977\n",
            "Epoch 4/30\n",
            "160/160 [==============================] - 209s 1s/step - loss: 0.2457 - accuracy: 0.9033 - val_loss: 0.2611 - val_accuracy: 0.8914\n",
            "Epoch 5/30\n",
            "160/160 [==============================] - 207s 1s/step - loss: 0.2346 - accuracy: 0.9070 - val_loss: 0.2611 - val_accuracy: 0.8867\n",
            "Epoch 6/30\n",
            "160/160 [==============================] - 206s 1s/step - loss: 0.2361 - accuracy: 0.9039 - val_loss: 0.2558 - val_accuracy: 0.8906\n",
            "Epoch 7/30\n",
            "160/160 [==============================] - 206s 1s/step - loss: 0.2279 - accuracy: 0.9117 - val_loss: 0.2549 - val_accuracy: 0.8984\n",
            "Epoch 8/30\n",
            "160/160 [==============================] - 208s 1s/step - loss: 0.2110 - accuracy: 0.9203 - val_loss: 0.2422 - val_accuracy: 0.9000\n",
            "Epoch 9/30\n",
            "160/160 [==============================] - 212s 1s/step - loss: 0.2237 - accuracy: 0.9131 - val_loss: 0.2426 - val_accuracy: 0.9008\n",
            "Epoch 10/30\n",
            "160/160 [==============================] - 213s 1s/step - loss: 0.2185 - accuracy: 0.9162 - val_loss: 0.2530 - val_accuracy: 0.9031\n",
            "Epoch 11/30\n",
            "160/160 [==============================] - 214s 1s/step - loss: 0.2082 - accuracy: 0.9221 - val_loss: 0.2502 - val_accuracy: 0.8992\n",
            "Epoch 12/30\n",
            "160/160 [==============================] - 213s 1s/step - loss: 0.2022 - accuracy: 0.9207 - val_loss: 0.2434 - val_accuracy: 0.9023\n",
            "Epoch 13/30\n",
            "160/160 [==============================] - 212s 1s/step - loss: 0.2075 - accuracy: 0.9203 - val_loss: 0.2434 - val_accuracy: 0.9000\n",
            "Epoch 14/30\n",
            "160/160 [==============================] - 205s 1s/step - loss: 0.2044 - accuracy: 0.9209 - val_loss: 0.2488 - val_accuracy: 0.8961\n",
            "Epoch 15/30\n",
            "160/160 [==============================] - 199s 1s/step - loss: 0.1987 - accuracy: 0.9229 - val_loss: 0.2358 - val_accuracy: 0.9086\n",
            "Epoch 16/30\n",
            "160/160 [==============================] - 195s 1s/step - loss: 0.1924 - accuracy: 0.9268 - val_loss: 0.2490 - val_accuracy: 0.8984\n",
            "Epoch 17/30\n",
            "160/160 [==============================] - 194s 1s/step - loss: 0.1935 - accuracy: 0.9260 - val_loss: 0.2329 - val_accuracy: 0.9070\n",
            "Epoch 18/30\n",
            "160/160 [==============================] - 192s 1s/step - loss: 0.1798 - accuracy: 0.9281 - val_loss: 0.2385 - val_accuracy: 0.9047\n",
            "Epoch 19/30\n",
            "160/160 [==============================] - 193s 1s/step - loss: 0.1670 - accuracy: 0.9395 - val_loss: 0.2363 - val_accuracy: 0.9031\n",
            "Epoch 20/30\n",
            "160/160 [==============================] - 191s 1s/step - loss: 0.1799 - accuracy: 0.9320 - val_loss: 0.2459 - val_accuracy: 0.8992\n",
            "Epoch 21/30\n",
            "160/160 [==============================] - 191s 1s/step - loss: 0.1676 - accuracy: 0.9367 - val_loss: 0.2404 - val_accuracy: 0.8969\n",
            "Epoch 22/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.1678 - accuracy: 0.9332 - val_loss: 0.2313 - val_accuracy: 0.9062\n",
            "Epoch 23/30\n",
            "160/160 [==============================] - 192s 1s/step - loss: 0.1655 - accuracy: 0.9322 - val_loss: 0.2321 - val_accuracy: 0.9031\n",
            "Epoch 24/30\n",
            "160/160 [==============================] - 191s 1s/step - loss: 0.1661 - accuracy: 0.9336 - val_loss: 0.2344 - val_accuracy: 0.9117\n",
            "Epoch 25/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.1585 - accuracy: 0.9381 - val_loss: 0.2306 - val_accuracy: 0.9023\n",
            "Epoch 26/30\n",
            "160/160 [==============================] - 191s 1s/step - loss: 0.1618 - accuracy: 0.9393 - val_loss: 0.2307 - val_accuracy: 0.9031\n",
            "Epoch 27/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.1570 - accuracy: 0.9418 - val_loss: 0.2391 - val_accuracy: 0.9047\n",
            "Epoch 28/30\n",
            "160/160 [==============================] - 190s 1s/step - loss: 0.1546 - accuracy: 0.9389 - val_loss: 0.2306 - val_accuracy: 0.9078\n",
            "Epoch 29/30\n",
            "160/160 [==============================] - 189s 1s/step - loss: 0.1490 - accuracy: 0.9426 - val_loss: 0.2313 - val_accuracy: 0.9047\n",
            "Epoch 30/30\n",
            "160/160 [==============================] - 189s 1s/step - loss: 0.1464 - accuracy: 0.9422 - val_loss: 0.2276 - val_accuracy: 0.9117\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_e043744b-c373-4dd0-bc4e-27a0c452c135\", \"cnn_resnet_tl_tuned_v2.hdf5\", 283623568)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uj94xyxfK1yA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 587
        },
        "outputId": "037f256e-7b27-4c14-a623-fe144ea51d09"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Examine the training process\n",
        "\n",
        "\"\"\"\n",
        "plot_history(rn_history_ft)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5de5L5nS7Dux"
      },
      "source": [
        "### 3.6 Final model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PW-0jqW-xGNn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27b3eb7a-9da1-447c-c596-ab0a4f19f41e"
      },
      "source": [
        "\"\"\"\n",
        "    Use the model to predict on the unseen test set.\n",
        "\"\"\"\n",
        "rn_predictions = rn_model.predict(rn_test_datagen, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "50/50 [==============================] - 26s 511ms/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7Rt5T0m_P15",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 808
        },
        "outputId": "e2c107b5-0519-4035-f932-f6f2557dae15"
      },
      "source": [
        "\"\"\"\n",
        "\n",
        "    Evaluate the prediction results.\n",
        "\n",
        "    Displays a Confusion Matrix and a Classification Report.\n",
        "    These are also saved to the 'results' folder.\n",
        "\n",
        "\"\"\"\n",
        "unique_labels = list(rn_test_datagen.class_indices.keys())\n",
        "\n",
        "y_pred = np.argmax(rn_predictions, axis=-1)\n",
        "y_true = rn_test_datagen.classes[rn_test_datagen.index_array]\n",
        "\n",
        "display_confusion_matrix(\n",
        "    y_pred=y_pred, \n",
        "    y_true=y_true, \n",
        "    target_names=unique_labels, \n",
        "    save_path='results/cnn_resnet_tl_tuned/cnn_resnet_tl_tuned_confusion_matrix.png'\n",
        ")\n",
        "\n",
        "display_classification_report(\n",
        "    y_pred=y_pred, \n",
        "    y_true=y_true, \n",
        "    target_names=unique_labels, \n",
        "    save_path='results/cnn_resnet_tl_tuned/cnn_resnet_tl_tuned_classification_report.csv'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_6e9a8070-e7a8-4ed3-b7bf-0f2f01fc9187\", \"cnn_resnet_tl_tuned_confusion_matrix.png\", 84033)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "                        precision  recall  f1-score  support\n",
            "dyed-lifted-polyps           0.89    0.96      0.93   200.00\n",
            "dyed-resection-margins       0.96    0.88      0.92   200.00\n",
            "esophagitis                  0.89    0.78      0.83   200.00\n",
            "normal-cecum                 0.92    0.98      0.95   200.00\n",
            "normal-pylorus               0.94    1.00      0.97   200.00\n",
            "normal-z-line                0.83    0.90      0.86   200.00\n",
            "polyps                       0.98    0.92      0.95   200.00\n",
            "ulcerative-colitis           0.97    0.96      0.96   200.00\n",
            "accuracy                     0.92    0.92      0.92     0.92\n",
            "macro avg                    0.92    0.92      0.92  1600.00\n",
            "weighted avg                 0.92    0.92      0.92  1600.00\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_db53fb10-a09b-4a89-95f5-39ac1fb3552a\", \"cnn_resnet_tl_tuned_classification_report.csv\", 419)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}