Training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Kaggle.ipynb",
      "version": "0.3.2",
      "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/Yumlembam/Kaggle-Distracted-Driver/blob/master/Training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "CVXiI19IfMvH",
        "colab_type": "code",
        "outputId": "c0ddd6b4-ca61-4612-e291-20ba1784d458",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "#install dependencies\n",
        "!pip install kaggle"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.6/dist-packages (1.5.2)\n",
            "Requirement already satisfied: urllib3<1.23.0,>=1.15 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.22)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from kaggle) (1.11.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from kaggle) (2018.11.29)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.5.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.18.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from kaggle) (4.28.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.6/dist-packages (from kaggle) (2.0.1)\n",
            "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (2.6)\n",
            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: Unidecode>=0.04.16 in /usr/local/lib/python3.6/dist-packages (from python-slugify->kaggle) (1.0.23)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZBKJ8z57fi49",
        "colab_type": "code",
        "outputId": "971b77fb-ef3f-45d6-a3cc-19e62d7c433d",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 57
        }
      },
      "cell_type": "code",
      "source": [
        "# upload the credential of kaggle account\n",
        "from google.colab import files\n",
        "files.upload()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-bb03c720-618b-4ebd-b199-26948256132f\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-bb03c720-618b-4ebd-b199-26948256132f\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "metadata": {
        "id": "brLCd9L3g03n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#before importing the dataset we want to use this code\n",
        "# The Kaggle API client expects this file to be in ~/.kaggle,\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "\n",
        "# This permissions change avoids a warning on Kaggle tool startup.\n",
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "iAAvEHE-hP2t",
        "colab_type": "code",
        "outputId": "1daf8661-fefd-4eab-c727-a5dc5d95e829",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "#import the dataset we want to use for our project\n",
        "!kaggle competitions download -c state-farm-distracted-driver-detection"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_submission.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "imgs.zip: Skipping, found more recently modified local copy (use --force to force download)\n",
            "driver_imgs_list.csv.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "sidy3WvLmyMm",
        "colab_type": "code",
        "outputId": "cadfc1e0-ce21-4c89-cdd8-008d7c0cc5ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " driver_imgs_list.csv.zip   kaggle.json\t\t        test\n",
            " imgs.zip\t\t    sample_data\t\t        train\n",
            "'kaggle (1).json'\t    sample_submission.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v3n5WJET1LaH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Unzip the data\n",
        "!unzip -q imgs.zip "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xBPbPKvAO9uI",
        "colab_type": "code",
        "outputId": "072edb64-c974-4f74-be14-93b71c2ef42f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " driver_imgs_list.csv.zip   kaggle.json\t\t        test\n",
            " imgs.zip\t\t    sample_data\t\t        train\n",
            "'kaggle (1).json'\t    sample_submission.csv.zip\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "irksJoXHwWk0",
        "colab_type": "code",
        "outputId": "d73ae838-b286-4974-91a1-8316d582ca6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import os\n",
        "total_count=0\n",
        "for i in range(10):\n",
        "  path, dirs, files = next(os.walk(\"/content/train/c\"+str(i)))\n",
        "  file_count = len(files)\n",
        "  total_count = total_count+file_count\n",
        "  \n",
        "print(total_count)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "22424\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "b7Okn0s7pAJq",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv('driver_imgs_list.csv.zip', compression='zip', header=0, sep=',', quotechar='\"')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DaF1B3C5pacb",
        "colab_type": "code",
        "outputId": "344fce49-41b0-42c3-bba4-75c68273338e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "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>subject</th>\n",
              "      <th>classname</th>\n",
              "      <th>img</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>p002</td>\n",
              "      <td>c0</td>\n",
              "      <td>img_44733.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>p002</td>\n",
              "      <td>c0</td>\n",
              "      <td>img_72999.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>p002</td>\n",
              "      <td>c0</td>\n",
              "      <td>img_25094.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>p002</td>\n",
              "      <td>c0</td>\n",
              "      <td>img_69092.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>p002</td>\n",
              "      <td>c0</td>\n",
              "      <td>img_92629.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  subject classname            img\n",
              "0    p002        c0  img_44733.jpg\n",
              "1    p002        c0  img_72999.jpg\n",
              "2    p002        c0  img_25094.jpg\n",
              "3    p002        c0  img_69092.jpg\n",
              "4    p002        c0  img_92629.jpg"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "3coOe-fF0N6j",
        "colab_type": "code",
        "outputId": "d29cd201-892b-458a-f1b1-789d36eb102c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install pillow"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (4.1.1)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow) (0.46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "q9ateq16zQIk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "dab89a69-6443-4bb3-a31f-8e4e11fa8cef"
      },
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "%matplotlib inline\n",
        "example_img = plt.imread(\"/content/train/c0/img_44733.jpg\")\n",
        "plt.grid(False)\n",
        "plt.imshow(example_img)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f77e7cfb198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VeALKWQLlTPnquKIE+Suv+jV+/hdfyanOCf7qza/jjs/ewY+/5JUQAqsgQ9ASrAKpFNY5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\nclyFCl1yRa2hGfZr68WTYCAzGWhOeImtjGCgRoWtnKwp7n6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/mDd88+vYYMDB\n408yLHJkMIG0vtFQmoKoGzO/c4oH7/s8D9z1WU4deZJduxe54YUvQEUNlLMIERAFEZgCKTOc6GNd\nCmL+a4ao55ZqWCP9PSxHga3A1qcYxpeXzm9XauqeA5QK8BhCnzH44OiDDzifsQg/21Q6qKlTDiUl\nZb2xR18jGqKUEh0q8iLFGk2st8r+7UX6+cOW/621vY9X8563Z2bWWoIoQtYQD+HEGMOoQl/iWudw\nUjBIh0TC8N73vpfe44f5l099ivvue5AL+xs02032L0wRaEl/OKDViLFCIqRGhoqJMGF9fZ0wDBkM\nBoRhuJUhn/e3bbfkNcZ4691xq+PcNsVg4KewURRRlsPxNTXGIEuJDBVhGCGsY2N9neUzy/zM+3+K\nv/y7L9FfWeH0kSdZvGAvBAqtJBubG9z4kpfxiz/98yzO76K/uclw2Of00hk2q5y1bEBV5QwGA1ZW\nVinLksnJaZ5/wwu48OLLuGz/5YRhiI5CkjChxKBQZJRsptU4w1teXjlnT1hrMc6eI3gwGqZYaxHG\njSfBo+dH38vagErUjpMj2t7oddsroe1TcgCR5eMA+Uz9+e3T5jAMt7QJghiURIcBOo7QcULcSAiT\nmOlum9N5n6luh+/4tm/hH/7ukyydXKHb6ZCnPcrKEUURSaNNWeTc+el/5YbnXcMP3/rDPH5yieLA\nAfKqpNls8n3f+z38l/f+GHGjyfrSEFMVTLab/M2f/SF7dy/w+EMPAzn/cu/tpGnKy176MrI8IxsM\nMWWBloIkCpEuJE8zJIrZ2VnuuvNer1UqJYN+DyUkL/y653PppftJOg1ec8s30Gp2qIwgjBtIuUaR\n5gyznOnJCfpFxu7FHbz8RS/gLz785zDsI02BDCSr6ytMz2liknprS1AhYKlMQWkyGiFfcz3nXOwR\n3lBUFqz/fnv/cbShRq/3j21li+eUsvVrlPSZm5Ij5seWvJeqM7aylkcSCoIwoNFoMD0zCcBjDz9K\ne3KC0pTemnWbC+D5UJ5/9xoFxzowCu1lyXToy1VGQw+txm2CWIcUhTdAKqsKGQY0Oi0qLN3JCZoC\nPvLhP/dSaaVBSzh98iQ7d+/i4cfX6LY7xHFEYR07ds4TSG/AFYYtgjBkeXkZ5Tx1bXSd1Xl/1jhL\nGV//bRAfoVB66/vhcIhSilbSQsoaI4efqDYnWh40rSDSih0zc2S9Ptdf9Tw+8IHfIOlMUqQZl1x8\nEbv27UNbwzArmZiYQeQpVdonCiQPPfwgU4u72HHJxeyaneGyiy8iiiJcfZgMBylhGGOtpV8UiLKE\nwYCsPENeFAxzD/NWMhoLo2xXnB8HQzP0j9VCBxjfh7TWYotyPHnenkWaeqotRpWG8+0fZ7ZUZbZP\nsUdBb7yHz2OOnX+vnP+5jEviWsVJBiFBnBBECUGS0Gi0MNMxWmvyfpd2o8HrX/Nq/uD3b0NQ0YwT\nKEOMKVnfOEurkfBnv3cby6sbTAYN4s4kFvi5X/oF1k6f4Mff+6P8+A+/i4OPHuKDv/6rvOzGl/CC\n51/Hbb/+PpJQoFjlznv+madOnOTN3/JmsiplY32NtdUlBv0eVenvwbPLy6yvrFIWhkFvyLFjx+m2\nZghkiBLw6ptu5pL9FyElxO0Gg8GA9cfXSYMWTx4+TjYscaLPcG2Ns0cPsWNukpe/8CbuufMOyDPa\niQbjSNox/XxIyxVE0tNUBXUyJSK0AiH/A0+xR0oihTW4ssRZz6tWgcK5cnxjbodE+GxmNDTxAxul\n9LbGskWKClFbT1q7BaPwm0jSneoyH8+jtabRaIwHK3mREkjFIO3TjAKCKBxPwJ9pKLN9PVvfcxSA\nkHXwl75klaMpfP241FtAYl0rEWmtvUagEohAwbY+oapKfuFnfoYrdu3l2muvxpUVBw8+wsbqOrML\nc6SVoypK0rKk9+QxhPNDhaL0/aMoiqhqM7HR3+QE456eG4kiCMbl6UgZZhQoozBG6VEANR67Jj24\nOGlE5GmBDhQGgxUWLSyba6tsTqwSxU2iIOH2T3yCKOqQIjm8usJgdQ0Teg+DPCt5wTXX84W77yFU\n0GjE7L98PxO79lAGiuOnzm4bxEmqWuDDWUD4gDMCv28OB2NZvWxz+Rz2yPaM0MObNr2N8GYfV9aK\n8GUtnVVngOdnndZa5ChrdM6jM0eyenYLGTE6jKzbAt37XqI5Z9K8fW1PGpxzVNsSB896kkipyYOQ\nQMdIHTAIYzaGTSYmJhgOM1rNhGrG8K1vfisf+dCHKAw0tcKVvqHw9ne8zdtVBAFHltd58tRJDuzf\nR7s7yR/8j1/jzOEneO8Pvps3v+7reccbX0Vhc7QUlGYApuLz99+LlDmvePmNZGWfo0ef4uTJ0+RZ\nydraBsOhH2htrPcos5JikHPvF75EHCZIoCwyDlx1MbMzE0xMtDl1+gQPHnyQ+x99hOmFOTZEG2Ms\neV7SSQJ+8Lvfxp5dc0hRcfunP06VbrJjdpqJVkIjiWhONGh0EpCituPwNaZxAikCBAHiWdwZ4Tme\nYu/Yv8tlw4x2q1sPXQKU8zeXETX3tA4So9NeSkleleNBwXbe9mhwU5mUZrNJp9Oh2+2OH8/znLSW\naRrdKEniTxYpJXGiUQjuvvMuJhotj63Di4bKcQV6LlPm/IxA1YyVrTJUUsmt96haOLTRaKCjkDDp\nEDcaBFFId34enfhyqTUxSSMMcEpCoFCBpln3yqY6Xao057tf82ouuvBCesurmLKsDwVHlmVMTrXr\nprojjgICJQkjhXA+y9kOVB4xjqSUaLFFGwtChVKCOA6JY5+JCO2fTxoRcewHA81mUmddtQka3pWy\nKkrWVlYR0uGcD1LNpEHcnKA7s4uLrnweJ89ucvONryJzJVlRIXTIIC/oNJo8tbbM9OQMzXxAJ2qw\n75I9uDDgFa97I5OLe5jYsUhlHXEc02632VjfREpdByuHDLe4yaOe9Cibyzc2xhnj9mA3Bmcbbxks\nigqJ8FjHuqdbnRe8tgcztW0Ao4QcW22c/7rRntteCVnOhadtX9sHO9thZ+MsVIwU+BVxnNROFIpi\ndoEoiogif43mZmZpNtvs2rWLgwcP8tj9n0djSHurPPjgg7zk5a/kHe/8fh546BG+9NVHESbjxc+7\niptf8DxaWBJXooEsS31mmqds9tcpiiFKC3bt3MWTp49y7NgJjhw5ysb6JmXpSIcFaerbIV5ETnHP\nnfewsrzKoDcgiWIiHXDBJbvZsWPOt7oAKzTXvfjllE6xWmk21le54dpr+K5b3kSarfLEwQc5deoo\nKhC0Ww2gotlK6HY7oDVRow3A5tIAUwkOXHYdXmJ4RCCB4FnMiZ7TDPKq6w9w5swyR588ShI1sHWP\ny7oKreQYkuH9VPT4lI9iCRiU9uKwxpRMTnVot9u+DzU3RRiG9Ho9SlNRmMLzL4VAaIh0uKXyHQhv\nXl6WVLakso7JyS6ysria8/vv4dRsL8FH0+yt9kDNppFeVNdZu8WuGfFwZW05KwQq9IeEN9dSKK1w\nQpAOBiinCcMW3/GSVzARCAanV2i3muSh9+qQgYYoYmNzg06nw8jPuqhylI78ZForrxZdBwVRU+nO\nl3/YfhOOIExu26Hgs3cFSKR0hCqshwkW7SRRFKC0q0tL/zMdltLmDPIBq8M+H/vHf2L/DS+iG7fR\nUURRVZTDjIWL9zM1N89Xv3JfXQZJHn3oQb7ne9/JHX/z17zqlm/i7LGTXHDVFQyGm5w9cbx2vfMe\nKc45SpOPg541nvFiSg/EDtjKHs//cs4RSouqM7+qKjF1cDQ8vVrYHvjctuzPCcnIsla60TV+5vfV\nm2fbxT/3KWO3yvNxt9157UivkWnrt1TYmpFTmRLb01RBhI5jBoMeR86cZHFxD/nKWWa6XSauuoR9\ne3by/KsvJyTkgScOsXLiCB/90J9y7KklTp96isnwu3jxtZchlaOhQ7CWKOkgcDglmWs169/JcWrp\nBF+854ssL59lY2MTZxV5VpGlFaBQLqS0FWur6xw9chJTWvbs2sVNr3wFRTbk0cOHKIxl70UXsv/S\nSzi+tMrZXkZuJZlUnD17lm+65RZ6/WUef+yrrK2cRgXe3tjjWf1wbensMr1+ShK3cU4wOLuGNY6r\nL7sCgcbYCiVD+DcYc89pgEzNgMm5LlMz13D4icMMN7NaNE+QpmaMs9NaMxx6Mnocx8jIjLPDjY0N\nqqqi2YqIYkUQCjaLARQDjDMYZwgagfeoMIYkjsclmdaawWAwNu/q9ftIB92pLqsnltDSA0hHg6Rn\nW9t7paNe0zi7FCC3ZZRW1L7VgR+AlKai1Wp4upRIwCicFEi1BTzXWhOpgOdddDmvuOGFLF54CfHy\nKdpxwuGTx8mMpZQelC61omVLlpeXaLcaWCMJA4mthHeRrm05twfAcfkmxdaXEMjAM3u8ArZD1yIb\nuJHAQTDO7vVIycYZqqrACUm73WI4HOKcRmAJAv+6HEsvy0jm5vitD/8V3/kdb4FBysLkBHHc4Njh\nI+y78GJe/5rXcfs/fIpeltMJNR/5/T/CIBlUhkFheNt/+SGklIRhxOLiInlWUhS+nVK5LW4xlUHV\nAifCQVGlT4duOYccPVZnnl6IdhTwxPjz/Zr7oBZF8f11QNUMHDxeb/t6GnNlmyze+RnkuX3gc4H6\nVeXbG8aUGJtTFt7fqKxysrNLDIcZWZEzP7fAZZcd4E03fhO7F/dSlAXNwBBQEddSHDsjzUfv+DQP\n3fU5gniS3tJpBusrfPWr97NvcYG1tGCmM0Ey3UGjofYGWl46xaGDj3LwsUc4fvKEv2ZOUxaWqoSq\nFGitAE2vt8Y993yBJG5S2JxrrrqWyU6XdWfYsbgb6wqyyvCZuz6PbnZpzexjmBsOHz/E7/zGBwHD\nowcfor+xho58sEuzIUsrZ2l3knEylQ0KitiQphk2HzA1NcXa+imSpEEStQGDcxIhEr7Wek4D5Ctu\nehl33XUjUQNEAAAgAElEQVQ3rhJcfMnFrK/2WDp5lkbcQElvai+lpN1u+wARRb5fpj0VKwgCgmDK\naxFubjAYbDI9PU1Z82RHfh6yhlgopcZTYWstURSxd+9ehBDMzs7iKJiZmOGxhx7h747+LWFN0H8G\nq6Wnre0l0Dm81nNf5MtwqcbDGKUUcRxTVRVTnQ5WSuxo8wfa9/5qmE0zSnj9q19DiCRf30RsDsiy\njN27d7Hv8svZddEFTM3N0Zme5Lq9i3z84x/jbz72UWam/fDJmBIhNFJu2b7CM2M8RzfoqNyGc+mf\nwJgJMhJirYy/SbEV1laEgSLUmmYrIR16T5YgUIRJTDg1ybGl01gBTxw/yZHTS+zftRPrvIFbrBS/\n+su/xE+8/yf560/ezutueilZlhE3ErAVbR2hrGTf7p387Pt+lp/9tZ/njjtu5+KLL6Hd6lBk5dhP\nRVhXZ1yMGS/Y0hu5nffZjZarJNa6sXCtk16cASl8Zvg1lrH1gTpSyWCrPfO0tFCoc5EN2xPIpwXI\nc4VChsOMzc1N8jwnyzzTpd9fZzhYY35+ml2LC1x+2cXc8vKbuPrqq5mYmKIyjqqy6DBCU0AAVZ5j\nsz4b/VU/ZENx32c+Q2IMZZ7STRqcOXGcL33x8/TXLqQtQx5Nc4ZBxvzMLFpLTjx1lDMnjrO5sUG/\ntwFOj9s3WIEzjjKvyIYeNfDFL99DllVQKrQKWVvbYGlpiXTY46kzZ3CUHDryBG/4xjcRt6d58nSK\nsZJbb72VIFB89av3Meit42yJwg88be3P0x+kJDaiyHNEJSmzgvXlNWanEqYnuhRZn421FRZ37SXQ\niUfP8B80QG4uP87Fiy2efPIoy0vrNOJp9l1wAWHQxEZbpZ1zDh0EDPMcKgPGEAQRVgqMLJmen2Lp\n9DGWlk4yzM7SaU0gheDSC/ex9+ILCeKEZqdNZQytKBgH11HJnuc509MNlJ4kCkKuuOoAf/uxv6GQ\nJQiFEArtnulSjYodf0MIUZscSYkIAkwtvRURAAKlNAKFkhJTOYgVuh66hHFEr9cjbjaZaE4QpBWF\nECRJREtHtMOEB+75PBtLJ5ifneflL76RfVdfycLOHZggZDMtMFIjgpCB0tw/dFzzum/jk/96N3mZ\no2XldfoUtCuHDCTWjHjrVY3bNFglPK1NgQgVFaYOphahBMYZpABbD2nyckhA4Cf9TmJ9L4HSGoqs\nohEpAt2kEeXosIuKuhRqFpgjz4fMTMUQVpw4fJgr9i5SYmlEGgn86Ht/ln37LuOdP/wjPPbQVwhU\nhwyFkr5bF4QB6XrF0Fa870fey0/+5x/h7//iL7j1/7qVCw7sZ88lN9BptWgmMcPeJmVlsUIQJA2K\ndDjeWyOywvZVinJscu2DlRvHL2XOPShGrxFC+JJawOjW8nA1r+qO7nhoT+kl5ZwpkWI0kQaqIUmS\njNkrVVWxurrK6dOnWXryCfbsXWRxcZHJiQ77r9rPS1/6Eq644goajQ4KReUqZM3nDnRYK5F6QqJA\nIJXBmJze+pKf3NuStf4K2XCIxFGWjnxYEkcV116yg3sfPsX85DRfuPs+0mHO6tk15mJFNw7opYan\ngiNoCWWZU5YFRW4IdEJpI5yrKArPPJJSM8gG2FLx2GOHWF2DOGkQxoLe2iqfu/cznFq7gIv2XcgV\nu3ehOgn7rr6c1sICd9/7FQb9gF0L+7jumh0cO3ofg/4JBAOwGc5A1u9j+n3sYEDcaGCrIWFdeRX5\nJogeSkeEkaDRiJmcmEWrBsgmWP20c+ucO/y5HNK8/9ff7U6fOYW1ljwrePihx5ieWiQdFsSdzngD\nblf/Hg6HZCZHCMHi4i4klsXFBZqNhFYz4fDhwxTrazRbHUQQMzk1TaM1QVaUTE1PY0ufWa6srLC+\nvs69997r6VH9PmlvSKvVZrLVoRkkCGp8opLbsoCt9Uxwn5GHdhzHqMDfJFoGflKtNVIrZKCJ4phG\nq0kYNoiSBEJNZ8c8Rgja7TaBjmh3OwRK0+12WTp9msOHniAOQoo8Z2pqgiBpoIKQIIxxQtcDJd+D\naTYVMh8QpH3+263fx0WLc0hniKIALfQYR6cDD7b1AzDGAxsdyDpjV56+Vg9xXC2nrwP/+Ihxo5Si\nqWOPk1SA8rYBVkRYJxnqkB07d3PBRQcgmmR1M2el16c3HDAsHGfPLvH8667mhmuuIVEBV+y4gOtu\neCGdVpc//chtFGnJTBIQMrKl9+uJQcn1X3c9jz30MBN43+vBcAMTSQrT56N/9dd8//e+ixff/DJ2\n79uLUAEq0AyyrQPyfDEGAG22PvCniaI8a4Ac9XLlOJMypvTX2WXEYUgch2xu9HAYHnrgAcqyZGZ6\nmtfc/DKuv/56Dhw4QKfToZW08PIbFary178oPEwpiSPA1geWGYdvS4kEsiKj3+9xdmVpDGS3tsJY\nr+Q9ykZzY3GmZLDZR6mA9ZUed37mbqrccN/BJdIiZ6XfY+fiDppxwJ7ZDs1A0UjiuqLw5nCh0pQ1\nj72y3jMqz3M2B0PSYcHGesa99z5AECQk7Q6A3zcSBoNN3v72t7O4uIvb7/hXTBgRdqeY3LVIZ3qS\n+ZlJ3vDar+fwo3exudGDokDkQ8qsz/LyaYJQEdYVUavVOmcG0Ov1aLVazM0vcskllzA7swNNgiPE\nuRApQniWEPmcZpD/dMfdvPrrbyKKAvI8pUJw9OgJgjiissW47JFSUhnDngsWCYKAiflZsizjgj2L\nVFVBb2MNrEEGmosvvYSjD95Pr7fO2ZUN0tzSnZjh1MklNocpSydOeaOqOnuYm5tj1+xuxJxAOd9U\n769toOzoBhBgZa0auLWeDQc5KoO2TxvPf6+oFXtkjWGrrFeW1mGIlQIRemOnYW+T+7/0ZQ4cuJLX\nvPZ1/K8/+mNe+8Y3sry8zGA4JBQSFUYooRFCeekvISjwUlHzO3YxuWuRfpHSovYRERZZT9yV1LUL\nokRKCKRAC+/zLIzxQx0roSq90lBNlYNzeexaa0IdoUNF6SxDU1A5zezOPdz4ipvIp+ZZXtnAipD1\nQYltJwQ6JApDsvVNdl2why898ADz8/MkKoBGxMOPPMjOhR1sLG2we65bSwr44CPxJINPfOzjzE7P\nEQG5tTSFRFtNIAJk1eN73/KfeOPrXs8/fvZz/Nbv/h4Gw3A4pNnwg7xWq0XUaJzT/wPQ2/oq4rwG\n9EhvdPs+GAdIWTIKjuD7yIFWGFshygFPHj6IKSviJOSml7+C3/m1X6YRNQCHq1lBoQrJq5ygtgAR\nGFA1JVT7fdVP1+oWiSUKA7I8ozIlg8EGlckQ0pLnXvXIupyiyMaBsqzyLRk3F2LLkqrMGQ43GG4O\niaKIk8cOc+bUcXJjmd2xwGtufhVVmXHm+GHQkipLxwejKQxDV2Dwvfcqz8mKguFwSJ6XKB3x+KEn\nkVITRw2qIvf3RhwxzAuECvnkP91OHMcs7L2Iiw5cg0wmCVstpDLc9KqX8PAjd1FtrBAKRVZkVIM+\n6bBHI0kQwlEVvp0WBEHNSQ/G1NlWq0VrYpLc2NoT3NT4XN/VeLrSwrZ98CzP/f+wGvzmb/4Br3r1\ny9i9eydXXXM1G5t9+v0+w7TP3r17mZ+fZ2ZmZhxs0jT18lrNJmmaEuqAKEp4/OBjnDl9kgceeACZ\nbrK+loOAMEhod6bptKcIdMAl+y45B/eW5zkrp7380mC9TxgECOvYMTePrqfH8OwBEc4FkKsadiSU\nrCfTjHnYKInS2vcXYYx9jOOYLE2ZaDaJdECoNMsnjvO5Oz/LXXd+lt0XXEijM8mPfeBnKYVg7fgJ\ner0+M1PTuKwkVA4latyk0uSFJYpjTBDzpjd/B3/+u7/Jrh0zlP0eQgZUeYEMQ4RWKOHZQsI6lB5N\nRAVY4wc7gUIKSaC9t7JwFi18Vq2l/4pDTStJcFIQKsm1V7yAF77wVZweZggZc0a3WBusM7UwTRga\nqkGGFpqGCji7ssJke47O1DSPHHqSN9x8M/Q3yZ1Azs3SbTfICoMOVY3C9EHS2opmEFKmQzLrCAqD\nDSRx0sRiCUUXVyhmunu55lrJW/4PwZtueSPTcYuN9cOcPHmS22+/ndtuu42JiQmfBTeb/oCpy9yR\nGMfos/UVhRiLd3iHPzv2s3YuR0pNVTkEEaur6xw//hSra2f5gbe9jZ95//vYu3v3WNoXL88LOJww\nCGkpqj5aKUozGEvUOWHH7QBjS6SEPM+pTEmRKZ8dmhKhfP/XuoKiyBkMtmwFjDFj98HRGqS+Z1wW\nKVmWsbGxidKCKA54xStfyvOe/wImJqf4l8/cyec/fzcveP61JI0mIvdByEvEOSpT4WpK8MbmgGaz\nSRCDjhzLZ1ZZX+/RbHiVe6kspXVkZYVDo6KIjWHBnosuZdf+ywg7UwRxl2any/R0wtlTR8k2zxIU\nBVlRUqZDqmxAMRyilADlHVIXFhbOwYsCdLtdH8SNqUWkSxpJh52zezFU9V76D6rm8wu3fdB98lMf\n5/CRg7z0ZS/k+uddQ5oOeOihh7jqwPPGUlDdbpd+v09RFBw9epRDjx/m2LFjHDt2jDzNyPOcnQvz\n9YRVEOCpUWUhMAacVRRFSZYWbK5vnNOUH8lJSSmxlSMKAs8pThKQtbG6AvkMY+ztZdaYo11PMZVS\n6DCoA2Q9kAl8YNRRSNJqEsYRSdgiiEKiZgPRSOhOTNJsNpnsTnHbB3+F++++m7ld+0izkmZ3iu+8\n9V2kFk6vrbBjfp4kiv1p6eryWPkS2AYSUXlJp4lI8o5vvYWrFybQZYrUjfHNH8chOhhRHgWNyG8W\npeu/QUvC2vlQa42OYj9YSkLiOBqX5mEYIozksisP8HUveind7gIZmiNnemz0MtZ0g0/8wz/w+m/5\nNnKtGZYleTqkzFPMcMDJU2fodDqsLi3znd/0jfzPn/u/+fiH/pyP/Omf+OsxOUmnPUEUSAL8yS4x\nlCjmL97PF+77IrONNh2hUA6ssD7zVdCr4J3v/XFeefNNvPn1N5MPShaaAypTolVAUeaUZclgMBhL\ne8nGlhbA6AYbEQ7W+j3W1tZYXfWDjaWlJb761a+ytraGNRm7du3migNXcu21N7CwMMf8wgydToOY\nCIHAOYsSgrLKKNKMsvS9OqW3KJ15no/LYGMMVtU2Im50sGeMbRVce5xNDtNND+sqM6qqxFkPl/Py\naiNo01aVU1qFMyVZ2qcsc4b9lKUzK2ysbjC761IefewQqys9Dj1xhNn5OYwtuP7aawicL6dloKnw\nSAwhPeKh0WiRDfpUZcmO+QU+/CcfwmaSQMUY4yjNkMyURM0OQaNFe3KG666/gbmdO4k6EwRJk4mZ\nWU6dPMbrvuFGlo/cS8SQ9aWzFFlO1t+k6A+2hFRCn8js2LHjHIzydnUtHTXHn2MYNLjiwLV0WjMI\nNLJ2yXmm9ZxmkL/63/8nv/iLP8eHPvzHPPLgIc6cWuJtb/8ufve3/xeX7LmSQ08d4vjx4xw5coRe\nrzfW0ZudmieKInZMzNPd3SGJYs6cOUM28CXK8qDPU+kyJvcYRCVrqa4wZqK7pdxhrfUndFHUWUDM\n5nAThaDZSryAg/QiGtI8WyL+by8n8EK4dW9EBT6QSV0L5ApBJDUhkmytx2/9P3/EIw/czY7FBZQp\niMKIYjDkbz/8Vzz/la9kttulUQdzYT2vz3m+CqBIVIe0qBgWFTOTk+y66EIG66foKjwdTnoQs6kq\nBL7ERtSlqxBoodBSeE1EAOcQzjLsb1BVBcaUngYZKpIkodNpceVV19HtthDSUJGztrbJ4vxeFmYC\npJJc9J/ewW0f/hA33vKNxEohkwQtobexwe5di/R6m3TaXf7sw3/FU0eP8uB9X6atNRVwanWVouY3\njzpGGkHVz/nIH/0hP/fTP8lv/PdfH1FoPNxJ1mriAk4sL9OZmiazMNkMcE6hazsCZw3NRotmY3Jr\nb+DGPthl7Ws06muNxGw9KN7vi8L4llCo5TbrB0thhliX46oCoQX9QZ/NzU3Sgd/LWIOSfi8awTng\n9e3QHuN8e6QyRV39eLaYn35v96rxCA0fYA3OqZr9I8AFWGM87KZyWOsoKZFs9firqiKOQ3pC8MV7\n7+GRhx8jHRqcEKwsSzoTbQ49eZi52WniRkK3MUkjSWh12gipcVKx0eszO7cbm+eUWcogN0xECXk/\n9036wNKME5LOBOuDlMsW95B0J7AyIAhihoMen77j47znPbeSqAE6T9lcXWbpzAmsMYjKEgImNwgp\n0DpC1Zn8diLHOT47ZoCU0Ov1mJiYYtBfo5k0UCrCd66feT2nGWRjYZdLe2vohuKVN93I/Q/cx8LC\nHEEQUQxST2APQ19KVBUbGxu+/F4b1srLhumJCXQdAEe4jjKU2LK2gTTbQNvGjCmM22EdY9WgwHOf\nw0DRnWgRxgFW+SwyrJ7O2fz3ZpBW1rzrKEQHASLUTExNooKARsMPo3QQEDeaPPrgI/RW15hodgn0\nGpPdSRYmF5if3okIGhzfTFHtDmGrhVX+JgrjGOMqPwhSPjJEro0KJJaKUBt2TzR4z1tvYb6hkZVn\n+iilCCNdy3z5DLId6zGkKkkihHS1RJeH30gKzxaKtx5rNGOmp6eZ33sBk1Mz7LvociwJd/zLF3nD\nG99GWSlaGgoBywbuOnwE3W2Brm/e4ZCsKOkPU09TLCtee90VxMbQUooM6JsSpwLK0hBKQaIgxBLn\nlirS3P/ko+xc2MFMo41CUOCQRR8TJjx04jQvfu0bue8L99KWMBsGaLz9RVVVhNuk9kpj0Eqhzfa2\nyegDr/91274Aa4ynjUqgqkApD2tQBsyQ9Y0zDNMNTq6dZHV1lTAIqKoCJfxbvI+MxcitALm9peOc\no8o9LVRI73vuYVU1/9t5cyqtQ5+hlSVm5KnuRtqp3icoS3OM8Vmkcw6jckyRk6YblGVOVRhWV9Zw\nlePM6pDHDj7JmdOrqCAmzSomZidJ84wX33QTCwsLlMaSVcZ3S3WAVAHGeWWn+ckuf/exv2T5yFO0\nRYBJS1zpmNjRJkwatGYWmN61h9bkLEl7gieOHGFtZZXl1eN84Od/lAv3TNFfPc1TX3qAhlOc7p+m\nygvva600SZIgtKK0hpmF+TE7btT2GIuACIFw/roNBgP277+UVnOCVnOSHQu7EXL+P2YG2aLARpLr\nXvR8fu13fodf+bVf4TP/eDuy32PzbO8cEYEoisa0w87ExDkQDQukdsu+zaVb3G3n3NhnxDlXl1/n\n4t9sHSBNPqwbuxDKCIwgQOHcdnv3reV/pv9vMXIBxIPCcc5PO5VDqpH2s/+9AqnAeW9iVVmiMCEM\nYu778v1IrdhxwQUkrSYTjWu9UxuOx3OHcCU2CcBmdAjHU/aiKEiSpD4QHAqFEiVlAXGzQTbcpOwE\nbAwt7TAksWVdrjmslRjjUErgnKIaAcGln8gqKRC6ZjgFjsS1CAJBEPgyPI5jJrpTtJIOQg2Ym99P\nUVo+8cm/561veSdaKCwVVJJQS+YUvPDi3Xzh4CH01CzoFrYRoYOSRDdwZYFWBSv9PvNJzMDkaFUh\n13rMTuwgE4rTeZ8sEUQCokATG8Pz9l2G6fcp1/qU7QaVllThBIMq50Mf/ijl8jpTOoSiRISCggxH\nyVr/DK1WiDMpytW2CSVU0itMKREhRQBOIghQQnkpT1TtmqiQMhgHzEo7lKywpFRmnTTt8dChh2jG\nTfpr61BUFJkvk4ttoG+vE+DRBLbYEoMeUx+t7yM6AyNrG2v9sK0kpqr0OFu0Vvv811oMXrZPArb0\nE3VXOWzpn0+txyaWlaXIK8oqA+ErgLlGk3xuirS3xiDtk4QBzSggHW6yPsyYkZreoI/SnpEihd+T\nofI2q67IWTu9gkSR2QIXGcJ2SBkFBO02famYbbWY2zXPBz/4Qfbs2c1bbnkJ//Xdv8mTxx7DDAcM\nVvpMdZr0exs0QsUgr1ChRtWojco6gqSFchKNd0t0BpQOkNKbrknloRUVJbIVkFLSTCLac7vIZPNZ\nUJDPcYCMoohXvviFVJHgJ9//fjY21tlYW6MVhsRhNPKmAyDLMqQQ3qNXKSpbjjPA8ylbrjJbrBV7\nbrAcsRy2T5nHzzk5ZoVkWUbSavj3i/OdrZ++zudmn//c6HcVQoy52kopAh1RWsPDX/kKQRyTNFq+\nn4ciNxXU/b/KWZpRhE4ikBIVhdjaGjcMfeY7+pvLsiQIPcA7z3OSJPEtio1NmGhtoxiKMU99xA4p\nKotXP6rLRCdR1jNENJJKFQilkFqgIo1ux7hEEXQaXHnldayup5x6/DFe/9rXEweJH6o4cKLwP0so\n2ihmGi1KHdDD4ASoKCYOQso8JRYJh44dp3HRPtphxMk0Z3JiBydyMAlUjRZpkTMRRiTSi+Zb44jb\nHYq0wKiAQZGjQsiLgl/5pV/ip37uF5AanAhInSMWcPzMKYq8z9mzfUJtCQQoIRFCYbRHHjcaHZQM\nEEKjlc/SVBigRxhDZ9ECBKEfHUmNtQVCCjY3+lhXUBU563nG6vrZMQRlu0C0rP9nC1DKeeC99SXw\niDYpzjugxwgJ4R0GfUZocc4jjYrCV1CVq4VaRo/nXl3IjZKCKqeq8YrGGqwRoAIkkmEeEHSmqdRJ\nRCMmabRZ3hxyxXU3ML9jp8/4gwgrZB2wPHZTi4rZqWke+vKXyYYpzTBAWofWngknjCNEUuQp6foK\nn/77B/nhW7+bb/rmN3HF4hynzh5js7dOpARllaJ13Qt3IarTQSIo82qctSslxuLNskZUCFErY1Eb\nyumQvBgQ6S2ZuMoUKP21WVHwHAfIK66+ivX1dXp5n+XlZfr9TSaabQa9DXTtJzLqx8BWxje6GLBt\no2xb4rzSefS6kYLK6Pun/eu2HPyGwyFJq1FvQp4pgTxnPVuAHGUIo4ZxURQ0uxMkScLq+jpHjz1F\nGEQ0ooSk2UCHHkNZKVDCT5ajMKLE86YD7b2Yx9Pybf+f26Xw0QFpmiGsRlCC+9rT+FGvpjAlQgSU\nFRTC+Wm7cQih6qGH7/ugFDqOfEsh1OhGwvqmZXWl4Oqrnke3PY8xFUp5CJINTE3lM7SE5Lrdi3zk\nc59n6vJLsQQIKbxeJQlFmbNZWlYrhw3hlz54Gze95JX8tx//ACce+DK//fef5HXPP0CROU5Q0I4j\nAiWogPuOHOOSyy9iaZgzLR3v+4kfpzM7w1u/6zsZGkeiBDmOqupz4vQJmokmyzZJAkkhpDfnEoLM\nZDXusEJrf2Bp7cU5wkaLMIgJdVTvSYOUlScBWIEUGpwlDGMef+xxyrzgzNIJkP5wdDiKcktFCecP\nIkSItzSWdVlvxyyl820/Rs6bIxjcM8HKPFrDV/3SgbFekLcyBVUt1OvKAlMUWGOxKAwSg6ASjmBy\niosuvJzW4j6eePIphmnOC666CodEqcj3YrV3A62MIQgkOohpxQKblzxx8BDtZhNtLYH23HyPclCs\nnz3D4oUXc/KpQ2ysn+U97/4/aUjNmbWjOFvRaYY88dhBIi0Ylt4SI4wCZBhgSi+4ggSpPaxnMByO\nVdVREm0tExPdLZ90K3DEhIlG63Ds9a3+DaWF5zRA9jY32Rz0CWLNqWPHSZIYKkOepiAiTFGe07C2\no81gzFho9pl4sdZtiQ5sz962B7HR13af4FFw8Sol+XiC6SEW/9//znOmadr7X4dhyNLSEk88/hRz\nOxZImg0mJidxQkHgTeiDZoITgtJZENBst7wEGa7OHr2STZZlY+myUQ8mL3MiHdR+xo5ut0trok1V\nVUTnXYPtgX1ks2Dr61FZgzHKw0GN9L+aA4WiLBxWWOJKc+Zsn9XBMb71Dd9OUZRIGVCVDmN9IMiR\nKOHFH7SDsig4e+IYF15zFRuVF3YwSnrpujhiYWaaBx47yBWXXsof/skfc+c/fxYVWphucf01BxBA\nUlnOZH2iOEIDl774ZhYWFvjkR/+UfpYx24r45Mc/QVoYUJJSOA9fchX5+grr62sEqouUkkGWIo0H\nEUkHpfCq9UVeEoZxfW28LoAqcpK4SRI10DokChtIHBKNsF5AxTpotybodCZY31ghy4bIMPKc7JpI\nwAiA7hH63inF+lpdSomxDlvTFre7CPrfxUOejLW4bS2kUVXgrMBUjiovt8F8KhyGvPST+qqqEEXh\nKYgoKiS5sRSVpjIOq0M2l9foTszyoldewvLyMsM0p6wsqswIw4iyKFGRohH5a+RMxdkTS9iyJO1v\nECqwRY4MFUJCWaQcfOQQTsFPf+AnePU3vJo069GWJWdWDiOdY2npJI04pNHQpL0e1hZYV3rtUSs8\nPEdKgiigQhCEnvyglNcwAGh3O16Upa7U8qysVfFjrIEwjMmyFBUMiGT7a967z2mAPLu2SlEU5Ksp\nzhj6/SFVUeJMiamFBsAncLYOhKYOfFVZYkYKz+dlRdslpkbv9//6kgR4xhNX1adNlmVYzNgYyVqL\n4FzjsPN5ydu37yiwbl+mDupaa6ampnjwwQdJkoSde/YSNxJfdgchQvkAqoKAqu65ypHMW5oRxhFl\nVXrbiLp/NJJyG4Fky7IkEF6IQ4UBxpacGm7Ur22ha2ms839HgLL0vdNACohCKiFJCRBOoo0kKQKs\nijlybIVKaA5cexEXX/cK9l5wKY3GAn1AqRJkiI7AOI+dHJiALE1ZWz6DVoKZ+QW+99u/lY/fcQfN\nCy8hbrdJByXNiSkqZ+lXlsldezix0ePE/V9G4xmzx84uIdNlpsJposTimtMEwDve/V+RQcCjjx5k\nkJZcuTDPPZ+/mzLNeNe7ftB/XkqQY2iIkEOHHqPRShhkA7Al1uRIKxBO+uBCgbVZ7fmd+35kfci5\n3pAwiAiiEKUCwiih2WwSxg26YQvQCOE9uC+6+AqmF2a49MrL2Ky58yMoUZZl4z67x9sG4wBnKg9h\ndsia0bpt4CAE1ahENnarP+ncWCV9ZOVgXekDZVVRmIqqMqR5Nt7TxoQYJ6gMZJXDOk1pnMc2Djy4\nfB8IreYAACAASURBVDDM4eTpLSZbllEMSobDIYPNvneqVArnDHEQEsUlaa9P1V9j+cwZyqLiB77v\n7bzrB9/F7r17iINaAwAo3JBmU1KUS3SiihOnlki0pRyuE2tLSoFwFUIasqwgkAohHVEconSAwKGj\niEajRZ7nWBxhHFFZw1S3SxRFpGmKVn7YayoPs0rTlH7/CJPTs3TmF75GhHquM8hez5fRZY6tjFez\nNrVE/UhU5bwscLTOZ3NsX/JZOob/O1P7NE3H/jX6GYLJ11rPlJmNhDeaTe/g1mg0vBtgq0kQR2gV\nInTge0pCUFnr1c+pm/GAlhKTFwRaUwxTpB5p2rlz4CdeHNibOVXOEqpztQcrU20TGN5y/JPSN7qD\nIELKgLR0xHGAVSHDIiMfpmjdZr41zZu+563sumA/ImqTVdBvzJMbTUMJGkr5A8NZnJCkxnLqdMp1\nz7uGZjvmh37k+3nPre8mzTO+/aab+OSDD2OAiVabLBuCDuusSiAIyAFds2T2NFqIhsJRUgjDjAg4\n1evzqd/7ba561dcTVhU7koDhZo/LL7yYWGre859/CB0oUuMVfSyei+6Uw1XWO9AIT2B0ZrTfNFVV\nIIWs+SyGSliUsmgJhbGYssBJQVTmvr8VJpStAYEKkVLTjltklDRb0wTNiCj0bn3D4dA7TZaOAoOz\nDmMtiKdXPVt7agT5AWt9n9JaP40elcujgeYIr+m/H1KWJUVpKI1nZlfGs3yUUmR4oYfCGApbedXv\nyoCTDPpnWVlZochzNjY2KIqMOI4ZDodEquWhc4FirtPCWc+6CrTm4UfvZ2N1nZe/5MX85B/9Li96\n/g0gLVIpL3dXrZCVFRv9vu8vKkeV9zFlQTNWGANDK5DCEAWC0qPZPABeOJTQmMqQ5TlOKSItQbZR\ngcYUhXe7zLw4b5R4IRhX+KCIUigZUAReSlHI/8A9yEE69Jg8U4HxN7PfGFsl7b9F2Xum5+SzyO9U\nPHOgfaaVpimdTucZM61/zxr97FGvKQxD+v0+p0+fZnbHThqNhgeOq9CDyJWq8XcADlFVqJraV5bl\nmJaoLCihKGrh31GbYaRxqZSiLEqc0pRViZWOQG/93dstQ8/XQSSHtUEPW60hdUCj02Zixxz7r7yW\nCy/Zz9wFlxG3m/Qqw9GsYqLbJKsCSkJmpc+aKiSpKbBFTm6g0WrzDd/wzVx+5fNJ7YC33fpuelRM\nRTGqqHjplQf40hOHsVlK1GyTW29voKQkCDRLgz774hYUkBCw0c8IW00y6Zik4nu+49t59S1v5I47\n7+Zf/+UOyn6f+XaDot3hyKEn0HHImbQkSgIKW3Hw6OO1iK8PJjjjd4WtqwxqlI4Kcc6rlI/2mbUW\nIS3OSKz201FjDEEZkucZqRnQTFooGSK1wpnKB/MyI9/wwSrPDUVucVbhrMIa4wOd2PKneVpP223t\n99HnPbZ9KNwYyD5GdtSB0roBZWkoSosTAVUlUSrBWChKRxU0KE1JWlpMJShKR9rPSNOc0089Rm9t\nlTiOvShx07thTs1MoZTXXsUZVs+eAVNx5MnDBErw15/6M2668ZVk2SYaR2VStJAYkzPIUlZXniIt\nCpz0+zUMJNKWBAKqylJVBUU2IB1sUpa595aqbZI9192SZUMq4yBQiMJXS0mSIHXt56QUaZ5RGq//\nKY2gqgyuckhpEUoyNzfn1aeeZT2nAVLlxXg4cO6peS5daLQxzv/+a2WD5ln6rs6e+55zf+4Is+Oz\n0HyQIq2HVjjltjyjnZ88bsfCKVnPGYW3LbBaYnBYBVWiMWXFW9/yFr7/+27lisuvBJkgiNEqBiRh\nGFM4i9CKivq0HdlE1NN453zPsazB3IF0uConjiI/xbaWdOgVYXAhzuSEEmyVsXvvhZQuQHV3oPI1\npufnCJIGjekpoiRGCkW73SZsNOl2u8xMzpC02lgHstUmardZXlmhfzpnIZ7j9GCTqahJeXbIzskZ\nOngY6npekCpFokOiJGTdpdx18Cv0Byvsa83zwKf+mS5+iqkAFWpmTcmNFyzyT1+9H5UsItod8gw6\nOiawkEcRfQXTCkQZMGE1g35J0or5w3+4k6I5zbHDT/KDP/BOrr5kH6FwePi7pRkHZDjaiefT93qn\nOXPkUebjGvztHMaI8bDDOENZlciRi2WtQK7qw8ki2NS1In25hSIIS//vIEspGh6XmGZ9rLVkWYYx\nhv+XvTcPs+wq6/0/a+3xTDV2VfWcdDrdBSQBIYRBQOZZiIhwVSaFC4jIJIoIgoLDVXFC7uWioldR\nEVAmkUAgEMOYkIHMSSXp7vRY1TWfOsOe1nD/WHufOtUZAOX3gL/nruepp6rOsM/eZ6/1rnf4vt9v\nnLljVSGw1q5K7domnbAcpeiXKdU4pee5CrRUA4kGYDOE1ppMC6zSaK0GnTYVcUTPNCisLnktA/y4\nRlZIMmNR2tDvtCmyhKTXo72+zGijzsryadZXVvHDJmGtzuTMNjqdDlEtxqQp2XobGaacmj+BtYaR\n0ZjnPPcZPPfi3+VRj3wEQmmyfA2LQnsGbXI6aZ+icHnPgi5eYNF5gUCgUyiMoa81nrWOwarpE4Yj\nrK9JesLH6JxAdbHWrSsnr5E78l5Pk3ldGvWQWugMpE57FFYhwhCrNTmu00gVOZ1OBylDfCxaCQ7e\nd4T93QHFZ2dn/xB4HM6g/g/gauDvcX3e88BL5ubmstnZ2RcBb8Tl+P9ybm7ur+/vuK2Rmj0TclON\nM8/rTAP5Hx33d71nHtcYw8jICPv376fd624ayHs7h6AUUZLSabeUgPEoiugIxU//t5/lkks+z+7d\ne/n3y7/Cgx50AQ+/6CJuveOQwzAKCGoxYcNBY/AkHv7Ae6280GElRFc9lWUhxp1TEAQufMstUoLn\ng7AFNU/yrcu+wDm7d0PaptZqYj2PeGIcbQ2+HzIzM8Ph+RNMjU+Vx4dCC4JWk8kdOzly9ChjQYOw\n2WR0907GxifwhM+ubdO0gojYgu9aY7GlFlA9DNgxPcb5FzyRPEn4yef/OG963StphgH+AElo6GhD\n4vl89chddKVP3JqkGTXBCAwbnN0aYT8g+s47sJHkdCfhZa94NYfvvIMHHDjApz/291gLIa76X6FX\nMwQJik7a4fobrsZHI5KVwT0c9toGkLHKaJaGUVQbq7EUcjPHXKEeHDdpgPYYwLiqtEZedmqJXjbo\nra6ez/N8kDsslBrk1aWUA61ygMJsSkOoCpFRhtVFqYaoy9hjgJ8U0M5d65HBx2iBNpK1jYy1tTa9\nXuLIogVsGxtl966dLJ0+yZ1zt2O1psgFfhiSZH26nTZZ0sMmCWm3h/UkYeTz5je/iTe+6bU0oojU\n9EiSDkUvw/MEvi8dOSaKJO2iym4jSwbaIIXLyVe6PXmm8KzBWIVSBUrl9Psp3U5Gr5eg05xcK4yB\nXr9Pnhco7YqSjckR6vX6GTUFb/AdG+FjjCJN+6yvr6OU42mIoyYv/elfuU+D8h0N5Ozs7BOBX52b\nm3vW7OzsJPBt4EvAJXNzc/88Ozv7e8Bx4EPAdcAjgBxnRH9sbm5u9b6OXW+E9r4+/75yjmf+/b2O\n+3vvvZ2L1ppt27YxOjmxxUDeI+weMpBxHA/o9ZVSHDt1krP3n4v0AuaXljn73AP8+E8+nyRNufnG\nWzh69Cjbd+7ACwPC2Mm+WimQItrCfl79XeG4jHT6N1prarXaQMJVKUUr8jl9eoHJiTH+7VMfZ2Jk\nhL3bJjFF7tIGYYCIY2qTE6QqJ/Ajtm/fTqfoEkiP0I8YbY7ieT43zd2FH8ds37mTg7vPwgYe0egI\naZ7RqDVZXVwiQLJ/ahs7pqZplPmyTjfnSU97OlEtplEfRRUJl13yaVoSJIYK6aqBTOV4fkjHwFI/\nZa7dxtSbiChCxJIwz7jl0i/xu2/9dR5w7gGuuupKrrnpRh44sw2lLZHnymjCOrCK8+6DkvEaMlGg\nyfj9P/htDpy7F+mJgXc+3EY4oDazlcqjKAlwXfGqMl5nwsSqopeWdoDDq+5FljnDKIpNmdcqHVL9\n5Hk+mC8DIz3UNudQaA4Pi5XkSlOU3m0gC4c2UJbCugq6RqC0ZXGtQ7/fZ7W9Qa+bYKxkYmKaRmuU\nMIywRlBkCXffdRfzp47RatQZaTbwfZ9jdx6lOdLg+T/1E1x9zdf598sv4+d++gW8+zffyc4d06RJ\nn6NH76ZQPZaWFwgCJ9TWiutEcdndhmt/zIu+69RRCj9sEtdrxFGdKIpLeI5rBBFKIqUhLxLW24uk\naZ8kyUjTlG47c4UYYxCeRy8pWYmswARyEIZHUTTQhgrDsNxYHJmHu1+SzkaCFBFGS57zjJf9pzpp\nvgJ8q/x7HWgATwB+oXzsM8CvAHPA1XNzc22A2dnZrwOPKZ+/z3F/XuN3O/4zBvM7jTiOOXVqkdHJ\nifv9TDPkAa+vrw8mfqfTQfdTbr7220xMzzC5YwcjIyPcetttrG20OXXkOAdmZ0nTFE9Ip+ltXeus\nwg6YZHShMdUi1M5wFyU5avUDLm8qpWRxYZko8DgyN8dovUktLEW5oggZNSHwEWGEsh5hbQThBSh8\nxmtjCCDpJXzzmm/wiU98ir/8278jVZrW6ChhPSaIayRKUas16CV9RODj+wGnOmscXZynFTeZaI2x\nb2aKCx7+CJI859+/cAmnD89RKI2QjlCi+goLo4j8EJPlbAtDWs2YuxeXKOqabtLBNzETzRq79x2g\nNjLBVV/9Ol/71pVM1Jp0NtqMNlukvR5x6BP5ZVdLlfuwrp0vsoK19jpPf/zj+MpXv8jkjj0Db7wy\njHEcl/lA43rkjcWIUhe99CCtAEr51zOHLenEVV6GuUNeprSWLN+k8KvywAP8aellGmOcbbd2AFmx\n1hIUOUiBh8RID5R2hW2j2chyQDqyCD8izRRr611OLy1zemUV3/dpNUc4Z/9ZJQNP1aWjkNZjaWmJ\nlZUVpqamMMr12DdqdQJfcurkMa668mu84IU/wYf+5v1MtCKKpE9n7SS+7zM24pNlPpgmlTEMAosn\nNQInJJcXOb4nHOxLFagipd9PmZmuuVSU8hDSI/Bi8AK0zvBDSa0xTpor/MgSYBkRcUlaU6CtIIzB\nUyFWCnKcE9FoNonjeMDCNNC998wA2lSvNQmjhJMnlnjve/8nz3nGy+5z/X9HAzk3N6eBXvnvK4BL\ngKfPzc1VvX2LwA5gO7A09Nbq8fscw72SZ9IU3QP8/T14ft9tqP6djgsu11OrBczNzVGr1ajX667/\nuF4v2ZLdwspKZhW/LH4EQUDS65N0uvjSY8fUDI3WKM16g/XVVfpKU280OOecszEqxw+ky4Fai85d\nNVr6kqzU4oGyEl2SBwPEcYTKMwLPo0j6Ay9ECIHM+2x0ckbqNWLPw7euJTGOQ/p5jo/Fk4K0axF4\ndDod1mtNxiKPK7/xTa69/AqoNZFhzOm7TzG5YwdFN2VFa+j2mZyZpt/vMTk5OYCrJNoQhCMst3tE\nzTFS4M4TxzjvgvN5zo8/EwE0fQ9hFJ4sxa2ExJOuMzrwPdCayPM4d8cMty4tMzY+RpZDdy3lvPNn\n6fQTPvbJT/OQc/eVPO0BnoCwUUNsubVlo3OZJ5bA5PgUj3vUj3HZFy+h3W4ThiGNRsP1Y5cEwsAm\ntEs46ro8zwcA8qqZoHp+EzVQsuYYNfBGq/lRvT7LssEcHM69V48JW2JPtXYaQKpU9DSakJwiVSWf\noY+ysLbRpdkapTY+zdT0dnq9PtffcAsn55fQxtJqjXLwATsG12OVZmVlheltU+S5ptvvcvfdx9hY\nW2fH9gmnyx0ITh47yqE7buYFF1/Mq171Sn7kwgeTFV18FCprY41Cigxjcuo1j16vTxi479z3QwLf\nBfVGZ2W3mCQpC7KekOSFgxoty2VGRi1jExG+CDB4CBmVGE+PqK5pWkN7Yw0PV/BqtEYwvS55mpeY\nYYsUHr4QjI6O0mg47zeO40HBUkhXPa/VY3dPvJil5dP8+tt+kzy7f6fsuy7SzM7OXowzkE8D7tw6\nE+91fEe3brj48p28x/sznv9Rz/N7GRUV/urq6pbc1dlnn10udLeDxVGEynLSXp9Oe4Od23e4sCJ2\nHZ8b622E32fX6ASe3fT4NvOK1gl8WQc/EdaiMmf4hqE5QohB0l6Xj8VBUBIVaNKkRyAER+8+ztL8\nKXbt2Ilo1nGehiHPE4qk5/R7spzbb7mdtNPDDySvedWrec3Pv4q40eTgg85DhSGJ1iit6UnB6OgI\nXiDRehwrDHGthpWWu+8+wfj4OA/ds5/VpOD/fPSTTE5OcMXll/Pbb/wlLG7C+ViEdUqPubD4iJKl\np8oaWmZqIQu+T9ZNSKWPX2/QV3D9Td8iLlyuU2QFMiy/k0oQy0LFtJiVWsiBFFQyn1mheOc7fo+3\n/OavMzU1heflZXWasmDiDmJKUpPKiGnK3CoWKyx4ZehrlPvfasf+U3qHwwZwGIJTzZvhavOg4Gjs\nFm10K12TgDaGTEZ0jaXb75MWlm3bd3LRhT+G9D2WugXXXHsthw8fxhMBO/acRRzEZGmB0ZuGWWKJ\npM/K0gJpP+HOO+8kjgJ275zg+JFDFFnCnt07ePvb3shLfvZF1D1JT/XodheQnkEVKSbpYoucIJSE\nYUiapU690ivJrT1QZa8/0nOQG2vJimKA/+x2cvq9lFxZhAyxwmd6po6QPp6oUlcKK3yCsEGrJd0m\nTJc8V4yGEf3Ti0hfYLQlCEPqzRYjo6NOTrlkJdJKYS0obYj8MhUVjfHVr36Dt/7aO9m9cz+29n3o\npJmdnX068HbgGXNzc+3Z2dnu7OxsbW5uLgF2AafKn+F60C7gyvs77neqRg+P/6iB/H6F3wOi3pIx\nRGvNgQMHNvNJkgEWUWU5S6cXUVlOI64hQp9+XuCFEVM7djK2bQqVZmilCGthGTI7EakBcQECJfQW\nbwZvK1gdWwyKW2mWoXx/cG5pv0d9dISFE8fZs2PXIIwrjMYkHQD8MGSiNYYIQi799nU04ha9WDB3\n+E7mDt1Fbiyj132L2YsezsjMNJPTUzRrE+SqQKUFRZHhBZIgdqTAP7LvAFlqeee738OznvIUPvSB\nv2LH9BSrx47znGc+gxi3a3rl3ukAMBDgKv8CC1I5chE8zts5zXW33Y2YmSSlQBWG04srPHL3jhKY\nXyBxLEuDbvmhttBclNhaPHwDUvhYGdMpksH9TNN00FFlrR3keisYlCrKogkCLRwrzIDkZKi4M+CO\nNHbQ/XUPg1jmF6ufM9tEA8/HKE2mCnKrUdaQlVRry94Ie86e5UHnHGB82wxJoTl0bJ5rr7meQ3Nz\nCCHYteccfOXIV9Ikx5R99n7JJoXJyUzBiaOHydOUkUbA4UO3c+SOjM/86yd43KMe4yILLEnRYy1d\nc7m/pEcUBaAKIglB4JPmPawwjjMVjSyZmSwChMN1utsgUUqTF3bwU+R9tCnodNpoA5nWjIxP0PQd\nxWCpAYmx0nmScQvPbxD6ARsbG6jMqZAmaY7GIm1ALYiwSLJc0U9crrLKQyI8cgXGCD76sX/m7/72\nH9l71ixaSdY3etzf+I4GcnZ2dhR4D/CUoYLLZcDzgX8of38euAr44Ozs7BigcPnHN97fse8L33hv\n4/7C5u+18PK9PF8N34AvBUWWE0URe/bsGoRmSikiP8AIJ49EHLHznLPQuArb2NTOQa7LWkuS9lwD\nvfIGxRwjNgk+TWkAc6MHsrfWWkRJ40RZDOr3XQgncIur6PeRFrIkIfQF133zKhq1OtZq9uzbSyfv\n44cBvoFGXHNkre0NCqOpbR+jt7rEJz53Naurq6z3OkSNOmNTk2R5zrZWTLa+jJB1WrUaUaMBwiCR\nZEXCwsIC03vGeOzTn8T64jJXf+7fOHrLbfzzwimIfaxSdJQl8gVaOs2ShITC5myIcUAgRECTEL9Q\nZN2E2mSdqalJVryclh8zWgvYENMcSgv2xh47YtfiB1AtR3BERMJCK/MghFzkdKSgIOBk3/KZz3yd\npV6N7qF1xsfq1Goekafp9Qv3fcsAL9xEBjRK9noo00LGEcQqpeglCcKTJMrlFzNZekDWENgcoxVC\n52A0LW0xWPIyn2w9H43AD50+kVebIWg02Hb2HpoTU7RGxwjCGlJK2qlEZyn9LOfIXUc5NDfH/NGj\njI+2eOC+h2CtJkv6ZN1VPGkwuiAMPLqLiywuLnDH3O2sr6/iS8OvveXNPOGJj+XCCy/Ek25jSpMO\nycZR0n6PPM3oJw6KU5G3FHmZqw1DtJQYpcmt04YSVpYpAolWGs/zscZSFAats5KPsgBbIFAEIqKb\n9Kl7EAqIjUF11umnCdH4Niptdc8LyExGmibkStHPoV/4rLUL1rs5ee6603r9HOUvkak6URiTK0hT\nhbESZApI1k9lfOQjH+Hk8RNsn5klzzK0VtTj+2YTh+/Og/xvwDbgY7Ozs9VjL8MZw1cDR4G/m5ub\nK2ZnZ98KXIrbw99VFWz+/zCiKBrInMZx7HZkb9PTsKIUsPJdzsUPQ8cYXqthZLDFW668DyHEAOtY\nHWeLprbYrGpW4V6VqzXGuCR+aeDzNEPnxaBq6qHZWG8T+h47t8+wdHoRWfMptCJPM1bWVin6KQun\n5jm1dJqZXTsZf9hDaPc36GY9Or0NltsrHDl6eEBXX+Q5Y2M7mZ6e5pnPfrbDAMYRI0GNsT37MMDR\nb1/DzP4HsLKyQjA5gSesI8WVDnpTs4JYQIxEUActSXRGEJVG00DL9xmbrPOopz2Hxz79qTzrDa+h\n2+5jxCiFMei8z454lKTQNMpuo+FASdgSXRICAk4vr+GNjHK63eZxT3gKwosQ3UVuvPEG/vsrXsbB\n2X0uB2xchRMrsHGwmRtfWhncnyAIkMqB/i0eYzM70VjGQlcUyEQN3xPEYUDkC4TVmDxldWUJm2W0\nRkaoNVsEYUzcbBLWG4RxjVqtRpoaglqdzPNJlcIPYopcuxZYVbC6vMKRO+/g6KG7MHnKwXPPYWV5\nkdBzBRfrG06vLXH8yCGOHzvM2Eidxz3iIi5+7rN54pP+kAcePEBeJDTDCENBmvUpjJtfRZ5CCSUS\nVSVe6rIS7UZVXBJC4J8xnytHo8rFQgU3KgXD9JAzIgxRvYbFzedCK7rdLp6X0ApdVRuglzrW97W1\nNaSUpIUi6fXodx15ticDtHIGPFvrYo1ERYJ2J8H3AtJMk2UFl37xMu688ThpmjI5PkGSJFubI+5n\n/EAJc2v14Af34d/jqIxXFEXsPessZ+B8z1UWPQ+DxAt8wjjCi0Jk4PghXV7R2wyd5SYDi5QSLTaZ\nfnzf39LjXakiVgu14sOsmNCN3JQa9Utqs37XyQaM1WPyNKVZd0nrWquJDiTj2ybxahF5knLOvn38\nzd/8DQ867zw0lgdf+FD2HHwAaZqi8hxPSALpkXRdD/FYa4T1tQ4n5hf47Ocv5Zde/wYe+ODzOfus\ncyiMJUw2eMHzfpIDZ+3j0J2HedOvvoXHPPXJZFaTRzEeEBloaKhLiF1Sj76Adm+DbtJlW7NFzXiM\n1uu8/Ffeysc+/A9cduQwSaro+wHGF3hpjwc0Wmw3gsmwvD92628AKwz9XFPg81t/8Ge8973v5byH\nP5RbvvIlbrnzNsYnx0iLlN/+3XezbXoKGdZAuja8orZ5z4AtSf+WrA3uv5SOJV7jnpPRlKNBw+JZ\nQxx5xCUZsV+rIaTT27aepNCGsO40cKTn4WsNvocNAsK4hikcLZnKNfMnT3DbLbegi4JtY6N011fo\nrK1x6K470N0e1153NRjDy178Ql7y4p/hgoMPxJLjA8rmGKOoeSGpTiiyHtoUJL1+udkqTNl+qvPM\nQa90gfSdwatSBhWuUAiBh9piGIfTDRXb1nAaYbMFMscW1hk4PyaIIsIgptZs4cmA1sgkzVHXvba2\nvs76+jon5o+XLb8FaerEwNLcpYwQHmEY4ceOrCXNCrKsYHl1nZtvuZ077riLer1OJBwhRdLro8p8\nqC3pAuduuuk/BfP5f4NNAortO3YQBAG5VoOEehAEaBkgAx8ZhiA9ZBAOpBR8I7fQv1eTbPjY1QQa\nptmvoCDAAMZT4cCstc67KA13ojRWOdBw4PsEAsYmxvG9kH6W0t3YwB9pkaQ5i6urPOc5z2H24EGO\nr6zypCc9ibhW4/TyEh2TEjUiojgg7/Q4feIUjTBmbeE0//b1f+Hqb1wJ/R6Nc87llptu5OOf/BRH\njp9g3znnMt2AO6+/loVT8+gs58lPeTxhGKAI6OJCOQ8LRpBplxPyPYgF5LUGmVakWhMFAWsKvGaD\nO06dQAJz3Rw/9kg9i6zFnFjfYNu2MVegghIDyRZaOi0FhYUwllz+pW/xyEc+kZtvvZp20sFmBXEU\n0I8Cdu/ei1+vQ20EI320keDrQfGsglpVRTRlN/kcbckyNchPeyNOo8f3CXzKHKjTbkmt80CNcDRd\nwloSo0lT16A5UguxyoI1dJIN6kFEI4i47pqr+Yf/8xeYImd8bJRQCtrrqxy+43ae/Yxn8uvv+GXO\nPnsvNRGQqA6BLxFsgNVkNiNLHF2YEg5vudFeLYHokKZOr8YT7hqqOWhUge85/fZN+JEmkAFCSARi\niwGs5rCbm7qcw0NcCkY4OjgZ4dcFrbFx1tc30BYnPZtkBIEh6DvxMKUUiyvLLC2vsrK26vgKpCs4\n5ZnCigApPYzVdDsbBLUGp0+f5obrb+KGm24GK9mxezejzUnCOMIkJcyncFFWZRzPJLY5c/yXMZDf\nr06a72UM4BHWqQ43m03q9TppkRNEJSzEkxiBI1ENA6TvE0ThPdodh4HIw7ut51ehigMRO7lKr7xG\nW/byujHgeix3QCGE66oojW+/26PZbOIJSZElZFKy1l8jiGp004yJsRGQHo9/2tM56+AsmRA85ilP\nZX5lhXqWE9abeIk79tr8IqqfMlpr8KmPf4JvfvaznH3hhXzgr/+K/QcOMrNnH4UQaDy8IMAC/cle\n2wAAIABJREFUMRmv/cXXcfP1N/Cvn/wUdx87wrkHDpDlGXvCppMmkBLlGwpt6Bc5YdBw1W3pMT4y\ngbIFnV7Cm3/5Tcx3NjA4zbkRKSmylFqrjrIaG4Us9AwjjVKR0Qqkcho72hiiRkwf8OOQsemDPOHx\nP87Ondv48iV/S65yYi/AGkMoQ170opfwJx/4ADvOnaQxPo02Aq2ygcdUbY6DRoEgGnj91fMDDzPY\n3OAMri3VCEuaZfgyJE165MqFnVYI0tQxNGVZRkf6pVpiQCOKCD3Jkdvn+MoXPsuD9m4nTfoEnuDg\nuft5+IUP5alPfgKB8KmRktueY+BJO6UEbO4o1jyHpU3TvsNyCoPKC6i4UisUSclTKkTJMSqFa+Ur\nhxAlWYQRCOE5/XXhiFV0OZ91uck71UXHcalLYDbCrZUg8DFWUxiLDGPSXkpY8ym0oZv2WFvu0e5s\nsNbusLy6gh+EbPQTTi7M06y7DqXp6RnWVlecvERa0G53uOQLX6bb7dKot5iamaY50iKUMRpBv5tB\nodElXliULZ2U3V73N/5fiH0/Y5gswItC9u/fT5qm+KHLT3lhgFfCc6RXc90pUmI8AVIMuP+k2ey2\nqBZUVbQx3qYCW/Xc4P/hcHEod1kBXl3BwN1kozQqzxkbG8NaS6vpFh1CkmSa0akpHvGEJ9AYH4WR\nMRqNBpNT2yiMJu31QWl67Q6Hb7+FqfFJvvLFL/GFf/wn5Pg44WiTT3/us6x3OyRJgdKGkYlJPD9E\nCYH0fQxwYOd2vJIYLgLSIqUVxAgsI92UVtNBnRSQY1BIFtIVhGrgNaJSUsjQxOMZT3s2y90+6/0+\nf/Vnf8Qzn/A4FHDtqUXMSB0RhBT9nEeMRbREQAAECvIsJy1yWmNN1oBXvurNHDp0mPf+8R9z/uw5\nTNRAZV3ioIkFEqCHYkPlfPrLV2DCGCEDYi3ueT/Kza1TyhmIsm9aKycf6nJtnS33LFObSIOwqKjy\nHCWZH3hkWVK+VhMGDfIiww8MQhecf/Bc+qurPPSCCziwZyc+HgUZab9LP+ngSciyhCLtbNlkw7BE\nM5gCYwq8kmVemJJwQ5UVdwTWpmBde6y2AuH5Q+mgrethOPI502usHrfWUqgErGOkt1aAlRjjfjs6\nNoWyhvW1Dkmas7reprPRY2Vllf5GnyCKWet0OXT4CAvLa7z9Xe/iK1/7Ou/+/d8nkgF/8Rcf4B1v\n+VWmxifJuj0ufOhDufGWu2i1WsSNOq1Wi1q9DtKRvChVkuEoRdZPsOXfaJeqOnL4rv/6Ifb3o+Pm\nex3VxM6ynNlz9zsSXeEmihGb0Zy1jtTAKo2RBmk9jCcc87Z1vbDDY9j7UEJvMcTD1zlMUTZsHCsZ\nT0w5QUvjmOc5ReYq2728j+cFaG3QwnLuA2fZefZeTi0vs3LsJOdfcAFeEKNVQS9ZZ/30EiePHGV3\nc5Tffutv4WvL6Mxe2qvL3HT8BJdfcyWZ1TRHZ6iFIaPTUzgEtus394QkwSkfYjXkOa24Rr/IqAcB\nvWaNHk4/rlBQ893q2xFPcmR5A6NygtE6kfAIgZGoyfpan8hIzn/AAZqAzuGBE5Pctr4MEwF5JFnr\n9/AbYwgLvge1ekggQhJtef7Pvp5du2e48KILediPnEMNMCohDiIwUGhLGAkyC7Uw5OSJo+w8ax9Z\nnpKXuMqKX3NYA6lQTgum2sB0obAlHEiwyaijCoO2ZtBGJ40e5OFEVUky2nVbmYLu/AIPf9TDeOHz\nn8dYs0ZLBHSTZYpen/bKMbrdLlnSB6GQnsAo16lSFQ91qU7Y6yeu1mQtvjAYXUKrJKg8RxjhdJME\nCDTVTBZSOvytFRRG49szpEzscGronnNzsBawGFO4sFoEeNKjyB2ZRq/Xo5co1tfXOXlqkeWVVUfQ\nI32SJOPQnUc4fnLebYDG0u6lnFpqc8kXv8JXbnwBP/fSl/DSV7+e0G/wtje/iclmk9APqdeaeDIg\nlK57BmvRhS4VODWicJFWJVMyEPW7l46o4fFfxkD+IEZVFJFlISbPXWidJAlxo74F7Bv5EmElwgqK\nQmMUg15az9+66w4bPs/37jXR7Yzz1o6L6vMGpAV5MWCbofw/zx0Mqd9PqDdaSN9j5/adWODk/CmC\nZpOLHvQQxicmSFTOqfl57j50mI2lFR64/wBve8VriIOQ3kaHCy+6iA9+7Wt86/pvYwPfGVwZENRb\nLK2tO68nDBkbk9TDCBAOvqI1rbgBQBxE9LMUG8X88lvfzaUf/zgkfX7up1/I7//O7xBHgt3bRji8\ntk7a7UIt5lVv/jXmT53ivAecz6WXfp6ztm8n7aWMhjEzscexIGA5SaBRp7vRZrzRQuNYzysU3ete\n/3pWVjsEUciePeMYXOHA94BcY2xAGAq6mcKPPKzJeMgF5/Pv37iS+sgIwg8GeeBqoxxUcXO3sKx1\nDOlGaTxbbmh5teAE2loMAmO6gJOGdTlmi8pT1tdXCXzBox/9aJ71rGewtxHgB5LFlRNsrKyz2GlT\nC6Do9QBJkvTol5hEo3J8X4AwpLm7DwaL50kMm+dbi52qobGizLc5OQdPSqTwHRQGnOaQlI71xxoH\nnGcrV2KVP3ee9OZz1fezmasssZ4apPTI0oRuJyXPFWtr63R7io1uD6MtIyNjHL77BLfcfCsbGxs0\nmyPUmy1SpTl69Cif+rfP8TMv+zlmtu9E+RHv+h9/yHve8yc8fPYgT3ziU/FUSntpiTiK8HzfUQiW\nhSVlis3W0bJL6Ux6vx/qKnZc8+2w91Tx3EkpMZpBgjwMQ3q93uBi7L3cuOFxv4S530P6sjq3KIrY\nsWf34DGkk3d1UJzy3MtwZLi3d1AF9cqcVamFbcucZRAEaLOVqWdLuKIrZki34ymVo8qGf6sNaPca\nlefOuy1Dh+3bt2NrEAYx4xNTpAai0TGecvFPMrNnNzqoEYU+J48d5dpvfJ2lk8d4wqMezbve8Q7s\n6WV6WcZzf/ZneNFrfoFU+qxnmjwv3OSVBb08Z2L7NNu27yhTAZKk10dITaveQmlNwwswCt72hrfy\n5Uu/SGOkxf49O3jyj17EK37mJ7j0c5dw6WVX8Gfv/0uiqXF6qmDUD/jgX7yfd/3GO5me2kOaWi56\n1KP4yIc/gF9o6llC2KixaC3XnF7A37GL+ePHmd2xnbP8EM9oTh45zqt+8Ve548hxdu7Yze3XfZP1\nzkl8FBIH+Pbw8bUDSyoLCRrr+Zw2KX/wp3/MaqdNZJvufuOVkh8VWYhAqI3NuThkPK21dHoJjUYD\ntKHf2WCk2XQefpbTHAlYXl6k19ngnLN286pX/jxn7dxGnvbp93su/MwSrM6cZ2c0VruiXJb2qKjT\n8jwvZV79Qcg+XEgaTuVUkcowemLLmpdi8LpqDQ6Yiuxm77g1Ain98jMCFymU1+/UbvMBoD5LJdo4\nOdhEKZKioLOROMlWDb28jzUeX7rsK3Q2UpaX1gEP34uRRhOOjHHVDd/mD/70j/n6V7/KyqmjTNTr\nLKx1MVJw49ytPPWZz0D1U/pLaywePYGWUGs2XA7ed3C2TLueeGUNYVaUDRQJuihJK8oUydGjR384\nQ+x6vT6gnK8MUWXsWs0mWmuyLKPb7W7NB30H/trvV0GnwnTt2LHZUu4mi0V6m1+dY25xgNNhnZzK\nwGZlQcXXGuFJV+BRTvYz8CNsyRLjRxGVEFO1yyFMWXFz3qILpwuUKpC6fG05qQul6Xd7dDc6REGN\nMHDnE/s+aT/h7LP2sNrtQqC49ca7OHroLtZOneJxj3g07/nd3yNpt9Fpj3MveDCPf+qTueKKK6De\nYHrXWTQaLRaOH2d8rM7ec/YzPjNFVmiWl5cIPZ9Ws0lYa+EhCYyg7sFb3vI27pi7BSMKOvPH+O2/\n/yAXPuhcgv46r3v1L/Lq//4apOfRBUb9gEsu/Rzvev0b2Pug8zm+uMb5D7mID3/4A2TWTVQpXVVV\nSA+TaZKNHmPjO+n0M3ojIZPS4/TyOouLi+zdvYebv/ktvvbNb6ISgIRWLUZa6YgrhcUKsMK1PW6k\nXYLA5/nPuZj3f+B/Y1S/lMgIMdoVVApd4lfRW7yP4RBTSo9ee931HCcd1pIO9VpElqQszp/m5S9/\nOY/50UfQjCOWlxa487ZbUHm/1L9JMGWfOsaBy43VCKPJ9SaioSoWVZGE54nBnBtmh4dNr2+YDWrY\nKTFsCtUNOyrGOLkGoMwqV5GMwJiCwKvOwWCMcnAkVUnTSpQxZHlOmrsmTWklcdigvdbm+PFlrrji\nq2A9tLYEQTggyrVCcviuO3jy05/G2OQEX//mN6h7lmxinKwQ9LKUotcnDiO6/ZS1tTVXea9Hg2tU\nSqG0k551+jwuNaWLskiDy27oao3dz/iBGsiqzavaCSuaeICOcsnuYczgAFxt7/+ivl8GMs9zJicn\nHaEBQ4vBWkwJmLXVZxhXoaX0BoWUgAELcRiXnqcsBYQydOAwlYV13RvV9Q97I0KX+SyVo5WiKDKM\nKkAphHZ9v9Y46n9jDEm/h/RgZek0+2Zmnc7Pepu42aLb6fGRD32I0clxpnefxa7pbYzLA3z6W9/i\nrX/+PqbP3odud3nZr7yJxz7+x7jzyN10kh6jcUyytgJ5zsMvvAgTBiyvr3H41Dyt0RHGRsYZbY2g\nco0QkhD49df+Mld96d+54ILzmD2wl8svc4ROhYXl9irnjI0gsNSEJE8LJiNDkeVc84XP0ZoY41E/\n+mieuH0Xr3jtG1gFGmkf4Yf4cR0E1AXsnZzhdJaxrDP8QLKROC/3ta/7JeJawNLiMdLkKEUOaZIw\nMtpCWD1g+lGygLIPxxOGVuTT7vV5x+tex+G7j5C1V6nX60xObmN6epogcNRZvheSic1+6WqOVfcs\nCELyLEXrgquv/Aov/tkX8JpfeCX1WkRvdYmF+XmuufwS4shDFxlFnuBhKVRGFISlI2DKLiWLX9J4\nKSPJSmNXeYW6Cm2VgaHnjLWIijF+KPQ9M7oRwuXJK2D28PU4Dkx/K0xHCocSQGLQjhDaGIy2FLoS\nBtPoLEEZSgEw6HUTNtZSvnrF17jx2zfQmNxDFI1isRRFUhp1i+dLTvcTHvPkJ/Gs517MZV+6nOc8\n73l84/LLOLl0mrrf4tTiAmjLSFzn7vk76HQ61GsxUaO+qclUSU4o5aRsjUEql0sW1g6KVBh7BsHJ\nPccPNMRutmI7jGYfDrGx8r7fOKTdcW8GsKoo3qtxlN+9wcxzxf79+9wXL4aMrRSuB1TKQZ5x+GcA\npi0nspGB0+j1PNdhE4Yo45ilvXJRVIJesPk5pkgHDNRoRZGXGMiyO0EV7ntTuatUttttTOES4eec\nN8v05BSdfo/RsW2IKCIYHaExOkq716UZ15hsjfJXf/Y+RuoNer0O27dv5+LX/jyB74SOkiSlUR9l\navsOtu/YyejoKPPtDdq9PjO7dlKvNyjSgm3jE4Rl9fNtr3kd7aMniY3l8N2HuHn+LgqgZy2RsNSA\ncSyhtURG4FkP1DqEEYUymLBBH7ji1jkaExPUR0Y5GEDD94m1I3NIgPk0Y27+FBt+TENKdo42+cWX\nvpTO0iL79+3jHz/0oUGixfGAFzjdQIs1IKVGWYUnAgp06SV5rsBgDbEoUEZx/fU3cu0113HVVVfx\n6U9/hmazye79D2BqasqJQfm+y00HAXmes7gwz9LSEj/5vOfyG2//VbLeOqdOHqPfa5N12iVhbkaR\npxRFhlZZKQzmEXohnr9Z6XYGrwybg/pgbXieNyjMuPBYDJ4bnkNSSmQYDMTGgAGCovpflmD3SrKh\nmrvWWqTRJYmHLgsugor9XOtSPM8YlLGu91tIpO+hMkW3n4DwOL20Rnsj4WP/8C9EfkQcxKReo3QI\nCnr9DkrlzMzM0Ol0uH3+FJ/81Kf5yEf/2RVvjObBDzzAbbfcwOLxRTa6HQqjmZqa4vbbb0clGfUw\nYmRifBBlVho9QogBe7ssiYpVvjW8FtZyYmH+Po3CD9RAVoS5w8UJ+OExkEopDh486EJtsxk2Ix0T\nzZkGsnp+2FA6r9JROAnPMaBY4WQ/hScpCn0PwzooCKQ9p8+RZShV4AuJ0cUAjKt1SfpQ3vSsn9Dt\ndimynJ7OefD5DyaOYyanprFBSNBq4tdjmvUGY80W7/+zPyfr9EApHvSwh/L2d7+D608dp9/vg4G0\n12d8fJJ9+w/QTxNHUCodWN6Pa2htmZ6YRGrXofDX7/tfXP5vn+esmRm67XUuuPCh/Ob7/wjTiBiN\nmnhYIgQNa2ggiRGEQKC75IXBqzVZ15ZHPvFJPOOnXsiuvXt4wU88l21WEQuf0BiEhBxLgmDd9FlX\nismwhVF9Rv2Qbned8WaL46eOQ1QaAVxYjHZ6JliLVAVQVuA958FHYc1pA0kfqR38ph42UFYhhY9E\n0k36vO6Nb+af/ukjNJsNZmZmyEpRq5mZGd7x9rfy2Mc+lqS/we233Ei3s4bKE1Se0W0vD3SJgsBz\nXmMUDIxVo1YfUNupMiLZ7NZxms/DRM2bnuAmFGnYQDrDupV8o8L2Vo9ZsekBD89ja+2gMq9VVa12\nQmGDSMc4Al+lhRP6kh5pVtBN3Lld/e3rufjin+ClL3k5O7btIBQBRmkKz0VURZHj+Q4LKiWsra3x\nzj/9U775zasoCo0uFCuLp8FkdNpr6H5Bo9FgfX0dgHbbdTLHUUSj2RwUiobxjk4UUGF14dJTSqEL\nVc4Ld+33ZyB/oCH2sHEeLk58r+Me7/s+2fxqwt2b9vbwRAIH5BFCDCRqpZQDyRppnH60sBYl1cDb\nkFIi/c1m+eF8iLUW4agvHF4N4ai1tMFaVRrJIVZq5TzNKAjxhKS7kXHdlVez/wEHmJ7eThD4LnTL\ncwqtuOPIYbLTp2mMT5AHHi96+Uu49uYbyeo1Wq0WvfUORmlajTonTh1no9+j3mwwOjqJyvrkRcr0\ntilW5k8SyZDZ/edy1Wc/y/bRETQFwWiTf/jI37IE9LEUg6sBKZycg7KWSAqafkwuDL2s4NbDx6i3\nJrj+qqt58QtegNQQeb4T3ZWWrEjwA4+V5WP0VApoTvX6NKMa8+ttmvWYhWMJRkCh0837VdKpCeM2\npMBWG5oDgGsDfhQP0h3jzSaNRoNetkGeK5rNJlKEhKHk/X/+Hv73+/6IOKxx7MRRjDGcvXcfx04c\n5cihW/nSpZ/A6IK028Uahc4ztMoxKqHXLReniMBoiswiIp8wiihSp8WO9JC+jxUOlG1xmjOlfsZQ\nxGUQfvlY+f0MvmPpwNln5sMrb3Ewb73N4k0176r8p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bFJb2ZyznJA5v9uSoQVGaPbeDYl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YWmDJtUAhpYWUaw51vd0oJ9E/bWrcdxTQg6KsNZL/uaK0sIiikCAo02632bNnN+eeex5JlmIJ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EIhSsd6RnF2OUYV82RtAVg1mvRpNBqsr6/n2oyCxlTE3Owu0t6AzCSFILVJsy3rzlrHL62H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\nLmATBzmxiU1sYhewiYOc2MQmNrEL2P8HYXTNjpuknoYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qcl5E3OB1bQ1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Converting classname into category\n",
        "df['classname'] = pd.factorize(df['classname'])[0]\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x6LcRMVBE7hF",
        "colab_type": "code",
        "outputId": "c2c9d98b-4b80-44a7-bc78-f379e03b2b98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "#counting the number of instance of each class\n",
        "count_class=df['classname'].value_counts() \n",
        "print(count_class)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    2489\n",
            "3    2346\n",
            "4    2326\n",
            "6    2325\n",
            "2    2317\n",
            "5    2312\n",
            "1    2267\n",
            "9    2129\n",
            "7    2002\n",
            "8    1911\n",
            "Name: classname, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GzRHTUmChuZi",
        "colab_type": "code",
        "outputId": "6a0f0fff-9368-41a5-852d-eb0935cf319d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "!ls"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IgR6YdqaiVyT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Lets copy all the training data in train set to one folder for easy processing\n",
        "!cp -a /content/train/c0/. /content/combine_train/\n",
        "!cp -a /content/train/c1/. /content/combine_train/\n",
        "!cp -a /content/train/c2/. /content/combine_train/\n",
        "!cp -a /content/train/c3/. /content/combine_train/\n",
        "!cp -a /content/train/c4/. /content/combine_train/\n",
        "!cp -a /content/train/c5/. /content/combine_train/\n",
        "!cp -a /content/train/c6/. /content/combine_train/\n",
        "!cp -a /content/train/c7/. /content/combine_train/\n",
        "!cp -a /content/train/c8/. /content/combine_train/\n",
        "!cp -a /content/train/c9/. /content/combine_train/"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "1QegTvWqkLLJ",
        "outputId": "b96df165-a0cf-4c17-da77-dfd9b5868abf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "path, dirs, files = next(os.walk(\"/content/combine_train\"))\n",
        "file_count = len(files)\n",
        "print(file_count) # confirming the number of file after copying"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "22424\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "H4GnoOzpqKb5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from skimage import io\n",
        "from skimage.transform import resize\n",
        "def convert_to_array(image):\n",
        "  \n",
        "  image_file = io.imread(\"/content/combine_train/\"+str(image))\n",
        "  image_resized = resize(image_file, (128, 128)) #resize the image to 128*128\n",
        "  image_data = image_resized/ 255 # Normalize the image\n",
        "  return image_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "deU-1A9bj46O",
        "colab_type": "code",
        "outputId": "c0977c27-50f6-4e17-c61b-71982c90fb77",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        }
      },
      "cell_type": "code",
      "source": [
        "i=0\n",
        "X_train = np.ndarray(shape = (df.shape[0], 128, 128, 3), dtype = np.float32)\n",
        "for fname in df[\"img\"]:\n",
        "  image_array = convert_to_array(fname)\n",
        "  X_train[i,:,:,:] = image_array\n",
        "  i +=1\n",
        "\n",
        "print(X_train[1,:,:,:])\n",
        "  \n",
        "  "
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/skimage/transform/_warps.py:84: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
            "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[[[2.40292196e-04 3.47943103e-04 2.24913500e-04]\n",
            "  [2.30680511e-04 3.38331418e-04 2.15301814e-04]\n",
            "  [2.74894264e-04 3.82545171e-04 2.59515567e-04]\n",
            "  ...\n",
            "  [3.86582082e-03 3.88312177e-03 3.90234520e-03]\n",
            "  [3.92156886e-03 3.86005384e-03 3.92156886e-03]\n",
            "  [3.89081123e-03 3.89081123e-03 3.89081123e-03]]\n",
            "\n",
            " [[2.30680511e-04 3.07574024e-04 1.99923103e-04]\n",
            "  [2.63360242e-04 3.40253755e-04 2.32602848e-04]\n",
            "  [2.67204916e-04 3.44098429e-04 2.36447522e-04]\n",
            "  ...\n",
            "  [3.45059601e-03 3.79661680e-03 3.76778166e-03]\n",
            "  [3.84275289e-03 3.90618993e-03 3.92156886e-03]\n",
            "  [3.90618993e-03 3.87543254e-03 3.89081123e-03]]\n",
            "\n",
            " [[2.74894264e-04 3.21030384e-04 2.13379462e-04]\n",
            "  [3.32564407e-04 3.78700497e-04 2.71049590e-04]\n",
            "  [2.74894264e-04 3.21030384e-04 2.13379462e-04]\n",
            "  ...\n",
            "  [3.21030384e-03 3.80430603e-03 3.78700509e-03]\n",
            "  [3.78508260e-03 3.92156886e-03 3.92156886e-03]\n",
            "  [3.92156886e-03 3.90618993e-03 3.87543254e-03]]\n",
            "\n",
            " ...\n",
            "\n",
            " [[1.38408301e-04 1.23029604e-04 9.22722029e-05]\n",
            "  [1.48019986e-04 1.13417918e-04 1.55709349e-04]\n",
            "  [9.32228828e-19 1.34563626e-04 1.65321035e-04]\n",
            "  ...\n",
            "  [2.42214534e-04 2.42214534e-04 2.11457125e-04]\n",
            "  [1.71088046e-04 1.71088046e-04 1.40330638e-04]\n",
            "  [9.99615513e-05 9.99615513e-05 6.92041503e-05]]\n",
            "\n",
            " [[1.38408301e-04 1.23029604e-04 6.15148019e-05]\n",
            "  [1.84544406e-04 1.05728563e-04 6.53594761e-05]\n",
            "  [1.03806226e-04 1.38408301e-04 9.61168771e-05]\n",
            "  ...\n",
            "  [2.67204916e-04 2.53748556e-04 2.22991162e-04]\n",
            "  [1.55709349e-04 1.42252975e-04 1.11495581e-04]\n",
            "  [1.51864660e-04 1.38408301e-04 1.07650907e-04]]\n",
            "\n",
            " [[8.84275287e-05 1.48019986e-04 6.15148019e-05]\n",
            "  [1.49942323e-04 1.23029604e-04 4.61361014e-05]\n",
            "  [7.68935060e-05 1.63398698e-04 2.69127268e-05]\n",
            "  ...\n",
            "  [1.98000766e-04 1.53787012e-04 1.32641289e-04]\n",
            "  [1.92233754e-04 1.48019986e-04 1.26874278e-04]\n",
            "  [3.74855823e-04 3.30642069e-04 3.09496361e-04]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "URADexxblJzD",
        "colab_type": "code",
        "outputId": "c2f54615-df53-4cb1-9617-94079cd5a104",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "print(X_train[1,:,:,:].shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(128, 128, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "AxcIranu8mYU",
        "colab_type": "code",
        "outputId": "54787d58-8609-4856-f722-298780d7e6ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "targets = np.array([[2, 3, 4, 0]])\n",
        "print(type(targets))\n",
        "print(targets)\n",
        "print(targets.shape)\n",
        "one_hot_targets = np.eye(6)[targets]\n",
        "print(one_hot_targets)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "[[2 3 4 0]]\n",
            "(1, 4)\n",
            "[[[0. 0. 1. 0. 0. 0.]\n",
            "  [0. 0. 0. 1. 0. 0.]\n",
            "  [0. 0. 0. 0. 1. 0.]\n",
            "  [1. 0. 0. 0. 0. 0.]]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "m5G_fwKv8CuW",
        "colab_type": "code",
        "outputId": "083b83fb-6b8a-47c8-e832-597e44c29de0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "Y_train= df[\"classname\"].values\n",
        "one_hot_targets = np.eye(10)[Y_train]\n",
        "print(one_hot_targets[1].shape)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Y5pUfK4dEDET",
        "colab_type": "code",
        "outputId": "bc8dd458-a9f9-4cf8-e2d1-3987081952de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 0,
      "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 /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "v_3ZU7K73oxy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#If you want to save image array as .h5 file for future use\n",
        "'''import h5py\n",
        "with h5py.File('/gdrive/My Drive/image_array.h5', 'r') as hf:\n",
        "    data_train = hf['train_driver_dataset'][:]'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "x7z3pKZEZvfN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_train, one_hot_targets ,train_size = 0.4,test_size=0.2,random_state=42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "dR_YiU6rc5sh",
        "colab_type": "code",
        "outputId": "986b01f9-6979-4036-b69e-96e5a1d4793a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "print(\"Training data shape\",X_train.shape)\n",
        "print(\"Training target shape\",y_train.shape)\n",
        "print(\"Testing data shape\",X_test.shape)\n",
        "print(\"Training test shape\",y_test.shape)\n",
        "\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data shape (8969, 128, 128, 3)\n",
            "Training target shape (8969, 10)\n",
            "Testing data shape (4485, 128, 128, 3)\n",
            "Training test shape (4485, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FTPJkNbcdd7o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Creating place holder\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.framework import ops\n",
        "import math\n",
        "from cnn_utils import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wL-W8UFUhf3n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: create_placeholders\n",
        "\n",
        "def create_placeholders(n_H0, n_W0, n_C0, n_y):\n",
        "    \"\"\"\n",
        "    Creates the placeholders for the tensorflow session.\n",
        "    \n",
        "    Arguments:\n",
        "    n_H0 -- scalar, height of an input image\n",
        "    n_W0 -- scalar, width of an input image\n",
        "    n_C0 -- scalar, number of channels of the input\n",
        "    n_y -- scalar, number of classes\n",
        "        \n",
        "    Returns:\n",
        "    X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype \"float\"\n",
        "    Y -- placeholder for the input labels, of shape [None, n_y] and dtype \"float\"\n",
        "    \"\"\"\n",
        "    ### START CODE HERE ### (≈2 lines)\n",
        "    X = tf.placeholder(tf.float32, shape=[None, n_H0, n_W0, n_C0],name=\"X_placeholder\")\n",
        "    Y = tf.placeholder(tf.float32, shape=[None, n_y],name=\"Y_placeholder\")\n",
        "    \n",
        "    ### END CODE HERE ###\n",
        "    \n",
        "    return X, Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7kRveisdhpV_",
        "colab_type": "code",
        "outputId": "a0f0386d-0ccf-4685-a054-22ec7bf6af92",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "X, Y = create_placeholders(64, 64, 3, 6)\n",
        "print(\"X =\"+ str(X))\n",
        "print(\"Y =\"+ str(Y))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X =Tensor(\"X_placeholder:0\", shape=(?, 64, 64, 3), dtype=float32)\n",
            "Y =Tensor(\"Y_placeholder:0\", shape=(?, 6), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "TQqcuJ9-hske",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: initialize_parameters\n",
        "\n",
        "def initialize_parameters():\n",
        "    \"\"\"\n",
        "    Initializes weight parameters to build a neural network with tensorflow. The shapes are:\n",
        "                        W1 : [4, 4, 3, 8]\n",
        "                        W2 : [2, 2, 8, 16]\n",
        "    Returns:\n",
        "    parameters -- a dictionary of tensors containing W1, W2\n",
        "    \"\"\"\n",
        "    \n",
        "    tf.set_random_seed(1)                              # so that your \"random\" numbers match ours\n",
        "        \n",
        "    ### START CODE HERE ### (approx. 2 lines of code)\n",
        "    \n",
        "    W1 = tf.get_variable('W1', [4,4,3,8], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n",
        "    W2 = tf.get_variable('W2', [2,2,8,16], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n",
        "    \n",
        "    ## END CODE HERE ##\n",
        "    parameters ={\"W1\": W1,\n",
        "                  \"W2\": W2}\n",
        "    return parameters"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6HXzwLWniegR",
        "colab_type": "code",
        "outputId": "34301f87-a6fb-440f-b5a4-49edd1c08fe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        }
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "with tf.Session() as sees_test:\n",
        "  \n",
        "    parameters = initialize_parameters()\n",
        "    init = tf.global_variables_initializer()\n",
        "    sees_test.run(init)\n",
        "    print(\"W1 = \" + str(parameters[\"W1\"].eval()[1,1,1]))\n",
        "    print(\"W2 = \" + str(parameters[\"W2\"].eval()[1,1,1]))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "W1 = [ 0.00131723  0.1417614  -0.04434952  0.09197326  0.14984085 -0.03514394\n",
            " -0.06847463  0.05245192]\n",
            "W2 = [-0.08566415  0.17750949  0.11974221  0.16773748 -0.0830943  -0.08058\n",
            " -0.00577033 -0.14643836  0.24162132 -0.05857408 -0.19055021  0.1345228\n",
            " -0.22779644 -0.1601823  -0.16117483 -0.10286498]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "IqKxn12fldLW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: forward_propagation\n",
        "\n",
        "def forward_propagation(X, parameters):\n",
        "    \"\"\"\n",
        "    Implements the forward propagation for the model:\n",
        "    CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input dataset placeholder, of shape (input size, number of examples)\n",
        "    parameters -- python dictionary containing your parameters \"W1\", \"W2\"\n",
        "                  the shapes are given in initialize_parameters\n",
        "\n",
        "    Returns:\n",
        "    Z3 -- the output of the last LINEAR unit\n",
        "    \"\"\"\n",
        "    # Retrieve the parameters from the dictionary \"parameters\" \n",
        "    W1 = parameters[\"W1\"]\n",
        "    W2 = parameters[\"W2\"]\n",
        "    ### START CODE HERE ###\n",
        "    # CONV2D: stride of 1, padding 'SAME'\n",
        "    \n",
        "    Z1 = tf.nn.conv2d(X, W1, strides= [1,1,1,1], padding =\"SAME\")\n",
        "    \n",
        "    #Relu\n",
        "    \n",
        "    A1 = tf.nn.relu(Z1)\n",
        "    \n",
        "    # MAXPOOL: window 8x8, sride 8, padding 'SAME'\n",
        "    \n",
        "    P1 = tf.nn.max_pool(A1, ksize=[1,8,8,1], strides=[1,8,8,1], padding =\"SAME\")\n",
        "    \n",
        "    # CONV2D: filters W2, stride 1, padding 'SAME'\n",
        "    \n",
        "    Z2 = tf.nn.conv2d(P1, W2, strides =[1,1,1,1], padding =\"SAME\")\n",
        "    \n",
        "    #Relu\n",
        "    A2 = tf.nn.relu(Z2)\n",
        "    \n",
        "    # MAXPOOL: window 4x4, stride 4, padding 'SAME'\n",
        "    \n",
        "    P2 = tf.nn.max_pool(A2, ksize=[1,4,4,1], strides=[1,4,4,1], padding=\"SAME\")\n",
        "    \n",
        "    # FLATTEN\n",
        "    \n",
        "    P2 = tf.contrib.layers.flatten(P2)\n",
        "    \n",
        "    # FULLY-CONNECTED without non-linear activation function (not not call softmax).\n",
        "    # 6 neurons in output layer. Hint: one of the arguments should be \"activation_fn=None\" \n",
        "    \n",
        "    Z3 = tf.contrib.layers.fully_connected(P2, 11, activation_fn= None)\n",
        "    \n",
        "    #END CODE HERE\n",
        "    \n",
        "    return Z3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kWegk_T9phVF",
        "colab_type": "code",
        "outputId": "64868661-f42d-4633-f601-f64e3c4fd4f5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156
        }
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "with tf.Session() as sees:\n",
        "    np.random.seed(1)\n",
        "    X, Y = create_placeholders(64, 64, 3, 6)\n",
        "    parameters = initialize_parameters()\n",
        "    Z3 = forward_propagation(X, parameters)\n",
        "    init = tf.global_variables_initializer()\n",
        "    sees.run(init)\n",
        "    a = sees.run(Z3, {X: np.random.randn(2, 64, 64, 3)})\n",
        "    print(\"Z3 =\" + str(a))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "Z3 =[[ 2.4164295  -1.1614199   0.981163    0.6824026  -0.05248657  0.7184313\n",
            "   0.22008131  0.80433124  2.404187    1.7046089   0.45491973]\n",
            " [ 2.0366726  -0.92503506  1.1718057   0.4065038   0.138816    0.90338784\n",
            "   0.07981753  0.5967737   1.9868093   2.1816905   0.11782407]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "b-B5NMhGqCn8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# GRADED FUNCTION: compute_cost \n",
        "\n",
        "def compute_cost(Z3, Y):\n",
        "    \"\"\"\n",
        "    Computes the cost\n",
        "    \n",
        "    Arguments:\n",
        "    Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (6, number of examples)\n",
        "    Y -- \"true\" labels vector placeholder, same shape as Z3\n",
        "    \n",
        "    Returns:\n",
        "    cost - Tensor of the cost function\n",
        "    \"\"\"\n",
        "    \n",
        "    ### START CODE HERE ### (1 line of code)\n",
        "    cost = tf.nn.softmax_cross_entropy_with_logits(logits= Z3, labels = Y)\n",
        "    cost = tf.reduce_mean(cost)\n",
        "    ### END CODE HERE ###\n",
        "    \n",
        "    return cost\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "JVwHIsBclN6c",
        "colab_type": "code",
        "outputId": "6df2fc75-0737-4e9d-a915-5676bda53234",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190
        }
      },
      "cell_type": "code",
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "with tf.Session() as sees:\n",
        "    np.random.seed(1)\n",
        "    X, Y = create_placeholders(64, 64, 3, 11)\n",
        "    parameters = initialize_parameters()\n",
        "    Z3 = forward_propagation(X, parameters)\n",
        "    cost = compute_cost(Z3, Y)\n",
        "    init = tf.global_variables_initializer()\n",
        "    sees.run(init)\n",
        "    a = sees.run(cost, {X: np.random.randn(4,64,64,3), Y:np.random.randn(4,11)})\n",
        "    print(\"Cost =\", str(a))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-31-0457baf7b79e>:15: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n",
            "Cost = 1.1419544\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "kC5k5Z5jrEwe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,\n",
        "          num_epochs = 100, minibatch_size = 64, print_cost = True):\n",
        "    \"\"\"\n",
        "    Implements a three-layer ConvNet in Tensorflow:\n",
        "    CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED\n",
        "    \n",
        "    Arguments:\n",
        "    X_train -- training set, of shape (None, 64, 64, 3)\n",
        "    Y_train -- test set, of shape (None, n_y = 6)\n",
        "    X_test -- training set, of shape (None, 64, 64, 3)\n",
        "    Y_test -- test set, of shape (None, n_y = 6)\n",
        "    learning_rate -- learning rate of the optimization\n",
        "    num_epochs -- number of epochs of the optimization loop\n",
        "    minibatch_size -- size of a minibatch\n",
        "    print_cost -- True to print the cost every 100 epochs\n",
        "    \n",
        "    Returns:\n",
        "    train_accuracy -- real number, accuracy on the train set (X_train)\n",
        "    test_accuracy -- real number, testing accuracy on the test set (X_test)\n",
        "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
        "    \"\"\"\n",
        "    \n",
        "    ops.reset_default_graph()                         # to be able to rerun the model without overwriting tf variables\n",
        "    tf.set_random_seed(1)                             # to keep results consistent (tensorflow seed)\n",
        "    seed = 3                                          # to keep results consistent (numpy seed)\n",
        "    (m, n_H0, n_W0, n_C0) = X_train.shape             \n",
        "    n_y = Y_train.shape[1]                            \n",
        "    costs = []                                        # To keep track of the cost\n",
        "    \n",
        "    # Create Placeholders of the correct shape\n",
        "    ### START CODE HERE ### (1 line)\n",
        "    X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)\n",
        "    ### END CODE HERE ###\n",
        "\n",
        "    # Initialize parameters\n",
        "    ### START CODE HERE ### (1 line)\n",
        "    parameters = initialize_parameters()\n",
        "    ### END CODE HERE ###\n",
        "    \n",
        "    # Forward propagation: Build the forward propagation in the tensorflow graph\n",
        "    ### START CODE HERE ### (1 line)\n",
        "    Z3 = forward_propagation(X, parameters)\n",
        "    ### END CODE HERE ###\n",
        "    \n",
        "    # Cost function: Add cost function to tensorflow graph\n",
        "    ### START CODE HERE ### (1 line)\n",
        "    cost = compute_cost(Z3, Y)\n",
        "    ### END CODE HERE ###\n",
        "    \n",
        "    # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.\n",
        "    ### START CODE HERE ### (1 line)\n",
        "    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
        "    ### END CODE HERE ###\n",
        "    \n",
        "    # Initialize all the variables globally\n",
        "    init = tf.global_variables_initializer()\n",
        "    \n",
        "    # Add ops to save and restore all the variables.\n",
        "    saver = tf.train.Saver()\n",
        "     \n",
        "    # Start the session to compute the tensorflow graph\n",
        "    with tf.Session() as sess:\n",
        "        \n",
        "        # Run the initialization\n",
        "        sess.run(init)\n",
        "        \n",
        "        # Do the training loop\n",
        "        for epoch in range(num_epochs):\n",
        "\n",
        "            minibatch_cost = 0.\n",
        "            num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set\n",
        "            seed = seed + 1\n",
        "            minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)\n",
        "\n",
        "            for minibatch in minibatches:\n",
        "\n",
        "                # Select a minibatch\n",
        "                (minibatch_X, minibatch_Y) = minibatch\n",
        "                # IMPORTANT: The line that runs the graph on a minibatch.\n",
        "                # Run the session to execute the optimizer and the cost, the feedict should contain a minibatch for (X,Y).\n",
        "                ### START CODE HERE ### (1 line)\n",
        "                _ , temp_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X, Y: minibatch_Y})\n",
        "                ### END CODE HERE ###\n",
        "                \n",
        "                minibatch_cost += temp_cost / num_minibatches\n",
        "                \n",
        "\n",
        "            # Print the cost every epoch\n",
        "            if print_cost == True and epoch % 5 == 0:\n",
        "                print (\"Cost after epoch %i: %f\" % (epoch, minibatch_cost))\n",
        "            if print_cost == True and epoch % 1 == 0:\n",
        "                costs.append(minibatch_cost)\n",
        "        \n",
        "        \n",
        "        # plot the cost\n",
        "        plt.plot(np.squeeze(costs))\n",
        "        plt.ylabel('cost')\n",
        "        plt.xlabel('iterations (per tens)')\n",
        "        plt.title(\"Learning rate =\" + str(learning_rate))\n",
        "        plt.show()\n",
        "        \n",
        "        \n",
        "        #First try to bring out this\n",
        "        predict_op = tf.argmax(Z3, 1,name=\"Prediction\")\n",
        "        save_path = saver.save(sess, \"/gdrive/My Drive/new_driver_state.ckpt\")\n",
        "        print(\"Model saved in file: %s\" % save_path)\n",
        "        correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))\n",
        "        \n",
        "        # Calculate accuracy on the test set\n",
        "        accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n",
        "        print(accuracy)\n",
        "        train_accuracy = accuracy.eval({X: X_train, Y: Y_train})\n",
        "        test_accuracy = accuracy.eval({X: X_test, Y: Y_test})\n",
        "        print(\"Train Accuracy:\", train_accuracy)\n",
        "        print(\"Test Accuracy:\", test_accuracy)\n",
        "                \n",
        "        return train_accuracy, test_accuracy, parameters\n",
        "\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "QgE8O-7hZwJ-",
        "colab_type": "code",
        "outputId": "6f732a4f-e765-4901-b42d-c1edbba59e0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1161
        }
      },
      "cell_type": "code",
      "source": [
        "_, _, parameters = model(X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Colocations handled automatically by placer.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/contrib/layers/python/layers/layers.py:1624: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "WARNING:tensorflow:From <ipython-input-12-0457baf7b79e>:15: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "\n",
            "Future major versions of TensorFlow will allow gradients to flow\n",
            "into the labels input on backprop by default.\n",
            "\n",
            "See `tf.nn.softmax_cross_entropy_with_logits_v2`.\n",
            "\n",
            "Cost after epoch 0: 2.196474\n",
            "Cost after epoch 5: 0.720671\n",
            "Cost after epoch 10: 0.410561\n",
            "Cost after epoch 15: 0.296159\n",
            "Cost after epoch 20: 0.224600\n",
            "Cost after epoch 25: 0.186237\n",
            "Cost after epoch 30: 0.169118\n",
            "Cost after epoch 35: 0.141057\n",
            "Cost after epoch 40: 0.132121\n",
            "Cost after epoch 45: 0.114623\n",
            "Cost after epoch 50: 0.086894\n",
            "Cost after epoch 55: 0.083453\n",
            "Cost after epoch 60: 0.068118\n",
            "Cost after epoch 65: 0.066109\n",
            "Cost after epoch 70: 0.079267\n",
            "Cost after epoch 75: 0.048555\n",
            "Cost after epoch 80: 0.052946\n",
            "Cost after epoch 85: 0.040932\n",
            "Cost after epoch 90: 0.038683\n",
            "Cost after epoch 95: 0.039979\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kSxEREflKCm+OWZxE571FRCQGKLzR/OYiIhJbFN4cs7KYznuLiEgMUHjTcrU5aKIWERGJ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\n/x/1eqVwRFb0aQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Model saved in file: /gdrive/My Drive/new_driver_state.ckpt\n",
            "Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n",
            "Train Accuracy: 0.988293\n",
            "Test Accuracy: 0.94158304\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}