7_Fashion_Class_Classification_Using_Deep_Learning_RN.ipynb

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  "metadata": {
    "colab": {
      "name": "7. Fashion Class Classification Using Deep Learning_RN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "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/raghunandepu/Tensorflow2.0_practice/blob/master/7_Fashion_Class_Classification_Using_Deep_Learning_RN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnKRe0p9h35K",
        "colab_type": "text"
      },
      "source": [
        "# STEP #0: PROBLEM STATEMENT  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ELuIQxKBrHm",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "<p align=\"center\">\n",
        "  <img src=\"https://storage.googleapis.com/kaggle-datasets-images/2243/3791/9384af51de8baa77f6320901f53bd26b/dataset-cover.png\" />\n",
        "  Image source: https://www.kaggle.com/\n",
        "</p>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HA5wDtIB_IjC",
        "colab_type": "text"
      },
      "source": [
        "- Fashion training set consists of 70,000 images divided into 60,000 training and 10,000 testing samples. Dataset sample consists of 28x28 grayscale image, associated with a label from 10 classes. \n",
        "\n",
        "- The 10 classes are as follows:  \n",
        "  - 0 => T-shirt/top\n",
        "  - 1 => Trouser\n",
        "  - 2 => Pullover\n",
        "  - 3 => Dress\n",
        "  - 4 => Coat\n",
        "  - 5 => Sandal\n",
        "  - 6 => Shirt\n",
        "  - 7 => Sneaker\n",
        "  - 8 => Bag\n",
        "  - 9 => Ankle boot\n",
        "\n",
        "- Each image is 28 pixels in height and 28 pixels in width, for a total of 784 pixels in total. Each pixel has a single pixel-value associated with it, indicating the lightness or darkness of that pixel, with higher numbers meaning darker. This pixel-value is an integer between 0 and 255. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fr6pOOh0hHaj",
        "colab_type": "text"
      },
      "source": [
        "# STEP #1: IMPORT LIBRARIES"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sgeamaKI_5N_",
        "colab_type": "code",
        "outputId": "64282348-5d58-40f8-d57c-19b51ba1c03a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcsD7hiN_XXL",
        "colab_type": "code",
        "outputId": "f9b570a1-e4fb-484f-e6e4-93450f663f58",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0.alpha0"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow-gpu==2.0.0.alpha0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1a/66/32cffad095253219d53f6b6c2a436637bbe45ac4e7be0244557210dc3918/tensorflow_gpu-2.0.0a0-cp36-cp36m-manylinux1_x86_64.whl (332.1MB)\n",
            "\u001b[K     |████████████████████████████████| 332.1MB 51kB/s \n",
            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.1.7)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.2.2)\n",
            "Collecting tf-estimator-nightly<1.14.0.dev2019030116,>=1.14.0.dev2019030115\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/13/82/f16063b4eed210dc2ab057930ac1da4fbe1e91b7b051a6c8370b401e6ae7/tf_estimator_nightly-1.14.0.dev2019030115-py2.py3-none-any.whl (411kB)\n",
            "\u001b[K     |████████████████████████████████| 419kB 31.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.8.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.33.6)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.1.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.17.3)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (3.10.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (0.8.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.12.0)\n",
            "Collecting tb-nightly<1.14.0a20190302,>=1.14.0a20190301\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a9/51/aa1d756644bf4624c03844115e4ac4058eff77acd786b26315f051a4b195/tb_nightly-1.14.0a20190301-py3-none-any.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 42.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.0.8)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow-gpu==2.0.0.alpha0) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow-gpu==2.0.0.alpha0) (41.4.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0.alpha0) (3.1.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly<1.14.0a20190302,>=1.14.0a20190301->tensorflow-gpu==2.0.0.alpha0) (0.16.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow-gpu==2.0.0.alpha0) (2.8.0)\n",
            "Installing collected packages: tf-estimator-nightly, tb-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.14.0a20190301 tensorflow-gpu-2.0.0a0 tf-estimator-nightly-1.14.0.dev2019030115\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QqPjanlXkVzi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "efeb282e-35cc-43b8-b31c-25ec6e231772"
      },
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import random "
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_je5sPfjk_r1",
        "colab_type": "code",
        "outputId": "29c54d2b-511b-40b1-fe85-b013e2df57be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-alpha0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qqOSkGp9R55q",
        "colab_type": "text"
      },
      "source": [
        "# STEP #2: LOAD DATASET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2CwugSPR_Ph",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dataframes for both training and testing datasets \n",
        "fashion_train_df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/fashion-mnist-train.csv',sep=',')\n",
        "fashion_test_df = pd.read_csv('/content/drive/My Drive/Colab Notebooks/fashion-mnist-test.csv', sep = ',')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3he8rS4hu8ww",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Alternatively, you can use the same dataset made readily available by keras Using the following lines of code:\n",
        "# (train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jWb45OshSDsr",
        "colab_type": "text"
      },
      "source": [
        "# STEP #3: DATA VISUALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ylQOrrruSIUz",
        "colab_type": "code",
        "outputId": "e8b229c1-4350-4a3c-d7bd-e2e245801dc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "# Let's view the head of the training dataset\n",
        "# 784 indicates 28x28 pixels and 1 coloumn for the label\n",
        "# After you check the tail, 60,000 training dataset are present\n",
        "fashion_train_df.head()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>105</td>\n",
              "      <td>92</td>\n",
              "      <td>101</td>\n",
              "      <td>107</td>\n",
              "      <td>100</td>\n",
              "      <td>132</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>...</td>\n",
              "      <td>211</td>\n",
              "      <td>220</td>\n",
              "      <td>214</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>255</td>\n",
              "      <td>222</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>134</td>\n",
              "      <td>162</td>\n",
              "      <td>191</td>\n",
              "      <td>214</td>\n",
              "      <td>163</td>\n",
              "      <td>146</td>\n",
              "      <td>165</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>114</td>\n",
              "      <td>183</td>\n",
              "      <td>112</td>\n",
              "      <td>55</td>\n",
              "      <td>23</td>\n",
              "      <td>72</td>\n",
              "      <td>102</td>\n",
              "      <td>165</td>\n",
              "      <td>160</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>188</td>\n",
              "      <td>163</td>\n",
              "      <td>93</td>\n",
              "      <td>...</td>\n",
              "      <td>171</td>\n",
              "      <td>249</td>\n",
              "      <td>207</td>\n",
              "      <td>197</td>\n",
              "      <td>202</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>21</td>\n",
              "      <td>25</td>\n",
              "      <td>69</td>\n",
              "      <td>52</td>\n",
              "      <td>45</td>\n",
              "      <td>74</td>\n",
              "      <td>39</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>187</td>\n",
              "      <td>189</td>\n",
              "      <td>...</td>\n",
              "      <td>230</td>\n",
              "      <td>237</td>\n",
              "      <td>229</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>116</td>\n",
              "      <td>112</td>\n",
              "      <td>136</td>\n",
              "      <td>147</td>\n",
              "      <td>144</td>\n",
              "      <td>121</td>\n",
              "      <td>102</td>\n",
              "      <td>63</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "0      2       0       0       0  ...         0         0         0         0\n",
              "1      9       0       0       0  ...         0         0         0         0\n",
              "2      6       0       0       0  ...         0         0         0         0\n",
              "3      0       0       0       0  ...         0         0         0         0\n",
              "4      3       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwqzXO-5SKA_",
        "colab_type": "code",
        "outputId": "31783138-3e05-42ef-b292-31478f7cd3b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "# Let's view the last elements in the training dataset\n",
        "fashion_train_df.tail()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
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              "      <th>pixel37</th>\n",
              "      <th>pixel38</th>\n",
              "      <th>pixel39</th>\n",
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              "      <th>pixel745</th>\n",
              "      <th>pixel746</th>\n",
              "      <th>pixel747</th>\n",
              "      <th>pixel748</th>\n",
              "      <th>pixel749</th>\n",
              "      <th>pixel750</th>\n",
              "      <th>pixel751</th>\n",
              "      <th>pixel752</th>\n",
              "      <th>pixel753</th>\n",
              "      <th>pixel754</th>\n",
              "      <th>pixel755</th>\n",
              "      <th>pixel756</th>\n",
              "      <th>pixel757</th>\n",
              "      <th>pixel758</th>\n",
              "      <th>pixel759</th>\n",
              "      <th>pixel760</th>\n",
              "      <th>pixel761</th>\n",
              "      <th>pixel762</th>\n",
              "      <th>pixel763</th>\n",
              "      <th>pixel764</th>\n",
              "      <th>pixel765</th>\n",
              "      <th>pixel766</th>\n",
              "      <th>pixel767</th>\n",
              "      <th>pixel768</th>\n",
              "      <th>pixel769</th>\n",
              "      <th>pixel770</th>\n",
              "      <th>pixel771</th>\n",
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              "      <th>pixel773</th>\n",
              "      <th>pixel774</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>59995</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59996</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>83</td>\n",
              "      <td>155</td>\n",
              "      <td>136</td>\n",
              "      <td>116</td>\n",
              "      <td>148</td>\n",
              "      <td>110</td>\n",
              "      <td>118</td>\n",
              "      <td>67</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>118</td>\n",
              "      <td>...</td>\n",
              "      <td>199</td>\n",
              "      <td>165</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>159</td>\n",
              "      <td>6</td>\n",
              "      <td>91</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>85</td>\n",
              "      <td>159</td>\n",
              "      <td>0</td>\n",
              "      <td>73</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59997</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>114</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>228</td>\n",
              "      <td>229</td>\n",
              "      <td>231</td>\n",
              "      <td>231</td>\n",
              "      <td>218</td>\n",
              "      <td>230</td>\n",
              "      <td>255</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>116</td>\n",
              "      <td>140</td>\n",
              "      <td>147</td>\n",
              "      <td>166</td>\n",
              "      <td>176</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>173</td>\n",
              "      <td>174</td>\n",
              "      <td>173</td>\n",
              "      <td>177</td>\n",
              "      <td>164</td>\n",
              "      <td>160</td>\n",
              "      <td>162</td>\n",
              "      <td>163</td>\n",
              "      <td>135</td>\n",
              "      <td>94</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59998</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59999</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "59995      9       0       0       0  ...         0         0         0         0\n",
              "59996      1       0       0       0  ...         0         0         0         0\n",
              "59997      8       0       0       0  ...         0         0         0         0\n",
              "59998      8       0       0       0  ...         0         0         0         0\n",
              "59999      7       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vpop3h1ISL9G",
        "colab_type": "code",
        "outputId": "1ff5f078-197e-4f1a-c0d7-33c0f457f4d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "# Let's view the head of the testing dataset\n",
        "fashion_test_df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>pixel9</th>\n",
              "      <th>pixel10</th>\n",
              "      <th>pixel11</th>\n",
              "      <th>pixel12</th>\n",
              "      <th>pixel13</th>\n",
              "      <th>pixel14</th>\n",
              "      <th>pixel15</th>\n",
              "      <th>pixel16</th>\n",
              "      <th>pixel17</th>\n",
              "      <th>pixel18</th>\n",
              "      <th>pixel19</th>\n",
              "      <th>pixel20</th>\n",
              "      <th>pixel21</th>\n",
              "      <th>pixel22</th>\n",
              "      <th>pixel23</th>\n",
              "      <th>pixel24</th>\n",
              "      <th>pixel25</th>\n",
              "      <th>pixel26</th>\n",
              "      <th>pixel27</th>\n",
              "      <th>pixel28</th>\n",
              "      <th>pixel29</th>\n",
              "      <th>pixel30</th>\n",
              "      <th>pixel31</th>\n",
              "      <th>pixel32</th>\n",
              "      <th>pixel33</th>\n",
              "      <th>pixel34</th>\n",
              "      <th>pixel35</th>\n",
              "      <th>pixel36</th>\n",
              "      <th>pixel37</th>\n",
              "      <th>pixel38</th>\n",
              "      <th>pixel39</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel745</th>\n",
              "      <th>pixel746</th>\n",
              "      <th>pixel747</th>\n",
              "      <th>pixel748</th>\n",
              "      <th>pixel749</th>\n",
              "      <th>pixel750</th>\n",
              "      <th>pixel751</th>\n",
              "      <th>pixel752</th>\n",
              "      <th>pixel753</th>\n",
              "      <th>pixel754</th>\n",
              "      <th>pixel755</th>\n",
              "      <th>pixel756</th>\n",
              "      <th>pixel757</th>\n",
              "      <th>pixel758</th>\n",
              "      <th>pixel759</th>\n",
              "      <th>pixel760</th>\n",
              "      <th>pixel761</th>\n",
              "      <th>pixel762</th>\n",
              "      <th>pixel763</th>\n",
              "      <th>pixel764</th>\n",
              "      <th>pixel765</th>\n",
              "      <th>pixel766</th>\n",
              "      <th>pixel767</th>\n",
              "      <th>pixel768</th>\n",
              "      <th>pixel769</th>\n",
              "      <th>pixel770</th>\n",
              "      <th>pixel771</th>\n",
              "      <th>pixel772</th>\n",
              "      <th>pixel773</th>\n",
              "      <th>pixel774</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34</td>\n",
              "      <td>29</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>242</td>\n",
              "      <td>245</td>\n",
              "      <td>224</td>\n",
              "      <td>245</td>\n",
              "      <td>234</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>69</td>\n",
              "      <td>94</td>\n",
              "      <td>123</td>\n",
              "      <td>127</td>\n",
              "      <td>138</td>\n",
              "      <td>138</td>\n",
              "      <td>142</td>\n",
              "      <td>145</td>\n",
              "      <td>135</td>\n",
              "      <td>125</td>\n",
              "      <td>103</td>\n",
              "      <td>87</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>209</td>\n",
              "      <td>190</td>\n",
              "      <td>181</td>\n",
              "      <td>150</td>\n",
              "      <td>170</td>\n",
              "      <td>193</td>\n",
              "      <td>180</td>\n",
              "      <td>219</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>248</td>\n",
              "      <td>238</td>\n",
              "      <td>80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>174</td>\n",
              "      <td>233</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>235</td>\n",
              "      <td>216</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>53</td>\n",
              "      <td>99</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>94</td>\n",
              "      <td>68</td>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38</td>\n",
              "      <td>106</td>\n",
              "      <td>94</td>\n",
              "      <td>89</td>\n",
              "      <td>94</td>\n",
              "      <td>68</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>87</td>\n",
              "      <td>12</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>46</td>\n",
              "      <td>50</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>53</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>161</td>\n",
              "      <td>212</td>\n",
              "      <td>138</td>\n",
              "      <td>150</td>\n",
              "      <td>169</td>\n",
              "      <td>164</td>\n",
              "      <td>176</td>\n",
              "      <td>202</td>\n",
              "      <td>255</td>\n",
              "      <td>183</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>129</td>\n",
              "      <td>221</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>215</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>254</td>\n",
              "      <td>252</td>\n",
              "      <td>255</td>\n",
              "      <td>101</td>\n",
              "      <td>196</td>\n",
              "      <td>254</td>\n",
              "      <td>252</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>168</td>\n",
              "      <td>232</td>\n",
              "      <td>225</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>172</td>\n",
              "      <td>147</td>\n",
              "      <td>148</td>\n",
              "      <td>153</td>\n",
              "      <td>155</td>\n",
              "      <td>146</td>\n",
              "      <td>137</td>\n",
              "      <td>141</td>\n",
              "      <td>143</td>\n",
              "      <td>137</td>\n",
              "      <td>137</td>\n",
              "      <td>126</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>133</td>\n",
              "      <td>224</td>\n",
              "      <td>222</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>204</td>\n",
              "      <td>235</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>69</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "0      0       0       0       0  ...         0         0         0         0\n",
              "1      1       0       0       0  ...         0         0         0         0\n",
              "2      2       0       0       0  ...        31         0         0         0\n",
              "3      2       0       0       0  ...       222        56         0         0\n",
              "4      3       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vzSsdNSQSNW7",
        "colab_type": "code",
        "outputId": "b38ebbbc-959a-4017-ad87-158197024a86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "# Let's view the last elements in the testing dataset\n",
        "fashion_test_df.tail()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>pixel1</th>\n",
              "      <th>pixel2</th>\n",
              "      <th>pixel3</th>\n",
              "      <th>pixel4</th>\n",
              "      <th>pixel5</th>\n",
              "      <th>pixel6</th>\n",
              "      <th>pixel7</th>\n",
              "      <th>pixel8</th>\n",
              "      <th>pixel9</th>\n",
              "      <th>pixel10</th>\n",
              "      <th>pixel11</th>\n",
              "      <th>pixel12</th>\n",
              "      <th>pixel13</th>\n",
              "      <th>pixel14</th>\n",
              "      <th>pixel15</th>\n",
              "      <th>pixel16</th>\n",
              "      <th>pixel17</th>\n",
              "      <th>pixel18</th>\n",
              "      <th>pixel19</th>\n",
              "      <th>pixel20</th>\n",
              "      <th>pixel21</th>\n",
              "      <th>pixel22</th>\n",
              "      <th>pixel23</th>\n",
              "      <th>pixel24</th>\n",
              "      <th>pixel25</th>\n",
              "      <th>pixel26</th>\n",
              "      <th>pixel27</th>\n",
              "      <th>pixel28</th>\n",
              "      <th>pixel29</th>\n",
              "      <th>pixel30</th>\n",
              "      <th>pixel31</th>\n",
              "      <th>pixel32</th>\n",
              "      <th>pixel33</th>\n",
              "      <th>pixel34</th>\n",
              "      <th>pixel35</th>\n",
              "      <th>pixel36</th>\n",
              "      <th>pixel37</th>\n",
              "      <th>pixel38</th>\n",
              "      <th>pixel39</th>\n",
              "      <th>...</th>\n",
              "      <th>pixel745</th>\n",
              "      <th>pixel746</th>\n",
              "      <th>pixel747</th>\n",
              "      <th>pixel748</th>\n",
              "      <th>pixel749</th>\n",
              "      <th>pixel750</th>\n",
              "      <th>pixel751</th>\n",
              "      <th>pixel752</th>\n",
              "      <th>pixel753</th>\n",
              "      <th>pixel754</th>\n",
              "      <th>pixel755</th>\n",
              "      <th>pixel756</th>\n",
              "      <th>pixel757</th>\n",
              "      <th>pixel758</th>\n",
              "      <th>pixel759</th>\n",
              "      <th>pixel760</th>\n",
              "      <th>pixel761</th>\n",
              "      <th>pixel762</th>\n",
              "      <th>pixel763</th>\n",
              "      <th>pixel764</th>\n",
              "      <th>pixel765</th>\n",
              "      <th>pixel766</th>\n",
              "      <th>pixel767</th>\n",
              "      <th>pixel768</th>\n",
              "      <th>pixel769</th>\n",
              "      <th>pixel770</th>\n",
              "      <th>pixel771</th>\n",
              "      <th>pixel772</th>\n",
              "      <th>pixel773</th>\n",
              "      <th>pixel774</th>\n",
              "      <th>pixel775</th>\n",
              "      <th>pixel776</th>\n",
              "      <th>pixel777</th>\n",
              "      <th>pixel778</th>\n",
              "      <th>pixel779</th>\n",
              "      <th>pixel780</th>\n",
              "      <th>pixel781</th>\n",
              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "      <th>pixel784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>37</td>\n",
              "      <td>93</td>\n",
              "      <td>72</td>\n",
              "      <td>58</td>\n",
              "      <td>55</td>\n",
              "      <td>63</td>\n",
              "      <td>66</td>\n",
              "      <td>89</td>\n",
              "      <td>159</td>\n",
              "      <td>120</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>67</td>\n",
              "      <td>125</td>\n",
              "      <td>151</td>\n",
              "      <td>168</td>\n",
              "      <td>...</td>\n",
              "      <td>112</td>\n",
              "      <td>115</td>\n",
              "      <td>117</td>\n",
              "      <td>117</td>\n",
              "      <td>113</td>\n",
              "      <td>146</td>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>76</td>\n",
              "      <td>54</td>\n",
              "      <td>55</td>\n",
              "      <td>51</td>\n",
              "      <td>53</td>\n",
              "      <td>53</td>\n",
              "      <td>55</td>\n",
              "      <td>53</td>\n",
              "      <td>44</td>\n",
              "      <td>38</td>\n",
              "      <td>37</td>\n",
              "      <td>32</td>\n",
              "      <td>23</td>\n",
              "      <td>14</td>\n",
              "      <td>20</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>6</td>\n",
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              "      <td>76</td>\n",
              "      <td>36</td>\n",
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              "      <td>170</td>\n",
              "      <td>78</td>\n",
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              "      <td>5</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>68</td>\n",
              "      <td>...</td>\n",
              "      <td>94</td>\n",
              "      <td>84</td>\n",
              "      <td>70</td>\n",
              "      <td>44</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>152</td>\n",
              "      <td>65</td>\n",
              "      <td>110</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>21</td>\n",
              "      <td>57</td>\n",
              "      <td>89</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>52</td>\n",
              "      <td>23</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>152</td>\n",
              "      <td>184</td>\n",
              "      <td>158</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>219</td>\n",
              "      <td>223</td>\n",
              "      <td>226</td>\n",
              "      <td>239</td>\n",
              "      <td>247</td>\n",
              "      <td>241</td>\n",
              "      <td>224</td>\n",
              "      <td>213</td>\n",
              "      <td>144</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>232</td>\n",
              "      <td>199</td>\n",
              "      <td>200</td>\n",
              "      <td>190</td>\n",
              "      <td>180</td>\n",
              "      <td>179</td>\n",
              "      <td>185</td>\n",
              "      <td>189</td>\n",
              "      <td>191</td>\n",
              "      <td>185</td>\n",
              "      <td>179</td>\n",
              "      <td>178</td>\n",
              "      <td>175</td>\n",
              "      <td>172</td>\n",
              "      <td>172</td>\n",
              "      <td>182</td>\n",
              "      <td>199</td>\n",
              "      <td>222</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>131</td>\n",
              "      <td>192</td>\n",
              "      <td>79</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>13</td>\n",
              "      <td>26</td>\n",
              "      <td>17</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>119</td>\n",
              "      <td>103</td>\n",
              "      <td>99</td>\n",
              "      <td>102</td>\n",
              "      <td>101</td>\n",
              "      <td>104</td>\n",
              "      <td>119</td>\n",
              "      <td>118</td>\n",
              "      <td>122</td>\n",
              "      <td>130</td>\n",
              "      <td>129</td>\n",
              "      <td>127</td>\n",
              "      <td>166</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>18</td>\n",
              "      <td>211</td>\n",
              "      <td>212</td>\n",
              "      <td>212</td>\n",
              "      <td>225</td>\n",
              "      <td>...</td>\n",
              "      <td>217</td>\n",
              "      <td>206</td>\n",
              "      <td>211</td>\n",
              "      <td>217</td>\n",
              "      <td>219</td>\n",
              "      <td>216</td>\n",
              "      <td>249</td>\n",
              "      <td>222</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>94</td>\n",
              "      <td>104</td>\n",
              "      <td>109</td>\n",
              "      <td>113</td>\n",
              "      <td>105</td>\n",
              "      <td>92</td>\n",
              "      <td>53</td>\n",
              "      <td>0</td>\n",
              "      <td>65</td>\n",
              "      <td>152</td>\n",
              "      <td>120</td>\n",
              "      <td>111</td>\n",
              "      <td>95</td>\n",
              "      <td>75</td>\n",
              "      <td>44</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      label  pixel1  pixel2  pixel3  ...  pixel781  pixel782  pixel783  pixel784\n",
              "9995      0       0       0       0  ...         1         0         0         0\n",
              "9996      6       0       0       0  ...        28         0         0         0\n",
              "9997      8       0       0       0  ...        42         0         1         0\n",
              "9998      8       0       1       3  ...         0         0         0         0\n",
              "9999      1       0       0       0  ...         0         0         0         0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XDUtEV4gSOlK",
        "colab_type": "code",
        "outputId": "406cf89e-6523-465e-a4ad-c2a6ab1803ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "fashion_train_df.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 785)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g9kO6BdaSQZy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create training and testing arrays\n",
        "training = np.array(fashion_train_df, dtype = 'float32')\n",
        "testing = np.array(fashion_test_df, dtype='float32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qBGw-XNwSRMD",
        "colab_type": "code",
        "outputId": "2154072d-105c-4bbf-a5ca-e358311b8b1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "training.shape"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 785)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3DCiNiDSSjc",
        "colab_type": "code",
        "outputId": "8f7ce712-5fa7-4edd-ed3a-7acf8d360f7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "training"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2., 0., 0., ..., 0., 0., 0.],\n",
              "       [9., 0., 0., ..., 0., 0., 0.],\n",
              "       [6., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [8., 0., 0., ..., 0., 0., 0.],\n",
              "       [8., 0., 0., ..., 0., 0., 0.],\n",
              "       [7., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7tSyzbOtSUKk",
        "colab_type": "code",
        "outputId": "fc512097-5a0b-46a1-a5ff-831bb548e9a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "testing"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.],\n",
              "       [2., 0., 0., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [8., 0., 0., ..., 0., 1., 0.],\n",
              "       [8., 0., 1., ..., 0., 0., 0.],\n",
              "       [1., 0., 0., ..., 0., 0., 0.]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fDPETkRESVQi",
        "colab_type": "code",
        "outputId": "31f11aa4-4a8c-4a6a-8b9c-5bf2c9d3c90a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "# Let's view some images!\n",
        "i = random.randint(1,60000) # select any random index from 1 to 60,000\n",
        "plt.imshow( training[i,1:].reshape((28,28)) ) # reshape and plot the image\n",
        "\n",
        "plt.imshow( training[i,1:].reshape((28,28)) , cmap = 'gray') # reshape and plot the image\n",
        "\n",
        "\n",
        "# Remember the 10 classes decoding is as follows:\n",
        "# 0 => T-shirt/top\n",
        "# 1 => Trouser\n",
        "# 2 => Pullover\n",
        "# 3 => Dress\n",
        "# 4 => Coat\n",
        "# 5 => Sandal\n",
        "# 6 => Shirt\n",
        "# 7 => Sneaker\n",
        "# 8 => Bag\n",
        "# 9 => Ankle boot"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f1a3fd9ed30>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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L8KAXb9wd90NJm6p2EkDjjObd+Ksk/Z+kjZKOrzP7oKRbJS3U0Gn8R5J+UryZFz0XR3ag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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R82p9OClSXw2",
        "colab_type": "code",
        "outputId": "5231752a-344d-4358-b205-08b6e90a64f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label = training[i,0]\n",
        "label\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rss5yne6SZfa",
        "colab_type": "code",
        "outputId": "5b35cca9-e7e2-4da8-d11d-c487a3a4dd48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 968
        }
      },
      "source": [
        "# Let's view more images in a grid format\n",
        "# Define the dimensions of the plot grid \n",
        "W_grid = 15\n",
        "L_grid = 15\n",
        "\n",
        "# fig, axes = plt.subplots(L_grid, W_grid)\n",
        "# subplot return the figure object and axes object\n",
        "# we can use the axes object to plot specific figures at various locations\n",
        "\n",
        "fig, axes = plt.subplots(L_grid, W_grid, figsize = (17,17))\n",
        "\n",
        "axes = axes.ravel() # flaten the 15 x 15 matrix into 225 array\n",
        "\n",
        "n_training = len(training) # get the length of the training dataset\n",
        "\n",
        "# Select a random number from 0 to n_training\n",
        "for i in np.arange(0, W_grid * L_grid): # create evenly spaces variables \n",
        "\n",
        "    # Select a random number\n",
        "    index = np.random.randint(0, n_training)\n",
        "    # read and display an image with the selected index    \n",
        "    axes[i].imshow( training[index,1:].reshape((28,28)) )\n",
        "    axes[i].set_title(training[index,0], fontsize = 8)\n",
        "    axes[i].axis('off')\n",
        "\n",
        "plt.subplots_adjust(hspace=0.4)\n",
        "\n",
        "# Remember the 10 classes decoding is as follows:\n",
        "# 0 => T-shirt/top\n",
        "# 1 => Trouser\n",
        "# 2 => Pullover\n",
        "# 3 => Dress\n",
        "# 4 => Coat\n",
        "# 5 => Sandal\n",
        "# 6 => Shirt\n",
        "# 7 => Sneaker\n",
        "# 8 => Bag\n",
        "# 9 => Ankle boot"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jvEDWWdcre7wsIVDVGM+iP00WqbRLVs7VsT5s6ScrYVNNBsOXkvem5gfz23sT\n9OT2/DVROy99x2ZkhsmKHhECTV/fdRFWfoKUaV1MtJ5VCqUaYhmYcDFWpPa/btELuH3nGwAAI1ka\n07XRvPTuA5DeG1/AKluy5PWRHP3OCjMlcwp6XEOjRd6fqvfoTmNZZLou29Xo7EDXm2ne2raDvFd6\nwkaLECMJCVpoWLfRHKbvloSHsTRE1ks/B+Nm1oW4TvPn9iUrq8qjG1RbnAyjfRGabycPTExIyEf1\nolRbXW0RiyfMXIwIuup+J4f9DpXQpzh2mWP45dB68asjc1KOk8YMYRajN1+AxmeI5um+shMAsTim\nE2asFpTqPdzSTYypr/VfAWsvjdG6c8hjdc7iHVIh/Fd7Tkc0Qv1yVZJG61A+juEMzQVe1oSRIPbC\nqlpq64huY3WEaIxdMWrLI6cnJXX1huQLaDHpb5UE3e13fKVUZQ8NV4+93ssaMJOCjSFeizlASQhr\ntv9mCE8OkUL9xe17AAD3bKuXzzthGgd6cea8kKcKg2eGYAnqpd8+YeZKwZgOa0TSi2t0mrML3JLP\n6/DgThaSQZmpEtOo77icISfCGGo0F/uFQn85Ry8wtI6eSf5wFgs4C/Dpt0mN+nlbOCWvddw7DHcb\nhQHgdvpnSXQEIUbls8aqpy+fCNJn015toNQLFMX8FyrPf+kCtWXzN56EP9P9bITEBD/e8gAe3E4Z\nCGD5FF/AM2amo1Y9jubFDTK9AJFH3JtwbdEXJjJY/V9gn6V6KnoG+sZJuLbQaE4QfqoaBMr9xvPo\nXJfhNobdJgDAeSGaH+7KNqL+rbQWXvLtTwEA9l39LRREnbi8JEOfBsX54KCTwndW/z8AwJ/gDcd8\nlSqsHQUFBQUFBQUFBQUFBQWFk8fse3SnyZMYxP7PXYCms8mSu31zFz3iMJx1zi4AwBOH6DvX1VAb\nJz52V/2I9PiMF8nKv6h5DEcyFIydyoaxBIOzXpTXjICVxcyQx83OWICIV7V1EY9paxC6O2DCY8sT\nHKaIZXJdDcwXaxKeXW5wMOEdTRyc43JMhrBEXVJLVpgnxlcgJeJp2o1RHGYkHOGLjjhFDYkQWWpH\nslFoQmTHF9spOgYch9rXL//S0ABcXrbopbqozqrJczUXGPgYeXH/9GM/xcs5Sqllcx3v6HgRAPCf\nz10KAOi++XlM8BedgvRa+Ubqv7V6DrbwrrSbo1hRT7GV+0YbxKuV29HxNOSFdd73TvsphYKfazUD\nYa2cW3dRaAwAsAuROSvPXIG75Zba8WcdaMiSR0+LUfmisYKc35qEF7c5nEZ7iDxiJnOxPU8W84xL\nlvGBQgJeiOo131ZdAm3B8qXYTDUAACAASURBVPrw08Fd+9sX8d4kebQ/cOAaAMAftf4Ow0JkwoRv\nxWVYIeK2bQ4s0ilGeVx4eRKM4+NtlM/zH7Fxropy/Aha7yd5ctmZa/Hqx0W/tR003tFX4Zd7beC1\nNnZmKQr2ovU78NgzpwEAxu6juNn/WVeP5PPULmEXYFlqyyMOeSqtrIdoHfVvK8qQHSP35sMvU6oM\nzwR+GRaMpwLVo7Y0i4YQxQJ/aeAKXFazHQBQEDmNIrqNMZvqVJ+aZveUQcvrYLVirfbFVHIcmSVi\nfu4bwsFt5JX5i7feBwC4r3CujMfzn9Hs6vToOuemYYp4W12M1RZdwwHhxdxdaJHx9n5beYH5H0yb\nkGv3aLCYK+N6w4xNSDFCv81Qe/rwayvMHMEUmYHCgrESFE6T3twAolq5A2uvL41VibHltBfIexaY\n2KPqA+TJN5ZlYD9ZP+WZX207AwDwH298FvWNNL+PDJGXkuUMzBDK/fqA3P97E/dnkz29/Njrd/4+\nYoFc0UR77vsOrkZHLbFcHvznC5Gwn5qVV54r/HkLrdVjXpnp0OPQ+L4+nsJvnqG5/JutXwYAjHs6\nlhgBRoDQOOo0iB0x4prYIPYUxtIlcPbNLN426wddpuvgnh+MXn7RI39BG/j6s/ox9AxRl+PraPN6\ncftebBujhXRTx34AQEQvYaAoXPOOKYVtGsLk7j6YrisXwqjORSEIn7oMWwME5dgriOr3AIjFQBe0\nJydvwBWq056jyevyIGxrMCO0UU4eqCzFQ6+num8wyDhhcw1jglKw1urDgE0GCF9JEygfahLhImw/\nJ2iI2tQPqgcgKQpJrSBp6i5nyHdWIbVPUBQ1q5znl1kW3IzQwAtyN6c7iIrn+z5xHq69+VEAQFR7\nAACwp9iC7SkaE0XXwIP3nAsA6P5igNISnDCPoTo3F/Bpdh7XcHiETBDaEg9n1BC9fPdI47TP+Rsf\nv90NzZOUXcZocYwwSx6eAWBFmA4Hj2DpbBdjzhAUFtNPIxr/GRv34cAYjZ+QGL9N8SyaIrQ7OrOG\nrFY5N4RRm8ZUfzGJg1l6xleodriGjEMT/eJVFVCknq5/TdenpzF06i3NuHMzqW28UPLwjXGqi+ub\nKP/poJPEGSHqM5bY3IZYObdmEPU6zRkFDpxu0a5QX7UC7qu7T6JQx0Aw/7svLOg40MJk1EPgu2AW\ngD3/RiJhyT1CSfs0D5G9vsgKsP8TpwMAOj/3hHx+JrGSE33X2QYv6Hj0AAmoNSaz6D6dDjI9Q4vl\n9dwmmvP4wSj8fbtviAHXYaZEHt2APKifh1dzACdCc72XpPKf2daLJpGc90ebz8XAKjo8v6eFjH8x\no1gOfeiunpOuOa6B0/lf5gl2wgxc7FHc0VG0PUZlPO2dZBD0DA5mivXfZz0Xq3C9A/DJtQ+iIArm\nCkJglFnYUaKDzEvj7XhbDYUuPZalA300cHrzOJMHZB8uNHkYHHRoz3dV7BV89zDtGTenFuNz7fcA\nAPpECMOYZ+GqDjJ+PFtlsl1iipJ0fvMoStMHHZrzo3q5fhI91b+XnQ75ZiprXz5R/lIM/476MVg/\nIKNtsFe33Cso7W+c+ntc59CcUyOy+poh5/KjtOXR1s2j3HPk1k04I059/WdPE917xcpeHP4tzb/t\nP6kOxXmJSaE7RtdirDRJTXlLsYhlJoWn2AFLxu0dT4pPcXmfmBIRnWb8uGCwhYHgwBfjaL/uGK90\nMuVQUFBQUFBQUFBQUFBQUKhWzLpHNyiqwc4my/XOm2PQ6oiGHOMMG64g+sYrg+TZfejgCrx92csA\ngLsPkOhUJhOGppFVIxotIpsVVnRhJfNsDXoveTRCK1MYf/f5s12Uk8NRqNuFRnp/raCBJcmTowmP\npefqcEUAv+N7eTmlKgAAljbARZC+/GXGEROiH0amst483kZS51FGf7/kGcgI4ZgOAxgskVVPF20V\nM0sYzpNAxdLkCPaOE6V1KEceq9pwXqac8VEK0Jt0xhFpzM1VcU4MQcubsFh5hYDFqVCY8XF9xVLs\n+BTV30cuJTrHMuv7eDS9csJ9HmdYW0MCLT998lys/OI0VrtgPzsF1GWhiwQXDKbIndljN0y9jzPZ\nb3XNQ8Sk/u8E8uzqgrJccqj/60yT1N2dmWaZvkqLClGbXJX0h+kg+khwLtzx/5GlcnGxJPt6Sw15\nrNqiKSyPUejFKxlKn3EwUyfpzLXhPOoEjdPPMxwzStg1TqIOl7XsxFPR2jkrBzg//v4VuK94DVmf\nH/7WN/H3g+sAAIutYUnV84VnlpkjqBUm14JPfOHAuOgf9Zorvbth4Q0a8Sw0idyte97ThK6/nQOP\nbqBMQTq253tvfWqa58qc0nv+8jR4grHT/By5L5u+vlXWi9Hagu2fJxr6gc+Sx2rJ3z8xVaBE/N3j\nwhyzOfS0jmvP3QoAuL9nlRSTK60RY3DMQnMd9eXDBQPwhRNLVD+MM8nJLTZyoJHqr6mBnnE9DYZY\nCzsTxPLakDyEb79M9QNbw7okpSTx55eoVkI0RO9xZvcBFFqaZ7/gJ4H4QY7saX6YEX3nWYBWKrdR\n9DCtETEh2gYNMr2QyNIGo38M1ejT3RA+iKwQl/I9lSbT8eM+YhztHmxEbAm9uU9R1hifSF+eBB3e\nlOu1moddR6hNd7otqO+kivHF6Dyu4eL4DgDAs1g7K2WbLZg5an+fbh3Vi9hnZ6bct6NELJ2oVsLD\necFy2zfz/qFawTvovfeNNsCPqWKdND8ceLoDS3uenPJM8kdEt/38Z7qlsOmjgroMjcuUXK87vJa9\nWODZ8ffQmabm8j48+zjlme9cT/XUM1yLJf9SZZ5cAWYKJluROsLBmzrktWEvij2CqXZ9nGjIb935\nZtiX0l73na+I9Jm1h+WYWGKkUJrEY6/VSjgkJsit5/8AV2HDjO8080H3aIvuDDQro7UFB99L+e8a\nriQ6Gt8bRV0NLfqup2GbyMmXGaQCm4kSfvYqvSjbQ9/xjqLMI5u2NXBbJBzP0CubbTmUWsR7FEyY\ndafYOR2oE0lZtGkh1pNJHLmIvguNMLgZOhTanXQ9kiggJxRqtZygK8dcxGvIOJABgKxQY84LClzE\nRUYc/pN1FiqZfa3Q4R9ky9SMuODrjHuupKT61OX6UBZjBeLgp+ww6iM0Afr32Z4uFZiLIhb5bbEc\nPicMHSVXx+J6OvScEjLLlITfEzH0wQtgi/yHjS+XEH6B4gXcQRq03hs2wPsHomu8v/NhPDBGMW4H\n8kTtzbkh1BjU1n7uwYPFemxKEDX8vu2b5N8yOmnScNrqkG+jOnXCGmq3DM1SYY8fpVpBtwOHZVBb\n7803YcymvhwSbaoFjBjBDY2l0fW8pyGsi/yJwrjzfLGEdpPaPOdYctPgnEV0OO3RF+amUK8VWoBG\nJ3a6fZ/chI1LKbZm13AT4mHa6NdY5U3Ni2PUrj6tuT6WQ3OUDgJh3UHGphGuiRCAXeNNODJCIQKj\nDVG467tnvyyirzPDmKjqKChZ8vA3aUwM3EmL8lfPIFnRL48ugybaT2ce2kUezkUiz2SfG0OBU134\nMboJzUNY7JhGPB31oq/0u+Uwgb1iofun3/sh/utvZ5/SLnNTB3NUe+Wy+vO7d9GZ6P4/rwAASrcW\noT9E1M1grfhrgtPXj0V3dQEAht8l4o7ffT5qfiBirIJ1KfoS09i0qppMp++445TDheYA0V6GDTGi\n1B9oqJfjubOJ2nE8HkbepvK1tIzLnLn+fWHLlnoMUbOE3hT126ExMv6EQjZ0Ucf7XDrIHsnU4J1r\niO6Wdy15aBp3aG4ZLCWkKvuOgRZ09m+dk7KfKOp35GFcT3P4MKPycQ0T4g3NHpqrs6LNPBNTFjZv\nqLqUxP0Y+xVmAc8ViaYcnMtf2Ek5dWFrkmronkSQpX94LnDAE/s/Y9CUqsT+niPLLZxmVacGf3SQ\n+qWvJF+j52FPE4ZhC8p1QstLw765lw4y1WjkmAm6WOsLJVNqx+iCrt95d/6ozwHA/QOrcV7Dfvof\nsc+HznEcodynBscbZhJUVZ5GgX/ivaLcYs9gtLUi8X4y7u3e0wqjg+rQD+dbctPLJ/7elQBjE8J4\nAOCSG59HkdO+ZcxNYrlJ++JekSnkhtbn8ZP1VwAALo89DgDYZ3NENZpzxj1TjiVd7iM4soEOMnLL\nBTO+lqIuKygoKCgoKCgoKCgoKMwrzOzRnca6DM+d1pKhN5Il1v2hAS1FlrYD+4la17J4BEPb6LNn\ncUT66Hx9w41EZzicr8Wzj5EXQAhyIrXMRTxGXuBsPgQuaAxu2Kf7lpWImeYh23EKfH1Bj7ewyDCd\nTcmJOHDTWniCelyMcGndtQ4SBYiXQtBrRFl8J6mnI2OS9ZoXNSAsvCglPzlfubzDp5lYdPesleqY\nyDVTt/GVUjVwSUOs0XQ0hoimUxQ8rMFCXHqnxktl1VxTK3v8QibV2ZjwXK998t3orCMaW386geYI\nPV8B2Z2pCPR3o6Mdr/wTMRI2X/kVAMCXR2w80EuexqtufhnbMqRGojPyfJ+dfAB789T/7xs9HTGh\nONIZJqt9UKxiRYgsulGtiH6b6KjZTo7cO88DAKQ7hZfHg/QCuCGg7tnK24DtBL1AygtD18p11F8Q\nHn/hxfE4m2D99ynLvkCZxrj0/vie/fvSZ+D9tSRW9H/1co7g9GKRW3H2izMr0CwTnqCvs7OIUrfm\nph0yTCMWKqEpQvNaWJR/z3gDBoV3NhoTSsOBvMMO1yR1tz9HdTuUicEpCpo3PECbO5rX8eR4dS8l\n5eNvfu8/cGeGxu2WAnl5tmfb8OY6skCfZvVjzKN5b0wotY+5USRFGMQIp+/S3EW3qJ80dyWl2e8n\nLZqNvcK7d2nkCP5rDkTKZLmPUn5t/RoAwH0/+Q6uWU+KKvrg5mmFRYJ1GPs55aQ9/Eaie9a+ZxD6\nFpo/JqiyCi/AUTVNKpQ6m3nAiENz/XgxIqn3h4dofuKcSY+OabrS69AQI+ZOjZWXjAQAWC5U2Y1G\nKliNWUCtSfeGAgKGgyWRW90Oo0/MKZvq9gIAerK1aI8SI+DCzn2odOKBo4E9vgXjWVKTdVtpntd6\nQ9CL5T7BbWq4IyLfLDxI4R6fuuxlA6pdVQDWTePLZBpM5nssy34SLS1YbCaHLsrie19noi3L5/39\njOjsNhjiddQnvF210EQF1er0XYGbCItxptfVwR2tHu+ua1G9PJUnAbcGPYNHc8un3HfYJs94mJVk\nhgF7Ka0TrPf1pcwej9K6lQwXcSBH3ndXZBfRntseCLmbyhLNfKsdK/6e5kQW8uc8hqNoeFUOx8qD\nO929wesnoKo82eN74H3LsNIkRh+zPLyxm2j6uz9B6wQz+zDyB2cBAIYuK2HxT6rEZzkN8/er7U/j\nNzma62JaEVExxx9xaR9wc3IAN//6RwAAl9P5YI+Tl97bAjeQEOFOvhCeCY4gyT/dNfNrVUntKCgo\nKCgoKCgoKCgoKCjMDmb06GoJkdMqGoHbPzD1BnFiH/joBchfTB43+6WYTCUQFyJJI4kYPOGRvOSc\nV3B2cj8A4JEREuDZMdSMUDcFJg83k0W/5VcR9F9EVuBoUxaFPJ3+u1opd9qK5KDM5wYAd6fWHXeh\nTwYy7tYL5EmcIAYUsL6Lehl9HwWTp1YA4SEhoqADRWHJtpcWxDMMXFi/mIjR5WGvLNDEAM0SnuzA\nO/kxzM7plRUwKDQIYRgmePeliPToAsDiELXR5hR5dCzdlTF6o5xJb9WYTW29vG4YKZEfGcIzmO+N\nY3UXeTf60wk0WeQlHhSpPbxjiD7NNnZ+jbwvy1b2YZnI2fzml98LAGCMS+GtZ8eWoCNKHi0/jcLm\n1BLkRWxhczgtf9MX5XlPcjtGPLr3Lw+8EwCw5eVlaHuY7mvPONKLLoxgiPV68J0fxRoGt+fIrJb3\neGDHqcwjblxa5JutNLY4FG/qe3RdzuB6ZZtaSbS/79EFyhb9hIhf3TzeiY/WU4zeSCEmBVBKiSoV\nqBCMF69QgJ4k7+zeTwuLdu8ihC0qa02ogME86RCM58h6WSiU40798o9ko0gXaP4L5pP28ww7jgZN\npBprttLgxtzVi9HRjvHzqE1Dow5CB0X84DD181e/0oXvbPo2AOCLA1egNUSeNp+p8EdNv0OX4Vtk\nOSwxLpaJXDO6mYMt5oeXS2T5HfOiSHtUZ2FmI+bPNR7V2aDLpDDRlw6vgxaeKvbyWqGvIC9W71Vt\nqHuV2sUaLwEvUbz13pvIo3n+X34YNYOBPIbHKUbS8ij1j9GBZgzeQs+s/MYK+RtejRBeMzU4URr/\npVr6t1CrSbGWQiPQ8vzcuXc9E1gk4uV1zZNzteuKNS1rwBETUzHkQResq7GRuF8UGMJTY4VsORaa\nYmWvZU92opha0ipIL3BYt2VO9g6L1hZD85AQeZbvevwsdOPp2Szya4It0gL66f+YF4ITK6/cLEzl\nWmKQd9KpdREaFfN7uJJvevwYOpe0A7xA324SOS0BSJaevTGDgpiv/Dy42iShyckeXhea9OTKNENu\nBGuaiL+1DbVwMNUrZgoWXf68FbDuffYkSzb7KKfd873QWfz34Nniajn2+nejtP99Z+NmGYPuhkXK\nvQq962wh9zzpjdx0/W/x3THa99bGqX+XLjkD5m+eoxunSYVY99tdOPRX5N0ORWnMFDKWFLs8ZTja\nPD4NY4cZ4mW5V2bvHK/GkaZP8egue/Ne9GZpH7G0YxBHcsRh6/s0raNv68oj79Kc119Moj9TwbSL\nvnaEn17PLqd3Y6GQjNHt+Rs/bvYFmRZsQ/gQ0qJhzwrRmrHqvz4imbzPfu4/AVD6sJxH8+QiIy2f\nSWjlda5PpCJbZ9nQ7Jn3PzMfdJP0Qwe/Wgvr10S9aPnpK3DHaCOTvZ42/+xNwygeFmTCqIvmNURN\nqg9TR7+icYeku2zNduCu3vUAIPPgtSXSMj/uSyDV0YFNBpKt5UPB2k5S5fJFa3rzNdg/SpNvbaSA\n5kdFUT4wY3lPGjPS9xiD0UFKmoXuFgxuoAayaZ2HkQXsGHVqN4wyjekQrWpOwpNKzNzPPahxKcAF\nl0lhBs0sUzu42GjEavMwlnW95jIeL/zDln/QBcoqyf/YfyGuqX0RAPA0p8E3mI/Lw23ULMnPPt11\nuBCT1ObBhKi0xMQF0ae0aa204fb2V46sVnjrufjR1TQAd5ZaJH3qzmGia17bsAXdIsD+tsHLEBd5\n8fzFa3FkRObKazLSuDlJRqOfZmjMbLz/42j9DQ3k2ldobK3ODiC1gVQnR1ebU8RKnAiDK/qKZ02c\nbCoFnqA2SXkRJEJUviXWENJFsUEVAlXBdvQ4Q1SoLvtjOWdb8p6oUHQdL0WQE5M/Y1zSvHwBrFOK\n6XLeBRaqVz9LYmOL62jOShXKtM29Aw0y73epKER7dBdLhLCPX3eFvAXNpzHbhtw81wtRv4ZkVo6j\nnGfBOjj74jU+Hdn8h14MP0h/y05ocONEw4dOm5sfbfq63KCuivZhiUVjYbEQnTKZJxO+l7iGFKcy\n5gT31uVcGvD8OaVWy+ORLIWz9NtJKdLmU7jPju5DvVDizjmWPDzMKsRCPr7KRbaTJj27kUELU/tC\nCGgNdDLc8jc0pp9NLcW4MOCVhNhewTVkTmjX01Av1rpSUaxpnGFpkg5wW1e1ifsYmKBzW4Yrx1LI\nV7Jnnlw/DeYhtbusbDnb4Dpt1gGgLZJCozAqjGToIJ7PGmAxkQdb5zKshol+rmscVkiEKWgcXIz1\nYaG6P5yLoeRQ/dREqE5ztoWQKHNjOAMnkFMbANbW9EpDYd3W6iKm6UIN3M7T+DajAHPKc4Z7hA5w\nHYZY68yy0TLUV525Q1NiH10I8OiD63/NPkFTvjQrDXP6NNKROjx4k/LeBvPqFkRy5TEvipVxGlMv\nxlbCFYeCMUH3bjXG5aE702ag/uSLNusoNAgDpxyrJWztp3HdHjjo7kvRW+tNnqwrOyEEuCr2trOD\npT+itorfVJCCkr5x+8gbTCz5TeDmSQdId2gYjw3R+aIpSXNLT6oBpZoqWOsnY7rDq6Yf//4rKFbp\n9/vA3uHQX5Hw6NXxp1B06CzUFMmgJOa/DS0kUPX0cBdyNo2VhkgO1qEKitf5YpSOeP+AWGtQiOo9\nNz4AABh1c2gVwpO1moOXS43iDprrV9y2F06fCEr8HP3jcQ21Gq35e+16OdcMunQmTWh5tEtDWwRd\nXyNqNz47/StX1wqhoKCgoKCgoKCgoKCgoPAaMaPhyPditt+0G/0fJOrF9v/TDbOfLAmh1XRKz/Qn\nYdbR6VzTOHoO0In9cJSe3z3cKD0SmsZREiIqbY3CM+xp0jq9rIEs22gYlhTQomtIYaOYUbYY+Neb\nrDR+1tF2EsU/fmgbyIo/vioJOyryZArjDNfKdF43DFjC0OAzq+04h5Gn6+YQUKoTqVIS4gYO8JxI\nH+Tn29MBLqiJ0AJWcj91Y04HhOWsJZkGjkPwYbZQqqV3yQnPTNws4oCw0vxy5xm46Gyi9tWaZJFJ\nmRGMCEtsbSgvvRvZInlJRt0IQroQuBCW/bO7DmDMJpqipnnSo+s2Ep0D++eseBK+dS08wvHpT3xE\nfj9wthDMuoLK2VNqwJ8+8i4AwLcu+w6ahPfDD5zvc2owIoS7tuXasfKO6wEAK75Pff20/BDyy6n+\n+t5ALAW9yJEWVvTQMBAZojrX3LI11Kcu1uw7NckIIkka80N2AjFBTXWhSUu2L0pju/oEr64vwuTT\nEU3dRZ1FfcVPOVQKeHDqQjn5vC+AVREcjXo0AzX10Gc2oXs9pZc6MEwWe03zkE8H84AL71yY6iwR\nKco8uZmSJe9rSZB129Rc5Gz6vjEi8gkzjlSJfnNPtgnOvgMnWcijY+8NNM/vXflrfChKNKTfbDsN\n0Z007pu2UDvees9H0Hc+tdeScw6hRrRlVPSJtfFerAqT99JkjvTOv1ygZ0adGMadskgdAMT1IhIi\nZdnS0KCkMY27dN9huw47C63y/swlq2at3D7cVyk3b/cnJuboNZZ0AgC8WhrT+Y44fvrtqwEATkRH\nqkvQjJNiTYgAnikYPRYwPEjf12+nflCo1fFCHVnva0TOpNRiA5lOkUohz1CzR+RP7hXMn7wDwxUe\n03QBye2COv392Sl7EHoR2FWkuh4sxtEQovnNFwscMB3kCkJY0SsLz/l54DXThSe8O3ZJQ86ltvRE\n+zPLQ00N9X9b3DeWiaImRv2oLmTK39yWJ891o5lGUdDZxrsB30dQDXBsn9JHZSnVchm6RN/TuPjK\nqAjtiZfAHKvCb3li4N3U5kdcCwlNME14efuY2EWMrLXN+3BEeF3Mo6gJTUdl9u/1PTdpN4IVYfLy\ncB2wBXXZ/9saPBnukO1gVeXR9Zl8lmAxasxDdiwy5b6xrNjfBDzar9fcsf5cuTffBE0w9YZETlwW\nP/aaXS/Wv6RIudfjNlZneqHp1n7uwbvkTABAvtGSYoMzPhP4noVC2PlFSq+697qvAQCW3vsBLFtM\nXvILavdIFqzn7/msvAwHPLO2B8+OVnAGnKY8WiCkMHcdCad+ppHSC96bq0OTLhibroVOg9aNbSX6\nnbs33yd/Z9yjOV9jBkJiTrg6GkxXRJ+3lwpoEGLEh5wM3OGZPdrKo6ugoKCgoKCgoKCgoKAwrzCj\nR9ddIqTOO5vRfNsTAIBmADiX5PMHziYBCWMxh15LlhjTdPD0Nf8BAPjKCFk56oysjFf8VN1+5Dyy\naI6If+/MrMHhInmybq6nlEMmuEyyXeA69gsvwKtF8tzuyzfh2f7FAIChIzWoH547T0/xmnNw4G30\nLpFDGoSGSjkVgAkphW6lgJKfKsilZ5gH2MJ764YYnIjgswvRDmZ5CAvhiqKI6+GFgDmLM5lIm4u4\nJxgczCh7zELDw7NZ5BlhN5Kl8rBNbRbWbezOUTypY+t4cJzSbmQdstw3hLLIBizWfmxhvUg/MZYP\noyji2XCELEOR5TYSRllwyvfoFxvJCloJ+/eSbxDvP3PRCoyuFGJkOpDYT+3X9+8UV/K9mm6s2kJu\n/H+4+wPSEmnkhccl70ErCTGxkI6mevq+5y1khcs3cxgipzoX1m69wGAKZoAdB7KLRf8OqJHxBqqf\n1n/NTRApqxTqhdhEXykJS3jk024YXTVkXfPFZEzNlcJTJU9HWNzri1LpzJP9Y0yknwrrNkzhUY0a\nNvptimcuNVXQe30US6xMpbaMvHDj3TEMvEnEC9YPoWeU5kU/Vi+XCcmfMsMO6pNUb75YV9S0sXuE\n+oIv4HPLxsfRLDrA/cNrMC4EgHxv2oFMvfSI9+cT0DA2GyWegJXfpb/1mQvX4eIaYi988NJHEL6M\n/u5ai9rK5Z4Uixn3Sni2SPXje18uDHnYYZMl9qn8UumJGxIpa0btqPybfnz7i+PtKAgBt87YKAYL\ndK8nmD/R+hJGShTj2RjOIOdUztPvHOihD8KJHnqxfE0H0HSCvzed/tCJaBJVIgtHvUFMglQxjIEs\ntUUqR29plwy4tp9er9wOulirQiFbiqwxlJk8RV94x3BlyrGcuNaUzMj5YbgQQ1uUxkJB9J1mMyXj\nGt3GyusTzASvKNhrEZqr7HqAOVO3Wo+OrpCfU6fT/NjwTHVGZ7Y3EPtu2I2hWcTGBz2ReJmEIy9K\n7kSfQ/OfxqauSu40vhWNcZmKqCS8xAXPxOoQsUDcEEdRxAU2iL9dCrj78otODaPpaDDE/tD3Uuvg\nMAanKivlRmjes5grNWz0UnXGaB8v7tm7Ft3NpNHwstAN4HXHFsp7/hCxZM5bvB8AwGxtii5JxeGL\nLgVS9wW1egY/Qiyn0Q0OmEj7GW3PIPZz/+ZpChCIx/U9n84fD+HPl9wDALjslj8GAKy891kc+jml\nJyy2mpLhlgvso30Ng3EnInWTKoJpNEqC4rA/+vIXAQC9oqo0mFJMNMZKSIi+vtig/j/kZtGo01q+\nW7BhuowSmsV379l/wBtTeAAAIABJREFUKbZ9nxi1vljfrz71BTRo9PyFL96EGkxkXU3GjLOqvpeU\nXHlrE4pXn0MPZGxYPXSoav4a5UZsnvTc2679JBWwJIKWdSbzxN0ZWJX9fLJGzoWeo8HwQo4KpI2m\nZGAzzxfkZ1/9mIVDaNKF6FV3GJnO2XdO++74wfUmzDEhplLnodAiDqp+Hl+HwciJhd4B3MjEPFpe\nU0kKR7Gwg1iEFmafzm0XDdglcZDyKcwOAzf83bEnB70vSgVWpoaZugu+tH12Cz8DapuJhpAT+TAd\nT0fEos37ly/4sTRG+BS1I8Vy1lMNXA5Qf3Ezda+suruSBGx6c0l0CRq7qXsy564dp3qsxEHXp0NE\nfvEMgsQjn8ZebBZ5jhlw6EoqI/PKlHUmNmpmmsPMCTpv3kNonMrS8hz1eXO0AJahky4r+tTEPDwx\neemNDUifTzQ35ouWjBRhDNOK6tOGKo2YEI7yeJmuvKfQjIJjiu999U0uBUpsV5dhCnFx+PW4g4I7\ncSoaysfxRIGODPVWFoMlIULQPPvquscCv3AD9r2deoDelUHIEiI5cWqfen0YxRRR6vPFcs90haHL\nCtvwxPjXNI5cierHETR9O1LE+maaa79z9qMAgD/uuRA//sHlAIDFV++XYkQHMmTwK7qGVGAeL4RR\nN+ulBvhzWwEA9/aswWAL1f/v2EqUhNHJEB09opcwVKTDT62Vx/YxMpD6YiTnNe3HkwPEw4+YtjR6\njBVEnWqevNfvM4sTo9IgojMuw1QcXhbgqreo/7+lZgv++cCi2a8ABQCAEy5TShsjGTnWhy3aiIzl\nw8gJwTXH1pGI01zmGzQ9T5MHWAAym4CvvmwZrhSjiodoTjF1F8kQbZ66YiNSydsXoGo1xrG7SP2s\nq2No1sv8WqAJhWkvQ+PcSOkID02lpPrjx7F1sKI46LfQfQ2VeNETQIsQi9xvN8kMC1aAmqzV0PzX\nbQ7i4RypCQfFqnzozIPLJ+7VgodfX5gqx8NYIsRmuMlxb5bWv3aznC93h039r3ZRWf25GhAZojL4\nRhkdHNp0Zz1/e8ccWQeafapPd68NxjMJtNxAua731dOesC6an3jTNAelUnrijo6HXDDvFHOXj5HH\nvG6nEM680ANL0VmhWDBhtFGYh3OUXMh7vkiq1Cs3kqBqz6+7cNcXaMRbKKuH37yKwlEeG1ku1/q8\n2FvZro6IEPVM22EAc7gv0vSywrJjT3uAZ6bIibt1lxTZ88VWFxsj0uhjcx1LTbr+v4co3OiRdRGk\nhNjxk+vJSvCV0W7c83tkSPC27kAznpjw937/0F/g8S9/nX7zF03AMQ66irqsoKCgoKCgoKCgoKCg\nMK8wM3V5SNBhh4YROSS8cm3NyJ5OFovSBeRF1ByO+AGy3niGBs8UnhzBKLLGbdhx4bE0WNk7Ke4r\n1puwO4R3UOQgcyL10gvsWoBguUkwp0wXjvZx1H+HKM/4yfEW/dgYeC9Rr0tn5IAe8j4wlyFyhOwD\nTsz37AJuWHhv611AUpbFvyMWNJFegLkmCsz/Lfo7PMbhhchiw4SAVzhaQjxCllPfmwOUc2o2RHKS\n2rW2phf3X3b+7BX8GGiMkyfFpyAWXANX15B3v1VP4f5xolz4NMSLa3aiJ0s+J0NzpSfX9wzUhvOS\nuuxb+8eLYayPksXrXrZGBt4XkyKl0hyW71jwtrwCAPDJSCaA+FHvPo7fO8Z1p68fkV/0T/m+EpTF\nmeC3VdEz0Ci8axrjGCsK+rHw3BVdQ4pMmbqLrBBWipvUPxyuwRaWyoQQo7A9HT8eIGpPW3gcKZHT\nqjZSwfzJIkRj94c0aIJbXsqbKOXoXVKD5VaPCuu1pnGZX9QSnl9Td+HIlFqepGkua6T59ZLGnXgx\nRdStiz76Ifq9/3kanTHixF75h7346cGNE16tNpyX42csFZ0Tj66Ppre9iq2/T/NL/1U27riEBDMO\nu7QmmHBlWpBmPY2OFqqrEeH5XWFoyDU9BgBIaBY0YV/14OdZ5vKzD5PpsEVu8hx3EWVUfwXx3RFX\nhy08Qzf+7sPo3rp5toutIMCNctqXhFmEJYQBfWp5SHfgRqn/m3qZhuynEaoJ5TFaJPbLQDoOS1D6\nfWZPyHBQL2i+MuWYY6FJpDFqstIyvVRUeBMTWl56CW5o34y7qsgH6jOtpICkC1ipqV6QZw6Ql1LX\nPfAM1VlodMptVQGfafGO+C5kRaqweuHluWzbuxBppjlxkc7RWyLq8tLQ4LS/5a//vmfX40x6NH0K\nb8YNQ2wFYWQ1fG3vpQCAu874HgDg0XwbNoaI5XHhon3YNTvFnBXo03hlZUrJILxyGiY/JGMatvfr\nCi3PFoAb6LPvhQwZx6aWGyNiLhH7BD3invK6MFqJMTJ2URfCw1SGYp0hBcPSi8U8N+KCW0IsFMCh\ndy0DALR+iTy6ekszjtxEYQr6m4Zg5GmvxP5IjJm9E72VAKX225GhcKGCa8pUQgVbnKM4Q94uH9+0\nOfDo+uxZ7jjg3tTdptFFoaO7/rkOOy/5nvz+5xlid/hhBi4YasS4LqLM7nt7cgsA4Cef/HO8uJ72\nFKc/9W4AQPt12wDsKL9LiBhDPrM3MlAOV2l8qZyP/WiY8aAbLKjkgI+NI7SdPsaEi95e2oKRtbTp\n00tcxhkwoRCbawtBL/o0Xw4jT5VmpAUd2Sn3aG76UsYc0ERHYoBWFAp2KVpQeS4P5GnT66bmhrqS\nOER/M9Ywjq6lRMeot3JYHaHYkV8NrAMAvHKwDdogdVqW1wFjErW5qYSkiMs7vakXy6NEtTozuh8A\nbQ6jgtvi89c9AELcGW1GHHeLeCh/cbCYi2Gh5Lva6sX/LDtnlkt/dPgKuyHxznVWXi5Qw14UG+MU\nvHbXACnFtVnjOKeevtuebpWqy1KdV3PlpNgcp8FxeLxGxgDHrRJGHdooVWVutQUKv/3STghNgtoW\nVNT0+2rMLMnNcTaXkJRnn7pnaU6Z5igmwZDuoDdHE2Z7ZGxKnt1KELy8EM1/HS2jKIgYO9vRURQL\nTEmEG7g5A7l+Mr1oCVvGJtoiRhmcSaNAQ20GV7fRBHp97XMAgA//4yeloS6KsmKjfS5RezR2WMbz\n1oVpwx/WbUkhtgtzH9eX/NFT4l/gM2veAwBwakUe1bYw8vXljZoTobYycmVDoFGgz0aBQy/4cehi\nU2N7YPbEXQ3zAi3scbhRQQMVa4a2vxfuCJ0Kurk65M4l7DhHrUbr13AxBsPPmZwg49tQKS5j7D3O\nUBAmQP/A25OulYfe1kRajgV/rgjrNpbGyOjjrwMeZ1Jp3wOTatt+uEyTkZJxw91WH355zqVzUfST\ngnGYxr3bKYxyI8a0m/aQyC1cyFvgUbohebA6qauZD5GGwJv/5f3Y2Ey5PL/Z+TgAIPIRDUeuoTCT\nLPfkXD35QOtDxvYGlnIZoyr6hMs1iGkC9esG0fBBmvean6F51gXD00IL4JmvbkQdnpydgs4CjJwI\nyWLlA15kYJrDb8bPt+vJg264jzbtr9fzrvnMDgwXJ7ohDgzUYyk7RP9zFN2LSL9weom9YTRWQGTg\nRJQKZh9jF1O4zVm3Po9fbqG9rBYuobmBzhstgjpsuzoWJ2gtiug2+tspzGfVH1KZzo8/hYfG6fz0\n1Dc2Ysk3qa/6vYOZ1pQ8vKOrQqgXGjeG5uHNi8i54o+llBPG9pQ4f80RxTsYj6w30fjeeesK/O+3\n/xAAcFN8i7z+QN43RJtoN6guXDHAY4EQhoRmY5swBPlv/eKnv4ald30QALDyw89M/zLe0edFo2cI\nxzKlKOqygoKCgoKCgoKCgoKCwrzCceXRPRr8YGvW24e6qd53ial6c+L3J/07E7xJ/1YCoXtEYPg9\nQL9QYOu74Aw8dM65AAD7DWTZuWXDE5JGtSLULy0ZvljDmBvFiOBex/UChmyy+Hz7yEUAgIPjtbCE\ngvJYhizXjm0AvWTRCQ1rsCaLqjHAyAvLuA10/z9h0fzY7JR9JvhiQmeEyUr3yNBK9NhkXa3XM1Ih\n9w31ewAAz6aWIC68wCtig9LSP14qSzz51l+ZYzcVxgNDqwFQHtVxkVO3WDt35VI4MfgemYJjok5I\nkQ/ZCel9bAjTdwmjiMO5GvlMXZi8Q77HJsND8hn/N+NmUXp5o3oJh11q+OYIeY6nErlnH9qjLwAA\nIn97Bg7/AVmp29f044JF+wEAG+JErc95FgZK5H3enW3CQI7G93ieLNLdDYO4ppGo/ZdE9uLa54me\n/Px1NA7q8aRUeJTwXHg6zSOd5ggSFnky2yI052QdCzWm8Bh5lWU5uNuJKOj/1aj4by7h146/Vpxq\n2v5CghvzMOZRC3ucYY/I2ejP3x5nctwazJMKob7oWNwqyfm9LZqS1DWf+hwzSpK6GhfUZA1cZmrI\nuZacK/wcnWNuVK65BW6i7/zEnJT9ZOALT2UpGgGeyaFNs5XKidyqyYYssn3Ce5OtLgVhH+42UlVu\nvBY4KL67ChvEp32ofSu1yxOF9il5co8G37PrQgvk5KXyJ/QCXiyRx+oz3ffgtkMkcHXN6ovpmQCL\nr5q8uQCF8k0Gm6ZZjSz1ef3/Z++8w+wor/v/eWfmtr3bu3bVO5IQHTU6GAjFDRfADWzHDRMntiFx\n4pLEDvnZjuNuJ3ZcYmOMMTYx4ILpFtWYIpBAXauy0q62l9vvzPz+OO/MvVtUQNoi7XyfR8+OZu7c\nO2fmnbec8z3fg0NO93AqMY6pOWMAJ5lkX0LESOORQpTSXCLPz2tHwzH8/riuoqRjYt+F0juExbTp\nDjihThiJ2WUzyJXKXDdr6fe80WBrv9CcK7YkcC15ri9pxsFL60zcvPRrNUVt1WfM5rIFgS4d8a7/\n6Tr61gnd2UjlWBvTkr+Ox5Z1yJfrqhZdY1t1Y8+vlvKvJ94DwIroXezVIrN3J2ROZyqHhK5zPzvU\n6ffb0aKHmtH7MkVq6XP1wnDO798/MpKr1NDovzF0jmNkbfblhdGTb917SBuCiG6AAAECBAgQIECA\nAAECBDiuMDmLtk1G6GRs9fgLND6u931N/qwtqnr4EHMP8UXlRdsSl6orik9VMPnhRWQ9NEQHWBFt\n8f//YkqS1HemdZQ3nKRlUIuFlBTysGJFYkVe+aCILikyq6nLLymScSxfzCpbeaxmrxx/8HJss45J\ntSnR29ZMlf8MY1pYZiAfKeRjm7YvLOZHgQzbl833EDbyxLVYVYmRxcHL+5qAHLY/v8T8IofjNv23\nZb6IZSUW19E7X7rSxAwHR9f1LKuQvLJtPTX867NvBmDxdzpp3rRh6PcXR3OLahlE90r0+tnEbF+4\nq1yLYrUmK0gaWtRr03gU2wowVWFUZ/yIW3NJ74h3NWbl/PYZNXN+vqFXR7XUyhLTypQZJ0SvZueU\nWZqlEO3z8zqLEdUaEFEj5/cZFZauQa1cygx5F3rtOH2nTJ5aurXrxK706bq8oqNGFSjy4LgKW+fo\nRto9AZdjC3PKJcc651pMC8u47UfcnZC/XVx7t9CX5332m19mRzl+rdyV0TZARwTHSI/lqEJH3Q41\nVoW0flBI2Qw4WqA0eWxHdAG/VFi5rp0dj2RJzdDlczYc8DQAQrrPGOwuoT47eeZ6docIq5kPdzA8\nI3Y4m8nryQ41UxnCmB2Wu+wkk/DUi7I92rkUWE5jdZe2fVnK+2xd9V3+rMtersvW+nnCPgvTtXzh\nqbQb8suKebVzy1XGf65RZTPNlP0fa5XyiQvf95eRPz7sfhhajMrWYlR2zGJglDHjQAgWugFeNTx6\n8ZMJoVYsjLdz7br3AtDbUcqtF3wPgG8PXAjIQmZRuSzma0MD/qRnX1qW9d1OCYNZacjLqkXo69G7\nTuXxVbLv9TPXs31A6HJufHJSu6YiPAGaiJn36SpnlO7gLx3i6PAmvz2ZEl/AzFDukKLnAIO5AnXZ\nozM2Rgd8saWXBpr9RbUx4VXkC7C37gAgunUHjYf4bK13zmgHR1E0BLBfFtXFZ08xABGwW+8f3YtX\nnbBpXIjcAaYqDOXySkrqFO9LVdCg0weKa1977/RgLkJl2GuZMiEazIdJqcLiOK4XuN6CuDVd6YvV\nec4xE8d3iOYdw6cu+7/nhKkwZXwoN1JcsGQjkwXRFqm/but0CzfqEO4b+Y579XbzeROlqzK4LXvG\n6SqPLs6vFIG9hBOhJy9pHn35whIgVPT8hjtCoEBz9AQuc67JjozQNcPKxpolPPD8zt2A0D4PlVo3\n0fDq6GYxcEfRC7K0WGzaDfnUTzdxaAXZyY7uVmn385aJS3j3QCWqRIuoHuCc8ICM656T3IrlieyT\ne3GsOX2OF8z/lKRuLnA/zBlrpH/9RNN9nBY5lGN9eLJqxN+yXYc1694GQMVlB699WwzXHtoK2s6M\nsDY577DPD6jLAQIECBAgQIAAAQIECBDguEIQ0Q3wqnHjrIcA2K49rnMj+1F/EGrxwu8+yb8iNT/N\nOt286upobVwASNmQUKeuOTqo/6bSxPZLQrlHC53uPkH7jasBuOLjL9CuxX6mLRVVriCGNfF4a9Oz\nAFxfvpvFD70fANNyeObs/wLge73LANidrvbPcVyDJl3/cFlMvPPrUzN8j/+iiET0V0Y7ePcW8fz9\n25y7SLtDuyqvjQUIEGBsEXoxzhfOFTG1TwNX6PqHnuhiSNmYRUyLas3Y0Vpq5FzIFpWYcRhKOYsq\nm4jyjhVwMC+8A7ycFTrksnAXt7xy6aszagzhlkgEo7pSC/QlLHJlEtIrjoXMnSYsjbxjsHunREGd\nYzSi98UNlwDwg1N+QpslTK0TwyJWmnAtBpyRUaDiNuO1pTLddl7KNDMjJHTojZkmyA+L6yljhIDP\nZEFimti6OCJzmhlmBic8kmap9HUvsAap0+W7VEwLdPYOVx+d5Ch6FqFuaeueWGJ/JM22Fbp2/K+L\nTtF1mN18nlxczh9SKucgJWUCjD08xsTcm5+kS+/7R870j5u1Oh2xqgKnUpf9KrHIlcpcbbBRi21Z\nEO2Wnr3ysZ1U7Dv8SK5/LZqy7GHWt9dz29OXA2Dx7CHPDxa6AV41Pv57Kep81hlCV/rvDWcx9ycj\n8wm8vAY6OrBeLuw/XAXthm+KlPdblt9AqFOa6ux7hRaneOGA5wUYH9x+418B8LUVERbdJZM2++XN\nnPOxTwCQmClPWE1LEyuRjqqhbJDfbpGadKVb5ZkmT0oRi8sEJ71D1FMX/qAbV1N3b77gw7StkMlj\n3YtCbYvwzNgaFyBAAACm//sTnPvSB/z/P5GV3HQzWaCOKlvn3ecdHGvoElXZhZ7eyOQx+mVS74aF\n4uaURHBDhn/ch7eAUQqV0wudDqnRuOfdC/jWDd8B4PKv3sy0r+qyD5Mgrc95UWh+qbXiqC3LQ/la\nmdwVL9dCH5YF0d6rGpi2aZITNEdbVHraAo7N9Ksk+fJTl36IrqXyXHVxCdKNeVRU7HNt5VO2i7NQ\nlG4yoXC+8DMbpVLF3G9sxu4cqqw6vO7oZEKlroDxd1e/HZDa6/XfGlmWpO678rlzZ95EaKHkHk/f\nd4gk1mMAc38ltqzNLweg8ak889dK0o0DfhsqpqPWvCzzgweeXwpA9XMm9qZgjjeZYXfq5a/3F3FO\nekTlyIgzOGS92wNheJqC3d+P9dChF7jF1xUgQIAAAQIECBAgQIAAAQIcN1DuJKN9BAgQIECAAAEC\nBAgQIECAAEeCIKIbIECAAAECBAgQIECAAAGOKwQL3QABAgQIECBAgAABAgQIcFwhWOgGCBAgQIAA\nAQIECBAgQIDjCsFCN0CAAAECBAgQIECAAAECHFcYt4WuUurvlFKPDdu3TCn1mFLqcaXU8vG6lvGE\nUqpJKfWcUiqtlLKGHTvu7QdQSr1bKfWgUuoRpVRz0f6pYv9XlVJrlVJfH7Y/sP84t18pZSmlbldK\nPayU+tKwY01KqYeUUk8opS6aqGscSyilVmj7HlNKfXXYsePefpjy7X8q236pHvMeUUrtU0q9sejY\ncd/2lVIlSqnfavt/o5SKFB2bCs9/mX6+a5VSP1JKqWHHjmv7PUzhuf9UH/tnK6Xa9fv/x2HHxtd+\n13XH/B9SUul/gceG7b8LmAE0A78Zj2sZ739AFKgCHgGsKWh/M/CDAxybCvafCnxfb38XOCOwf0rZ\n/1bgU3r7m8BJRce+AawBSoFHJvpax8j+RiCqt38GnDjF7J+y7X8q2z7KvXgaKC36/1Ro+28GPqu3\n/wl4w1R6/kCoaPtHU7H9T/G5/1Qf+2cDtx7g2LjaP14R3ffpxj4cVa7r7nZdtxWoHKdrGVe4rpt2\nXbfnAIePe/uBSwBTR3S/qZQyi45NBftXAvfr7QeAVUXHAvuPf/vnAi/q7ReA1UXHTgSecF13EBhQ\nSpWP98WNNVzXbXNdN63/mwPsosPHvf1M7fY/lW33oZSaC7Trdu5hKrT9bUBcb1cCXUXHjvvn77pu\nrui/GWB30f+Pe/s1puzcnyk+9mucrxkNfzds/7jaP+YLXaVUCDjPdd2HDvH7apTjxzumgv0NQNh1\n3QuBJPCGomNTwf5KoF9v9zG0Uw/sL+B4tX8TcK7ePp+h9puudm8y8t4cV9D0tDrXdV8u2j0V7J/K\n7X8q216MNyMRrGJMhba/BVillNoAnA48UXRsSjx/pdTrlVLrkXlQ8UL/uLc/mPtP+bF/H7AQsf2i\nYRT1cbV/PCK67wJuO8Axt2jbGYdrmWyYCvb3AY/q7YeAE4qOTRX7PW9VOdBbdCywv4Dj1f57gJhS\n6kHEq99edKzY5uH35riBUqoa+Bbi3S/GVLB/Krf/qWx7Ma4E7h62byq0/fcA97iuuxT4LfDOomNT\n4vm7rnu367rLgD3AFcWHiraPV/un+tx/So/9rutmXNdNuK6bB+4FlhUdHlf7x2Ohuwj4sFLqD8BS\npdSNRce6lVLTlVJNFDy/UwlTwf4nAM+TczKwo+jYVLD/SeBCvX0R8FTRscD+49x+13Vt13Vv1IwG\nG7iv6PCLSqlVSqk4UO667nF3D5QI8N0KfNJ13bZhh497+5na7X8q2w6AUqoRyLqu2zXs0FRo+wro\n1tudQEXRseP++ReLbyE2por+f9zbzxSf+wdjvyor+u8aJJXBw/jaP87JyY8h4iT/pP+/HHhc/zt5\nPK9lHG0OIflJPcCDCJVhytiv7fwPRIzrTmDmFLT/68BaRJBgSrX/qW4/IrbxCMJmuE7v+6b+O13v\nfxK4eKKvdYzsvwbo0PfgESRPc8rYr+2cyu1/ytqu7fwg8NGi/0+Zto/QEe/T7/39QNNUev5Imtaj\n+t//TDX7h92LqTj3n+pj/2XAs0iw64sTab/SPxogQIAAAQIECBAgQIAAAQIcFxi3OroBAgQIECBA\ngAABAgQIECDAeCBY6AYIECBAgAABAgQIECBAgOMKwUI3QIAAAQIECBAgQIAAAQIcVwgWugECBAgQ\nIECAAAECBAgQ4LiCdbCDrzPeetSUqlq+sAqAU87fxDPPLJQfH5Q60Sedt5m+s4ap7ysFr0Eo637n\nl0et+PRrtX/Pp1YD8K/X3wrAz9vPpCosyvIpO0TeFf9C1jblr2PxhVn/B8D1//F3ANR/+wleCybK\nfnPBXAC6V9RjvXM/AD2JGADJnhhWVwiAkjZFtFO+1szJ31StQXKabGfr8gDMnrOfXeunATD/F0l4\n6sXDuo4Je/6GPEsce8ShXZ9bzc+v+yoAL2aa2ZCaDsCcSAcA7y7fwVvXXAVAfufuEeerUBg3lz2s\ny5gM7d/D5h+eDsDTr/s6X9x/NgC/Xncqof3SFnIVcq+uW/0Yn6t7GYBFP/gwALM/8+Rr+s3JZP9E\nYDLYb8TjADipNADtH11BplqOVW10KN+WAGBgjnyuc7ki2yDv/Qmf2KS/RGH39o38cqXNO8DYMK72\nH+JavD5h12dX8PdX3wnAv/3mKmpfkM+3r5G/n73oLm554VIA5r5H7Hczmdd0zZPh+XtIveFMfzsX\nlzEvU64IJfROfaWDzYpZd8v4b2/YdCQ/OansnwgE9k+8/db0ZgDaLp8JwOBMmPaY9G/Rh17EKCsF\noOVDiwAIDcL0/9sDQL5l1xFd84TZr4p+9iDz9u73rqJsTw6ATKUsP0rveOqAn/e/+zDXApPh+U8k\nAvtHtz+I6AYIECBAgAABAgQIECBAgOMKB43oHhAH82SP4n3Z/J0z2fHG7wJwzktvomJuDwDZvPz8\n56bfy33rlwJw37Lykd/9GqO7E4XKc9sAuKikHYBc/bOUmRLRNXExcAAIK4lo9TolnByR2uL9q+Rz\n9d8e10s+fBimH7XsuU6i9INXDFBdmgRgbtkmOtLisQyVy+dSsQzhGbL96yW3ct63bgIge7Kcs/nc\n/+X6XRLx29DV6P9U3aJOOf9fbLoGpX1E7pf2UfdfTx46ojIWKG6L+veVFTpoxPWL7/wxZYZ4MS+L\n7+Td5WLXrvwgAJ0ObLqlFoB57xgZ0R3y3QeJHE8UzJpq2q8S73TPUhdtKiBe7C25GG+vfhqAU9fs\npMYUu6NKPthgDvKf3csAUI7c0y3fXIGRFj9c5Sao/fEzALj5/JjbE+DVw1wqzz9fEcVrmVav9GWN\nT/Sz/RPSbme/bhe3zXkYgG/2zALgP596HbPvkHNUudSYzzdV44bkHLMvjbN+o3xgoseB4ve/6Frs\n804FYNelEWpOEkZLeViisk7nIEsirQCse8fX+fKlJwPwiZrnAEi7Nj+rkzGx/Y45ADRX9PHyBokI\nzbzXIfL7Z8bSqiOGNWsGAK/c3MQtF8nDvKjkcQAueeF6HEfe5b6+EiorJaT7gfmPAbA4so/zbpQx\n8YN7ZEz54/PLmHmP9AXRe/88TlYECHBk2HnHifzzSfcCMDt0NwAroyaZ62Ssi6hQ0acfBCDpZHn4\nozKvCene8yvvvOawWWyTAsPmREOgFK03y3t9+dVPUGYK0yftyL14SK2h7BcHiepOdJ9/NDHKWsaa\nNWNUJl+Ao4eR03egAAAgAElEQVTXttD1MNpCo2h7662nAPDFM3/Bp/efCMCbpz/PJXGhKTbqOftv\nErOpMGXR858tGwD45JILcZLJkd9/DOCUGpnUPJBsAKAjX07WFWPDyibtahqvIROh9lwlf9KT/xVz\nWgAYRuSePChaYCWu7AegoXyQ9j6ZoHYNxMkkxb6mhl4AwqZNxJIFyk2tl9L8xaG07Ns3VdEclc+2\nhIXj2JuMkUjK4j8UzlMVl0mz8SZpE8bPynAGBuQLxtMR4roj2r2by2I1yrPe8b55rLjyJQA+WP8I\nALvy1byQaQLAVI6/wIsqmfyl3RBfPONXAMzYIU/+gYFlfP+xcwFY9P0E7vPyXnj3X1nWhC/6PIrq\nxq/MgYRMVEu3mcQ65L5YGelePvb4DaQuk7bysSUPY2pHT78j1PbrH7mC+ifk/ajJyLFknUm6Rn6n\n64wc3WedBMCC654da7MCvEpYs2bgvX2h3V043bJos3X/rUyTOddIW+0CLo2sAAr03IX8pfBlmvZn\n7drvFZ3HaarDWLZYtr0F7ySA987v/E4trqupxlmbtrZKANps6SfMnhBX7/8oAJeuXMf7av8EwC0d\nQu39xSOrsZLy2ew06Rv6eksI18n96/xrh+q/lQVw6btlkWi37x9T2w6I0SZqc2bxnvseAaDR6mN3\nTl7cW/vFOXnTwvu5pETGxJdyJSzW3OUXMnKftmXracnJe39VtbSFv7nkIToukv7l3zvfeWxN+gNM\nOeQvPA2Afzv5l+zOyRwmqj2+m3PtlCl5Z0IqS871tuWd77AVA7aMhV22BAn+5tY7+Mb8xeNnwNGC\n62I21APQeu18APoX5amUKRG1oQFuqt4GwJoX3yzHZxu0fVPGhJrnZU5U/cPXlro02aHCYX/cy10s\nqV3n/sdabt18BgCZ3fL859ydxXxYHKGsXE7bStmfky6RXJmLqeMf+RhUTp5hcUyw7+OSDtr83+sA\ncBIJlCXzy8OZBwfU5QABAgQIECBAgAABAgQIcFzhtUV0DxI92/1Pq7nnA18C4PGUJNj/vns5V1TL\nSvym+67hd7dJpOrEb4mb5+zyzaztF4Gql5MS+fr91ieY/zMRppl307Hl3TGUeKdzOopbjLQb8ikb\nIU1dLjNTDDhRAGaXSESva5L5IIq9J9YcoRzWlYlnfm93hf+5UMhGmdI+9nXI/mhJltOahJrxQnsz\n9Yj7yV0lUbrnk/BQqzz/nv4SOWYXKDDKMOlNisdzZpVEi3quXEr5bYcQMRgLFFG3PXTcvYizmrYD\nsCq8k/1ZiW7/R+slAOQdkzc3SCSyz47Tp723O5JCV15RsZ0/dEj0o8QSL3BtZJDr1gi1jzXws5fF\n4zfnGnmP3Hx+wmnM2/9hOQCxTYqQEBJwLbAlEM/ALGnD8VaXqtvEI/mjyOsxtAPOSsl7Mstx6Rfm\nI/1z5Zxwf+F36h+z0Mxmdv6LePZmfe61ibUFOIrQEQm3tATVJYwMp38Ad5FEHzPT5F2OPbIBc5qk\nJNj7O3yPttenmA31YMhzd1PC3FCRCCokx429HTjT6wCwmmV8yLfuHVvbDoSise/lL0ijDWWz5Lql\n/1aOgpAz5BS7OocaFFse+t2prB0UmrP3nlDrkGvW7nnd77lpk0xSzskYLvm83J/UD2TsqL5igiK6\no4z99T/v9lkqTyYWENGRLG9825ur4rs9EuVJOmEe1vtNPU7Wh/p9lseWrLSTdemZzA2LWN9VP3qA\nX51QP1YWBQhwxNj3YenT4kaGRktE9P6SkH4wqnJcWiLHO+0E0ywZC5/NyDv/bHo2cc3ue2FA0hXK\nKlJwprAg+fNL42PEUcLmm0SY1C71BnoH5Ui/8dOtZ/L+08SevS0y/ynPgMpLv9d9gdCac/HVNHzz\n+BvjlVI++ylTIfO3U0tamHui9OdNp8r8Nn1lyO8Lf7mnn7OrRKQvp4Vsa0IJSs2Mf/4NT147XiYc\nGUZjX46SDlj8mT2fWs3dH5I15eujNwMw/ZYnXlVE96jl6P7LdpnIp90XubNfKMveQPdX1S9yV6cM\n7p993V28cpZMVr5QL+fcMVhPuaUbuF4cfq1nNt960w8B2HjZNH77ofMBMNY+X7iGSUppNjRNxdaL\n1ZxrknbDAIRV4aF4tg7YMeosmdmXeHwEouN0tYcH1ync69QCmXRWR3cC0G6UkR4U+9wYMOyxpFNh\nnmqRTr+iLEXDk5KPMpiTyerdW0/06c6uztE0IzZ2Ru6PbbqEQjrfNy+f238mlN/mXdw4toOiReWW\nbwvd5qNz7+cvfbL4358p849HTXnWg47J7fuEpthc0kfckg6qNSmOgHszy6mJJChGZ6aUtnS5///r\nlsqi/vvfOweAhR94ZsLzdO2Y3Hcjp9CvOrEuh8Hp0u69fakGRbZcnmUo4aL7ajJz9UInA95r4eX3\nqjxYOnNhYJbyt/MlRR3iJH3/pwosTVFzXReismpz2tppvVjadWKh9GULf5fEqBM6q1lXW/iCsH7n\nBwptX0Wl33NzOZRl6n0RVEYak1sy8f2iWVUFwMI5osWweUcjWDqNwSha5Lp6nLQVbolcf6Y0T8bQ\n7dYpHCenXwrvmOnKP8CI2GR6xG47L/ek8txTMB59/qjbdkgUvXe5i4SueX7lb3y6cpWVIKrkuScd\naRMlRoZSnZfnuAUHppfCE1Z5sq70Bd6YOCPU7VNAF0Ta2P2ZN8r+zx9/k98Axz5Obxa15BlWL1sy\nskCZHu4G4He9y9mf3wHAO8oc/qBTsn7cfgEAXek4yyuF2v+5afcBEFaKf75B3o8Fx1CKuhGPY5dJ\nX1eyU67fCUGsUwd1yvtJu9JHzr1D9u1bbfkVWKz9EgToX2DTMK5XPj5wsr6ACbFO2e53on5f56Vz\nhVXed34sq9rnnxPSzsGQYeNo2fotmUYWf0YnO75jbK//NWG4rsXBtHWK9rV9TIIav//wl3jTuvcC\nssD1UHwvD4XJFTYMECBAgAABAgQIECBAgAABjhBHJkal0fr3q3k2LR6FlnQtC2KiNtyTl8zp33cv\nZ15caEhZ12JN6WbZn5To1yupJj/6m3HkkhzXYF1KaBynxFp46v9J9LBnjf7RSRzNWdctgipXVorH\nfUemjmpLuJ2NVp+vutySk8iojaLOFGGl9QNN+lu6x/GKXx36Z4inbl5EwmwV8RTpLvFE5cCPVChL\nRzdUIbrd3RPn8d55Q77PyZqgo+CGPsd1lB8RcfKG/7jDhrQTp+LwvTlHG9ZsaZdvXS1Kwn/pm+VH\n8SNGUcTekehEXXSQRF4i3pWhJClbtmeVyjN2XMOPdHjfYxguEQp4eVBqCr97hXi0nlx1OurJdUfd\ntsOBp7DqeW7tMhszK23CyCsylfpe9IpNkS6XXLls22Hl05i8KK5jgqWJDGFdOjXR7FIubHB6F0Gk\nW87Pz5Tnbs2cfkwpFbprRGl371klTH9I3vW9Z0v/Fxp0qf3eyPQMn5rjuBMevR8V+vqcSAhDe1et\n5iamf02YOh5F2WpsgLy+/mLvbk43ANeBGomS7rlUosTT/2832Lr/sEzciKbpm8VvxcQgtUJEVvb3\namG8SOHZOCnLj8T61BbFkOiuH731YLp+/+fDADSdTxkQKpcXJBKV+9wzv4KaR4+KOa8OReOu+akC\nfdqjHnfmypgV6RxySne+1KcpR4wcJYbYMsOS/m/AifrpHFHdKRhFon29dpznP/R1AF7/+TOOukkB\njjKGpdS4q09i1XdFZGzLYD3dGUlp6EjI/ND8VQ1VPz620tOGo32VMPLeceMnuOYD9wNwiZ7npt0Q\nLyVlzDy35RzePkP6x3+dfg8AA07Ij9R9cvfrAdh022IWfk/u2eSd6Y6Ee8IcjKR+/kUXno9Jn7fr\noVlc514NQLpW5gz5uEukS4/vJfrUkItZp+fHHR3jcOXjBLfA+LH6C7XS6yyZEyQ0C6bGHPQjujXh\nQZJ6zlilqW1lZtpfU+zNVR5x/eWjigMobwMyfgxfu7nuiD7DbKjnn274GQB/2/Imaq/cPPL7XsWc\nKIjoBggQIECAAAECBAgQIECA4wpHRYyq4vw2WjPikc84lp9nMyciHt/ufNwXYNqfK2dlTEI1H910\nDQDlkTRn12z1zwc4NdbCw4MnAPC7vpOoj4jHo1fXm/WiBZMRuzZI9G32QglP/dGOUqnLJ+3SuUwA\nC8OS4/VMag51nhhBq0SDZ022iG6R96RnmTz/Lu2ZnVnew/4SySd1k4UmVSwo5eooh7IclM5Dc7TA\nCsqFlLQZx9L7LBd0HVVcE6tc1yHWOXCh6MSV1tn3V/KMzrFeAWAn1UMiuQdDzjVJ2fIu9OUk764i\nlPa3PXiRa4CyUJqE9uhFdMSj5coS5kyQEzyxTHKQVFaeT8VGE/36k6pXvpBUpFs/55BI4APkq12/\nVq4T8XIVIa0delHt2S3Zp+hZJs9a5RT5Ut1+tKhP95pmyidpRHe4SIIKhdn5MXmeJzdvZPs50gdc\nN0vqjP546wp2zJU6g3P+ofBQRxVZMIoE7rR3WFkhXHsCI74GuCU60mqaGJ7dOm+XTHaoR3fYtapo\nFLdb+srmP+Cf43oR3ZJyjD5dqmhQ/g6VexpfdJ4o728uJ8/CCtvYts5LDzu4+WEebUcVohsK8Jgu\ndlGU13+sup2bLq7+TidnMKNZxoOuhPS5fQugMJKML9zVIiL484XfAeAHvSczPyIsrj3ZanZkJBIz\nMyyR3SsqN5HUEe20a7IxK9l3X99xIQAfnP0nHJ24HzFz+ntqsHUOWs61+IvWa9j2ZXlPjjWByqkE\nIyZjmZOQ3Pstf22xMCsCTF3pOIsqpK28dZr037mbTe6+R+pw213SzlUofNDa9CglVAeQfnCSMPwa\nvvkEjz4g7J1r7hNGX08+zruqpb1eWfk8NYbMZZJ6rtthl3HLtssAiF0iubz1PHFMRXI9JJtLfGGp\ndL30c+XbDPrnyL5Yu0vLUxLdNt8mc/rY2nJ0QJvkdNkwB03yC2WepY6niG4RjKS0bxPX1yiYERJm\nrIPhM1oMXP5vo/S5nzhZ2AJNoR7qNQv0Cz9/OzOZRNoFh3gXRxWRGhad3fXdOnL6nmSuDRcOFEd+\nD5brOwxHhbr8ufn3cGuHDEB5x2RGVC6sIy+LnzPj23klLZTcHclangsL9bOpVCY37cky+vRM2Fsw\n3NN7Ms0RUfJMOyHyuuA8S0SdF6+u6CREyV651lpTi03lo5wfk5f15HuupXyT3PZ1N8tE4edd9Uyv\nEvvdLaXjfbmvGuY0mWx2p4V6VF8yQCwuC/V0d9hn6bkR3XuFnILICgXBKW/B6zoKYrqhe5O/tIGZ\n0hO9uiyNZfJSD2Zl8jyrfuIcAb2LvTp4cs1xM+svRONmtoh+r4b8BXk/LL2I9WjKM2PdlJRqgSq9\nYqwNDbItKRPGrGNh6ZHAowC6s1NjZN2hMdgo9kU6xK6B1SlsvVKt+1PIV0jWGQzkywsT/UhP8SJA\ntwMbn1ui2d5kK6D2WTneP1eRnimDgtUpi4z+WYpyJieGL1C3/r9T+djS3wPi6DD0zfjpNhEoy+dN\nPvmG3wDwZeMNAMz/7POoOZoi/sqWwpeNQtc56IRwDOHqfl5lbJwSeS6Z6WXEN2gnpDcQRYfRja1h\navSmCd539Q34+wZXisBb2Qtt9J0uzsNsqTSUqh+3H01TXhUGF8oEJKL7r7l1XbyyXcY3lTJBC08N\nWdx6KOoH/f3GyIFamQ7mgNyT2LQBZpdJf9cxIOODMz195Ia8RvR/WhYwP+oT1fWcaxLSDjhTOVxc\nJqqq3bom6EU/upncPOmvmut6OadBnNr7ukS07NwTdhIuEeqdV1v3ZaeZBk3ny7kmrXnpF2+96lsA\nfO6m08bQwiPAgVRFPYwyKdt702ri50tQoOdZ6fNnf+ZJX2F864dnMWe13J/KiNzHL824m/sTQqG/\n44TGo2rCkUCFwoUFrhZrJOuw5QwvMLGHTXprE1qJ/bxT2fEZaevz/1ZEF938IVKTXBfcSZTOUTQB\nH9JfA7Zr8MOuswCZM3iL3pa8uKqyrsnu7fLcFyILXWVZh6UmO9mQaDSx9NTknW98BIBftF5Aepp2\nftoWtadJgGcgrYNWRcPBG88W5a3f/nYFqQYtZjcO1z1uUIbfblWyIMDridR6An1pJ8RsSxa9Hdky\nGn4t92L9gukAVJYnSejg2Nwf72FStpSihagK6cXq8gU40dCQj219W5TKjfLZxl9LjeX3LHyK7+88\nG4DInpbCh4vnP64XPLOGiOWOhoC6HCBAgAABAgQIECBAgAABjiscUUTXozD9JZnyqUeV4RRNIYnE\n9triixlwoqyKi5drf7aMn+5ZCcDljesB2BeroFdnoS+PC51lRXwb/bq27FO9c6iPSOL1vnPEC9w4\nAZUVDhdlOyXqVqrEC2O7igpDIrYNjxvUPNwiH7y5cE5IiVsrPjnZmENQUyEe25Ap3pX+bJQ5NRJx\n2NhS5uuq2KVyH4ywjaujmqpIdMWjLivD9Wvvei3SzZiE+uT4KSt3kNftyysvtKe7kplMzM1aebr4\npJNOscCUXNdAPkLcGhphM5RLRJcaStkhYpqe57XpllSNL1zlMRpKjKwvYGUol8Zo/5DfvGD+JlrG\nxLpDo/cEHdHu1yUBtsY4+xKJ4rzy8FKiXfKskhKEI1PtoHSk3siDkdWRbl2SJVfu+uVZrL5CaaLk\nNPlc5cp22jeLx9trPrmKSUrsKqqzbE0X6tW8k/f46Ry37zyN2hJ5f7xI/9sXPOc/1/f/1QMAdF5U\nSsSQetN3bDyFOVe/eMCf7LluFXV/2nfA42MN5ThkaqSvNjMOrq5/64mOuUZRREsplKYku6YxZD+A\nPU3KD5kdvey+WA4t2llG1xK5f5kGubdVPx4TUw4L0UrxxBs6jcJQLlV1OrVmVyXK1EwW/Xxdl6Lo\nLYVGPEpE19A1eJUqlNr6yMI/8Uz/nCG/WV9TVGh6HGEuWcgF06Rd7s8Kp6I+3M+ALotxWskObtl1\nBQA7e3U6U2MOEtImetZN45flEsmrOlWoze+44eN0LJfj5S1in/Wu/Xxx4Z0AbMvWszUt51SWCJuo\n5fOrmP2ZSUhfPkTJDLOhnlf+eTYAlS+JzckmBzLy/v/h3V8G4HMXXcbSUpkztbWUsnWfiLStnCMR\nv5/3ncJJMYnyuqsuGVthQqVQZiHspsK6lKAWk3NzWVRRSplRInO57W/6bwAuX/36g0aczEee4y1f\nle3ff0JKikz7yhP+97i5vJ+aYeiSZF7dbQAnnZl4sT53ZDJFgxbOu6hsA99rOxeAadE+vtch2++s\nEbrpfruMcNcwlos6NmNQ2QqFrVOS/qpMxqxby88n3CH2Rc/sIqWFKwd3S/8Ri4ggFcCn6x8D4Hf2\nShL1cg+Oq4huUTu124TF8abS/dw5KP2bV5It4UTI6jjkrFgnj79L2K/nVEjfK6kduvZ8d+/4XPvh\nQo/lXp/h5vPs+fjpAKQaHb+UlPfMz12xgbdc/gwAn79Sxo4Gqw++Xqe/sGX0n3kVaaxHtNDdf5pQ\nVyusJCFNx/xO81MsfPQ9ALxpsXS+jqv4Q0r4jFnbpHNQzrukVOjHX9x3CY89I/m4z8wVWnPfSzX8\n/GpRWmyK9dGZERrUwAL5nclD1hmJit9pWvV/yp+68GDh2NYk+da9Qz4/I9pTON4yKUkIQ9DZI2qx\nkVp5wQbtMFc2i9NiQ9n0UVWX/bw1ozCZ89Sn/UUuhbxeN2r7dKCr65/mh3uFxpC1ZV9pLIMRl3bk\nUaXGC0m9AN2dlolcZSjFKRUy6diZqvVpzBUh4fBkHQujaCB0hnAZhe7s5egZmpq8L1NBWi+e55Z2\nMjMijoStKZnw7EtVAONPX7YaGzD0s/QUkvPxgsJ050mKEr3mspJafblbkanWz9Uo1Ne1UnqhnCzc\nj9h+OSfRrKjYJh+sv6Kf/a50eqE+nf9T54iaL5Bvmzga63CokIWra752XCh92eW1j/r5hh27qzBm\nio1/t/hBAL7yykVkMjpHR79T00r6qQ5Lu75swcvM2yB9hKdO+2D7InbuE+pbLN5PpG8Cqg7qyabb\nsodQjVZQTeVQKT3w6AUvVgjlqS4XZ5551CPH9RfHRkZWd+7goJ/i0XFGObP+XRRIjUpxdE7UtNaa\nPRNbO+jCMemrLeXwoQVrAfi+cRad++QazbjYYjuWv7hVhuunbnjdQLGjz2N7xeNp8oPyrG0M35Hm\nFqmzG8sXA+C8uHFMbB0Npd/r4rwy0SZYl5b2HVI2tr6uZquXdRuEcr7k89InmpdVof04xLoc2mQt\nw+JqeW933ZhjTYWk9jz41IkAfH/Bb/yqBPPC+5kZkv5vl643mW2aONX9Q8HLQfMWK24u69ccbjkv\nTLR96PM3G1OYDwll+5JXbgLgS2/9KTfd9S4Aoh2KhZe1AHBzkySx395zJkvCQm3c/lGDeWO55nfd\nITTa0Si1xZPNsj9Kuz3x6WsBaGp5+ZA/sf4ymdE98GdZ6L/rK2twkskRn3PSk4iufAh8pUvmvCEj\nzwcaRSL90cHFZBy5f8+mZwOisFuydxSl2mMQ6WrX197wnLuhAYWOU9DXV0JpmTgKXZ2uphyTsK7Q\n8LVuUVXPVjiY6WNzsX9QFKc2aEewRVHqh54TZ5XFgFPQbblqzgsAlBty72qtfv/+2v0T4/Q8IEZx\n9jU+rZ/5pzv9oE9PWvqJMivNfb3S7587TdJa/jI4h873yftfo84k/kwLAHZ7Qem/uM+xph18RXgc\ntqQAAQIECBAgQIAAAQIECDCVcUQR3b6lsjLflamRqBTQYyeprxIaV7UlEYm0E/Jpejv7qhjYI5SF\nj8alnlb7n5qp3SVegNNXiRf4vs3VnBYRN/CjkW62Doh3t2JG35Fc8rjAGRD7exyJuHmiRQDWvp4R\nNB4DF1tH/ErWS7R3ssZ1rcYGYiXiSfEEwlxX0RCS5xIqy5JLDE02NwwX1yhENHwUObZ9SrOmsDpZ\nk3yJbC8N7/cpy5mcNNmyaAY1XXNjN209egYeAoNvW8n1jb8AYLeOLgBcEJcoxwPuUtb1i2CA6Ueu\nXTiIw9ZB+RHRcqsQpS215D5fXfU0L2ZEmGhWVLz4J5fu4vaLRakx9Me/HLFdh41YFDusn1GkYNSC\nuHjaXllcj/WiRBr753oKyi66dCZOUdNwi9xsnsh0ql7OCQ1C9r0SxVlS3sb6tAivmJ7OkQPppXKf\nrUkU0S1WFNaaZKyIb/PV1i86ZQOtSYn4XREXGuLWOS8xaAsNp0I//z2pKr/f2JeuIJGX40tLWwEo\nC2f48KkSJfj17pMpuWscxfl02NHZIakDbR8+ncGV4n1d9PEOXy1ZeRHfPftQ1VqWO1xoAGo0AQlN\nh3RSaWJd8j2ZCgOzQfp/t0KL9XV2TojSatvFzUSi0tfVlwlT58yqFhZHpN/u2VSNqpFoo/92KNev\nD+7kzIJktFkQ47PCuia1jhYPDkYpOVOi+zdU7mZXRtrPzpjcx1Q2RHqVFq47MKv9qKPvrC4+8h/v\nA+CzVwi1+C2le/ljSvrCtcmFLFosbXTZ70R05tqSZ/j02jcBYD0WwtF1hz1mzL1Lbuchff4zWyUd\nynYNv6bktmw9/3bnWwGY+0VhDi0cGMc+71VieMSz669X0XOORDTizytK90oDSF0jzzf8RDVvev8j\nAKzrk3SHTelp/NdV3wNgY6aJBzslev+JbXIf2vrLWFkq495li9b7Ak9jBbNW2p/TN0DmAhEh6zxJ\np9ZkC4JCmSqXv6m+V2zRFST2f3Q1aS0RbmQhrPXmSlulHZQ/2YLTJ1GpCkO+c8vXV7Lwp/J+2VHL\n73OyFSG9ryBwGP/V00fb3FePUfqiprCwcBJOxK8g8r6qp/m3ttcBsD0paRpX1/+Z5LSh5x9SjGuS\nIt+Q9dkp77zrBgBiqlAf19gXJbVHRyordZ8Xg8rN8k78dK2IdlGdI5MtUts9XlAkRpU9V6KY96fW\n+amfnupya76KGZa8E1/vWkCJToc7s0TEmppDPXzwaWF8zOOF8bv+V4HiftB85DnZeKTAxvLERIv7\nrtijwkz7UPMjforfLf/9HJe88/3yPUUR3e73igDygvdt5M9PzT7otQQR3QABAgQIECBAgAABAgQI\ncFzhiCK6DTMl4vJSbxNnVO8E4DeJ2X75AI9DPi3cy6Cus2gph6f7JJ+rW9cENDMwcIV47xaWiBd4\n0/K9/KRfPF5NoR6ienVfEZu4sgqvFo+mJOJYYSX9iK2bLvDKvX1RI4ej3ZP5Pa3jfJWvDrm5jURD\nEr3xIrph02ZXVly2Vsgmh84ncwoRPy8v17VVIQ/XK7szSnkN7ILHdluuyhe+ioTES1QeSZNpFF6+\nMdbu7CKU/fovfLFW6j/3niZethtXPMTbHv8gAG9f+ixZHZ312r+D8nPYAEw10l4vx92L7HWkS2mI\naoEbp4Sn++YC8PAzSwGYtlZR+senjq5xh4Ft1zXj6NxEnS4MrqJHu2z7NtZQUie26rRispWKrBaP\nchUYOS9fV98HA7/2nhexdS0wfi5t6unrZ5Orkt8M9WmPfnWe1nN0qakHj76drxo64lDsxczUyL5b\ntl3G5U0SiXrwL8tYvqwFgH9uuwCAtnQZFSHp1/anJf+9NjLoC5PFzBy7BiV650WDS0MZSnSYPH1v\nA+VsGzPTRkDnHhrVklfY+OQAO6vlunPzpmFtkEi118p73rycqpckCqpa96PKy4Z83RCxKh0FNiIR\n+mfLdtkuF0d/v9EnLCGjpGTcc/MB6n++nkS7RGe2v1WLzdRv5PM7rgQg2mGQ0vUjwxEZs/KJUKGt\n28rv7/zqC44qyuHVH8uYpHV06327zuLGBmnkv3xCSrZUrTNovH9i2D/zPin9zs8+KYyKnzGd7bdJ\n7dAvn3En+/rFV/8Ps6Sk1rZsPZ9eI1G+6Nk5v87uD54S3YVze9/NjQseASDVqAVKYkk+8mdRI5t3\n7QvMRgGeGDIAACAASURBVJJQJ7J+sgc/B7cIo+Wtdt8rpRAto4PoWuksk00O5rkSyU0kpf38/XW/\n5qn+eQCcVCHj/4bBaZSa0ifMCHXx6/lSP/O89W8EIPabCrYukPHvoooNbK084+gYNwqMk5cw+/vb\nAWiK9LM8dhsAe3PSJyWdCG8pF1rBT3pP98e9vznxYQDes2YLAzov9db+k3ymyrXlwkJ5JNXk/9YD\nKXnPP/G633L9VYU+7c5B+cwZ0V3+Pk/r4m/3fwRj7QSrkxaXUjlFxuiTIxJp3pBtYktKIlWPJGfz\n+mq5VltH8XbnajjjQmGEdXwG/3uORZRWpvz64vHN0r7T1eDqKK8dcWlYKPn4+zdqlo7hkotrJpfO\n1c2Vu+Qrj5187MOFEY34ued9c6V/v7Qkw0daTwHgtNIWQOaOa23pE06r3EWJnhitTwuzb36kjdgL\nRTJd6iCUwckArbcjGiYHFo9aUyNrxxlWL3f/RsQcXvhnMJGIcMsXJIp74SXP05CX/uPpB5cy79Na\npODvRv/eI1rozq2QMHtvNsY7KqX+1Vv/8yamvUEWvW9slJB6nx3zO7+4lWX+NGnovoLukgwrm/YA\nUlwb4NrmP/ONLwtN529u+iVNMZkoZTUf8NA6WxOPB/uWAKIknXJlUlqcTN1qS4MPKZs9+Ymri/pq\n0H1CCaWaptyTlGTyxqoBLiyTRveTvlWocGFRC+A4CkNP7uy84dOX/Tq6DBVZkQ0Xu0S+57/2nudT\nN8JmIUE/0Sgd6dBp89jCzeep/46oJXrrvPsoZz4yeC3euNenJOU9sQBXjbq49WDg+oO2t7hJ5MIs\nrBFK7o2/ei9z/15e5AVMLE1r3g9303mOTHAHZmkBmvN3c3/LIgBmPJBjzwXyXlfocoJOSBEa8JSW\nwY7JvfDUlwGinUN/J9Hs+gIV2zY2UTldpyysE4pjxR8tqh6RidC4TvRHGVCUaQ5d4F4mk86KC8Rp\nt7ullmSjDGpnnryFNZVy3Z5AVbmVojYkjr5dGbEvZYdYpJ1+g3aECl0/0xMoyzuGP2H0KL7jBSPm\nUc9kQaNe3k5klVBOu0+IUfOk2GKWC824a7mifJvYH7IsX3jKn8wVT+qsgsBG/XOyUGxbaVH1klZw\n1DV5i1VgxxPOwACx38hYN19KH/MQcQytAB/66AyID6trbBe1GVWkMO/3CQo7L/Z4/SQONGthsj0r\nB/l7ZIFb/P5PpvSWfFKe6UN9J5B9QRZALy2QSVnSCbNhUJy+ecekP6fbj74tp9a38kC3jJUxLdT0\nfMYgHC6y0HvvPE/AeKvsekqiVuigdatTbzyTnvdI+5+jax+/vfEZtjbLQufn/3ce71/wOAAtaRkn\nHu1dSKke36JaavvksoJS+/ZsPQ8a0r4uahThsTmfWktc19FcGe3g0+9depQMHQmVyXFhhQhK7czW\n+gvc7ry83znX5EvtF+ptg6uqhVb+ZGIBAP+yf5WfkmHgktPTztv65ZrrrAFmh2ROuCUri/d92Ur+\ns1so0lVWgozOedmSk3s24MQ4MSJOgT0XxJi5dkxMP3wU9WGbPirzopAWlqwxB9k+KNe9umwLcSXP\n+o8DIlZ1YfkGekpl3tthyF+cghjnhCtKHwaMMpmFRUN5auIyr+3S+6w06KaCcqH3aS2cOF/ahOqN\n+ffPS20yLAdbzyNVKDxhteKPNjylcoDuM+VdTzpZPyXNe+e9fgBkfRDVYlVedYa4kaFi+8iasmOG\nIkfOiDmQ6x66rer9nlDngeA579vsUtwlEujJXHYGj/zP9wH4x3ZpE3f+fg1z/lHmxPPmtx1SnDKg\nLgcIECBAgAABAgQIECBAgOMKRxTRXVImdUQe65yHqYlqzT95hdzrxX1TaYpnpzNf5pcHao71ko/L\n+npbn66ZGLYp09S9QU90payVfIkuY6JsFsQKkVBgzMUXjgYeb5Xah2tO2MLu/MioS5umqUaMHOuy\nk7lgUgH9C0A758hqYajeTIy0dsWpkOMLr9j24UddPB+R49OZ8d0w6/dO4+J58sT7s4WI7sAsTW18\n9Wa8dhgmho4qOSkdhS/ypnXmy0ec4rgGpjo8r6wXpYuHsnRrdoNXrxYKtcOUUjhZ7fUbR49vfudu\nKn8q0YVKb+cXYLre3Pfx1QWaZpGT0fRuVRyMTKGmLugob0R/TlM1jJzy6/AuuGH0KPa4RbSUQlme\nq1n5ZQE8L/Nw2qLzMQlPdz4t7/R5F68XQTKk5NRJMWG8nKOb8j+2L/fP9RgrlaGUL0DUli733wtL\nt4/9yTJasxJZSVzdR9ntR8nWVwG/JFBdDXkJYhDpwW+PSnv0539+PUoLC7o1VSMjua6LG9HftV8E\nXLrfu4qqTdJoXNMiVy03K6T7BMOYQKrWQbzXoUGX2goREdm2W3M+LMeP4rp5hpQakp2uz36JxuUF\nSOQMvwxfc/EPFEcWPcGaCaI5qpBXTzVLpFyuuz48QNUr0v9/6y4RyyvdBTXrZS6w9dooc36t0xAu\nlmd+efU6bm1bCcDgDF2jW9lDaq4X2so4RreKoxju0Hce8CmqyVlx+maLLelaF+vP0jPuyMnfz5zY\nxJnzWwD412t+xh3twvjwWGozY4Xygh5yrkmpjupknBDbshIFO71E0gJOjXTTpsfX92+/irLdY3df\n7IqYv70p2cCsqESqvVJnnlAeyFj3uz5hd3jRmYiRL5SaUTZtGUm/6M3J+Qvi+/3jvbbQMTOORW9O\ntjtzpVRZ0n68aHLOtUiHdW3tOZOL33fBUom6D+jo213dp/L+pj8BMpet1vPi2ZrGtDHT5I8PA2+T\ntlF2+/inJR0JjJoqf3uBLhXWkxO6uRPGFxYFyJYXaoUDZGptorqOsJGXz1VXDdDeIe3EqKzA7ugY\nWwPGCcWlxjb/ldSZ/mLXcqaFhL3jlRmKG1l/OxSx/ffCmxPGVZa9r5f+YcGd43HhHtPSHH2+Wbyv\nuN88CIrHDw+37ZL2/9ezH+NDS4Wm8bf/08LC//0wANMflM/OzGdwNYusdUWcxq/tOOhvHdFCd7ku\nWP6ws5D7E0JddDMZrpsu+RrbMzLQ9+VjnFoun32qdw49GXloEUvnW5YV6qV1ZGUZtTxs+5PfF5Mz\nfGqfRwe1ZjSQ373nSC5/zJHYIgNdaEl+RO1UKNAUKs0Ezydnjeu1vVaoOQn6UjLp9CYi+3rK+cn+\nNQC4WaNooXP4k1Hvu7y/dtbw8zpcx/Dvn0dxTuTCDM6dAPKeYxfq+3kT3mGTLy932VNSLt42lDOC\nxmwoh1Tey2G39T7XnwjUvFL0/VrV1xklJ2y84OWouZ7CcFGHlmxysBLafr3mV25BdTEfc1E6d9tL\n4TZyYEcLn4VCrd2D/vZ4TfBd96DUKWt6M1tukJqiNSfvpyEqfVW7fvxrH1/KJVf8wv/8e/74AQAW\nLRTqXdi0uazuJUBqygHsTlb5qoNQUKj1qP15x6BL95WNZQNHZt+rhLewd8qlzeYro5S2yuSlbGdh\n0umWyUNXsQgqo50CruvX2XUj2nmgFMpzBGpKcrw9T2iP0LminTOwBuR8o0+/e6EJVOQ8iGMpW6aI\naxoqg3p4LbF9SnKRbIGvYeDmFUp/NK8pzKUVKQb3jHSajbbgmii4RQrjmUFpCz9at4rSJq22rRX0\nBy9M0LNU2sJnL/o1P3xA8kznrxCHz687T+WKOpkzLH6dpGt02GUFDYeJwij9i7vqJHa8QWzJV4j9\nZnkONB01HM7TWKkVhMPiqJle0utXjbi97Uyml8ik1ktTqbYSfspCmc7LLTXT/vxgeriLelPe8da8\nLCjevOFyup4SR1r5Nhfb9zoefew9u5TZIVmUnRDfR1RJv7Q6LrkpvU6JP9ezUb6CbIVe0KXdENWm\n9IlhZY+Y1LfnKxjQi+amkCz6S4ysT9O8snQ91Tp3v9SQdvZgqoQFllzHzKauMbH7tWJAj+XfahM6\n90v7p3F51ToAXk43s6RcV06ISvtvydX6KXvZ0iEdxHhd8hHDKZd3oj8R5uz5EpR4KiwLEdcEK6FV\ns6tt1py8GYCn14rWQahoKmOm5XNLqtpp2yXvhNtcB8fJQtc+/1Tu/1+h4X6/T+YM9aF+GvU74SHh\nREjoxW3aDVOnFZi9dyvhhnnvyZJCt5Yo44YDjH1mnfRvdkfHyH6zuHYw+PPm0cawvfukf2uc38dz\ng7ImuuzcecztexaAzV+TXOa5d+bIx2XQjHUe+j0JqMsBAgQIECBAgAABAgQIEOC4whFFdJst8ULU\nRBM83S+qsE5ygPu6hdLTEBEvZMoOsy8rNITaSEEpc++g7HNdRZ+OXnnRsBIjTLxNVup37ziRf1l2\nDyDUGYDeVdMpneQR3dCgeKcqjSRdTmzE8Q5bPPY15iB9HvdvUkmMjEQ8lsHWzygeFY9MTUmCsyrF\nu/tEZhFmXDytDodPXfbFqLxosOWgMnJ+PmNSHZJ287ItXuy8Y/gRscmkT9ieK/ejbgkdhYtbWV9V\n+UDwItbdWfHsllhZujKyXbaxp5BsrybeNzVaJNc/ZkiNWygE9JUrghQgkV3vuKe3YIzS5O2oO6ob\nbghN+DApMkcDRlyeRercJXSeKJHI5FIxqrIygTMo0Zuolad1QPq1XIUYavUb3N52JgDvmvYkpQ0S\n3VhVI3SbvZkKv814FOWImecfp/0BgO90nsMOp2bI9UwvK3iAr2h8iXupYrzgCUFZHdK/JxfU+C9h\naF8vjkdJsrSCcn8CTNlWmWxhO62jvLEI6Nq7bqUWNbnvefL6WVsXxMhslHtasl2UhlV0HL3YrwYK\nB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BC7YjIohgDCPIwwF5OqfFUrwB+l60ZGOZQVVAIGVsX7VdRRcMfxaKuVXIeHL5nDnIeO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MUZU8+l57bYoIOL8gFa0kallvpCdPqbMS2QjHSTudBkTVwR2Y4avrdWiooFEYtUlAiVXfSp\nmZ54zY1oJ07U1a6jK6Iw+uIujF5A3vdyLb02fEkFly4jGlc5fxyDvSTc9uQ2EhBqX6fBFFGZco2O\n4bOojY1XkMe/NTGGoavELGZTn14Sz+HIGEX8HrnvAsx/il6XVQgM01WRtb7jtWjMngYRjgl4NEdz\n+l21zymOy3NjXQBofajZT2PeA0etmD/cV5gnqommf38GALD0YyNoj9P65YEpeu2AoKhqzFNzedSw\nURGRzjpBZx6pxDAkKIeG5uJwnthhljAEdo21qtQdS0R+HG+8D17SZSGuZEsganJjw3b8ANWpI6lV\noMq+DQtqnguG2ACdd1wro+QIJkdAjEqCTzJmGeMq4iXX1LRRhpmj1w7aOfW6fyLT1aJTw/Jkn4qq\nXhWjiNtHjjcjfhv1hYJXUZRNeX8zThwF5jOeVFk5cU9N5uKlPInRLI5TtKfgWrh+MQl07pp3jvr9\nYYf6Ua+Tgp2mNaHqI0ac/zXNdH4XRUwc7CP7eDGOIC76h3keReI+f/b/YFuJqJlf3365EmlLtvoU\nVns1LXwrWqn99y+/H28A0Zhtrinq53CB7Ee3VUdyZlr3qsA0wdLygHId3ZHN+S4AwIXJg2oMHCnW\n4deZcwEAb6sjW/elShN+3E+1d9/dsh4A8Ffbb8O8N+8CACzAevU7md+j9tfumPkBMPIeYrT8QeMP\n8enDbwRAfTou1nPZz2O6rSK2Ed3BoKiT6yh2hoeELhkrnmJvyNQNj2uqzrgU6qo3cjgrQmJ7JW6q\n16Vo03ePX4KDQ7QY/ri5BvkuSXQ//WAyD3uP015pEY7By9O1GhAU9jY9M+PnEEZ0Q4QIESJEiBAh\nQoQIESLEGYUpO8O8bvLepZ69AFpF5FYmyQvx++mt+Nry6wAA2TUczU2UZ3HoGOVQmAkbCSHsM9qb\nAlyRwyfLcNgMXoQ8tWPLRa5HYwnOIeGzYhxciLQYMtfFBvQSnUehdWaVGZhhjJNPl9j1fyhv7Gcd\nX8HTRRJDiTIba6IkvPHfw5S3mxtI4NmbviA+lcBxhzx5vXnyjL4xXsLXrpdiKofFjwbaNJkXmAOV\nyix5/8W9kIXvC56hBGncupgqBSQjW5bhKo+0x5l6HrVOFFyp0fzIZcMLsvwIh6PKKlRPhYMJL2Ql\nzeEmRPkc7pf4GbTJIxXVbJWXpDMOUySn+lm2mkpYdbmmchdk5Nfmuor+yse8F4HHfD9U1h0fy9SZ\np37Hhgs3NQu5m5u2AQAWu6uwJ0Ee7WgtlDCLE3Q5iy7Mg6418Zxx7kcMhVCdnWCAKL/DRy3opVks\np/MyCObLOlHZJ/xGayXh0WWeyi00DRewRL8QubrFsqX6mixNBPiiVlyfpM9XIZprtJOYyNG3dSK7\nkNrKLQ4jTdFVJsuG5Sxs3ETRW73MoC+gSF56Hs1zPbekkNxGHmlrlMMV6TjNcYrKdcRGVN7nriHy\nWG/ftAjRQdFuC+B/S2vKTc1U8uaJQV98qqN2FDuHF05jy6cHz43Q+vCHdZtVju5LI81Ii4iueYii\nN2VuK49zjzu5sNds4g/e/2Fc809PAwC2ZtoQFdFHlVc7IWFURnLz4kZbuot6i9o8WE6oe11jijJs\nrKBytGTpIYdrKvLhcA0JwXiRuWxF11TMmmGnerEtvciQlOXjRP/vsesQOzIGgMrflETEmwVydIMR\napl7J+d/zpmKeMn3JYyyYrRtLHWo6LDUf9CKsxOjWBbtVRFdXczZTslEZg3dt0OOo6LTUjhK5jQC\ntFbK6FTw9ZQQ+OkXAkQx3VY5mnvKZ+GwsJkks6lZz8EWJfWqbQWVWqnf3bOVIn5/fs2LMPZSdH/0\nXZfin3qIsVAbozYtN4ewv0IRLdN0cV0jldDZtp7msNRZw5jfQFGtlw7T/Ocu89Sa+PHut+Afu0jg\n6gecRJGkIGjVIO3RyfJyuTeOfTiybDz77Md9axUj5F0t6/GZ/VQi6eo0RWx/O3oO3tlC7JEP/vS9\nAIBFH31m0tOoE8ZFy4YMZlp+qf29JCrbZIxhUZLWn+58A3okk0OMVZczdKSofZbmqDlKrm9xraL6\nbUovncDOc7mmxoSM7O4rteARUTOo4hk4kqW9lMz7z5UtJKI05p7NLcKRN8+SdsdJMCZs1qjlwhm1\nTjjeXaF88674CIZVucCRE943HZi66vIlRMPi146g9CwN5Ji46D8YW43O+8REHTcx1kmDulHUgct1\nWtCOU0MW7a2oCUpav55BIlcA4ESp89Rt9wCdOopWtJFbQiP76PVEXWjYUYKRpRtdnDezSrzBQczH\nyd3S+fe4NePqun176HIAwC+3EEXjnuu+iUbdJxj/3z5yCvzLsnsBAHf3XA6n+/CEH50g9DLRsOXj\njeKqwpULtayj51//XLuFYZcMD7nQM8bV86RVVgq0ckG0dFfReeLMHxBNTxK1w/pfrvqMW8WNbrFA\n5+RZHEYt9bUC19GsjxcbsD1dqWrq8E4Q43LB/NeYT9mqBCY8+VyKUunMU3Rmk7m+CEkAsl6vDobY\n4erVF54Irmuo6aCFutJTD0d2h2AXli8ZXNF8fdVlpsQ4hG0LJwF0/Yiea7YD45HNM3b+04ngIibr\n5C6IDSujbku5DXpUiphQozWNIxmjjfCwqLfLPQZDOJSc2OyM8713U+3C6294HoOCejpUSqDkCGGZ\nMrUp62nQUzQ+lrf2Y0iI5I2J1Ir2ecMYSAgl1j1J2B3Ub+VGaddYKzJCVdYSAi9mVw7nv46UWL/T\n+QSeECJl3+ijepMa4xgp0VowOJJCw7Zpb/5rhkzHcDlHXKNr1nu4XomoeMO0qBe4q1I2sp5+4hfN\nMiK/eRaFT9G9jhs2xkT924YIUdCMAHXZA1MbuZQQWzEDkuopozyOygsQnS8lNr1ybozptqK+5p2I\n+k7pUIzpthLus1n1rpmZg0pTkevf0UoDWKmizl+2zwq0W27aDeadUEeTMY6iM37+NpmLUgO19Zhd\nh3SEro9cB/RZEuVebPZjwKHN6PMij4ANm6hdQn15Z6VVtc9PvfGQd2gtHa4kFOVTYtSOoSDaL2sT\nD5USuHwBUYPdiI51+WUAgHaLFF1L3EChia5pDNWFkaU+WtMlVNU1C52/Iftvz3ui0MbougyO0HX6\nXmYtnhf1nx1HxwMDqwAA0eW0Zt7YsQsP9tCmRhukcXZvrgFDd5FS8dBBG1HhaBw9TBuepC9EXR1M\n5lidTKAQQO1VZLcdKdI+4ax0L1bHyb4dcpL4yKIHAAC/HiFq9uXpvfjgujsBAEuDG9yAwBWLSAFX\nuubelp2voTFTQ+EGcl697/N34e2XEs368vr9quZvXmxOe8p1GBNK7DbXVP+Wc+KYE8WoTWtit9eA\nBTHqw3IeubXmeXxv9CIAwJc3CdG/RT14e+uzAIA9pXm4pJZSg3Ji85gMbJjPj3Vj4/yuaW//a4Xc\nBwCAVj7RMScDRXHmTpraOJ0IqcshQoQIESJEiBAhQoQIEeKMwpQjutYeEg4xf7kQ6RFRE+5yIaDC\ndRx/nagpmmUwyRGCXIcQqBgEYoMUnRheEUGhTVBCW0WUikEJt2h5esy116CwlDweZl8KLZvo89dc\nuAMA8EhkJZiIMkQGdCz45ak0+9SgpVLQGslL5xw8pF5//Wo6l6/3XKU8soxxVfbni1d9HwBwdcyD\nzema9blF7B6jiPclbeSR+cg/nYsESHJ9UopIgNopX9dLDJox0+SNl4HwvsREUv62Uoc6NLyKoc+m\n6LsnqR0B2law1EJB1CHk3Fa1BU3hnTcWdsLZRzL8b2o4pGqTSdGyasAbpvMzxxisBaIvMg+1GrlT\nawSN5lCxEUvifQBIjERCV6FL/zujzFbRXU0QGu3AMPQCvidJASdqizgXQdeLMltRo0vcRdPWWaD2\nij5ZboiiUBLCcAzKfcYFdZ15zK8Zy6iWLgAl8MU8wDPG31cnBpTq6bq4EYa6mWvFtMAMCOIZBjVQ\npmYMVFKYHyVqUzxSQbEgWAuCulwpG4hHRERI1NNlGlfUyFkpHQVgyb8STfiF7WswLGojx1ePYGUj\n9fX6Bur/i2J+kemsG8Vek+a3xfPo9TZrRI35f7VvQOpZmh/33U9jOjrsIFYRqSmiRntSA/pdmldu\n7r4W7gh50TURDefuENI2PU97ru/9/9a0Nf81Q7bZZAwlMf9Hev3InaS+VzhHVIylLE4/6C3NqDdo\nLh7UkpgXE+wNVV6IjSuPIaOvsryQxriiscY0PxTpBuY6FREO1BvXZHkKs6jECCVsrqm5cN3xsxDD\nwelo6ivCzHNEGbUhagnRIc2B3UJrnlwbAJ/RZDAXBhuf7gP4EV2NcXXclRRl5qk5cdBOKvaDvE56\naXZYHk263z7JQkoe0dB6HvXctybHMOqSjSTpmCm9qNayghdBSbCWUppfSFoymWSd+RsSL2GZSfbd\n59h44UoAiDKH0ltmAa5gpAwN+REro4/GxJKlIygLxosu1oFNI12KunskUafaUhf3r2WuSNeqbjlF\n+/7j8JXIz6f3LfiZhvk3UF+584onAQA/GL5qBlp2EgSiqxJ6LfV5VleLocvnAQD6X1/BW5qo1mdH\nlNrylXU3Inc1te/36p/B+gKVWJOR///uuQRLP7Dx5D+/gNJoBlV98ZlP3eFlsuWW3b0Jsnopi9QD\nZ1H09eBtROGvtDq4YAXNP9c07MbGDKXR3FxPNKMxL4YRIaL2zpoXcNs2qqOc/ixFNH/7TJP/W3gO\nAOCevQINPyeWwG97VqAxTpFsyRLRmIe8YEE+bSyB/S0SiMQN03kFXhtkmzUARv7EsXpMlJdLaEwx\numaKkT/ljS6vUAdL9LuIH6Kd7EvdtPlbZ56Frr8hysHInZfCTglqKo1tMIej2CA2MnmOzl+TgWSM\n0CMby8OdTzl+blTkt7ge9F/Sza+0JJBZOIHjrXOYo/Sdpfkza+Qfufsc/O+7vgeACpn/eN95AIB3\n16wDAHx99AqlpvrW1hdwa5IMxGadDLpBt4hDYiP8bHE53jrveQDAhpKoI/zTwCCXeZl8Ai1kwuua\nw5QhXHWIBVpudNePLAZABeOdeX4+guf5tQPl5B6kaMVN6lOG5kkxTZS5yFFd0YyIcCqMunFFf3Oq\nuNFNHKF2tG4sApfRQl6rERULABZZUiEygkUW1TdLaGW1qAdrqsrcKpM5KIkNrq42qkwZfXLzanND\nXccS9/PR5GNKL6k6uh5mqa6ecLpEfvMsFm+grehL/2eZum5OXBivgVmG2UytUfJ1rjNIZrZk+9lp\nD9FhGteRweJslY2cMoJljiWlUVKX50dHsTAiiqTbBmJx6vf5iqg9qnuoj9FcOBalCd9xfDqmG5QY\nr6LSMu+gfLG6Z/tR9yvKUYJlImPQXDzaQBvaPbUrUUlTn3YSmtqY73eIjhcZcREdIKNu6WgGGCEa\nGxNthWWCR4XTSxftjhpgZZEX3N4CvoJo1LYpHAGcq6LbetGGlvMdTKcLSq4c5wxZMWekDwQUeIVx\nk/F01GuT0wBPC6QSiq632VmgKMlSSRmAyrs1mHcCNdnU3HH5thKNQqlUbm6C6C+nlHqxB7+ObpvY\nMGTtKOyIoK4aJ6Z1zBT0Clfju0GMWdsz4KSEkrhuq7YGIe0DI9BWI3DP5evy+jSbY9CF6nxEc1Re\ntJz/J8lkqQpqNOav72LNivd6cK85BgC48YJ3ou9iMlfHlogc5IYKamrpWkUtG45Q5Za110cKMeT6\nhSr3CC0KX+/5HbRspDU3sulZLPoyra87y7TheanSCneWJPgjUTrvtjoaEwWvAufQUQDABxfswid2\n3gIAqEtRm7ft7sDwArIFO2uG0RmnDSCEUO6qeA8219P8dmSEqMmXzzuAo0lSEo/1FpHUaBGQ97/q\nbQ9scLXVNK/nPkvzwNG+OmiiAkUqUUJfmTaA0qZ57w2PYkmU6Mx7K604UKTczA83PwwA+MC1N026\nvjOT+gIvu8gvbwQA9BRlDufM5HJOikDqIC+XgRcowNX1gv8W6aD8BRoA0P7oHnSe8FWP4HWow/ia\n35O13dv+Er60hK5zHfaqZLjJGOtFAGmQ8xnfe+XmVAu9oh8kNAYrc6LdPlAiR1GSmRgYpsEwUxvd\nkLocIkSIECFChAgRIkSIECHOKEw5omufRd6JY3dUYOyhSG5qPnlVHl11H6689Q8BAPEBByXhsYsO\nkR+i2GjAyomaukcLYGXyiGVX0Pfk5jepqIVwDMGJA5Va8oLpRYa63fRd279KNdWa4Uc6rBmuqdX5\n/cPY+PbFAIBzEkdx98onAECJMry/83FFWfWg4Yg4sUMO+WrcCXIJ76+l+lhnffWDAIAOrAczRUTD\nnprKBLMBXZ+diC6L0O9KJdnnjnagU0R0ucOwJUM14yImeaEZ48qjsiA5gr2j5NGT1GVT96ld38gs\nAgDk2gxVK3d7vl2pMqKKbKW2B8lja9dHsSRFEa1RD4gKr+pSEcUdcpPIeqK2NLN9yrKAyVwVvR1w\n00otWYoZeFzzhVUC6nwpRv47i7lKjbkSiPJK9WcN1VWjVgjQ6XknUZd43AVkHWVNRjYZ9IoQE4sH\nVRvFx4PDV0Z7oxzxPRQFDaYLnK4IRtQl04KJdIzDxXq8PkU0Jp1x6CK6o1kiYsmZGktKgMrRVeSo\n1Dg7FD05T49c2AwnQtQoo8xhjdE5yjqfWtlFZITOP9rvQpMK2Z5UjdbhJWmsOy01cDvrxeui9iID\nNDFXMhGllYqzAOBZGjRBbQ4GC40snZ8XNcer1J8mkCkbGmMoCXZLw9YT1UKHvBha9HyVz+4UMJJR\na92drU9j1KXo25sSFMXyOIfJJCPlxLXYg09Djms+M0uyd1zOYUOK+dF1snmQ4svgiYjKvbkl4jcZ\nLowRXTDnRvDidLRzCtArwEMjRLmX7KR6IwfrcRrfz5eblbBWVoh2DRaTE2rGi74sqc2ah1FRh1xG\nw49XapH60QYAwHmf7MaOLM2vcm0xZqm7xJl5gphiJONH+/jmHWgWuoHNU/zOqYhJPS3EqJpFXtyA\nk4admB2eT02C1uXDg8RiOrrEVhHPK6KDOL+FxsXxIkW0LjjvRaQN+kzOjWBpjKJvB8tkB5nMwfIa\neq0lTu1ri4zAjQoa+0AGm4VdvD1b+cSE+AAAIABJREFUnXrRJ8OhW2n+TthkE8WTZSyoo71A3Khg\ntEJ3dHGC1u/rU9uxpUT7hydGlqErTrbiW//1rwAALVm/Tm4QTLB7OIBiIz0/mqOId6yaEd1q1qs/\ngyAjtnGmw5gkFC2p1zpj4JkTVZmnE2FEN0SIECFChAgRIkSIECFCnFGYckRX30C89Hn1a5DaQvXN\njr6ZxELOevZudNxHXpmR91zqR2UsUSeXc+Tmi7wVFlclREp1Qswq5Udnc+eKGo3DFtL7Rb3VYQ/J\nn1KStr6cIn6sUAIviCifXcFMZjg5R46ir0yepAazTgkDSe/qkVI9uqLkpZpvjqDXIaa5jMKltRLG\nPFFTSrPhCm91x6d8KXXunDzpRrNEvlJJCt34eT/VhiFyVKSMurYt5R90NDgnKZExVE6oUkESJcdA\n1BifZx2MYh3INeCcWsoBkvnf1QAXuRjl2y/G2Qn6/S3l+VhlUb5JSkRmF1t9KqIL+IIkUnRjyE2i\n1aDocKuewagox5QQLIAEqyjveIHTQOh30lhgkhhBhesYdekz9aKMlQcNrTp5fx8vzkPsvk3T2/hT\nhIzOsYKuhKcc4XG3hplfXsgM5CiKR83mUKF68aJe0FBcTHk55sFDk4u0nUaQwijzrMwJx2K6jW6b\nvPdl20AsMp61ESwTJsd0hRmoEXUYe9Kz02Z3F+USpXfthZYQ+ZhLO+HGaC6S91zmJwIkKuaKEnGa\n7UdpZaQWCERvxaMWiIbL79TL3rjayZ4hvtOhzqWVHEA85xEdWvfx19ze6YYUHjKhoyTnvP1HTnhf\n1ovCFOyN74xcWrXzmyrcoWH89v1XAAB+1h5FsZHuxf8VCVWe5dfONoqYPOmMyePcz8cX91+vwB/X\nYpwzlyuWimsxZR9IMUK9zPHvouzWvHu2oVoyXsnDBdSYouybRhG7EjdVvvW1sWE8kqBoU3+J1sVg\njvFEUSUJGQWW0d5bap7Hs7gQALDAGFH1dZ8ZI2ZZ3d7ZSdK14Sr2kaynGx0o+XN5PK70XIJ1xk86\nbzOmNEiYKDPJPT4uL/SJfork39FOJVdG3Oh4dlAV0XucIrlLOikKmwrQTN55/q3ov4XOVdoq+X3t\npCkgsKePao5rB8mmeOrai2EUhYaHYMkMPg8sHRPaLR3tir0l641OJu4zkzj2V5fR714+jLoI2T8L\n05RrPFqJoV6UGksaFSyJEdNtUYQeny924YUc5SC3RMZUnnHLlwKR3FdY34vNdDwzSmOqazoaFWJG\n4QWoelrlxPs6VvHFR6yhmY25Tl2MSlBqY/dtgpcSRb2HSBgg7otuou5bz0BvEaSVWpoIuWnATQma\nZlSHMUqLQlpMhFzX4cUFdacsNk/ZMfBjNJF42Sz0JjIU995Jxu+S74+gsJroPJWUhvT3N0y50a8G\ne0bo9z8y7wF0OyScNSQK1Wf0uNr8HrHrUSuKPkthIc/Q1EblqtgBXLqFVNfquEhKn6xO7kRo4zuC\nZgPmLFGXIxG6R1KMJN7rn7s1qGNBgibAAyNEcXE9DbYQ18nbFso2dTtLqtMyjhqLjAd57Rq3+gv5\n4ZE6rKohQzbfVv02J36yEY9+iKhTtzRvRbdDC50esOhqNTpvkznothvHfb7VGFU1xVJaUS1aUn35\nsF2vhD2kEmVCK6PHpt9J6UXVf47YdE07rUG1kZaOlaojIJDGbGqLNaJBlpJltli8NF9hGRwQdhLc\npF9QV+qyuMJ41WwOOyWU3BGgMTmzoC49Bcg5Xc4DACD8IDhWqEFtHRkCks4P+Btcx9NVHWnpBGIa\nR1bUqfWs2d/ce3nBldyyUzkt5GM1aEETf2PiLHAaSznBhothQfeVm4AT3iOcn8dLafHKWDVObcrQ\nniLd0RSUhs5pg6quCBtexMOHibp8Y+cuAMB/7b0U80DPz/nFh/zOGhHrm8bBdJHG4flifAhuenNC\nhFPMmXc8txRLQDbNh/e9HZc3UR3N7gLZHta6Z6e/bSeD2Ih8efg85NzxwkharqTGH69UTn2O5lyJ\nbPKXuZndh4SjcD4tHnvyLSo1pNpI1kn7jm7kZwauBiBqow8MoOGbAy/zSYK8/fKaJe89UXE4OJ95\noxn8Wy/VV+3PkR3B9equCfP/mTalR/7uMngXkCOntoFsthXJXvU+DVylX71QILrywUKDL+AWHcaT\n7zpfvHuX+JA+eU3eQDpKsVmokg8FVLgmUYIOcfoga/sb2cnE80ayZNNq0GbccRNSl0OECBEiRIgQ\nIUKECBEixBmFKUd0i7dS7aj7vvIFXPzUHwMA3r3qMQDA93ZfiPT3/fe6fURZgHyE7/3XEXBoBr6f\nTXhtoo+mvJqoD1+9/RsAgA8vfTt0nehKpu4C38eMou6NFH396wvei71/RpGW/7zsOwCA62Kv7FEa\ndCnK2agn0fAO8oAp5+UU6JjMGH+rPBO4pKUbgPKLVQ2y/puM3A6IumEA0PV3G/DUaop+zk9TVGJF\nTZ+ibC2N9WNY1NeS0a/dYy1YW0slR774qbfTb/zGp3Wbuou2CHkRVRSwCmAR8kjxchn7/4dqv938\nFz/HASEyIinIZ1tDqg5mxuNYJHh8unitxDmGXd+lVW8Mj/udy6JZRBh5qvfZxHZo1QFXjIYBl6Ez\nTvc/wuhxc8XF2aYsWXRAyNrPHrw43Uu2MgdXUNm9qBAWsn0RKm55qNSK0Z6ma1Kui6JSK9oi6IqV\nenfW6sdOFcEosxul8z4v3o3n0uTJHm2lft6VHML5Fgl33NK1DVtHSaytKUFR0qjuYG2dqD1ZR9fx\nxUybEqh6oS3gxT7NKdwhfCxKUjpLjeanNUiKaxCt+hgadeorf9r6EADg47ioCmcY4tUgn6XxeE6c\naOgPseXq2LK7pz+FZCgfx9ouEt76+YHVAIA2jE7775wM7HyKYq+O/QybC7TeK9HFgcB6xqZxzg5E\n7JK7aV4891qaJ4edBMzFs1N1mgtbJi/ENNckDmMXfJEoKSwKScO2/Qg30xjRsgNguq5C2fIY05gf\nGbdttMWorYNJmicOWPXT2aQpo+MffbrxwUVdAIAH33s+zr16DwDgTY1bkdaJlfZIZiUAoCWSxY01\nJBf3iY++D4mtEyLYU4jIsvn0neYhfy71ae6voiEhZhwJUT40qUXHl0gU6GigOcxkOryZ1aKa+kZX\n5gBesvYvUEd9Gr944BoAQMeewvSdkTTkmOYbknYFxsMk5ff5298GAGjfuhN6M9FZ3MCGeqbBN+/A\nknfR838GKUB/1jBQvJHoGMUGHeV6QUkUYzIywmGN0QRW870NeDX5RF5h/DXuvPc4Nh5aCwBIY2Zp\n2xMR+RTlKz92AVHH2x/Z7TsmOEfTLVRHGOetAgA8edECeCL37qF6v+aoJS5DbNDD4z8kmnod/A2u\nxLy/cvBAlHLXlm2tXi5q0Cht/QJN8Lcf/DCOvY76Zc0KMmSvmH8A+7LUF69o2IeVUVLV/vejVwMA\nbmregU5Rc/eYXTdOWRMAHu1bhsPHaKP6x2sfAwA83L9C1ZFcnB7E8320Ocruo8+4KVd5hZZ/YCuA\nqal1TyuCyqhbaFLo/Oel0Ico98hpJhqmPlaClxCpC5YOXdBgHaHEa4wOo9RGlKzIkFTXZtCPidrM\nmJDvdZogeE7z/5OonR9Y9m6wpDBQxuj+3ffCGoBKb+O3B1egOEwTgz4m1amBrbWkd4AKGYqxo/7U\n3LLt9KRrhzg5Hv71BQCAv31zAfcLumsTdp/wvg+8dAdu66D+8+8P3gAAirYa4vRD04M0l33SfCMA\nIPl4fEZ/T3ukDh+z3wIAiD2cnNHfellspX778W1vUWrRq5rJYe8ODqm3vZLWyCkhsL503Ef2wceu\nuw0A0D+cRv26qeg1Tz/abtsx7v8fYLwS8skqZ0y2KeOTbPSC7/NKJaz/x4sBANF+skm6+GQVVasL\n50A3AKDrb7shlSm+p3XCuXoNACDXLrRsKhwv/pD6TAIn0rRfDkH7K/0Y3eumzRQ84Th905hCEA79\njPSUll18J5b8bD+A8QHM4R+RTXvzHTej/eGZlZE/vUMmIUKECBEiRIgQIUKECBEixCmC8ZACFyJE\niBAhQoQIESJEiBAhziCEEd0QIUKECBEiRIgQIUKECHFGIdzohggRIkSIECFChAgRIkSIMwrhRjdE\niBAhQoQIESJEiBAhQpxRCDe6IUKECBEiRIgQIUKECBHijMKMb3QZYxczxtYzxp5ijH1+wrH5jLFH\nxPHrZ/pcZguMsc8zxp5kjH1xwutni+vyNGNs9Wyd30xCtHG9aP89jMn6UXOm/Tcyxh4Tf8cZY28O\nHDvj+/9cbz8AMMbezRh7WFyDtsDrZ3z/B+Z2++d6/2eMdTHG+kT7fzvh2Fxo/1xe++d635/Ttg+g\n7vPzjLESY8yYcOyMvwZzee0DTqP7zzmf0T8ArQCi4vn3AJwTOPYlAJcDSAJ4bKbPZTb+AJwP4Ovi\n+b8BuDBw7GcAOgC0Abhvts91htpvBp7fM9faP+FabASQDPx/xvf/ud5+0be/+TLHzvj+P9fbP6G9\nc7H/dwH47sscO6PbP9fX/gnXYi72/Tlv+wCIAqgD8BgAY8KxM/oahGvf6XP/Zzyiyznv5ZyXxL82\nxtcMPgfAes55DkCWMZae6fOZBVwC4EHx/CEAlwaO1XHOj3DOewDUVv3MqgDOebCCfBnAkcD/Z3z7\nJRhjiwD0ib4uMRf6P4A53f43ANCFV/fLjDE9cGwu9P+53n4Ac7r/A8A1Iqr14Qmvn+ntn9Nrv8Rc\n7fuh7QNwzkuc85GXOXymX4M5v/adLve/ajm6IjTdxDnfGXhZ52JrDyCDM/OG1wIYE88ntjF4/RnO\nUDDGbmGMbQfQAmAocGhOtF/gNpAHK4i50P8l5mr7WwBYnPPrABQA3Bo4Nhf6/1xvv8Rc7f/HASwD\ncA2A6ydQ1M709s/5tV9grvb90PY5Oc70axCufSdH1a5BVTa6jLF6AF8BcNeEQ17geRrAaDXOp8rI\ngNoGnNhGHngevBZnFDjnv+Ccnw3gKIA3BQ8Fnp+x7Rf4HQC/mPDaXOj/EnO1/RkAj4vnjwBYGTg2\nF/r/XG+/xJzs/5zzMuc8zzl3APwKwNmBw2d6++f82i8wJ/s+ENo+r4Az/RqEa9/JUbVrUA0xKgPA\ndwF8hHPeO+Hwi4yxSxljCQBpzvnYid/w/z2eAXCdeH49gA2BY8OMsXbG2Hz4nt8zCoyxSODfMQDF\nwP9nfPsBgDHWCqDCOR+acGgu9P+53v71AGQUaw2Ag4Fjc6H/z/X2z+n+zxhLBf69HMD+wP9nevvn\n9NoPzPm+P+dtn1fAmX4N5vza9wqo3jWoQjLyHQAGQMnIj4HyVL4sjrWDPB3PAHj9TJ/LbP0B+CKA\nJwF8GSTO9Tfi9dUAnhZ/a2b7PGeo7beCvFqPA/gGgPlzqf2inX8E4AOB/+da/5/r7f8XMff9BMCC\nOdj/53r752z/B3AzgM0go+8zc7D9c3btF+2cy30/tH0AE5SfPgLgYQBXzaVrEK59p8f9Z+IHQ4QI\nESJEiBAhQoQIESJEiDMCVROjChEiRIgQIUKECBEiRIgQIaqBcKMbIkSIECFChAgRIkSIECHOKIQb\n3RAhQoQIESJEiBAhQoQIcUYh3OiGCBEiRIgQIUKECBEiRIgzCuFGN0SIECFChAgRIkSIECFCnFEw\nTnbwBu1tU5NkZsx/PomK895vXYAbV+0AADz//9h77zi7qnJ9/Fm7nD5nep8kk0kvJAQIJKDSi9K8\nAhbwK1YsXPHart37s3vVe23Xq1esCIooKAoiHQVCCQGSkN4mUzK9nn52+/3xrrX3npKQkDMzh8x6\nPp985mTvU/bae7X3fZ/3efuakMoFAACraw8BAJ795zLM//RTh/9+RQVs66gu5UH7D+zl33V0OOr2\nj0P28tMBAKG/PnvMn2UaPRLHNOGcuRoAoO3ugNXPy9ApKv09zP0ohvar8TgAoPvaFQCATA3D3L8n\n6OSzW933aXOaAABdl82BmqVjFb88Qj84ChRD+2cSsv0z036m05zmGPlJz3907w4AwPZsIxr0IQBA\nmZoGAESUHD792Q8AAEpu95Xa5PMqU1U4pnlU1zFjz1+sAZPM/2p5OSJ/oXmrJpREbzYGANjVXwMA\nSLfGsfCjT0/43JG+83CQ/V+2v1Df9Urar8bj6Pp/KwEAyTn08YVf3gw7nT7i5/IXnwYAaH0TXX7V\nMxoqf7MJwOHnlMkw0+2faRRr+8Vex5hTCa2P74UGhwEA1sBgoX6mqNo/et8CAEBtJIltnfUAANti\nsPO0FrTM6wUABFUTOYv2vZfUkZ3w8PXr4Gzadsy/OSPtZ2zyda+slF78KQrLppjigY1zwAw6bC+g\nssqabmHuNVsnfN79ej1w1HNAMTz/3T8h+yfYQ8+U2YDDQ6rMov8DgKN6n2HcnMkuzAEAln2+G2ZH\n5zH/9uHaLyO6EhISEhISEhISEhISEicUjhjRfVkcxuNunXsKAOCs7z0DALij8n/wQi4KALiqYiOG\nLXpdp5FH69Z3PIY3rr8YANDzf/MBAPHf+jz8RxnNLRYMvjsJAGj467F/1h/R7fgYtbv5s2UAj+gy\nhe65YxfgQqcA2pwmdP2Inm840AMAUE0N6rl0T04qA2r1UQDA/kwfAKC7OwJVpQZ1XEZR4JqfhhG4\n/7lpvXYJiaPCJKyKI3lc9353HS4MPw8AsHAIc/i8p4P6/MbsXCTeQmOi5HbfB/m86pim95ti4BdT\n/fPDeLTN808FAJR/+QD+o/EvAIA9RhX6TGJ8fKapDQCwbC2wUrkJALDkM1sAgCJgvu98uYi5hEQx\nIHRPEPOcfQAAG7RWb795CYIh6rfp0ZD7XkWjsawFLBhZeh0vpSiPeaWDPo3GT9VPj4/lJFEE4Iwc\nrS8Bp52YjIqI+J2g+OvKWwAAf0wsxvsa/wEAmKMNw3ZoXFh8fESYie35OgDAVTFaB//v2guxYNN0\nX/FRgo0LGvrWqfwla9FxDu3h1752JwDgwGgYi8por9ueYMjW0r7hqqWbAQDbRurRdhfte9XHqU80\n/bENZnsHfb2RP+JvFhPU8nIcuOKnAIA7ktSWC8NdUPj1p20LOn8dYrSnCbMAbksQu+sd8X4AwIpd\nH0LTN449ons4HJ+h67vZu/9vLQDgI695EDeU/gQAkHNocO8ydDyTJhrDvYdWIh4knuoF1UTnS9md\n+FnLXQCA8m+HAQCbvmbhA1vfDgCovmLXcV3mdMBvoJomPUCtqREAYHZ0ehs1axKj3bbcjmxns+7h\n5kqitFi7D7nHJv18EWH3jXNwaiU9r229NHk5DsPOAXq9t7saKl/gFYX+mqYKRaG+xBj9tT42ANw/\nrZcuIfGKwdaeBABo+5SDqpIUAOBnS28FANyfHMJ1rRcAAE4vbcVdSaIpro0foM/kKnH+3N0AgE+0\nPQEA+MD+a7DvMXL6tfzffphd3fyHCsZMKhx860D/DetxxY20qXl9nNaB+0ZX4+MHrgYAGLYKk9O4\nfmGdCQCojSTwk0t/Tl9wKf35QccF6PwDtb/mRxukgSvxqkDbrxei6jpy4OzaTes/Ajaqbifnb/9K\nDRa3dXXy/aJ6cx5t76S9UqKTnEC1Lf2ISwP3xEEoCACw9uyHumQhAMDp6p3JK5oyCJr2iE3rQpdR\nhoRNnX4Pq0PO1gEA5Rqtk4ajIm3T/rjf6gIAhOYnpvWaXxH4umdcdBq6b6B9e028D018D7trsBoA\nkDc1PPH8MvrIHBP6INkHf3yabCa9IotomNa30tfTXn/gwhB6WokCvPwrbe7677czihHmsrm4ZbQK\nALA7S3T1vdm6Me8RDkCL85l1ZsHgPOZe6yC9qcDbHEldlpCQkJCQkJCQkJCQkDihcHwRXY6R69bh\nwOXkve8wkzhgiixjMstVOLiihML0/dUxKNzjcV6UQvtZR0WPRTb3IYs8GyEGPH/a7wEAi7/5wSOL\nVRUB/B4Wax+JreQWkudK7eh8+YjEOCqCs341duwmL9diHJrsE0UJqy6PmiB547qj5J3u6CtHpISS\nzPN5FbpO9yqoUXQ6r9pIp8njaVvUZ7rypVhUXk7fOTQ0fQ2QkHg5jEulOPD19XjfFQ8AAE4KdSDr\n0Lh/OjMPAPDk8AK0J8oAAF9u2opSLkIlvJgLQr1QuULDE5k5AIBTy9ugnEtzwsorD+Hxr6wHAETu\nonSQYxGomGpY556CU/+bqNkp83lsS5An9+HuJQAAVbFRG6E5oSqQwrIozWetWfL8dmVL8YOOC8Z8\nZ314FM3vJO5a3fuz2PDWVfRb23dPcWskJF45av68C2tvopSdXaCI7pzGARx6G9H4Ku9VoOU4aylA\na93QjUmUcnZTWqe5JR7ITet1S0wd1IXz0b+eolp9H69D+BAX5ttEa0KoMwn7pZ0zdn2FRu+FtIZV\nKLSnV+AgxGjPl3U0N3pbjpT7GXE+waPAVTHvXFFhEmHckQ+PopTvZfuTUaiCqchtmkjQQGge7WEb\nS0aQMGivO5wJ8fepyOZpz5DJ0V/GgLr5lK64/UtzsPgGiugWayRXINUYwnUlxFT4cq4WAKArJkoU\ninhHlBwSNrF2RWRfZxZ6DC5ga9HYSM83CnpdBTF0By/LuK/7LR0RhR6GDuq0OrORtumnloa73A1e\nmje0QvXougrn7xtQ0GtRZ7/yoqex5dOFuNLpQWCYc9D/P6Jh9P5yPbQs3Yv43sQENTm1rBTmCqLp\n9a+iTvDNj/8Mn/nWeyd+eZFy8wWYZiNh0gBeXk6D8+D+GmR4aqFtKlBVaoPGJ4RkIuQq8cUr6Zmb\nlgJnLqc8SENXogihRImO+NWrf4vHRoiaNGRE0Rik/irmOQUOegZoo3vj3rfi3Goy1g5kyNALKiZO\nKyEa84BFTrID6UocHCJHT1NkGJEbeb4KZXgUhZGrLqZ0lEXf2obeXAkAoC8bc6nJFSEy6G0wpE3a\n3HTbCvaMkpp8Q3QEADBqhKCxsaIDA7mI6xCNajmEfkL3NPW6qWyRhMTxwRoYxJARAQCsP2kPAODF\nvy/DovNaAQDaDTZiGhmxW3oaAADfWfEn/Ouf3wUAeMM55NwZzEfRN50XLjFlYJaNQcpswWkn70Xn\nPxeNOd/zmnJUvzQDFzZFSNXT/nfQpjndcFQvH1fJo1Qle0Hk6oaYgbRDxl8ZN45Hs0FUT+tVHyV8\nRm7PTZR60xzfh/ZRWt8jwTwMHugLcOPXtBTkDLJ/etSYq8CczlKbVdV20/gcfk90zUKSn5/X3Afj\nAsrX1x/iicvHUIlmOpErVWCCrmsgT3uZSCgPi5OH03YQSZ67IajLUOA6+vfkKVe3cc5AQa9LUpcl\nJCQkJCQkJCQkJCQkTigUJKL75mXPu6+DzPMyZLnFHmK2e3xp0KPhlirk2VThIM/fKxKVhSIpAETU\nPAC9EJc6LTDWkMrEPYvvAwC8/8b12NxPNCYnmMWqcmrjthGi+NWGE9g3QtGNK+uo9u654SxCbyIK\nFH46bZd+3AiF8y41oyNF1JxoTQrpJB1jigPLpGedsjld2VRQXUftbykjT87ugWpYEfL8FKH8joQE\nut5DkcmosglbBig6s6isDz05ouGEVaLf7B2ugpWiqda0FexLk6+6NVlB79MMRHmUZ9ggRkdbogLJ\nBPX/7UN1mFtCwnSd55FnV3tk5iUp97yXvK+LlYPI5al9K0q7XE/9KGd2mLaKhBnkrxUENWL8ZCya\n0yuDHk0tqlKkukxPozNL88e+ZBVWl1JE+4nXngEAUB5/YeoaJiFxHHjiUAsAoCFOCrKZlhz6bqE0\nhnQdwxfe+TsAwK5blgIAvhq5FHDGrnIvdDWiCcPTdckSUwgnmUZgAfUF22EYWkL7H/UUYrzE7ojP\n2LVNBTJNNL8P8P1dRMkjxIvHlqlpKDx6p3LGZ59ZgqxD60dcoTVjeDBWnBFdH0ZWe6wqEb0V0VrA\nE1Y1LNWtKmJaKjSV3hvQPRqyoDn7IRiPQdVE+3q6l3Me4ieLMJoLALlyhrQ9lnZsOQpst5AukOVM\nXhHFNRwVCu8L3SZFxpvjgwVltMiIroSEhISEhISEhISEhMQJhYJEdBeHutzXKnPc3FzByzfAEODW\ne7WacT05OndipnxeEGHZ22DI8pqRi0PdeBpzCnGpUwKmaWOSxL++5s8AgN0GRSpuqnkE94RJTOUn\nT56L3YwiuXqcPEI3rn0EjbUU0RT5zc/mwvjp0tsAAJ+svgJWH/k3irWepLp8MQAgGsqjgwvvDG+k\niM+/XXM3ftVKYjqj6RBsmx68qJRy+UlbcN8jVHJlk0N5i4tOP4j+xRTxKi9uHTKJAkDUXNUePspI\nJWMA4/PGDHk3E/N5XkmuDhmRg5MpQUyn6GxbinJsB4djUEcpb6cvEXM9tV3D5MnXNQsGF2EoDVL+\nkq5aQD95cVPlASQ5S6L3NPrb8MjUtu1oEFtCebN5W8PeYRq3g7mIez4gPNeKNzeWBzLoy1Hujoj8\nisgu4M3/exNz3c9XB5Oo1Wl+bL+QPP7zHi98eyQkCgGNR29CnNFx+UlbcI9CSZqBsIEdWWJ/qFdQ\nzchPLHgA7XMqAQCrw1Rew3YU7JnWq5aYMlSUwuIRu9ZfL4JOcgZIZ2gvV5Yrbt2VY4ZO/V9E8UKK\nAZ0zOnVm4Y1RYio8mCH20gCLuTaBytd0pS8wrZf8ShCI0R48a2mI6DTWR7Mhd11TmfdcxZ7XD9vH\n4rC5DeRFfhV3HjEdBc6qV0G5JQBmFEhwu01T6JkbjurqldRqSQyZpG2S4Lm6VXrCje4KobKolito\nRLcghu5cfdB9LYxcYOyDtnwEVGHgCvM2wGxkfcItAuL8dSUDuKWIDV0/1HgcZ4SInj1iU5uyjooP\nl5MA1aeu8JavtE0DJekY6OMTYYKH9S0oaOJPZ/DiBSi9tbilKUaX0aa+NHQI+3bQQr7kTuoXc64b\nwFCCNsCMOW7CvcEFqBqDw6gS+5YSAAAgAElEQVR6gdfR5Yz1T1/zN9xY/SEAQPn0NKFgYEEyRpwc\nV870CQcwTXMNNL+zYvRt6wAAZX/ZCgCwUyn6HDDGkBtTj5kfH3zXelT8chq9AcJDMZkwmt8A5RPe\n+PcpIZrg9n1xDQBAzTKcfzkZuAcuJ6U+s7vnyL/jOIDD78sMCTPYYWrf86NzMTRExptpqRMUIxXV\nArdjkc0EUF9PRlsiR/3EsBRUhyndYdSge5M2dDhctC0ayLubhny8eDZFp9W1AwBqA6N445wtAIDt\nyfoxxi5A1GWx6EW1nJueIgxcBY57rCdLu8CqUAqNYY+6WaHR/cnVFrfqpMTshtY8Fx9b+NCYYxVq\nEt++YAMA4KCZh8HH8o0VlKa03wihk9Eqt5+Lsbyj6gn86/U3AQDKfy09va9mJJdWQOdFk2sf6cLg\nGSSyWXftiwAAdtpKFM+sfvxgAVoXxZ5fYbZbR/cN0b24LdEMYKzdMB6xtuJNWBP7u4YKWscdh6E0\nQA7qylAKO/poDwNuvNo2cx0dtgNXZNHxGbpCjCqfV91jkSAZz4aluueLHUaJDYt3ZpPbPzaY2xcG\nzZgr1icc/rajIG3RvlYYxH7bsRCQ1GUJCQkJCQkJCQkJCQmJEwoFiejWqkkAZKXrDDC4MS7oCHlH\nGRvd5S8V7tDwR3vd9zmA59t49cBKplwZLcVXXmkLr5PlJxxH3Wbr0Mcl6AvPBgAkGxWUiv84xenZ\nMaLkM/lq8734+O/fDwBgnVRPK2GFYXHJdUW14AhPV4a6X9oOoHwjCW/ZJdSPXhcCCuzUmTa4kVw3\nIuk9M8d2AHsc7XzdKlz7eRIu++7FVE900Ts3geka/z5ftFIMGsOCyusMpy8bRdXvQgVuxVGAt4+p\nKkWYAcBxwFTRsemZ+2n9yWvOwC3f+S8AwCVPkhhLdjiAl4aIzj/8ZmID1P6gZ2wkd5LortY8FwBg\ntrYVrk3HgLUr9wHgc9Ywje+3nPwkzowSa2PYpr78QOlKPB0hMZpTaztwSTlF7V8I07FRM4Q3lFFE\nVNTgfXx0Me5L0ufroqNuOkC+qnhEKBpCFHHVmYWWII31ASPqCmsJZCwdeV5eLq5l0Rikz+1MUWTD\n8nm2Ixq9rgik3DJFNYEEDC5WEqsp0vqK0w3GwAIijcUsWnGS8WDBoDc/ugcnSUPwsTTU6mr0XrEQ\nAFD586e8zxRhqb15f+jFHJ0EFTekvTIyv0vQXJ13NAR4zVDFJ7jpUva4QGfaDuJjn70dAPDLX8+b\n+guXmDJEOlLQ/4dous5IN/pOo3mv//KTAQDNP2YnVMQpWkLlQt2SMlYQF5fQmpd1gF/feCUA4IFb\nbgYAvJgNIOYrMQoAFbuKKzVvDJbTXBRQKKKrq5abrlQfGkU7X6tNTuNSmIM03//6o7g6T80xbQVB\nLkxlcFFHRbVRFqYocdrQUV1C655W52O8FSHskINBPpcJ0THLUWDw9d9mihvRVvn8J8Sp/CjRshOO\nHQ8KYujmnLEmqZi+deYdEcas6iNp+A1iyzXwhFKzBZUVL33BD8f2Lbi2hbka0RjbTKKrGI6NEO/U\nfoVpv/Hv3hfX+M8gxOjxBEb9G/7inBI5GwE6M1F7x3b6D6d4AEAkSh03qFmuGl06RAMipmaRbaEc\nJf2B5wAAQ1YaRnQ6rnwa4DieoeanIZ+6AgAQ+/Yh17FRUpb2PpafONmLTSJbswKpRrpBzgsa7Gxh\nJ4ajAt9ojilirqgTipqr1dXo/jnlWwf1frzlK58EAJhnUftWLG938zgXXEsGX9ff5sPae2DCbwlk\nLzsdpZ8kA9c8u0DtOUbUhUhJ85TYQTxd3wyAaDrCwBUT+HA+jLzJJ3qHYdii8zk++ZuOijx//nnf\nXKpxJcc18Xa3zmzVMlrwvMrlM4d6nQzWHqMUy4Ok03AwX4WDw/SsIxo937ytoSZIOUajZshVpRYK\nyzaY+/wF3SlhhnBKCT3fCi3pKrRfOZ82TBtfBW7Qvd+jdIRnrvov9Fk0/j+6/xq0PUyGS2YOtfmX\nF/4M31hENP4xBusRUwSUsQbjq2Wt9F3zGL0JZ5yh7rsPVl8fbH3huC8qPiMXoLGesMmoqVBprIYU\nAwp3dkaQc502li+Xb16A8nUTFn0276j4xo5LAAB12DF9DSgg/M9XXUZG/953VKHqRXp2sXaaxUZb\nwii99emZuchpwPDSEvScQ/25vnox4nu4g3gxrfXtHw6eUJoD8TDtRdJ8zlaZjTka0XD/nmp2dThE\nPm6FmoQ9ztQPPb0bxeq6G1lK6TW1Go1Z0/Gu/c1lz2LzEFVYSeSp/ZrK3HxcTbXdfb8wbmForgEs\n/n521X24o3stfU8uiNIg3dORs5oBANE7i9PQhe3ZMqYzcY1OWCHX0LUmce8ICnNQMVHIUGdxWk0S\nEhISEhISEhISEhISEq8QBYnodlqlOBnkcQgxhtx48RkA4Fb+ZJa1zuBGdHPco1+iWNBf5RVUI9zL\n3md7qtN5n/fHT9kTEW/xPp3ZsPh7q7b44jdFSl1OzKe/fVYc1jBROkauo4jGS5kumJy6YVkKclny\n2tgmtfmpwRYcfAN1xYUP0PfsMoKwQsXptX9F8I2J1q+RAvXrL9kIgKidIvrnFzJyqYm+KEjyzXRP\n3//lP7pe0K/9+aopvPBJMIm4lBtR9kVi2j93JgDgK9ffik/+/W0AgNhBFed8gNqd4HVWR/IhXFRF\nLAChznjmQw/g6p9+AgAw9/ub3e/sfhfVri25vAtrykgM6fdffh3mfXH6BVtawhMF4gxHRYVKTI4+\nhyKXmmIhEvSil4KeGFG9iL04VsnosxYUhLjHt1YfwQsOifGJNm/AzKlSCsr4nMBOAMCBnFfxcF6g\nHy8odK0VAYpY9GZLEOdUpKQVRMqka6+OUJR32BwrXgVQ2odQoDUcFc+lqTapUPjfiKbCNmoKsPoU\norbfOrrCPfatljvxtqH3AAA+tvQJAETxuv0ghXSufueHAXD1cTHOfDRdpvF0BtOENofugdneUbQR\nzskwQawPQOtXaE5seIIiP4H7n4NaTf3KWNKI4XU0VkZeQ5HvJV9LwtpBKQLjqx7MJJ7dsBQ3XUVi\nVPscEpYatUITIlZ+2I6nQCqiYFElh8yOsim+2qmFEFtUa2vQdgU9Sy0NpGt4RNOh6LWWdZC58nQA\nQPjuZ4/6+9U4za/5UxZCfez5gl13odG3Frho9UsAgCf3rkHjf5IwWfhtREPdum/+jF3bVEB1I3b0\nnBNWCDUqsc9u61wHoAMA0MUZj1El7jLa+i3a/1ijo9N5yceExFway41cYFGDhTBXWI8w043KChaX\n6hOSUhXbpSyL/b+i2NC5wrLNmT+nhtpxF6NKFNFAHgH+W8OL6D4VK9lRSyhuGqbBbbmQ4tXVLVGz\nrl0zwCmbqmq7UV5BbE1aQQCFm9NlRFdCQkJCQkJCQkJCQkLihEJBIrrbs424NELe60OWCp1n6ZYq\nPBcTDo6kn245nncjyCM6VUoAacc4/IeKCeOirI9lyH+wnuegdpi2G6lVmTMmkiuOidzklOM9kn4u\nWqS39hbQtzE1MKvpWXUbpW7EZ/AyikQ/2deCMI9oKcyXm8BxcKQcZQtIal6bT/lru/MdMKOvniiF\nC0U9bFkdAFAfbcDH6+4GAOzJkEd3d6oGSyKUc3FgG4kxLULbmIiHcQF59y74HEV+9uVq3ehnfMXA\nFDTkCBiXO+jPD2anrkD9jyi38qaqXwAAPnzf9Tj9VIq+/McV9+DyJ28EAMypoWf+P4tuxx0jVEe5\nL0/5L4aj4ufv/SEAoPb9GRziea0b0t0AgM6cV3TqrIu2ouOLBW7jUSCiUJ8etiKoKyMPdF++BPeO\nkMiIyMHdPVSDwRHyXu5Rq9EcJuGtrSP0rCmXhepsl2o0Zp7vn4ORJEU8LEdBmkdB13Ohqw3wooTT\njeRKElOpVMj7njKDqONe6mF9CFUBOt7P6+VWBZNuX02ZQVQFyZM/4EvCT/CySqIMUZmexnKdvucf\nmXr3fauCnQCA24opouuPuAaDuHUvFTn+fYLE1nakGlAdoOj1fqMKP15D9dFDjObM7blGtOZpznj4\nNz8HAJzylQ+i+secpeCbR0TkUlm1FF+7+9cAgA9+7iOI/7bIcxx9pdLGi1Htu20N3rCEtBlil9G5\nni/FcWE5HfvnaALKKNVpjnLRl1zOF9MoIt2KipeA4X+huUr1iU35hacmK51RphP7odOgeW3YiiC+\ndyqvdOog1qpDr6XotJ4CQv08L7fbhBWg5xUcov4/vCgIoUWUuJFYQOF+G+X/bAUAmF3dk/5O/hTK\n2+58XQjznizeuqulOxnafkH7mty1Dnb/L0Wvl19Lkc05qyyoS6gt1q5X6UP3Qffp0QBAheax1PZv\nmoMWHtHdbpDEakjJQ+X73s35+HRe6itCpp7Xt+URy4BqIqbSvGRAwVCW1m0RyWXMcV87DoMlxFh9\ndoDQrVF4ScFfDJzlijmFNcPNA07NL26bSEt5pYR0Xx3dGp32R48PLUJfhvYFl9eTHktvPu5GdINc\nq2M0H0IhI7oFMXRLFG+je80zN+CqxVQf7Ou11JDdRsrt9H5VZnFDQsxBgC8E9SpNWCsf+iCePv8H\nhbi8qcc4g+aB0ZUAgHPC1P48vM5pOcytGel93nspOr/ObFfUyh7y6kmOEb4qIjBOvXhmtAVWNU1g\nFy0kauPjnS3uQNc0Ezm+ac9mec1gQ0V9NdGdB86iTe1tnevgBGeYpn0kMRj/2zRvGE1Goev7wHqs\ne88LAIBTY8/jvn7qH5v2cjVNm6EiSBudBStpI8/0ANRGMih2fKweD15JSsW3DtMiaTiqa2hdN/85\n3I8pWCD8Ajf+ezDufqjLFqH+l138fwfx6DPUvq1b6a+yzEHrCAkU/b9ffhyN3TQBtr+Njl128MMI\nxmmhqIrTotgUHMKmbDMAojOPcJGWtEWbpyo96dKdRowQ1Orw8bb2mPHGGBmdX+05B30JGquX1G1H\nV576v1gI86YK8HEd1fM4lKPzwrjTFcs1igWFPZENQuGOwqXBQ/je4Hn0o41T3aqXR6qe2iWM1/3J\nSpQr1JaEHcLKKG1ktqS82udJ/txqg6Puxmdvhqidhq26Ss2V3LhtCgyiitPd+swSV5W2lNOg1PJy\nWENDU9TCI8MV2THpWpRg0HX2qNVVuPyld9B5/syvmvsCDmbIUHtjdRIWd4T9JkEP86RgB1oNOn9n\nksbx81/4Md70tgsBACNfmusKuAjVzS/ffQs+tZ9SFkbmK1Mx+l85fGrsgFCa91IalNXLAABD36D7\n1/JVG7s20GsW5JvExip896y3AgDKb98EGNSn2t5LFOfK/V6qgqv4XgTQ0/YYQTkASNjhsUbvONVR\nABOc33X6CPR0ca7146GWl2P0vMUAgMHlKkJ9dN0h0upBw31dMOpozsvUBhHuobG+/2peT/3NP8bq\nZym1Jf5r6sn9q1X0nkaUXmbPR2CY0zz5o86cnIaQPbVSBpR4bApbeHz4wEfuxl0/407NqpNx/4Xf\nBwDcdPMNAIC2yxQs+vX0r19ThRyn7Ir9fdBHXfXXxxXpKGvCrTC4KSLWlGKGUkNzvTA+FVtBjK9f\nJcz0xrfiBbfy/FhAtTxHgKida6rIGdT++kraB9+7bwXmVNK+vzKUwnCO+kftnJlZ844Wap7uAeDN\naUkziDURuu7n2+YAreQIPPnN9wAA/phe667/guY8kIuikJKbxeMKlZCQkJCQkJCQkJCQkJAoAAoS\n0X1H/ADAxVFW1HfhrJLdAOB6rnU4vtqyk6OKe38P8ohY059U1FxUrCnXR8bAuLo4tqO43q0J0dzD\nHFN8YV47nZ5wvtgQjXtR/WwVeWrrguSdCmgWwjqPfjAHoSi1Jxuk7jeaDiHDPVpD66jdzaE0WKhI\nvHv+mo2KCiVM7bNTFH0aH8Xt/RDRr+a/lSJ+Nzd/G9/pPRcAcPuhtTjYS5HMhnrPO7drkKJbr6sn\n6tIbdm5GNS9PsSoQwn/0nQEAbr257lypG9H8cs1GPDzvTYVqrQd/5JZTD5nC3PaqK5YAAN75p7/j\nc3++FgCgJRlYOfe0B6hfz7s3j6E9JEYSuboHIZ0i0Q0/JC/30CINWz/6MwDAA2lekseOuGU4/NFr\nP3tkX5bu2YJYP5476dSCNftlwe/FVk6zqgkk0FhKfX3QjOLpnmYA5L0FgKHeEqgj1JaOUClSBs2V\ng7xOrm0zjOZ4dJd/JrG3DFYFjZmUHUSyi7M7TuKlyWprYPX0Tl0bjwB73Kph2Qp0RvfkE7e8G5+5\n9g4AwLIIRfn3Zatd4a22TAX6FGqLoOsPmlFkLHrupRrNDU+MLHKFeVqCPS7Nv4TXkc6vng/1sWny\nbo9jNgiRHQE/db/3J1GcU0vj/iNVlGaQc4CtQWJnXLb79di2nyK5n19PHu0yJY/lgbH0zF+NNuOW\nlr8CAGK/Cbml6rzSdXl8pvlvAID3LL3++Nt4vBDzg6651GT/vCjSWYbX1qP3VOrD1gGe4rThGfd9\n4rPm/laU7W+lY76f4QxB9Hz4TNT+kER9iqmGcHDEcsuHiflLUBDHw19eY8SkuUREtEqUDFSjSCO6\nfDy4kfllcaRrqS3Nf+xzRcIEHReJFLq+RffkrKZd2PaflKYR7KPPfHtwAUb7ac9Ukab2RzsVGDE+\n1kuB9AIac/EqLlbUEwPL0+eZzdDxzqVT1NjjgG/esE+nVJP4tgAutv8NALAsTfNX04NRqCMUvSqe\nnvzKURaitoiScLZPgFVPeH36maFmAMBpkf3QOU31kOGlJBUrykupDwr2pcIcl4lVrWqIBqivZk3N\nPc+XLU90yQdVcZDOcTHSMkrn6t5UB/CIbkUgjYEsjY/VVcT4ay10owoEX/B+TIqGEOg0MjoqKcsV\nLZzO7Ld/hN2TNLzSpIXAcRm6ojaayja6Ru2/N/0dtSp19CFb0HA9urINj9IgoINqzQJEYwaAvtXe\npRmOhf73E2Wp6v+mX131eKEw26VuvxzD3q03PMmAeDVgc28DnBYatE8PEvUoqHkbHsNWvDpifFMf\n1E2U8Bze0RRNitt666AdmuG8G59xyzROwzPyroHrh3ER5ZiOfHgUf1v9LQDAja3/AgD40P5rsLub\nDL2SaBanzCXlXDGYR3MhLKkgo6U9QxP9c4EWrAzT+/400uJugB7tI+MyoHhFygGg56Kpy1lkmgZV\nFCrv6HQ3ODs+QPm0X7n5OpjL6Vrsehtrmum6X6yla0q8BrhuxT8BAP/oWYT0j2ij33El9YuzluzG\nr0ZrxvxmiZJFnE+OCmzEFaHqzmkxYGjQaaOQdXQ8XrOu0M0+LNQYLTp/HSYF2Jiaw2V1W/m1aFhR\nQUbL3DAtWg84S9EboY3s/MpBvL6GFDifGl4AgChQZ5XR7C/G/8+TUTTEaSH4Z3Ip9BHqf8+nmwEA\nxqIGKDNk6JoRusYs38g3l3g54vPuHcHSd5GB+0yaNrq2wzBiEvUqquZdB5hbO1rNYpQrcK8L0334\nc+fJePwJ2hxufMt/415OXdZ5PubQ4iCqHpua9k3Ay6QujFy3Dov/lVTD7593B5I29dXn82SUvi4E\nBBn1iZ/YCt55Khlo7ymlYwcMb74v4X/Pi+zHhizNBbtyDe5vteXISZazNZfmfcPJT+CR6dThHF+z\nlymusenkLCgRXif6NcsBAEOLA9A4Dbf6TzsR+wON23230fhp++KZmPtluidCkRmWNcZQTl1Fjr61\nN5G67sOti4Efwv3M+LzfmYKWMhEYR79UYU+6wRUbZb/BK8ZUiBmw9OKoOqGUlIDV0frlRIJwNLre\n7jOIjly230B8H18TTQtn8yoRv9pO/WDRZwzMLadn/vryrXh8KT33mhdpN3TX/gtRx3/r0LvJ0XXz\n2p/hxs3kPC37QxzdjXQvPriY1pEf43XIbabxYS9JIrKoCIMBfN6440OXQH+R0riaDpbB7DwEADDP\npAoC0QOJEyI3V8CwaF4vU+iZ9DEvsULPeOOgfZQbtY2eXsF9o6v52eIYz5NhTTWlUbQmKgEAlcEU\nnu2nNLQrB+aiMkRjoT0xUTXddpgXxJokM0zs6SJdDHv30qg4e/0ebLfodQNfOw/qsQkO12IAs4Es\nd2yUaJ4DuFKhOUEd1FC5hfZ1jSp3CNpeqodIaxrMRFBIl4ekLktISEhISEhISEhISEicUDiuiG77\nZeTlCzIdaa4QfGpQRRt3xArCjgovkhliDIKMlOCWvKrY6OP1o5YFyDPddHY7DhiCrhXB0Cr6tqrj\nueBpQnUgOeHYmPq5R6gPLCK/WZ+ghRqPu3XFGOdAFFs53YZ/2e6+7uXKiSFeW8y0o9CEAh28JHW/\n6tzJleQlszdQZC/8Ke/7Zhy2BcdHj8u9fi0AoGctRa7nnt2GU8tJLOZAqhJn/u1jAAAWoYGwdE43\nGiro+cUCOWRN+tyb66ie7OMjSzCUp4hXTYj6TmeuDAez5DEcNULY3k8R1YDGVWlDGYzkKQp2xc5/\nwciigrfapSMqZaVjDu/9FEVdzmqmFAXlZBvDefLObXu+GS8M8EheiJ55ReMw7txHSsTW5lK878t/\nBwC3TmqfGcdLGYr+LguTt/vRkWVoS3MF0mwYwxleZzLN6VB9IYR7uHqhBsy/f0fh2v0yyK2lm/2J\nahIVuTu5DJfH6PfvTqzAheXbAADVGj3zbbF6ZDk1vyXWj+YA1d/tj1JEPGdrWBKkdot6uo9ULEXe\n8iKesRUUHb6ghKLBdy07D5VPTGEjj4BcuZijqB/PCw1iyCLvvbNpGwYtimQKERKdWa6a4tzwgPs5\nb67zlqEmrjpdFU4i9DtSKi6/NgKNCQVH6lPZqumLdiklJdj3ORJWQ3MKjVww5O1NRLk9KfRDPJEi\npkXLQ+/G+Ut2AQC+3/goAGC3YbhtfF/T40jY1Je/1EcRz9ZMpVtbuDfNVcdtBZVhuqc5U0NthPrS\n/AhFz0+OtqFR86jb/6y7sNDNfnmIMIRjuWkMXedWugt/2T7aE9T86Cn3vf5Y5+nNrQCAvjpPSGiy\nyOyBb6zHZ//lTgBwacFPPjqNqQrHACukIsLHsGAs6MwcQ3N03wuhvqq4jJ0sr6e7M9eAsntoHpmp\npV7UMbbm10EdpHXJ0VXkqmitCo7SlSUbNER0OpZaHUOVTgrglkntH15bj9xvqf2frXsHQgN0D8T6\nqfgeuZmnz3SbpcjnvXkhxinLHXliNDSWjmB3hCJmoYCF0qAXPSo2BLYehMXTz1hAh3MWrYXqC7R+\nOkax19Q4Nuzj7LXGBbwSgeVFdIODHqfRX19W9H+x5gOTK20XA8p0WqOEGNWaWBse3Ekst6rKBBqj\nRDn2j3Whqqyrlvta9SVliEokg1xNPjnPhpKmsRBTs+5nSoQ8+epTgOdeKnzjjhNq1kFoXKqG7TBU\ncBW52EEFeHbrmPNhNe/Oj2LuHByOyYiuhISEhISEhISEhISEhMThcFwRXaNkYt5Jv5VxrWfx1wKg\ncge8DgZrkqK6Jdy7Yzhk+VeGUu5nVKaAlRYfH92FX6wIwFdryGMhohxRxtw6uhYY4Iz1L+jMdqOc\nBs9B80d0c2sXueUlXg0QpdDcHCRbcSO6DjCpd3tjH4mV2BFqfzGJ7SsnL8fO91PUYf2qPTgwStG1\nedyL7DgMG3mORk0k4UZyQxFRO9jBQIoiEUPpMJZVkwiPECu5pvJZJGxqsYh2JayQLzKg4efzHqTv\n4qPqR8MLkOMCCADQOq+i4O1WK8hjznR9TP+OPEttebqTvJh2xEbzn6jNDXEHvW8hj2dzFd0nGwxD\nv6PodKzTwG1dFwMA/r6Vxof60n7YCYrebYQoSZOD8OqGceT+wPQArGnMV8nHRc0/4pcMmV5+5I50\nPRpKvdxhAVFqJq5l0ceFZ0TJHYU5bl6f6AcKHAyk6XstR8HSSsrHPWSSnzNdx1A5BW07GohuJ9pX\nombRbk1krHTlqf/UBBKYF6BaI1vSc9x2nxKjiH5HpsGdC3osimzOjw5gy6Z+9zvLeZ1Rg/fDTP30\nybZ8Z+sDWBEgYam0nXcFE9N8/HZbcZwdpRy8016z3xUmuS9N/WNXtt4tmxBRcu64Xx2hetNnx3Zi\nIRfmaNKOrkzKkJV219EqNYpvR6ZxxhyXs7z3v9dBLFdLv773yCJpiurm8+7+FQkIveuj9+Avk/Tm\n9s8TM+g7V/8KZSo9/+sfey8AYPH/bnDLPMF2oIRCr7g5hUQ+rrn5hi8HUV5IZbYb5RfQmQks4HPh\nizPEbqqhNUUdTIKlaE5XACilNH5DnFCQLVeRK6XxHztk4vu3vBEAEORTQv9JDtQszQmBEcCI842d\nL/DDteoQ20T9+JtPXouYKEOpA8o/aC65c/Nr6TpyQJRXH8nm4hg4wDcd5x5/swsFoWGz+z2VmPsA\n6ZWMVmmoeJzYa9l1tH5anx5A6FJi9BRj3uWxQjlIfXn+2TQp7Mx740F/5EX3tRBrHPPZSWyDYoLW\n2IAKjcTWRD3cOYEBYJjm/Neu2Icdo5RPG+LaNLbDvPKazMvXV3xRXrE/EDV4WUMWdh/dxxAz3PNi\nT5itDqOwck2FgZYlTabxKFVorg4NTeSn6Mxy74WY522jsDHY4zJ0ldzEFlkAQsJAFUrDDLAcZ8x7\n/Mg7DhK80zRx0Z+Do+UY5EqcczVAOVQcC9nLQSkpcV8P2/RQI77bpMBxaRqiQwQYQ4q/V4zzhOMJ\nMe2/WsXih/npSeq0FgU4zRW25S5aIdW7VvH0VeaMoSwDROsQR8ZodhxlHdupgrKKNmL9XzWh7KH+\nufHxpe619umcjhexwaLU1janAkjySW8p0VnbU+VYUEGUw7nRQVxf+SQAYJgbNXcOrsVDe4n6h4Nk\nROojDJzFieCQg5st+i3BCrECDNxegKMALRt4reVrCtV6gPHNoxMNQ8gGqvFFqPveholv5s8/oGto\nvmss/VABUMk6+MU6E1j0QY4AACAASURBVCfokhJo9VyOJMhrlAZ0QKfpydEUOApX2OR9wWEMjs7V\nWwMqtM37XmkzjxnZMvpdvyq0UMN9onM+riinmsltBm3eTVtxBToylg6LO7qEAJPObLQZtKm0uSMj\na2kYTdH5jlw5qoJEHRzl1MZM48zNA8JPJ1Q1K7QkHkl5qqeruVE7bJGhbjgquk2ivwsKM+AJkOjM\nQkjj9fP4Z64rfxpbQAJE96ZDbsH5rNDxKJv6DaEQRnrfpz6K0fnU6HSjhWgTOWWEw2pVvBPro7T5\nWa6PYJivZfs5dXNJqAtlXEF9Z64BHXlyVvQYtDmPKHncxb2DCd4nMpaONKcz50wNvUnqXyMjND9g\nREe0nb4/OOigqr3wjlDXkAS8XBmmuJtx83yiD89Z2Y3gRa0A+Nou6uhqNA+OqXPry7mpvJmEJS/5\n/A789CP/DgCo+z7NLVpLMy59E53/6+Aa9GRpXV387ue8r/IZBc7R2ZZTjkylApVP0odTWxYQ1GWd\nWS6NcYSLrgWYhe7XcEfRi5N/fqphRela8nPiULN8z9KfRvAgOTCF8RtVFIDPz04yhbkHKc1G4efh\nOIDF74Vtu/M6OGXXMU1AGEOir4RDbn16xzThjPDUrQBfHxwHTpavM45NgmgAcHOhWn/86FtPjq5/\nv/RufKP8DQCAd572T9y++BwAgBmmZ35Dw1bcc/H5AIDQPc9O/4UWGA1P0nMNXk/Pj5w49PyUaMR1\nais+Cq9w+gT4nrFwFVQLi+SaJpSqmwF4AZs+M475y0mAMablkMzTuhHh1SVy1uRmlsbnB9thLjU5\nnaP+XV6awsh+2h+eF92N3zqnj/lsPq4WpaGrGA4C48UKAbcqQ0mrl2IwYNNT1hTbff4hXl3Dyago\nJCR1WUJCQkJCQkJCQkJCQuKEwnFFdH3sWhi+OG2ITbSf/Va+6r7P+0xUGev9HEmF8ccRKtlycs1W\nhPqKQ2p/UvgijsN3eGVSEjziU8JMGKJs0MtQM0SZiTo1jX4u0PX8Zd/DWz94ZkEveSohaqUJj52m\nWm67VMUeI0IAcMl1fj6+hbzFxVBPruscirKltjsQLJv5p7e7HrpLG4iivjrU5pa/yUNFlJFX6vYh\nikh1J7wo/+bt87CJC6nE/0KuejubxXxsPq5rnRLBEs4eYHnDjdI4mgrlZBLRcURuAWNgeU+BjvHy\nE+55ACzHb6ACOLxmNuPee2ZYbsTWEdEgx3HHFTMssHHREQZQgVIALKhjOiGouwq/64eyZejgdU6z\nO8uwZxFFp7ckSWDr4Eg5Mhny1O5J1mBRmCKBIwZ5bAOKiedTzQDg1gtO5oPIJ+kz3ZkSnFZGNNdH\nh4nupqRn0EfJx6rhK4Xyh/ZTAAAx7EcJj+4fyJEoSVNg0KUkqXBcanOeLyC1+igSPFItIr8XRbwQ\n3Q/bzscPFlBt3o05Kk0VCE19RJstIrphrpSh/knyPusDKTf6lOKiW0+xpXgKFNF2EimkzqDPdbyF\n3rf42xnkqylSrWYtwB5bXkLJme74cIK+RVWMBQDVOh2v5nOnGTZh8GlleKEK+7RlBWu3wMvRKNvf\nR+2b+40KjKnsKFgXR0nDvOabn8Ttn/oOAODfvk/r3PZPVeP6GIl5ferht2DxBw8f6VKi0UlLvs0E\nRhZ6r4UYVUTJT2AxAR51mdY/j8YMUFrA6AJ6XTPhk9MELhoTLi+HM5fmtHxtFCYvlabwmpGOAih5\nLjbpAGqaxqZZxhlBugJm8veqzO33tirWFMVliTDLe594reZtWAHFfS0+awe8OVC8t5gQ7ab78MTw\nQrTMIzr/Y72LkJ1D42LFAqqJujXR6FK/Xx28xSMj3E4RW1Wk4dk6dhs0PpMXLEfkTyTi1xQbnvBZ\n7WVYEDON4QUaBk2a98U+tiNfgfNrSIDwQKbK3cs6vhQ9f5re5LV06buEKGFVLIURPk42ZucizBlP\nYs3NVCkomfAtM49AwoY+Tmw3qJhQ+LHA3i6IlbuVtzWi5N10JmEfqanC7m9kRFdCQkJCQkJCQkJC\nQkLihMJxRXT9jok0F5iwAeiuoJIXl/OEqXyS2tzwNxy4EU+BeCSLnQnK9UDNVsQ6is/T488hUaso\nH++p1Xe6ZZF4Kh90UBsBysvNivI6k0R3hShVieL4Si5FkHjLOjr++6enpC2FRHCEJ9lzSfGgakHn\nIVG/Z1u8DqoWUnny7oS373bPMx75m6m85IbfUI7twGVLUfVPXv7oS/0IrVgAAHgg9joAwMPJvBuR\nYYYJp4PyNUSUoRq73O+s9v+AyOfO5dwoiLqYvtvafZicU5EL7dhuvhILBMDmNrzCVh4ejoio0n/E\nQbAkRbcYj8JCVbwolW0DeR6JUvl526HjAEV/2bjnqTD3/OF4G44mvss75r53mt11Jk+6b+c5uAO5\nCF7K02sl74kxJUzyUmbzOqwMzRU5U8OmRDMAYM8g5XBVRdLI23ReeLSHUmGogxQ63jdYhfUV+wEA\n2wYosuIUNoXlmCB++5BBOYQ3le/EFx6n64phP2KM2p3mwlK7M3XuZw1HRSlPPhdaBSVqBmmem5j1\naRMI7H9mLhYsoej3izwtL6BP/Zxgb6NxWxlfhcQ8+v38shDsAI9EiS7pW0WZDWgZzmjppBt16Nww\nLK4VpRgAvy1w/KuvGF7+vsy8Y446PgrMINKd9QSgt/a84nYeDuryxQCA4VUViLUTY0UdyYJ10m8t\nrqMyWc5OB0455R2zaARWHb02S/gzrdBhcJHBXBlzI9FiKcivSGNZgHKPd/+MWFy/O+8n6OTCa8xm\n2HvrGgDADaupptaacCt+1fMaAEBNKIGH7igOxtPqs/ZggEf6Q8wToxGYLLJrwSsvJBgdKTuIFae0\nAiBZvpmENTQEDJHylAZA57nbYn5npXGghBgLTigIluN5dlGKT7KML7JvWWA5ztbwr+vKuEnctuHk\n+edsB0yIrfF1ws3PBe0PRN5nMSHRRAM8rBoI8tzTg4PliJRxYS++gT4l3oaXSlbMzEVOAZQR2vdY\njsi7NKCLHPQWFVxlAMtjXe5nRP8fyAphR690WjEhNddGN9dTCPD97ZtLN+Gtm98NADizvtWNzoq8\nWwWOy2hkYyK79L6gaiHHNTwE8bU0kIGeoP/c2XMKKkN0T9tyxDJM1xcfgwEA9JTp2n/uMWYh43Bd\nh25vnXomTfSXiJpz1/8yhZhf+mgRiVEZsYk323C8Wod+iCPKJMdUBmTtsQvA2fV70Z/zFChDQ8VA\nZh0LvwF24MeeoZHlu5UyHw3D4MdCzHJfR4QoFbzawjbfyRgADN/davjXvQCAxO8L3IgpQCBB7RIK\nerpquUav7TCX0mHy9oX1ySluY0RMZgAWX9yrHjmI/vNIVdkMN6HufjJ61QHeZ3N5OHwBtnp6oURo\nKhdiNrAsqLVEQDPnVEHrJHq2WOjtRAL5B+n7P9p8HwDggeGT3Ou47/E1mPsAdxr00USQLw8hV86H\nL2MupauQYGFOPQsFPDERv8K4+GvZ3gytKN5x0/I+IzYyim+cTyJaMEZ4zC9GJn7ef168nmYfmKDm\n/76dNuUZQ8et2noAgJplGDH5pn2A3BrZTAAsxWtqqhb6sjSvZfNkyA6yiEtNyvAay4bhWbKjQxF3\nI9DXw0WdRg7nEph62CVcVZxv5INMR7Rr4loQ4ap0WVt3r3+u7tXRFYtbiBmo0mijakxiwZft8mhw\nCWv61YXZhs2IT6K/JqCWlYLF+FoV0OFwA6A2wMWYNG8ed3TV+16fc8w9Zk4+5zl83LAs3VN7ZBR2\nMule51SY/flaalPv5Tl0cUeNHgsgEKC1ThnmYos/NxDSuFHDHGRN2sjnTbrWTC6AfI7uhZVVPWVC\n7siNvRjB2b+7AQBQWUPP/zN/eD96b6DvWbWyFfEAvb6zjWqQ/qTrbOi99J21G200/ZVTm79RyDtw\n7PjevD/jniQJC4qNrM4sV3hqUvj2SzFeJzNtB/CJOVRv/Gs4eYqu9pVBUNJdAbBsFii8n2UshorT\n8DkS0nXUv6+qeA7vf+l6AMClJ23F/iQ5Rdv+2AIA+GlZCyr6im9/+0phHmyfcExkMSUWmKjnx2p1\nqkeed1SU8UV8fw85f1vQMeXX+UpgV+WR457NkgCN1eezTRjZT0656nlbsQNcjM2lMI9dG4UzW/MZ\nxGJPHNRpUJm2inwZfe6FPfNw+WpKbctwL6kZKU5D12ETq+rozEKfNXGFenSAHKkXVW1HPydil3BH\nHyvwcJDUZQkJCQkJCQkJCQkJCYkTCscV0bUbPKloYYDrDFB4JEaUFNLB3GP+aK/fylbHJWiXqhk8\nM9rsXWimSMvqcOw46zcAqL6hPyoLACHGXDojgDGvAbpfrm6PCJI5Xi3dISuNPy54CABwcZF5dyeD\nmhMeq0nqpDEHihCj4qfjehbb9pLIzOJJRE1mDLzPmp2HUPabQ+5hm5cdytVRxMPWGGxeK8pR58Pm\nQSkR0M/HFKicux4YtZFbSCJFKhdTirXX4OB2igJ+7EXy/Kp5uN+jOkD/SRS9iHRz2kzSRnw3Sfbb\nm3cUsNEe7G4S0FDKSuHEqa1OSAczuSBIgKYPZllwxGvTBjO4sA4X0GGG5Ua1HE3xhEN8z1d8xo5S\nFJzlTU+YJKB53+lGjuHSuO2wBi0wfYJUI7wS1PVNzwMAWrOVuOtFEmPSYw5uqqASKE0Bitw/m5iP\nDZ0kUPTayr1uKZrnKsmjvz9TjasrNgLwatM+XL4cd4Oi+s6hCE4KkZecqXTvc9UzFwHQYxMZGPGD\nE4/V6yQ2YjiqG9HKOrpbVshfb7RSo+ikKFnkR+k+b50RYj0loZkmdHqwhkeA4ZFj/lxx+uQ9qI9S\n/17w6LF9biL5/OjhF+Npup/+ZuCVGynnZUrKx31upu+leR4JDD6S7hhTNkhAnYR2csQoL4DnM80A\nAOOCU6E/VPjyURJTDL5UffDpt0MZpfVx2AjjQD9FdK94N9FE9iWrcLCDau5GJ37LqxZdFqehMgti\nyWcxbx9frYmScTp0Pq+ztmlk7LwCaAELQ3naq4nymTuyDQj10lieH+zFozZFKhWNszTHiVGNx1ih\nKvqbMIJQWjhjZ8C7Jx1pSheyQ8WXygkAimW7ZWVF7fiQYuCgGZ/w3qFcxD0vUjqEQHGhU7OOL0fX\nN0+nHWHIKlC4BSOUlgdtr06Wf2pPC1VCZrkc/n08v3VluB3z5vW77xVKfjO9oE0GdVELhiyatAZt\nG/o4SqYCT03M8DVAdFUdKnR+zwStWVUs96EnHNtd2AUF1urpLXxDCgRXLdGXoyCKa9MBbijBU6XT\n+qdXOfeocBhD296yEwCgbzm6rxm/eE2mrLjoFaReT/VUZ2fJwLC7s4DIrVBUqDFqkcKpzcgbYNzQ\nZLruqcX66iW6fpzM5BXyBM1bOcTVU/UAbG48KM1NAK+vLaidzPI5zIZtmAODx9HSY4Nw8Ini7e+t\nfALP3LcWADC0iGGrQRO42MjajgLLotft2QqsDvPcXS7fnLICrgJxr0kUnowVcJMYQ/0KXhOi35xb\nT+0czc5cFb1AQNQBpzm530rB1iZSqQd5TdwKNYVBgxwlumIiLRQWffVGBWXZ2/wPuN+jJvPYkU/z\n76L1IWNoJ9SmUOLVjXwZzXVxNYsEr48eYhOdPypzPHq+I455/V7snWxHgcrH19DSIGoemsqrl5gK\n6NxOuXjZVsRUWitUZuPFTVS14I5+qo365nXPYm9s8Yxc41TimSylOFSqSVeDp7bacwiKShUDVsw1\ndEv2T/NFHiMch2E0T/ueOVFy5O5K1kLnKeLRSRTWHYe5ebuO75xLYWZeJZIUr6NbHTWR66d5pOJF\nFeF14wqFF2sRGstBkPE0FW7LBBXD1Vvwoz9JK7jOLNcpqItCHpK6LCEhISEhISEhISEhISFxeBxX\nRHcyJOwAAPJkVnDqartZ6kY0y5SM670Z5jQ1gxmoUMljcYgLucSVLBbpFNFN2xqUIXKPFWPK/t53\n16JcpevuttJuTWARvQ0pquuxyPtkNcUrlTE3CixozYYD6BD3ScNc/t6Ot5NSWf1/FX9EVyhI66oF\nw/LEeIQny+DcXF2xoKWL1UUlMQa2BWuUKEcQf6cY1p7icvOWlVLUOWF7sfnYAZqful5TggA84TXx\nN8hVgsv0tPsZl4arZd06s4NcsXVZ9BD+ESIF7nCnA4XPFhEu3CZUGmcCc8tJGMZPuRpaQp7o2vs9\n4SiDi3YMIopSjdon6GoAefoBIGGHsStLEiVCoMqP4eUlCIk1w6J5VlWKkdsjMVsh1rw3RpP44VDl\nEd8rKPsWHyeWw1wJbr8Y23lRYg7dHLy04NcrMQ3gU9TmwUYkc7TXPaP2IJZeSpUlhMKwaStupYoT\nCf8YpRyfN5VvctPw5scHXa7O8gCFQR/LlLjny/YeXe3tmYLTFUJsDkXnoxr9fezRVYhxw+TZVIu7\nRrvrP5i7VjLmuCl9JrcFwpqBkRztJYRAVUCxwAzOCNMZmgK05u5hJHCpZIs0RjmJwGiJksG2dOOE\n48k+6v8hZvgYLeJ7CntZRXq3JCQkJCQkJCQkJCQkJCReGY4romtlPe/jMPfElyh5X27p5Ga5OC+i\nvMN2GA0aRTxE5NNwNLSbVEqjTBkCRpLHc6lTigsueMF9bUBBiIl8XC7GxVS3FMewz2MrcpgVKG7t\nKb9QVYBHMQK+bMzECvIW1aO4wHgWvWMDRoy6lchLMW3FrR+m+UouiZJDGrOhjteW8ZexkZAoIpSF\nKbeonxcEvW34DCitVBNQqdHRZlKtu0GTorN9vjJpbZlytAaohEJvnj7fn4uhm891e3NUmkBnFtIp\nigLUdOTx9X4SpkobNM/mzZmL6J5STsJYjTrlCydsB2yShPEIH9RlatrNR7YdxRXcEqhUk1gZpu9s\n5/WIdxspV+An2aRgvk73MO3Q+xpio5g821tCYvqRqZg4Hm342VuTiFFNsj8SkZ+srbtRrsNsoySK\nHIllFLlPHKhBSwtpXNxQ/Q98ct/VAICDu6i++JfecDcerFoHACibgeucKmwbpl3q1RW2G91sjgxg\ngI+LGpUieiocDHKmTnBLK4DiZG4CQM2zgHKG2NfTVdY87yDLU1BL1OwYpiJAzK2cRXtimzGY/Hye\n/7Ud5u6PBfOxM1EKrZZYUA3f7MXgDVwXReyjk0U6KTgObkvQcxfPPMAs7E9X8Td4WiraMN0Tldmu\nhtM/MvOm5LKOy9DV+rwNyxy+qTlkBVxBpaxLx9HQxztyJJAbQ98FKBm9myu0iU1Q1tGxOkAkBwOA\nPU00yWMB44XT31H5JNK2Z7wPu2saPegRO++qzqlw3AVMDOeknUOKq1FnnTA/k3cN5pStoMMkQ/+1\ny4j2MtVl644HgRFeP5E7P8TAB2ijLmqGisFtOgocyS2QeJWgPkJz0bkl2wEAJwVG8U5cDgCw+kI4\nkCPBuOeGKeHgpZfmQU1SB3/O0DCapzHenSJDN5ULoERfTa9NMm7ztopgiI8TK4AKjejSlzZsBQDs\nTtWhbQrbeCRsPJnG8wv159CBgI6ag16hWYvPZa+N0Fy1z6h2N/p5R3VFKoQjwHA0RLmw1/JQJwCg\nWmFQ8jQ/Nv7nBqzr/AAAIDhC3xN7bqZaLyExEakGWuuHrLRLvy9VaczaUFyj13YwwdGjMts9ZvOx\nkbYDbmqEXYQ6jRJHAZP6xNVnbMTGftrAt5tlroHjhGh+25yZByM2+Ve8mrF/LxnygRYLow6ta/WB\nEYzXTLfAsC/PRVb7B1DMcFQgaVBburLknC59aRBlgySy9fn/2IkNA1RNQRh6CnNQymvuVoeSCKuC\n2uxtelsVco4nMjTmb1z4GL72l6sAAGZXNzq4JS2UnsN9xWnoqjkLeW73CTryqB3GUDbC3+EZuqFe\nakPeUd3axALKOO2t44U0LyQkJCQkJCQkJCQkJCROKBxXRHfB7ymysWLkQ0gvJo/8gYt/ji4efazX\nyPPRaw2iXBE1tXQA412Uo9iRJ099pUKEtNeFgPl//hgA4LxTtkOJcMnqXPHUT3QM8sxct+G9+Pn6\nXwMAqtUUFuuTFL6YlGnoCa8IH1cTfyJtpoMQpzbPDUTc911QTlGk29B0XNdecPhqTTmcxlyhk0f7\npPJDbqRqXngAO5Pk6Qsq5J1qCg/hnzVjqV0sEIBTRM9aQkJg48PLAABP1lHtw8vXvAg7SX196f/2\n41PXUJ3cn/Io7L+//u8YtmkM16mjrhhfK5fc7zPjuDhCglt/TKwAAIxYYXyk4UEAwNd3vQHffe58\n+vERmgfVrIIWPDV1jTwKmF3dkx6//OSLAQAtf6P14Wv1j6FU4UwVx3bFqvy4I0necTH/n/3dT6D+\nCS9KXHrr2PpbxV1VXWK2QTmVIjqHLIaYStEbITBXpqbHRG/GwwLzBKp47KFWH0YJL79irEpN2XVL\nTB3UND3LdbF9uHML1Vm/dGUWjQvvAADkF9L5YSsCo7Q466IeDwJ9tOldqGfdNL7VpTvx96ar+Tte\nBACcGerE/w6cOROXeMwYWMWwJkZRyV0jFIXWtu92z18y73Qwlc4zhY/5YBAGL8m44/SV0DL0rMNt\nJMbFbBssReteU+cuAMAdJSvQMuSt7y8N0p55TRUxnkYXWqgtfPOOG9mqEFYF28ccOzl0EC/Gid22\nz3ec61PipEAXEhHaH6wIHAIApJsKS14/LkPXeWEbAKDJS1HFeRe9F+3n02bMLKGLXbPyAF7qpJpa\n+pYozAh1+nwtbVdKdulItvCGldCEv+wLfVh88FkAQMfxXOQ0YOHbX8A3sAoArwdq8/qx80hpzIkE\noSSoI5sHDkKNU/Hk3rfQprZsbw7BnfSAB39BA+KvK2/BQZPu469HFuA3v6DNY/1/e5u/YoJjelwD\n5QmawJ76ONWJG1gRRLqe7omjAA4vpK1ypeXQIMOSX1JfEt3bMeRWVqI40fyFsQbmLt9ra9devOGk\n8wAA/ZeT6mS2kiE5l9fMq8nAyHBHH1dOVLIKvhSn/q4N0LngEMPjj1BtXnRtxaLrJzcqZwJM48sG\nN1gdywJsb2Gy+voAAHv45b8Z66FWUe6tubgJRgm1UTF5rtNg1l1LgPkAgHqMnedEmgiEFoBhjvlN\nCYmZRDhA69+AHUaOc413ZGn9782XuAasaatQuKPLFLWjHYYRgzZ6Wb7mK8xGeYD2DKUlMhv91Yjg\nAD3zH7aeBy1I8/vC337AVZRlnNpslpmIDJx45MqFP6Od++vXvAvJDAU6Wj46CLODjLUzPv1BAEDv\nOQaqnqB+XzHDztuXw/xPP4Vtl9G+NlNF47fcl0TkGHk442m36TQwRKrJ0XbPmjmSa8Pi7xfo2U85\nro+laJ6Y97fidIxEnt2Htz1MaUbaIO0TmAXUbKLrjeIZ970N95JBfOnyj4Ll+bqu055g4e2FDXKd\neKNLQkJCQkJCQkJCQkJCYlaDOVLZVkJCQkJCQkJCQkJCQuIEgozoSkhISEhISEhISEhISJxQkIau\nhISEhISEhISEhISExAkFaehKSEhISEhISEhISEhInFCQhq6EhISEhISEhISEhITECYUpN3QZYxpj\n7HbG2KOMsW+NO9fAGHuEMbaBMXbBVF/LTIAxdglj7DH+r4sx9kbfudnQ/ghj7F7e/rsZY0HfuZWM\nsScYY08yxlbN5HVOFXgbNzDGHmeM/ZIxXhwZs6P9AMAY+y5v//fHHT/h2y/nv9ndfmDW9/9Zu/7J\ntW/2PnsAYIydwdv3BGPsu+POzYb2NzPGevjzf2DcudnQ/tn+/Itn/DuOM6X/AFwD4DP89Q8BrPad\n+wGAswDEADw21dcy0/8APAMgNpvaD+BNAL7IX38OwJW+c38CMAdAI4C7Z/pap6j9uu/1LwGsnWXt\nPwXAzfz1j2dh+2f1/CfbP7v7/7h7MavWv9m+9s3mZ8/bWAcgxF/fBuCkWdb+ZgC3HubcbGj/rH7+\n4+7FjI7/6aAutwDYwl+/COBM37mTAGxwHCcJIMEYi0/D9cwIGGMtAHp4WwVmQ/v3AYjy12UABnzn\nyh3HaXccp5OfO+HgOGPKh+cAtPv+f8K3H8A6AA/y1w8BWO87NxvaP9vnv9ne/tne/wHM2vVvVq99\nArP02cNxnG7HcbL8vwYAy3f6hG8/x7mczfLRccdP+PbL508ohvE/HYbuLgBn89fnYuykrjrcvAcw\nghN7wn8TyIvrx2xo/x4A6xlj2wCcBmCD75y//zGcoGCMXcEYewlALcZudmZD+8sAjPLX4/v4bGj/\nbJ//Znv7Z3v/F5iN69+sX/s4ZuOzd8Gp6dWO42z3HZ4N7e8CsBg0718wjqI/G9oPYFY/f4EZH//T\nYej+FUCYMfYwKKLV4ztn+17HAQxPw/XMFC4H8Jdxx2ZD+68H8FfHcVYAuBfA233nHN9rGycoHMf5\ni+M4KwF0ALjMf8r3+kRt/wiobwMT+/j/z957h9l1VWfj7z7ttrnTqzTq0qhZcpUt2djGNhgwGILp\nOAFSSCHwQRLywZeEEPL7AsnPISGEACHUBCdgSnDAGIPBNmDLHRfZsprVy/R227mn7O+PtfY+586M\nJJDmjsbSeZ9Hz1ydc8+5Z5/d13rXu86F8p/r49+5Xv5zvf0rnIvz3zk/9zHOxboHAAghWgF8CsBv\nTzl11pdfSulKKYtSSh/A9wCcFzt91pcfOLfrP4Yz3v/rvtGVUgZSyvdIKa8Due7vip1+SgixRQiR\nA9AopZyY+S4vbAghugFUpZTDU06dC+UXAEb48xCApti5ESFErxBiASKvx1mFuAAJqIzl2P/P+vID\n2ArgOv78EgAPxs6d9eU/18e/c738OMfbP3BOz3/n9NwHnNN1DyGEBeCrAN4vpTw25fS5UP587L9X\ngKj8CudC+c/p+gfmUf+vdxAwSGzhXgA/AfAOPvbP/LeXj28FcH29n+VM/QPwewDeHfv/OVN+ECXh\nLm4DPwKwAMCfUFhpUQAAIABJREFU87mNAO7nfxec6WetU/lfA+A+/vf5c638XM5/AvAzkBhR97lU\n/nN9/DvXy8/lPGfbP5fznJz/zvW571yuey7jWwAMcv3fC4rPP5fKfwOAx0CU/b9L6v/cqn8u57zo\n/4J/NEGCBAkSJEiQIEGCBAkSJDgrMBcxugkSJEiQIEGCBAkSJEiQIMGcIdnoJkiQIEGCBAkSJEiQ\nIEGCswrJRjdBggQJEiRIkCBBggQJEpxVSDa6CRIkSJAgQYIECRIkSJDgrEKy0U2QIEGCBAkSJEiQ\nIEGCBGcVrBOdfKnxhrpIMls93QCA/lcuAwCUOwVEUPsdaQDSpM/VJqnPL//g1hPe+0fhN8RsPWe9\nyl9PJOWfn+U3cjkAQFgsznz+gnUAAK85DQAw7338lH5nTsov+CdOotje/57LAQBtrzmE/glKqWf/\npAnZAcoVPrKW7GwbXroDj2xfDgBY86kCACB8cvspPfN8rf+5wnwqv0hRCmnpujDbWgEAS39QgsWD\necb0AACLU8Ow+Zgb2gCAySCNUT8LAHhmvAfb9ywAADRucwAAC+4ZjdqIELotzqf2b5y/FgCw+y3N\n6L2Hypp5bC9Qpc+wafqduGYVBl5P6bVXfrgEAAh27D6lZ55P9X8mkJQ/Kf9s3aum/PE+P0P/N7s6\nAQADr1qBwhI61vUwjWnp7z18wt8xGxsx+RIaK468tkrHDqWx8ov9AIBg1/O/9DMn9T/35a/ceCmK\nXbRZqbQLhJdQStjWBhrLjw42wTpA67qG/dF1kt2MbotAcOEkAKAxV6H7eBbKJZo/m+/OoGU73Uts\nffKEz5LU/8zlP+FG93QhLLq99H24N2wCAHzon7+Ih4o0qV+S/TEA4JpMBa6kyb8iox3vXaXF+vOg\nTwvlH31qPQDAP3S4no+eIMHsQQi9wTX7VsDraQQAGPf9AgAw8ltb0PEzmtQsnkTNRb3wDx46Aw97\nHIjY+DFlgW+uX419N7UBABq3DODanp0AgKPu0wCA+/ctw2+vJwPVBzbv0n39kO8CAP5wz5uwdOkA\nAOCK/9gBANhfbsM9P98AAFjxjRLw4FP1KFWCOkKYNPlLAOHSHgDApxfeir0eGTNsblJdZgaF0OVj\nNPs3GGl9n0LnVth9dK/UDbQRvuoVr0XmZfw7jgPpunUtSw1m2OCaq1cCACbXtWFiCT2rXaTvhb0V\nNP3ZEQBAo+Phif6FAIDlLSMAgJ+v+ld8doyOfeXiGwEAEzd1onEfGYTy+8oQqv2r345t7hMkSFBn\nzNTvDOrne27ZhK71NH+VCpOwDDr/qpseBADc9C9PosJem4N+MwxQvzYF/f3WyCa02g8AAJ6dICfQ\n/uYWXPbN5wAAX/nZlVj17odqn8cwgXCKd+iFBjHDnuQkY1rxB2QQb3yvoY2BwnYgveqsP97JMPa2\nLQCAcrtA2zP0++1PVGD8j3oW2v9UL01jbDVfsyYqn2yj78myhdzjtL/JPk3zXvv9OyGXkHF3YnUa\nQ+eT0TfcRM6DBf/+DIKx8TqV7OzDnG10D93sAwD2VLsw4FGl7vM6AABf8oDxIAMACNnMEUhDDwQp\nw8PX9l8CAGg6dGqW7gQJ5hrx9m+uogE62LkHzkQXAODZz14KAPjvl30Cf3bh9fTdXTR4hfn8XD/u\nicEbkPjkeuDDNOiuvW4X2ss0aE9WUvjmjgsAAIHPG51A4LP3XwMA+KxzNQQvBKRL9xSuAenQsS/s\nI8t4trWE3g3HAADuBuDYGG16l/8hHQsGB39pz1qCMwMZRG3FODQIAHjMrSIAWapNUL2Nh1XkDRrr\nKyHXZVCEx3OBLUJ4XMfrHdrolm7rRgZ7AdCG+ky0AHMlMZL2vbkHplrbhIAzQU+jWEg933Gw/QLu\n/8sqCKpUrpEMWe/XPfDraP5GAwBAUvHgTAKFhfS98ZU5iBfTomrpl8m74x89Vr+CJUiQYGbE5ppd\nn6A1qUwF6H+a5nTTBaRP89J/3E+WuM+3Xo9qN7M4DAnD5nVtmo6ZZojigUa+F51DCPxnie5/3aZt\n2PptcvAsvOkZPv8C2+TyXC0sG0aGNnPBxMRxv77rU5fBaKFBtbW5gHetuA8A8PoGMh68avl7kNrB\ntzYNsO18TlC5kdZtIe+een4+iWoLzWluWxqyg/YyVpnqqHlHCa1P0v7HKJQhU8RKMsbJ4IswRNDT\nWvsbl6xEaLPRd18RDfvo+Og6mieOvG09uj75wOwX7ixFEqObIEGCBAkSJEiQIEGCBAnOKtTHo8vW\nm7BS0YfW9R4FAGwrLsTC1BgAoNMii05VmjWfAWAyyGA8IHf9UmcI/f1NAICmujxwggSzD+n7+vMH\n7/o2AODtP34n1q4k2v1Xl3wWAPBgeTncb1LLHi0Rdanj1Tvm8lFPjpgF2chSv8xcPAwA2DXcgVKR\nLJqGIWFa9N3QiNF0LLZUVwwgYE+syecbfX3MzNA7q5QcHOB7mlaIVT1EDXvuL8kztuo9g4kn94UE\n9tQ2G1VN41PIigDsyIQhYnFv7BI1hUCVQ1oO+GQF73h0jAmAgPSifjYn4Pltz29SXzVcwJ5Q1MYo\n9kpZ/IvdJtq20dN6e9LgMGRUS+QF6hgLUeqge7ot3A8qkWfYHIqO73sHtf/ejyUe3QQJ5gxMU0YY\nwFxPPFTFQnIGLYCntCAF+Dnq636avZgScI5Sp5cmEPJ15RwdM8oG4Cg6tBr/BFZ305z3+MBCvGLp\nswCAR15JIYCpOx6Zv4ym2LsCUEP3ll4VwRSacXDNRdj/Tnpn5y+icK3P9nwRf7nzNQCAwZ3tuO3t\nxOj66394FQCg14loz+Fchq0AKPRQ+dJjVCa/wYE06HkML9Tf83P0vaDVjl3dEPs8fTdjukxrL4ew\nC76+f5Bh7+4hendDG1M1jMEEJ0ZdNro6NosrwEinsTg7qs83mLQBHvap0j1pImDncs6gRhvAQCl0\n9DUtD0ef6UeSGKV6Q1HzDr26B5MrqS5Xv/eJMxIPMROMjWsAAGPrm9H4Xw+e4aeJEBfhAUiUptmg\n5/vmS/4Fu6q0wP15kSbMSmjj13pIZCDL7f82dM/pM58Usclr7weImrwodxAA0D/ZoDe3MjQQBtSX\nLT4W+CakxX3V8WHypjfwDL5GwOINbhjShCGl0N8TRojBIo0V7ctH6lbEBLOMMGbo4Bj1ydBGSm9g\neSMXC9VyePFWiY3tNoA0H3/ep3mgRqxMRouLuYD30ovpZ7lLWGUgVAHHAlA8asPjDyFQ4Y2q4QMG\n0+yqed7cNlsQgeR7ReUOeTEXpGgzDQBBM/01V688ZcGqBAkS/IqIjTHHrlI0UzboWtSvAaIuo8KO\nHt7fSBOQak9qSW0Is8doABEe4LVy/x+jJXl+zQgubj4AABgorkOPQyFN+19N1/bdgfm7/p1Kq449\np7h4PXa/hcKy3nH9vQCAXud7+OddLwYADPw9GfI+fvsQmkDjWxN2Q92x865VAIDz/uIJ7Ll9+v3r\nDWFZCC2qTLvIG9GcCeHzRt4UeixXMMNQb4DDlKG/eyKEtkBoRwZhqxjo3wIAEQLGMtIw+lXEys5V\nJNTlBAkSJEiQIEGCBAkSJEhwVqEuHl0Z1loswkoFAy6psi7IjONolczSvQ55ZwIYOq2EyYS0UpjC\nuE9B3QerbWh6fkq0+Xy1Zp1F2P6/2wEA79z8Y3z9+YsAABc9XMHFuX0AgOerJCa2u9SJg8UWAEDZ\ntyHZfFnyqE6LFQc+CxN5QxlIRU/MkJXKSgVamCFl018/MGGyQI1pSCzMk0VzQZb+Hik1oTdLYjRL\nMkO4+7/mj3jTVCql2H8EYyEJMDznLoDHriCDxdZCCFQkdcXP7yCBp26cWnqduiFmpW3exAqTXL9Z\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1C+xRbQUPYWg69BKmLnqBGQmXSQvbHmExjitnr/ynDfZo9DojmpJtilBb4l0ZebTXpI8C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BEiRIgQIU4rhBvdECFChAgRIkSIECFChAhxWiHc6IYIESJEiBAhQoQIESJEiNMK4UY3RIgQ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X2+C90DM9gB38gtX+w8NbsK+J1plAuHzvC7X0hoUydCvhHqBz7so/AH7xDaoZrLSVgALv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LdS58tFNUpdbtIelzE3QZP2upR0MP7yzn6SSrv2LZc+LYVdp/log+UMVigs4ZIPBFW0lS5hVUKr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\nRUFtdBUUFBQUFBQUFBQUFBReUfj/Vwyc8Q1FwCwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1224x1224 with 225 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPcXYuTkoko7",
        "colab_type": "text"
      },
      "source": [
        "# STEP#4: BUILD AND TRAIN A DEEP LEARNING MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dGFiOGmYego7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Prepare the training and testing dataset \n",
        "X_train = training[:,1:]/255\n",
        "y_train = training[:,0]\n",
        "\n",
        "X_test = testing[:,1:]/255\n",
        "y_test = testing[:,0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ig-EnEK-ejys",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from sklearn.model_selection import train_test_split\n",
        "\n",
        "# X_train, X_validate, y_train, y_validate = train_test_split(X_train, y_train, test_size = 0.2, random_state = 12345)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGPgYOIwelU2",
        "colab_type": "code",
        "outputId": "2bde52cb-526e-4d27-eaa5-59ba8a8ec3e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qeVBBV0vemcJ",
        "colab_type": "code",
        "outputId": "02ed3866-8331-4807-d7c9-f3973754ebf5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_kdPQ8zeomx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# * unpack the tuple\n",
        "X_train = X_train.reshape(X_train.shape[0], *(28, 28, 1))\n",
        "X_test = X_test.reshape(X_test.shape[0], *(28, 28, 1))\n",
        "#X_validate = X_validate.reshape(X_validate.shape[0], *(28, 28, 1))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfSxOtU1ep32",
        "colab_type": "code",
        "outputId": "06588e57-27be-4153-b69e-5dcfe0c4c56b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zFvDfpg_erHy",
        "colab_type": "code",
        "outputId": "66027762-5b07-4e28-b070-df435aaf6db3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gpq3BBKetX_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# X_validate.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMMvZyIUfOhb",
        "colab_type": "code",
        "outputId": "ea7da5bc-9cec-4c47-9036-139265c1b605",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "from tensorflow.keras import datasets, layers, models\n",
        "\n",
        "cnn = models.Sequential()\n",
        "cnn.add(layers.Conv2D(32, (3,3), activation = 'relu', input_shape=(28,28,1) ))\n",
        "cnn.add(layers.MaxPooling2D(2,2))\n",
        "\n",
        "cnn.add(layers.Conv2D(64, (3,3), activation = 'relu' ))\n",
        "cnn.add(layers.MaxPooling2D(2,2))\n",
        "\n",
        "cnn.add(layers.Conv2D(64, (3,3), activation = 'relu'))\n",
        "\n",
        "cnn.add(layers.Flatten())\n",
        "\n",
        "cnn.add(layers.Dense(64, activation='relu'))\n",
        "cnn.add(layers.Dense(10, activation='softmax')) # output layer\n",
        "cnn.summary()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 26, 26, 32)        320       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 11, 11, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 5, 5, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 3, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                36928     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 93,322\n",
            "Trainable params: 93,322\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Oguq-g7fWZR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cnn.compile(loss ='sparse_categorical_crossentropy', optimizer = 'Adam', metrics =['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rLEIbQ0fZgs",
        "colab_type": "code",
        "outputId": "3440ac57-b6ea-4d16-d8c0-1c5c1fb2f382",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "epochs = 150\n",
        "\n",
        "# history = cnn.fit(X_train,\n",
        "#                  y_train,\n",
        "#                  batch_size = 512,\n",
        "#                  epochs = epochs,\n",
        "#                  verbose = 1,\n",
        "#                  validation_data = (X_validate, y_validate))\n",
        "\n",
        "history = cnn.fit(X_train,\n",
        "                  y_train,\n",
        "                  batch_size = 512,\n",
        "                  epochs = epochs,\n",
        "                  verbose = 1)\n"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "60000/60000 [==============================] - 8s 126us/sample - loss: 0.9023 - accuracy: 0.6748\n",
            "Epoch 2/150\n",
            "60000/60000 [==============================] - 4s 67us/sample - loss: 0.5118 - accuracy: 0.8135\n",
            "Epoch 3/150\n",
            "60000/60000 [==============================] - 4s 66us/sample - loss: 0.4410 - accuracy: 0.8424\n",
            "Epoch 4/150\n",
            "60000/60000 [==============================] - 4s 66us/sample - loss: 0.3955 - accuracy: 0.8591\n",
            "Epoch 5/150\n",
            "60000/60000 [==============================] - 4s 65us/sample - loss: 0.3664 - accuracy: 0.8696\n",
            "Epoch 6/150\n",
            "60000/60000 [==============================] - 4s 64us/sample - loss: 0.3405 - accuracy: 0.8779\n",
            "Epoch 7/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.3233 - accuracy: 0.8844\n",
            "Epoch 8/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.3088 - accuracy: 0.8896\n",
            "Epoch 9/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.2942 - accuracy: 0.8943\n",
            "Epoch 10/150\n",
            "60000/60000 [==============================] - 4s 62us/sample - loss: 0.2809 - accuracy: 0.8993\n",
            "Epoch 11/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.2789 - accuracy: 0.8992\n",
            "Epoch 12/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.2662 - accuracy: 0.9045\n",
            "Epoch 13/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.2535 - accuracy: 0.9088\n",
            "Epoch 14/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.2463 - accuracy: 0.9117\n",
            "Epoch 15/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.2439 - accuracy: 0.9121\n",
            "Epoch 16/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.2300 - accuracy: 0.9168\n",
            "Epoch 17/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.2274 - accuracy: 0.9174\n",
            "Epoch 18/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.2216 - accuracy: 0.9201\n",
            "Epoch 19/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.2131 - accuracy: 0.9224\n",
            "Epoch 20/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.2076 - accuracy: 0.9257\n",
            "Epoch 21/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.2030 - accuracy: 0.9264\n",
            "Epoch 22/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1957 - accuracy: 0.9292\n",
            "Epoch 23/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.1883 - accuracy: 0.9327\n",
            "Epoch 24/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1864 - accuracy: 0.9328\n",
            "Epoch 25/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1786 - accuracy: 0.9360\n",
            "Epoch 26/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1744 - accuracy: 0.9369\n",
            "Epoch 27/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1711 - accuracy: 0.9377\n",
            "Epoch 28/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.1615 - accuracy: 0.9420\n",
            "Epoch 29/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1587 - accuracy: 0.9436\n",
            "Epoch 30/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1553 - accuracy: 0.9436\n",
            "Epoch 31/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1495 - accuracy: 0.9462\n",
            "Epoch 32/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1456 - accuracy: 0.9478\n",
            "Epoch 33/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.1426 - accuracy: 0.9481\n",
            "Epoch 34/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1386 - accuracy: 0.9498\n",
            "Epoch 35/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1320 - accuracy: 0.9530\n",
            "Epoch 36/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.1250 - accuracy: 0.9560\n",
            "Epoch 37/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1221 - accuracy: 0.9560\n",
            "Epoch 38/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1192 - accuracy: 0.9574\n",
            "Epoch 39/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.1161 - accuracy: 0.9598\n",
            "Epoch 40/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1091 - accuracy: 0.9603\n",
            "Epoch 41/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1090 - accuracy: 0.9604\n",
            "Epoch 42/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1035 - accuracy: 0.9639\n",
            "Epoch 43/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0970 - accuracy: 0.9664\n",
            "Epoch 44/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0994 - accuracy: 0.9640\n",
            "Epoch 45/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0896 - accuracy: 0.9687\n",
            "Epoch 46/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0862 - accuracy: 0.9695\n",
            "Epoch 47/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0816 - accuracy: 0.9715\n",
            "Epoch 48/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0789 - accuracy: 0.9721\n",
            "Epoch 49/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0766 - accuracy: 0.9728\n",
            "Epoch 50/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0721 - accuracy: 0.9745\n",
            "Epoch 51/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0682 - accuracy: 0.9764\n",
            "Epoch 52/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0703 - accuracy: 0.9748\n",
            "Epoch 53/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0654 - accuracy: 0.9765\n",
            "Epoch 54/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0591 - accuracy: 0.9795\n",
            "Epoch 55/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0574 - accuracy: 0.9803\n",
            "Epoch 56/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0529 - accuracy: 0.9820\n",
            "Epoch 57/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0487 - accuracy: 0.9840\n",
            "Epoch 58/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0480 - accuracy: 0.9834\n",
            "Epoch 59/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0510 - accuracy: 0.9815\n",
            "Epoch 60/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0486 - accuracy: 0.9822\n",
            "Epoch 61/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0441 - accuracy: 0.9848\n",
            "Epoch 62/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0448 - accuracy: 0.9839\n",
            "Epoch 63/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0447 - accuracy: 0.9840\n",
            "Epoch 64/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0345 - accuracy: 0.9882\n",
            "Epoch 65/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0422 - accuracy: 0.9851\n",
            "Epoch 66/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0310 - accuracy: 0.9896\n",
            "Epoch 67/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0335 - accuracy: 0.9884\n",
            "Epoch 68/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0302 - accuracy: 0.9901\n",
            "Epoch 69/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0265 - accuracy: 0.9916\n",
            "Epoch 70/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0294 - accuracy: 0.9901\n",
            "Epoch 71/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0276 - accuracy: 0.9906\n",
            "Epoch 72/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0236 - accuracy: 0.9925\n",
            "Epoch 73/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0171 - accuracy: 0.9952\n",
            "Epoch 74/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0228 - accuracy: 0.9926\n",
            "Epoch 75/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0230 - accuracy: 0.9922\n",
            "Epoch 76/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0268 - accuracy: 0.9905\n",
            "Epoch 77/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0264 - accuracy: 0.9905\n",
            "Epoch 78/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0384 - accuracy: 0.9860\n",
            "Epoch 79/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0170 - accuracy: 0.9944\n",
            "Epoch 80/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0152 - accuracy: 0.9957\n",
            "Epoch 81/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0136 - accuracy: 0.9958\n",
            "Epoch 82/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0201 - accuracy: 0.9929\n",
            "Epoch 83/150\n",
            "60000/60000 [==============================] - 4s 61us/sample - loss: 0.0244 - accuracy: 0.9910\n",
            "Epoch 84/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0172 - accuracy: 0.9941\n",
            "Epoch 85/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0110 - accuracy: 0.9968\n",
            "Epoch 86/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0093 - accuracy: 0.9976\n",
            "Epoch 87/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0297 - accuracy: 0.9892\n",
            "Epoch 88/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0372 - accuracy: 0.9858\n",
            "Epoch 89/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0162 - accuracy: 0.9947\n",
            "Epoch 90/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0087 - accuracy: 0.9978\n",
            "Epoch 91/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0139 - accuracy: 0.9955\n",
            "Epoch 92/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0110 - accuracy: 0.9964\n",
            "Epoch 93/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0069 - accuracy: 0.9984\n",
            "Epoch 94/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0036 - accuracy: 0.9996\n",
            "Epoch 95/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0020 - accuracy: 0.9998\n",
            "Epoch 96/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0016 - accuracy: 1.0000\n",
            "Epoch 97/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 98/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 99/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 9.9850e-04 - accuracy: 1.0000\n",
            "Epoch 100/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 9.1189e-04 - accuracy: 1.0000\n",
            "Epoch 101/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 8.4450e-04 - accuracy: 1.0000\n",
            "Epoch 102/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 8.2294e-04 - accuracy: 1.0000\n",
            "Epoch 103/150\n",
            "60000/60000 [==============================] - 4s 60us/sample - loss: 7.7626e-04 - accuracy: 1.0000\n",
            "Epoch 104/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 6.2957e-04 - accuracy: 1.0000\n",
            "Epoch 105/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 5.9871e-04 - accuracy: 1.0000\n",
            "Epoch 106/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 5.6933e-04 - accuracy: 1.0000\n",
            "Epoch 107/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 5.2742e-04 - accuracy: 1.0000\n",
            "Epoch 108/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 4.7933e-04 - accuracy: 1.0000\n",
            "Epoch 109/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 4.4096e-04 - accuracy: 1.0000\n",
            "Epoch 110/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 4.3878e-04 - accuracy: 1.0000\n",
            "Epoch 111/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 3.8075e-04 - accuracy: 1.0000\n",
            "Epoch 112/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 3.8884e-04 - accuracy: 1.0000\n",
            "Epoch 113/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 3.6126e-04 - accuracy: 1.0000\n",
            "Epoch 114/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 3.3554e-04 - accuracy: 1.0000\n",
            "Epoch 115/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 3.2187e-04 - accuracy: 1.0000\n",
            "Epoch 116/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 3.2478e-04 - accuracy: 1.0000\n",
            "Epoch 117/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 3.1494e-04 - accuracy: 1.0000\n",
            "Epoch 118/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 2.5146e-04 - accuracy: 1.0000\n",
            "Epoch 119/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 2.6284e-04 - accuracy: 1.0000\n",
            "Epoch 120/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 2.0233e-04 - accuracy: 1.0000\n",
            "Epoch 121/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 1.9144e-04 - accuracy: 1.0000\n",
            "Epoch 122/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.1962 - accuracy: 0.9418\n",
            "Epoch 123/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.1011 - accuracy: 0.9631\n",
            "Epoch 124/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0487 - accuracy: 0.9815\n",
            "Epoch 125/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0330 - accuracy: 0.9874\n",
            "Epoch 126/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0245 - accuracy: 0.9909\n",
            "Epoch 127/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0157 - accuracy: 0.9949\n",
            "Epoch 128/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0110 - accuracy: 0.9966\n",
            "Epoch 129/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0088 - accuracy: 0.9973\n",
            "Epoch 130/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0049 - accuracy: 0.9990\n",
            "Epoch 131/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0067 - accuracy: 0.9983\n",
            "Epoch 132/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0129 - accuracy: 0.9954\n",
            "Epoch 133/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0351 - accuracy: 0.9878\n",
            "Epoch 134/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0209 - accuracy: 0.9923\n",
            "Epoch 135/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0107 - accuracy: 0.9963\n",
            "Epoch 136/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0105 - accuracy: 0.9965\n",
            "Epoch 137/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0072 - accuracy: 0.9979\n",
            "Epoch 138/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0044 - accuracy: 0.9991\n",
            "Epoch 139/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0062 - accuracy: 0.9982\n",
            "Epoch 140/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0054 - accuracy: 0.9984\n",
            "Epoch 141/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0053 - accuracy: 0.9983\n",
            "Epoch 142/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0145 - accuracy: 0.9948\n",
            "Epoch 143/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0177 - accuracy: 0.9939\n",
            "Epoch 144/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0262 - accuracy: 0.9911\n",
            "Epoch 145/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0282 - accuracy: 0.9894\n",
            "Epoch 146/150\n",
            "60000/60000 [==============================] - 4s 59us/sample - loss: 0.0148 - accuracy: 0.9945\n",
            "Epoch 147/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0150 - accuracy: 0.9946\n",
            "Epoch 148/150\n",
            "60000/60000 [==============================] - 4s 58us/sample - loss: 0.0186 - accuracy: 0.9933\n",
            "Epoch 149/150\n",
            "60000/60000 [==============================] - 3s 57us/sample - loss: 0.0070 - accuracy: 0.9976\n",
            "Epoch 150/150\n",
            "60000/60000 [==============================] - 3s 58us/sample - loss: 0.0019 - accuracy: 0.9997\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0wR3uKW8g5po",
        "colab_type": "text"
      },
      "source": [
        "# STEP #5: EVALUATE THE MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZhkrXWmffa87",
        "colab_type": "code",
        "outputId": "a7f64fb2-5025-41c1-972a-7ffcc5705409",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "evaluation = cnn.evaluate(X_test, y_test)\n",
        "print('Test Accuracy : {:.3f}'.format(evaluation[1]))"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/10000 [==============================] - 1s 107us/sample - loss: 0.7568 - accuracy: 0.9166\n",
            "Test Accuracy : 0.917\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlOnUbMjg90E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# get the predictions for the test data\n",
        "predicted_classes = cnn.predict_classes(X_test)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RQZVEJAQg-75",
        "colab_type": "code",
        "outputId": "85736407-411e-43a7-8da9-abe8137ef81b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        }
      },
      "source": [
        "L = 5 #length\n",
        "W = 5 #width\n",
        "fig, axes = plt.subplots(L, W, figsize = (12,12))\n",
        "axes = axes.ravel() # \n",
        "\n",
        "for i in np.arange(0, L * W):  \n",
        "    axes[i].imshow(X_test[i].reshape(28,28))\n",
        "    axes[i].set_title(\"Prediction Class = {:0.1f}\\n True Class = {:0.1f}\".format(predicted_classes[i], y_test[i]))\n",
        "    axes[i].axis('off')\n",
        "\n",
        "plt.subplots_adjust(wspace=0.5)\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DbQId5k0Ap4hIkYhMB3CuY/sbgNkicoaI5Pl/B0oaLZB/q/+bAK4UkbNFpFy8UCeHi8iN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\ne5zCx8yFY49lqPtOOsyOfqkXjdkCYAuGju8A9J/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e9cp7Z/Nu3UoJxs1BrRC+a8Lihv+I85qm75iVYA29Bpk2Ye84+PKLv6O22I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\njav281HWYOL4D31nP75TIBPJdwD6z1D8x+zrl3pxlK9dY20CHeZZABeLS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            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_MqyoTl9hAWc",
        "colab_type": "code",
        "outputId": "c7d426d7-b452-4440-83c6-0516b093a7f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        }
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, predicted_classes)\n",
        "plt.figure(figsize = (14,10))\n",
        "sns.heatmap(cm, annot=True)\n",
        "# Sum the diagonal element to get the total true correct values"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f19ff223da0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mMjyKkqWKAVCnXk0OhBymQaM6dH3rJf77n67ExV2xNqSTedo+v5G/fx78/HwB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\n970jAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJ6CGR50hBxj",
        "colab_type": "code",
        "outputId": "08b1eef7-c20e-4793-f5c0-1cc15f47b966",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "num_classes = 10\n",
        "target_names = [\"Class {}\".format(i) for i in range(num_classes)]\n",
        "\n",
        "print(classification_report(y_test, predicted_classes, target_names = target_names))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "     Class 0       0.86      0.86      0.86      1000\n",
            "     Class 1       0.99      0.99      0.99      1000\n",
            "     Class 2       0.90      0.86      0.88      1000\n",
            "     Class 3       0.92      0.92      0.92      1000\n",
            "     Class 4       0.88      0.88      0.88      1000\n",
            "     Class 5       0.98      0.98      0.98      1000\n",
            "     Class 6       0.75      0.77      0.76      1000\n",
            "     Class 7       0.96      0.95      0.95      1000\n",
            "     Class 8       0.98      0.99      0.98      1000\n",
            "     Class 9       0.95      0.97      0.96      1000\n",
            "\n",
            "    accuracy                           0.92     10000\n",
            "   macro avg       0.92      0.92      0.92     10000\n",
            "weighted avg       0.92      0.92      0.92     10000\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7pUBTXMw75-",
        "colab_type": "text"
      },
      "source": [
        "# GREAT JOB!"
      ]
    }
  ]
}