CIFAR_with_Tensorflow_KERAS_CNN_and_DNN(ALL MODELS).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "CIFAR with Tensorflow/KERAS - CNN and DNN.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aayushkubb/Deep_Learning_Tutorial/blob/master/CIFAR_with_Tensorflow_KERAS_CNN_and_DNN(ALL%20MODELS).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ij2ahOKYIQ1I"
      },
      "source": [
        "### Import the Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opb2RYYyIQ1L"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "sns.set()\n"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GHjBdX16IQ1N"
      },
      "source": [
        "### Load the Dataset and Prepare testing and training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuXgk5EOIQ1O"
      },
      "source": [
        "from keras.datasets import cifar10\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Conv2D,Flatten, MaxPool2D"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17vp1CzIIQ1P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e5f6b34-0549-4af8-91dc-3c499d50a746"
      },
      "source": [
        "(x_train,y_train),(x_test,y_test)=cifar10.load_data()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 2s 0us/step\n",
            "170508288/170498071 [==============================] - 2s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WPq4C628IQ1R",
        "outputId": "664b58f9-9a3c-4d5d-fe58-0e1fe3fe8e05"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 32, 32, 3)\n",
            "(10000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "x1SRuGLcIaLD",
        "outputId": "9b4a21cc-5e4c-4b53-c279-a595c48e3008"
      },
      "source": [
        "print('Training data shape : ', x_train.shape, y_train.shape)\n",
        "\n",
        "print('Testing data shape : ', x_test.shape, y_test.shape)\n",
        "\n",
        "# Find the unique numbers from the train labels\n",
        "classes = np.unique(y_train)\n",
        "nClasses = len(classes)\n",
        "print('Total number of outputs : ', nClasses)\n",
        "print('Output classes : ', classes)\n",
        "\n",
        "plt.figure(figsize=[4,2])\n",
        "\n",
        "# Display the first image in training data\n",
        "plt.subplot(121)\n",
        "plt.imshow(x_train[0,:,:], cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(y_train[0]))\n",
        "\n",
        "# Display the first image in testing data\n",
        "plt.subplot(122)\n",
        "plt.imshow(x_test[0,:,:], cmap='gray')\n",
        "plt.title(\"Ground Truth : {}\".format(y_test[0]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data shape :  (50000, 32, 32, 3) (50000, 1)\n",
            "Testing data shape :  (10000, 32, 32, 3) (10000, 1)\n",
            "Total number of outputs :  10\n",
            "Output classes :  [0 1 2 3 4 5 6 7 8 9]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Ground Truth : [3]')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x144 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5k1v-oCNmD0"
      },
      "source": [
        "## Display images for each category"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0f6Culv9NpTp",
        "outputId": "8a53b9b7-71ad-44c6-e881-0c7995e1b658"
      },
      "source": [
        "for category in np.unique(y_train):\n",
        "  counter=2\n",
        "  print(category)\n",
        "  for idx, label in enumerate(y_train):\n",
        "    if counter <1:\n",
        "      break\n",
        "    if label==category:\n",
        "      \n",
        "      counter-= 1\n",
        "      plt.imshow(x_train[idx])\n",
        "      plt.show()\n",
        "  print(\"*\"*50)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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StV97p75A986f2xTZ/oU/+qzax17ws7L8NFJ4pjaSiXZkc1NzDYXoc59M6ouf85L6RGSk9DoN4G8BXDmf/RFCFo+GZ37nXBeApIiMOediAK4D8Hz9eyqUI6OsCKkgiXj0sK3ccxZT01Oqbdcu/ZfJKztfiWw/flz/5TM+rkfFhWfH4HbcmCmsCUab4eNhWTFAW0o/j+GSYpUHM0yKbdrICZhIV+YtDEZzJpL6pZtO6/kOf/TfUWvWwMbLLlH7fOS6D6i2Qq6xO7IzgVqlvjsAfADAWQB+6Jw7CuC9AP7FOZcAkACwHcDvL9RACSHNpdbV/lsA3BJhurS5wyGELBZ8vJcQT6HzE+IpdH5CPIXOT4intDyqL1fIl8shVZZF0nWjl17aEdk+OjpqHEnf3/DwTtW2deuWuvtNTenS4bFjesLKQqgsVHC72bFjWmQkAMSVMmRFmy4DZrN64k8t6aop9aUqS41NT81tT54cV/tZDyJNzUR/Nt+871tqn3SH/qDMu37lGtXWltbPYyzWQH2t0Cdd+RARE3gSQmqEzk+Ip9D5CfEUOj8hnkLnJ8RT6PyEeErLpb5CIV+WnYLykxX99uCDD0a2/8+T/6v2ae/oUG0T443JRtlsdFJKK0owpdSsA06XvYJHzuf1uPFG4ugtrHh+S1LS5DxAj223Yt6nJifV7Vh8TO2XSOrneMnS6DwA+w8cUPt87W/uUG2rVq5UbVf8zEbVls/rsqh+TsLtc9sxI0pTgzM/IZ5C5yfEU+j8hHgKnZ8QT6HzE+IpLV/tTyaSmF21TAZWaY8cOaL22bkzOqDmxAl91X5yUg8gsVbgrZCamLLKnkrpp1XLpgsAHSFFIrg9eXJCH4exYq7ZcmaGXtUEM1GfYdOGqCRABgDkw8FHge3JkyfVfkuWLlVt4eCpWXp79CzC4+P6sf75O/+m2i582/mqrauzXbVpMVenncNC8G39qg5nfkI8hc5PiKfQ+QnxFDo/IZ5C5yfEU+j8hHhKy6W+oPSVDLzv7u5W+yxb1h/ZfviQLg9OGnn1Jk7qQUQ5Iy9dIln/d6cly4VlwOB2Iq5LhHlDttNy7llSn1lfsmGiz1XMyCWIfEi+Csh0MUMjnDihf57a3xbv61P7pNs7VdvW7S+rtpGR/artbRfo5e61XIhBCTaRAIJxVI3kBKzq/M65fgD3ATgPQAbAMIBPishh59wVAO4B0IFiCeDrReRQ3aMghCw6tUxdBQC3iYgTkQ0AXgFwq3MuDuB+AJ8SkXUAHgdw68INlRDSTKo6v4gcE5HHAk1PATgHwGUApkRkc6n9bgAfbvoICSELQqyeZA+l2f4RAN8FsB/A74jIuwP2UwDOFhE9Of0cQwD0+teEkGZxLoo/yyuod8Hv6wAmANwJ4Nr5jwl4bXQUuVwOZw8OYt/+uQWSkX171T63/eVfRba/tF3UPtaCn1U4YjEX/GZmZsrvjxx+DcuWz2WJmZzQn+3XnlcH9AW/TCY6C1FxjKqpYbRsQykj1iEXWPA7OXUSXe1d5e1YQo/H6O7VF+96+qKf4e8xFvwSKb1ox1IjjuCv//LPVVsjC35BUqk0ZmbmPkPtukoaWY1qvnqdc7cDWAvgIyKSB7AXxdv/WfsyAPkaZ31CSIupaeZ3zn0Zxd/47xaR2WnyGQAdzrlNpd/9NwN4qN4B5Av5cvRWMIprpZEb7aabbops3zui3y3s2aP/wnjppZdU2949+j4PHToa2T55ajKyHbC/1cORWcE7kqQRKZiZ1mf+4N1ExbHMn3uN2axdxuNaDj8jEjB0rOB22BZkJqPfyWkyYEenLud19eqz+9Fjei7B555/QbWtW3ueatPO4+mz+9x2A6kaa5L61gP4AoAdAJ50zgHAqyJyrXPuBgD3OOfaUZL66h8CIaQVVHV+EdkGJWWriDwJYEOzB0UIWXj4eC8hnkLnJ8RT6PyEeAqdnxBPaXlUXz6XRy5blPhmXwFYVaGwYUP0GuNFF1+o9pma0uW3o0ejJTsA2Dsyotp2Du+KbB8ejk4wCgC7dkX3AYCDBw9WbA+ePVh+f2pCT056clx/AOjUqVOR7aclxwxgS3b6QzlG1TD1IR/rWFaUYzxpPByU0+XU7Ez0OI4fO672KUA/lvUA0H89+rhqu2rTJtU2uCpa5j7tYa6Kk1f/k1mc+QnxFDo/IZ5C5yfEU+j8hHgKnZ8QT6HzE+IpLZf6MplsOdItk5mLQNOi0YpEy3YxJXIMsOPUOjq6VNua1eeotiV9b4lsX71mjdpnaEjfXzi68JJLLi6/Hz1wQO2nyXmW7ZRR687KfWDlN7CSgmYV+W0mo3/O4Y8zuPeCJVXmdVtnZ3Ri2JkpPb/B6D5d7u3tW6La9oyMqrYXtu5QbYOrBiPbYyGpLxb4MwsNTOOc+QnxFDo/IZ5C5yfEU+j8hHgKnZ8QT2n5av/ExEQ5k+yJQH41K9jm2LHoHKFa++xxNMIBJLXatDx4VjZga5U9nUqp251Gjrm2tjbVtmRJ9Gp03lgRt5QWy5ZO60Eu48r5n5rUA67Cx1o1OLcKPj6uBzpNGvucmtaUjNqyKoexVId9RlDYI488otp++uL1ke0rloXUpUBOv3y4tBlQNdaHMz8hnkLnJ8RT6PyEeAqdnxBPofMT4il0fkI8peVS3/T0dFkamwoElIyN6WWQ9uzZE9n+slF2a3RUD7KwZC8t9xygF0e0inFahCWlA4HCpVNGsI1VeksbvzVGy7Z8+XLV1tsbXQQT0OVIS0rt6emp2H7f+95Xfm/JipY0NzERLQOeGI8u4wUAY2O67fDhw6qt3QgYSxmXyPHj0fkEVyzvr2wIfE5RwUzVrsJaynX1A7gPwHkAMgCGAXxSRA475woAtgCYPfINIrKl2j4JIa2nlpm/AOA2EXkMAJxzXwVwK4BPlOzvEBH9CRpCyBlJLbX6jgF4LND0FIDfW6gBEUIWh5hdqrkS51wcwCMAvisid5Ru+59B8Uvk+wD+NFDCuxpDAPS62YSQZnEuilW0K6h3we/rACYA3FnaXiMiI865XhTXBf4EwB/Xs8OfPP0MpqencdWmd+CJzU+W2w8YmWu0whdvpgW/H/7wEVxzzS+Xt8+UBT8tVgBY2AW/22+/DZ/73B+Wt9/oC34bL7lEtd30id+ObHfr3lp+n0wnkc3MZUfKRmRRisWAtnY97qNmqc85dzuAtQA+IiJ5ABCRkdLrCQDfAHBlrfsjhLSWmmZ+59yXAVwG4N2zt/XOuaUApkRk0jmXBPAhAM/XO4BMJlOW+oLRcNZMp+el03PZZY3ZIJvVc89Zeem0iK7ICKtZm5l7rrLficCMUzCyEFp3J43M/NZsbEUsWvJsf39/ZLs1g7e3t6vbg4PRee4AYOXK6HJXALD0LdHjsKImLazowlQypdoGVqxQbSsUOTWXm7t2kqFtRH2eVW5Aa5H61gP4AoAdAJ50zgHF3+q3Abin9Ls/BeBJFG/7CSFvAGpZ7d8G/TvkouYOhxCyWPDxXkI8hc5PiKfQ+QnxFDo/IZ7S8qi+RCJelpYqJCZDpsgqJaOsUlJx43suETPkN0MSyyndCpoBQD5vSYeVcl4+IDPmDanPekgppkh9CUMetORNS4K1ZDttjEuXLlX7hB8MOhlIfhpM9hrGku3C8uEsXV16HyuS8YLznX6slC71WZ9ZTpGKwxJyxXbUZcoEnoSQKOj8hHgKnZ8QT6HzE+IpdH5CPIXOT4intFzq6+3tQ3t7MeJuyZI52SeT0aPwTo5H17t7/aheq29qSq/flp3Uj2XJJXFFBrTkQcT079twxF+8agrG0vEsmyI7Zg2pyZKhrINZoz2p1CjsUKQ34PQIwuC2FV1oyZGTis2q76eNHQCSRgRktqNDtVkRf6gxErPio4jM6WBfP5z5CfEUOj8hnkLnJ8RT6PyEeAqdnxBPofMT4iktl/o627uQThaj8bo7u8vtybP0oXV2REdgdffokVldfXoa5Z3Dw6rt6DFdPtSir5DQJZZ4wZIBK21BKdEqrxBVp61sq9sAxA050lQxDWkpNxMdcZnJZNQ+ltRnSXOWbUqxTRvy4IwxRi3CFNCjPgH7OlBtoYsgmPA1qks1oZgzPyGeQucnxFPo/IR4Cp2fEE+h8xPiKS1f7Y/FYuWAhWDgglbYEQBWKKWOrNX+5UZ5pNWrV6u2rVu3qba9e/ZGto8bhR1zRrmu01ZzA9tWuS6zzrIiE1jlurSAJaCxwqWAHiyUMcqohZWA4La1oq+Vc7NsVjCQZbMCjNJpPWgpHtcDgmolZwVgoXrB2Fpr9T2MYpnfPIpVej8tIs8759YBuBdAP4CjAG4UEV03I4ScMdR62/8xEblYRC4FcDuAfyy13w3gLhFZB+AuAPcswBgJIQtATc4vIsHyq30A8s65FQA2Anig1P4AgI3OOT3PMSHkjCEWzhWv4Zz7BoBfRvHBoV8F0A7gWyKyPvB/tgO4XkSerWGXQyhW+yWELCznAtgdbqx5wU9EbgIA59wNAL6KJpXj3v3qPmSzWbx17RB2Ds+Nz8omoy1+nZqcUPuMHhxVbTt2iGpr+oKf8pgrACDwuOb/PvsTXL7xZ+b6WUU7zGd/G1jwMxb1Gl3wSyajL7Wevl61z7Jly8rv77vvXtxww8fK2/39/Wo/bUEYAAYGBupqB+yiHVbRkZ6ePtXW3qYvBiaM7ECz9PZ14sSYvrAJFD+Pnl49m1DdUp+I3AfgFwDsAzDonEsAQOl1FYCRevdJCFl8qs78zrluAEtFZKS0/V4AxwAcAvA8gI8CuL/0+pyIHK5nAPl8vjzLB2d76+eINsN0durBO2tWr1FtS/r0b+9VKwdVm+yIFjaGd+xQ+7y2f79qOzleeeeSTM/leSsYASSxGn+6Ban1595CY5VYs6S+hnP4aYE9xv4atbWldVvCkPqqSXSzBP0lskd8/lJfF4CHnHNdAHIoOv57RaTgnLsZwL3OuS8BOA7gxppGTQhpOVWdX0QOArhCsb0M4PJmD4oQsvDw8V5CPIXOT4in0PkJ8ZRWBvYkACCZnFv1DGrBViCLli6qENOfDbAWUNva0qqts1MPFurri9Zxly3TNei8sbp9qru7YvuslWeV32ezObVfIyv3jagpQDWdXz9eIhF9qXV06jp035LK87t8+Zzu39e3RO23ZIlu6+npiWy3PmcryCyV0ivvJJL6ir79LEVtq/3V/l/gc4wcSM1P+C0AmwA80aqDE+IRVwHYHG5spfO3AXg7gNdQlBAJIc0lAWAlgKcBnPbQQSudnxDSQrjgR4in0PkJ8RQ6PyGeQucnxFPo/IR4Cp2fEE+h8xPiKXR+Qjyl5UU7fM/975y7HcAHUUxoukFEtpbavTwvzrl+APcBOA9ABsAwgE+KyGHn3BUopofvQDEh5fUicqhVY10sFqpuxpkw8/ue+/9hAFcD2BNq9/W8FADcJiJORDYAeAXArc65OIrp4j5VOiePA7i1heNcTBakbkZLnZ+5/wER2TybH3EWn8+LiBwTkccCTU8BOAfAZQCmRGQ2QOVuAB9e5OG1hIWqm9HqmX81gP0ikgOA0uuBUrvP8LwAKM32vwfguwDWIHB3JCJHAMSdc29p0fAWFefcN5xzewH8BYCPoQnXSKudnxCLr6P4G/fOVg+k1YjITSKyBsAXUaybMW9a7fwjYO7/KLw/L6WF0LUAPiIieQB7Ubz9n7UvA5AXkWMtGmJLaGbdjJY6f2mldjb3P9Bg7v83G76fF+fcl1H8jf9+EZmNQ38GQIdzblNp+2YAD7VifIuJc67bObc6sB1VNwNo4BppeTy/c+58FOWKpSjl/hcRvX7Wmwzn3B0APgDgLABHABwVkfW+nhfn3HoAWwHsADBbYeNVEbnWOfcOFFe02zEn9R1syUAXCefcAIB/Q7F+xmzdjM+JyLPzvUZa7vyEkNbQ6t/8hJAWQecnxFPo/IR4Cp2fEE+h8xPiKXR+QjyFzk+Ip/w/mmfs6SH68YIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "3\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7cGra3fb0026zFkAcyfcVj5h/4owPfK8qm/pKVdmL8PT/dYdVLAhBIgH212Qf9ald7gCYALHnbSrV3P3vesaeeMQMuMXyJ5tT61Ny/1dX3b75RY9ZrNuTPTHjrtwWxPL7GHrex27H/Wx68daxarU2y1HRcc9niNOjM7+i5BQVfkXJKSr8ipJTVPgVJaeo8CtKTpm8tr/dIm71U0ptfAIQyprS6AwOCy4STzC+RIgGDLC+5kmTJWh6e77Af5776gWpWF73xCDcMy871FTKbotEWnCn8QJIPZr0MJJVyO227LTU7bivl8aeGH4jMQ3DIKPu90QY7ngcnSqCQ03Ro333OR/1fNaKRH7WBWSrjxRheDTYZJgpt5rbx7fdkseh3wdFUXKJCr+i5BQVfkXJKSr8ipJTVPgVJaeo8CtKTpm4qS+IuwQDJ5Mg42zSa8vmN6nXJU+mzqjqcbLwpTXyBIsLcJ9zbm6XeMzJU6fFttp0TSyXPP2oT8uOLLuEvqwvyUkke13ZRLS2siC2ze2XTY5LghmwLJm1gGgkb1WUSfmVeBKorq/L/b/04KVim8Spk3IWulJRNjuXI9kZq9WSY/8Fqfvdj0fuOTsGBU/cQomxhN8Ycyvwdvppta+21j46qH8KaA3+AXzYWvu1s+6FoigXnXFn/ruBPwa+7Wi7fuPLQFGU5w5jCb+19jCAMebC9kZRlItGkHq2SY4yWOa/cWTZvwwEwGHgI9ZaOffzVi4Hnhz74oqinCtXAE+NVu5U4fdqa+0RY0wZ+ARwG3DobE5w/Q1v4NixZzj8wMNc+8svGdYnsXxMsehOouBT+JW8Cj/Z6BF5FCmj+dI3KERykgefwm/Prrnh33f9l7u58Xfesnktj8Jv3+5LxLa/f/QpZ71P4RfJwYuo7PIk7fAo/I4fc1/Pp/ArZRSxd33um9x402uHZa/Cr3HxFH6JJ0GLT+F3ckFW+C0uu/0g4kykqr/73sO8+JpNeSlG29/FAwcOcO/dXxavsyNTn7X2yOCzDfwJ8KqdnE9RlIvHOc/8xpg6ULTWLhtjAuAG4JGzPU9ULA3TCmXTCyUF+eeI9FMlSWQPsUiYpc9ErycvQSpSCi2Ph9ievXvEtgJb+5914ipV5BVInMieZUVhHHfPzznrARbX5VXB0qLs5Tg1OyO2FWL3OE5NTYvHxCOx7MLMcw88v1brkWz6XF9yx/Arl+UVDT35YuVQfq9Wl+VfwJ2W/MykuIZxWhj5f5tepaFjBZUm/p/045r6Pgm8DbgEuN8YswC8CfiiMSYEQuAx4HfHOZ+iKJNnXG3/h4APOZpe4qhTFOU5gG7vVZScosKvKDlFhV9RcooKv6LklIl79UXlGlGlnwJq4xPAs/eDVmvdWd/tyUEpm03ZZFcoyOYa32ajZsNtkqnMyCavA5fKG3Laza0bP4qlTfNeoyUHx5yqyGaqirDfaHVhRTzGE7+TwJOTa3nBbUYD6DTcZtiVnnxMdSRQa9La7FjR88waa+73A2C55Ta/zc/Pi8eUC/L4Li3Km7YWTi+KbbW653pCgNpWd+vLWNiy8ctl1vOb+nTmV5ScosKvKDlFhV9RcooKv6LkFBV+RckpKvyKklMmbuorlKcIK31PsbCy6eG11pB9qAslt9moUvXcjsf/uxTJx8UeD72m4Jl1elE28QSR7Jdfq2y9VjuTn295RTYpHdi3W2x74ZUHnfWPflc+X2NVHqtWVzYfdXuyObIs5Chc9ZjleiPPeTXjURik8jiuN2TPw0LB/TyDxBfTQTYrdjtyoNnAk48vLHjiRAiX64x4Fxa2vJuua8nXB535FSW3qPArSk5R4VeUnKLCryg5RYVfUXLKxLX9SaFAPNB8xhkNaLkmx2Gr1N1azGokf5ctHpU10XTl2H/4oggLCttOR47P1l6VHWqq4dawuWk3m75MPuf6unxvs1Nu1XGlKqeZClZkB6leWx6rQlFuq8+64x2efEZ27Jmd2uogVSptOtg01+U+djueWI5l932vrsv9qNXlKLw9j0Y98ViKUo/klQJ3Y29t63PuxZsx/Ohuv1bc86S8Q2d+RcktKvyKklNU+BUlp6jwK0pOUeFXlJyiwq8oOWXipr6wGFAcOLsUM04vzTXZXBMK9rdyUXbAqFfk7JOFjidonSeIX0FI4jldk2P4+dKGlcOt38WVTHnP3C7xuFpFNkU1Wu6klesN2VRW9Ixj0WM9qtVk8+HuvbPO+qXTsoNROpK+LFsOQtnE1onl55mm7ucZBvJzDpBvOvE5/RQ8ZsCCfL1UMB+GxUAsJ460cq66LGcUfmPMbuDzwAuADvAE8H5r7UljzCuA24Eq/RTAh6y1cqI3RVGeNYyz7E+Bj1trjbX2auDHwMeMMQXgTuD3rLVXAt8CPnbhuqooyvnkjMJvrT1trX0gU/UQ8AvAS4GWtfbwoP4zwDvOew8VRbkgBFK6axeD2f7rwD3A08B7rLXXZdobwGXWWvmH3CaXA0+eVW8VRTkXrqD/s3wLZ6vw+xSwBtwGvHXnfYJ3//4HOHHqJF+58y+57tD1w/rm2inxmJm5trN+uipkqABWT3j2pHsUfj2Pwi8VFH6lkqz48ir8MpGI7vjP93Hz+94wLHd6bsUdwMFL9ottJSG3/I++/4R4TKspTwjLTVn5NbVbvu/LLtvrrP+xlb//56c3E1v8j/se4m1veMWw3G7Jz3PhtOw/EVXd41+d9viS1OT3anlZVpyurMj9mJubFtuSxK3wO72wubf/B9+3XP0iMyynyfb7OnjwIF//+tfF64xt6jPG3Aq8EHintTYBfkp/+b/RvgdIxpz1FUWZMGPN/MaYj9L/jX+dtXZj2v0uUDXGXDv43X8L8IWz7UAYtwkHs1qYmd0qHo+o3op7Nm51Zc+3Xlee3aue3GCpJ+WRtCbwzfwzM/I3PiPmpkrGi21+TjYfljz9b6wuO+uTVB6PYlE+XzGSZ9zYEwdvZdm9Yih4UmHt3bdXLBeL8hgfPf2w2BaV3LN4KKwIADqBx1txZkpu83gDdrpynMHGqrutPJKWLVtuNTzmaoFxTH1XAX8A/Ah40BgD8KS19q3GmHcBtxtjKgxMfWfdA0VRJsIZhd9a+0OEMKDW2geBq893pxRFufDo9l5FySkq/IqSU1T4FSWnqPArSk6ZuFdf2mqQNvubFzY+AQpdeTNJKnhtrTfdm38AQo/5rVqRN3jEHpPYStu9waPoSf+VJPL5knirqbKXCQR62hP4c85jBiwE7g0ju3bNO+sBOh3ZvNmRu8GaZ+PNSuh+NtWabA5bWlkSy7FnZ2roCU5aEEx6bTxBXD0UE/m4tCe3BYHc/6kp9/u4uLB1o1cSZ8/ves7+3bs68ytKTlHhV5ScosKvKDlFhV9RcooKv6LkFBV+RckpEzf10Wv3/238PSDyBD+s19xmqlg+hHYqe/w1mrJPts//vl53BwUthEISP/xegtVSWSzvnZHNeZWq7Bl3+vSisz70BMD0BeK8zOOV+PhTPxHbJJ/4blvOM9jsNMVy7LNi+QJnCia20DMNJoHHPCsEBD3TOT0p/sT3p1yJxPL62vZ3uOcxNYLO/IqSW1T4FSWnqPArSk5R4VeUnKLCryg5ZeLa/l4vptvtayU3PgHqM7KzTbfrdhJJCrKWvR3L2v5qIB8Xx7I2NxbiArZj2SlppianDZsd0aSXo00tftlzb2lX1ur2hJRN5bJsIahUPFGQhbEH6Cay1SQoufs443Hs6TS2XivK9LmxIlsJZqblc0YVtyUjLPvSf8nvztqaO0YiwKX7LpGPayyJbR0hxdpobMhSaWfiqzO/ouQUFX5FySkq/IqSU1T4FSWnqPArSk5R4VeUnDJxUx9hEYoDB4XipqNCUpA9N3qJ25SWIjvhFD3ONiVP6qeOJwWYFOuuE8umt8iThqw4P7e1nOlz7DHnhUX53splt9kuKMjmyPqUbOpbWlgV2553uTsZJ0AhdI9V3eNExEj8xFIm/l7rhJzuampmVmwrC2NVKMrPpVKWx7dXlt+PUlm+t0oij3G75R7jUbNztuxKsRZ63nkYL13XbuDzwAuADvAE8H5r7UljTAr8ANh4Su+y1v7gTOdUFGXyjDPzp8DHrbUPABhj/gj4GPA7g/ZXWmvlHReKojwrGSdX32nggUzVQ8AHLlSHFEW5OASpJ/75KMaYAvB14B5r7ScHy/7v0v8S+Wvg32ZSeJ+Jy4Enz667iqKcA1fQz6K9hbNV+H0KWANuG5Sfb609YoyZoa8X+DfAvz6bE97822/j+IljfPW+B3n9G145rJ+Z8ihZmoLCryAr/HwKxPo5KvzaQlQen8JvdkrO537FwQPDv//9f7iTD//LzYznkeecPoXfStv9XRx4kqLMzMj+B08vnBTbljw55wuh+1Wbrsk+HOvLm3vc77rzAW489MvD8pGfPC0e51X4CRGFCmVZ4RcIykqA08fkvf0H9h0Q2xpNOfvJ8pJb4dfrbI7h4W89wrWv+cVheXFh+3t68OBBvvGN+8XrjG3qM8bcCrwQeKe1NgGw1h4ZfK4AnwVeNe75FEWZLGPN/MaYjwIvBa7bWNYbY+aBlrW2aYwpAtcDj5xtBzopdAa2gk7GslMI5RmhXHbP8J22PEtXPF5s1arHi21B1mUGkXvGrfhiyLVkz7dery2Ww0j+nu523F5gAHMVt4fbYkfux7rHO296n7xyidryCkrKatXuyKuFtBCL5d37donHdT3vAYl7Fu96Ur1FFfl5BoHs9RlF8kq0vSjHBSQ9ewt8WNx+X666LOOY+q4C/gD4EfCgMQb6v9U/Dtw++N0fAQ/SX/YrivIcYBxt/w+RY42+6Px2R1GUi4Vu71WUnKLCryg5RYVfUXKKCr+i5JSJe/V1kpj2wA7UztiDCkXZ/FbEbTfymXgCz07GrietUakimwiltFAlTy6mqif9VziS3ylbTj2mvrVl2dMuit0mpSSV7/mnx06JbfMH94htnZZs9mqvu016QdETIHUkJ9dWLzZ5Y1OQyGMlpbDq9OR3J/WYbttt2VTZbMpmYp+XqRR0NSpVRsoZL9h0fdv/96USA535FSW3qPArSk5R4VeUnKLCryg5RYVfUXKKCr+i5JSJm/rK1QqVgU93JePbvdLYbrrYQPKaK3l8w4PAFxBU9rAqC15xAG3BJz7xmBXLddlXfrQX2fJo3rosvnyCSeDuY9dj2pqZnhPb0p78yrQ9MQfauPs4X5Wf2dzI89y/a/fw77Vl+f1Y9uQT7HTcbR2Pubdcl/u4a172LmwJOfcAfEF0pD52R3JDNhqbZkaX6bDoye8IOvMrSm5R4VeUnKLCryg5RYVfUXKKCr+i5BQVfkXJKRM39UVhSDTIMxZl8o35Qg/GgpNVw2PiqZXk4JL16WmxrdmRTUCS91gs5BIEaLTltmgkt1unt2na8eXqCwLZ66xcd3slRj25H0nq8ZiL5Vem0Tr7vHWpEFAToFLZ6gFZyQTEXPeYN0NPqO0wdI9V3JbNvaMmtiz1quz12ViTA6GmHs/DRIh22u1uveesKTEsbO9H4PFGBJ35FSW3qPArSk5R4VeUnKLCryg5RYVfUXLKxLX9YRpQTPtayY1PgGLoSZy4zQWmT+qxEQRF+Xyxx7SQBvIQVQRHohRZ693yxHxjdauzynK27HPEqcka51XBIShJZU1wq+VJXeV5ZVKPg1QiDbInNuFovL1suSfl/wL27JWdbepttyWj/bPj4jGJJxSeFBMQoONJiRYVZWehWt0dv3JUo79r18zw76XF7c/sTPm3x83Vdzf9NL8J/Sy9H7TWPmKMuRK4A9gNLAA3WWufGOeciqJMlnGX/Tdba19srX0JcCvwp4P6zwCfttZeCXwauP0C9FFRlAvAWMJvrc0mIZ8FEmPMPuAa4K5B/V3ANcaYvee3i4qiXAgCX1CBLMaYzwK/Rj9p5+uBCvA5a+1Vmf/zGHDIWvu9MU55Of1sv4qiXFiuAJ4arRxb4WetfS+AMeZdwB9xntJxv+eW3+LEyePc+8X7eePbX7fZMY/CryAo/JKerKiqe6L8lKtyzvm2rM8hCN0JOHwKv55H4VfJJKK448/u4+Z3vyFz4Lkp/Molt2Kv5dm2vNrybEGuyuSyOcIAAAROSURBVJGITq0vi21RyT3JzFbkJCZT4ea1PvHJe/j9D/3GsLxwckE8rj4vP+umoPA74lH4FcvydudqRW5bW1kR23wKv4agpM0q/P7X/Q/zK697ybDsUvgdOHCQ++69X7zOWZv6rLWfB34F+BlwqTEmBBh8HgSOnO05FUW5+Jxx5jfGTAHz1tojg/KbgNPACeAR4EbgzsHnw9bak2fTgWpQpDYwp9WyZjXPrxHJYSGNZOedxBPDzxe/LU7kISoIMdLSQJ4NCiV5lo6irdeKypsmnzCU+5EIKbkAlpbcceQKkW8288RC9EwXwuTeP04w9QWe1GbtERtbthyU5PGoepxtFhbdq5OaZ0VT9qys4lheyflSihHIz2x7NEepfrPsGkW/W894y/468AVjTB2I6Qv+m6y1qTHmFuAOY8wfAovATWOcT1GUZwFnFH5r7XHgFULb48DLz3enFEW58Oj2XkXJKSr8ipJTVPgVJadM0rEnBNi1a8+wYu/e/ZutHlVlUHBrjlNkrX3B40AShm5HCoDYF9IqdGuBUyFLTr9RbquNaOB3797cLBn2PGGrRO2wnP2l4NFEFyLPa1Hw7L8oeawERXcfp6qyhSYMtrbt2rVv+He1LO/NmJv3ZFnquPvYasumilLFY0VK5Kw8rbocHi7wOIw1G+49HYXC1n7s339w+Hetsv292rdvKE/Ohz32Dr8LwLXAtyd1cUXJEa8GDo9WTlL4y8DLgGdASOSmKMpOCIEDwHeAbUvASQq/oigTRBV+ipJTVPgVJaeo8CtKTlHhV5ScosKvKDlFhV9RcooKv6LkFBV+RckpE0/akffY/8aYW4G30w9oerW19tFBfS7HxRizG/g88AKgAzwBvN9ae9IY8wr64eGr9ANSHrLWnphUXy8WFypvxrNh5s977P+7gdcAPxmpz+u4pMDHrbXGWns18GPgY8aYAv1wcb83GJNvAR+bYD8vJhckb8ZEhV9j/4O19vBGfMQN8jwu1trT1toHMlUPAb8AvBRoWWs3HFQ+A7zjIndvIlyovBmTnvmfBzxtrY0BBp9HB/V5RscFGMz2HwDuAZ5PZnVkrT0FFIwxcmK+nyOMMZ81xvwU+HfAzZyHd2TSwq8oPj5F/zfubZPuyKSx1r7XWvt84CP082bsmEkL/xE09r+L3I/LQBH6QuCd1toE+Cn95f9G+x4gsdaenlAXJ8L5zJsxUeEfaGo3Yv/DOcb+/3kj7+NijPko/d/4b7HWbvihfxeoGmOuHZRvAb4wif5dTIwxU8aY52XKrrwZcA7vyMT9+Y0x/4C+uWKeQex/a62daKcuIsaYTwJvAy4BTgEL1tqr8jouxpirgEeBHwEbCe6ftNa+1RjzSvoa7Qqbpj45z9bPAcaY/cCX6efP2Mib8a+std/b6TsyceFXFGUyTPo3v6IoE0KFX1Fyigq/ouQUFX5FySkq/IqSU1T4FSWnqPArSk75/1gB9CVCn2OWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "5\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/EQBvvBAHJQjChUe29wqCTxHjFwSfIsYvCD5FjF8QfErHN/lEo3HEYpUIvtojAOQtu9vW1sx1zpIWqW9omG8aypNILwAoFC3F+gJEYiNRWQAQt0QrFjOtm5eaNzNli1yqDGTMUY4AEMyapcrsullOAoCeXr7hZTXC5aOsZ958BQDZsHmTUjHC6wJ6kShtp5OW82HZpMQSbk5PT9I5iVyKjtnqPDYnpG1ndJif4yOH/mDs99zutnbj7zRtiuvr53UhAVn5BcG3iPELgk8R4xcEnyLGLwg+RYxfEHyKGL8g+JSOS32uG4bnVuSj2iMALC7zxJnJlFl6KZW5xJPKcElmLcUj92yyXaFknpfJm6VIABhMcPklE26VAUNN7bU1ixy5woMpc7Pm2nRnJrm0Fcmv0LHQ3v10zIlxqc8hkljZEIdew+1O0HYoz2PsQ2GecLN3wCy1ZgpcLo1GuNzb08X/Zi/A/7ZdY2N0bHTEXBtw185WeXBPU3ugd2N9xd5uS31HyMovCL5FjF8QfIoYvyD4FDF+QfApYvyC4FM67u3PZLJIpyu5yGqPAOBY6kmx6lQFS9mqU6d5Ft7lZe7d9jx+iorEg51Kr9E5uQQv5eWGwrQdS/AgkeQCz5t6PGNWRhbmeEmrmdNH6Fjv7GV07OC1b6Zj8Z0bvdEA4PXxHH7h/lHaDmR5FuFufqrQ1WdWW3qiPN9ePsXVm9OneS6+p546RMfWUjyH4r79ZkUlUG5VJEJNeSnnpjZe30VL/klAVn5B8C1i/ILgU8T4BcGniPELgk8R4xcEnyLGLwg+peNSX6mQR7FQkSRqjwBQKPCyVgvz5sKaLOAHAHIklx0ABD0u8wxZpKilFXNZqIylJFfaIlE5bYU/y4GGlJiI8xJaa8un6Vhx0Sxxllz+N6eWuVSZ/wM/x+Eil1pDN5r1t4Tl/PbvupS2Z6ZO0nmFDM/l2Dd0lbH/FVdcQef87hffp2P//P0f83m/OUzHumJcj+zvMn/WTrY1uCvX1M6kNv7NsZg9sGdTxq+UuhvAuwDsBXC51vpwtf8EgEz1HwD8jdb6J5t5TUEQOstmV/7vAPjfAH5lGLut9mUgCMLLh00Zv9b6UQBQitciFwTh5YVTLvPfae1Ub/Pf0XbbvwLAAfAogE9prXl95Fb2Aji+6TcXBOF82QfgRHvnVh1+12utJ5RSYQCfB3AvgNvP5QU++K5bMTszg5/++jG87bpr6/0pi2Ps5GnzXvbzdfh5Fodf7zY7/Pr7efEQx2l8HDOTpzA6vrsxFrA4/FL8/YpFs6Bjc/iVPO6MCkb4+dh52dV07MCNNxn7bQ6/Z3747frz5755F1797v9ab8/85od0Xg+4w++Sq6839p+vwy+5xPf9jw3tpGPn5fDLNByxX/nWw/jIbbfW2yaH3+DwCD7/fx+g77MlqU9rPVF9zAL4EoDrtvJ6giBcPM575VdKxQF4WusVpZQD4H0Anj7X13EDDrxAJeda7REAchke9RQOmQ/b88yRYwCQSfEcbSMjvHSSF+Ir7uqaORowEuZzCkV+B5LPt97tpNKNVSUW5rniwrC8JpFMSwG+8hQL/K6gy5IL8czzT9GxF/t7jf3quhvpnNEDr6Ht2UOP0nlJi1RZjpo/6z2vfhOdMzvFoyZ/9E/fo2MrK6ScG4B9u3gOQpTNJdHGulr7m8uBxRMbr7m+QV4KDdi81HcPgFsBjAL4V6XUIoBbAHxbKeUCcAE8B+Djm3k9QRA6z2a9/XcCuNMw9LrtPRxBEC4Wsr1XEHyKGL8g+BQxfkHwKWL8guBTOh7V5wUq/2rPa+wYGaJzBofMSRjdIP9zSrzaFXI5LpWdnuaJPz3XXBZqsN9cbgkAwhGzjAMAszOt0YrhpvPRl+BSX1/PDjo2c9pclmshyTenBINcquwO8kix9SLfXLMyaZbLlmZ5ItFEb6scFmpqj176ejrv+JN809PhE+b3W/6hKWylQnaRn6tQN5eJu3u7+TwiVwNAat28eSzf07pBLB9oXNROcOMFXvS4vA3Iyi8IvkWMXxB8ihi/IPgUMX5B8Cli/ILgU8T4BcGndFzqQ6nY0OGa9LiAw7+XHJSM/SzaDwASPVx2WVnlUWA9XVzamp2fNfYPD3Gp78C+V9Cxye5WOey1Bw/Wn+/cuYvO27lrDx07fOg5Y/+vH/83OqdsyUdQKPOcCak8/8ziObPW6lhyEay6rVLVaq7RHrjiGjpv3eWf2cJJc/LXhSeeoXMC4FLf+J5X07Hi6gwd81zzNQwAr7nkgHkg2BpR2TvSiJRcWD224b9HHS4PA7LyC4JvEeMXBJ8ixi8IPkWMXxB8ihi/IPiUjnv7y6USyqVS/XmNtCUTb6Fszo1WBs8vt7JkzrcHAH193Du/f/9+Oja/uGjsD7r8tL5yL/fMxx23pX1w3976c8flwTb5NA9M6u42B0Ht6Oc55ObmefmvVJ7nVkxbMgz3k2ChsCV1fCHSmmcw1NReK5hzAgLAnmvfRsf2X2ZWdvJnluicSNRiJmtm9QAATvyWB4VdeQVXCfaNmdUKPdXq0S84DfUjjY1/VwYJ+h6ArPyC4FvE+AXBp4jxC4JPEeMXBJ8ixi8IPkWMXxB8SselvlIgVC8d1VxCqljg+ccCAXPuPDfAv8vWi/z1CmUeXBLxYnRsgBTdnJrgAR2nJs3BQADgtZX5Kje1k2uW/Hirq3QsGDCX3tq3lxeRzGR4IEsmwKW57rCl+OSQ+Tym02a5FAC8ZGu+w3KycQ7CMR60krN81oke8zEOxLh0mF0y50EEgOcPP0LH4h6XD8NhnlRydpEEH61M0LYT2CjBOgFe7BbYhPErpQYAPAhgP4AcgKMAPqq1nldKXQPgPgBRVEoA36615sKnIAgvGTZz218G8DmttdJaXw7gjwDuUkoFADwE4K+11gcB/BLAXRfuUAVB2E7Oavxa6yWt9SNNXY8D2APgKgAZrXWtXOqXAbxn249QEIQLglO2bK9sp7ra/xTAdwFMAviw1vrmpvEUgJ1aa/5jp8FeAMfP6WgFQTgf9qHys7yFc3X4fQFAEsC9AN659WMC/sO7b8PczAx+8KtHcfP1jRrpOYvDzyFOp3CE7y1fTfJsPQMDZscdACS6uCPoyJGjxv5lS334N7/5BjrWXATkS199AB//8B319rrF4VfK8zrwzOG3tMi/n58/coSO2Rx+qbBlv/0b/szYHx45aOwHAG9gvP78F5/5c/y7z3y33s5bHH5lyzH2e2ZncdwSs2B1+P3bT+hYPMedu3/+Vp6JyIX52n9x7vn68wf+z89xx396S71dCGwsRjI4MIZ7/p4f36alPqXU3QAOAHiv1roE4BQqt/+18UEApU2u+oIgdJhNrfxKqc+i8hv/Zq117WvpCQBRpdSbqr/7Pwbgm+d6AI7rwfEqq1PtEQCyKb7Sua75mz1qWQ0GBnnkXjBoXh0BoFTiudaCnnleco2v/Pqo+W4BAPr6W1fOqbmGcDLY20fnFcGlyqBr/n7fuYtLfYvLPAJyZpGvZt39XXTssv3m8mtrJR6RODPVeq4KTW2nm79XMMbvAJPr5r9tcY6v7rMvPkvHZl58io6NJPi1c+IYv9vs6TNH462uLdB2j+H0Wi5rAJuT+i4F8EkALwB4TCkFAMe11u9USn0QwH1KqQiqUt/ZXk8QhJcGZzV+rfWzAIw/lLTWjwG4fLsPShCEC49s7xUEnyLGLwg+RYxfEHyKGL8g+JSOR/V5wSC8qibhNWkTsQQvueS65v4ui/zjsEkAcjkuN2UyPDKqt7fH2L937246Z6CPb4QpFdukoaZ2V5wnY8x7XOpLxM3RdKUyPx+eZbOUJXASY/38M9vXa36/VYuki0LrlpHRQiNacnV+mk4LxSJ0DBmzDJua5JtNi4s8EWcoy6+PTIF/LkvzZ/hrxswaXbwrSNue4RpgkngNWfkFwaeI8QuCTxHjFwSfIsYvCD5FjF8QfIoYvyD4lI5LfZF4DLGuioxVewSAnZYEk3kSe51I8GSbpaI5jhsAshkuyaQzPM47EjZLhJ7HZbSgx0+506bMRJrq2wUsGtvwCK+7193dbeyfnOGpFhfO8KSaOYt8tWPQ/F4AEM2aXzO1vEzn9BVSbe2GvBcpWtatdctlnTPXgEz08NfLTPFkm+t5nndiuN9cJxEAQkF+jLm8Wf4M97ZKweFoo112Nn4uZYdL2ICs/ILgW8T4BcGniPELgk8R4xcEnyLGLwg+pePefi8YhBeqBvaEGoEK8S4eyFIomAMfgiHuZV9Z5Hn1HPB542PjdOzY8T8a+3N5HuzhWbz2A72teQa7m4J5hgZ5zrdimeeKm1vcmNUVAJ46xHPPTc7yQJbeHh481dvLvf2Lp82BM6srPMAlEm19vUig4amPhXgQkS0dfYHkZFxOcVUns84ViZDl89w9zhWr7i6eb7LkmMulBcM52s4YFIJSyaxs1JCVXxB8ihi/IPgUMX5B8Cli/ILgU8T4BcGniPELgk/puNRXQAmFqlRVaJKsTk2epnO6us0BPJESzz03Mcnlq64Yl68GBnhwBhuzpAtEPsUDYwrZHG2fWeKSWNnlQUvPaW3uP8JLUBVL/BiHx3bQsZExLm2VV81lvgIuLzIaGRhpaQ/tbLS9AJeCXSdMx5aWzaUkF1JmeQ0AXEsZuFCGB89Eovw4SiUuLQJmWTrYFkjW3F7PbQwwspVxAzZXrmsAwIMA9gPIATgK4KNa63mlVBnAMwBqVvtBrfUzZ3tNQRA6z2ZW/jKAz2mtHwEApdQ/ALgLwH+sjl+rteZfm4IgvCTZTK2+JQCPNHU9DuCvLtQBCYJwcXBsWyHbUUoFAPwUwHe11vdUb/ufQOVL5EcAPtNUwvts7AXAk6ULgrBd7EOlinYL5+rw+wKAJIB7q+3dWusJpVQ3Kn6BvwPwt+fygnd+4uNYmJ/H177xTXzgfe+u95fBHUHU4RfmDr9jL5zkr2dx+L3yla+gY8sr5uw0y8SpBNgdfs371b/68MP48K231ttDO0bpvPNx+D359BN0znqK7wl/1aWX0bG/ePuf0THm8Ftbt+ztb3L4/bf/+Y/473/buOHcboff8YkTdM4zz75Ix9JLq3Ts6tdcQsfifXQIiJtjCbp2Nfbv3/s/juI//92Bent5beN5HOzfhc9/msdwbFrqU0rdDeAAgPdqrUsAoLWeqD6uAvgKgOs2+3qCIHSWTa38SqnPArgKwM2123qlVB+AjNY6rZTyANwG4OlzPYByqYRSNdKq1BRxlbfkRluYM69MXpB/lzllvjrGIjz339wcz3UXi5tXmFicS0PrOX5HEwg4tD15eoLO6xvmEX8lcgeVsZSZci05CMd2cjkv1sWj+oLkpizSy++6CrHW1T3a34h6zKf5Z72e4vLbzv2vNPYXLSXKTk1bSmtZTMhWcs5z+R2DFzZ/ZtGoQ9tJg3Jo+Rgr4/ZhQCl1KYBPAngBwGNKKaDyW/1zAO6r/u4PAngMldt+QRBeBmzG2/8sALZsXrG9hyMIwsVCtvcKgk8R4xcEnyLGLwg+RYxfEHxKx6P6Msk00muVzQu1RwBwi1yuCTJJjyRnBICYpUxWiKuAWFvhkszcnDkyy7UkEo1GuAyYyrRKmKl8ox22JIpMkc1GAOB6Ztmou4/LcokYHyvmuER4kiQ0BYDuiDnpqhM091cGWz+YdLrRPrNgTkwKAKeO8eN4S/+bjP2v2jNG56wd2E3Hsutcut29a4SOuUEuH+bIpVp0p1vaAbdxvTsBQ0mxAD82QFZ+QfAtYvyC4FPE+AXBp4jxC4JPEeMXBJ8ixi8IPqXjUt96KolkspKwsPYIAF6ByxTRKJGHQvy7LBbm8d+RaISOeZYkjLnFeWN/ATxBytLqCh0rplulvqWmOnaDXTz6rZTn58p1zGMH9u+jc3oSA3QsGubSXGaN10NcXzLnMXBD/Ny70VZZcWGqEWGZWuPn0SlwmTi1bJbYShn+d40N99Kx1WWenyES4X+b4/KIvyypRVnIO7QdNEimQYu8DcjKLwi+RYxfEHyKGL8g+BQxfkHwKWL8guBTxPgFwad0XOpL53P1OmPN9cYSloJ3ubJZvgpYShDkHT44fYZHxTku/350SdLHsvEi4IwAAAaVSURBVCW6cG3BnMIaAEJtNRQK+YZklc1zSalc5u/HEqH2dfEahL1dPXQsHuPSp01+C7BjzHFZLptrfb3sSqNdtpyPkX6e0DRtynQJYHKCp3bP5Hgq8/WkRZK2nCvX45/Z5JQ5dXdkV+uctZVGOxDemIQ24PAIUkBWfkHwLWL8guBTxPgFwaeI8QuCTxHjFwSf0nFvPyNfMOQkq+IEzJ5Sz+PJ+NKW8l9zc1N0LBTiZZz27ttr7A86/Dt1bIQX3Ay2Hf6OHTsajRL3KmfWk3TMId70kK04c5F7otdXePBO2RJQ4xL1JrXOcyQGvNZzX0itk//ZSqzbEgRF/rRwmHvGswX+vjmLEjA3x5Udz1JcdWba/H6JUOv5XZhttPtHN16nhbzdvDdbq+87qJT5LaFSpfcTWuunlVIHAdwPYADAIoA7tNZHN/OagiB0ls3e9n9Ia/0arfXrANwN4KvV/i8D+KLW+iCALwK47wIcoyAIF4BNGb/Wunm3RQ+AklJqGMCVAL5e7f86gCuVUkPbe4iCIFwInHLZ9uOvgVLqKwDehkrRzpsARAA8oLW+tOn/PAfgdq31k5t4yb2oVPsVBOHCsg/AifbOTTv8tNYfAQCl1AcB/AO2qRz3O//yFkxPT+Px3/we17zx9fX+sOVLKUQy9nhR7pyzZYyZm+Pbe8/H4RewOPxyKe6ca3b4Pfit7+ODt72j0XGeDr80caj19vOCErEE3/qLEnfElgvcqRpkDr+0ebst0Orw++rD38OHb72FH1cT/QP8+GOxjdtgASCd4Y7M1XVeIGRpgZ/73l5+E2xz+P3x9LPG/sSexrl6+IFTuPWORjERk8NvoG8c/+uTv6Dvc85Sn9b6QQBvAXAawLhSygWA6uMYgIlzfU1BEC4+Z135lVIJAH1a64lq+xYASwDmADwN4P0AHqo+PqW1Nie2IwTKgFtd5N2mxb6Y5YEbqYx59Yk5PC+aZ5Fy4gk+z7Ws4sll86rqWYKBElF+HOFg6+oYizZWqQCPc4JryRmYJ3n1ggH+0adzfAWfsgTADPbzXHddcfOKWyJBWgBQyLXeFeSa2sUS/5uzeS71FVPmcmPBEA/CsZmJa8mTl85wGRBlvvJHQubAqpWldFu78TwQ2mgTQZK/scZmbvvjAL6plIoDKKJi+LdorctKqY8BuF8p9WkAZwDcsYnXEwThJcBZjV9rPQvgGjJ2BMAbt/ugBEG48Mj2XkHwKWL8guBTxPgFwad0MrDHBYCh4eF6x2hTIIuTs6WtMnv7o11mjzIAhLu66ViceIABu7c/HjW/n2vRcOOWKi4hr9WlPzDY0Ikdi7c/a1EyQuT7vXtw2NgPAEWX720oFvjn0t9jOccx8zHmLcpCu0N/cLixN8Hm7e8f5Gm8XM/8twWD/HP2grY9J7wSFHsvAHZvf9qsPKRCreY6Mryz/rxnYOPx9/fVg8iMV8+md/hdAN4E4FedenNB8BHXA3i0vbOTxh8GcDWAaVQkREEQthcXwA4AvwOw4Rark8YvCEIHEYefIPgUMX5B8Cli/ILgU8T4BcGniPELgk8R4xcEnyLGLwg+RYxfEHxKx4t2+D33v1LqbgDvQiWh6eVa68PVfl+eF6XUAIAHAewHkANwFMBHtdbzSqlrUEkPH0UlIeXtWuu5Th3rxeJC1c14Kaz8fs/9/x0ANwBoz4/l1/NSBvA5rbXSWl8O4I8A7lJKBVBJF/fX1XPySwB3dfA4LyYXpG5GR41fcv8DWutHa/kRa/j5vGitl7TWjzR1PQ5gD4CrAGS01rUAlS8DeM9FPryOcKHqZnR65d8FYFJrXQSA6uNUtd/PyHkBUF3t/wrAdwHsRtPdkdZ6AUBAKWXJM/6ng1LqK0qpUwD+HsCHsA3XSKeNXxBsfAGV37j3dvpAOo3W+iNa690APoVK3Ywt02njn4Dk/jfh+/NSdYQeAPBerXUJwClUbv9r44MASlrrJfISf5JsZ92Mjhp/1VNby/0PnGfu/z81/H5elFKfReU3/l9qrWtx6E8AiCql3lRtfwzANztxfBcTpVRCKbWrqW2qmwGcxzXS8Xh+pdQlqMgVfajm/tda644e1EVEKXUPgFsBjAJYALCotb7Ur+dFKXUpgMMAXgBQq1JxXGv9TqXUtah4tCNoSH2zHTnQi4RSagTAP6NSP6NWN+O/aK2f3Oo10nHjFwShM3T6N78gCB1CjF8QfIoYvyD4FDF+QfApYvyC4FPE+AXBp4jxC4JP+f9bUOlY9g1+fwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "7\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EaXrgT1VAGyu/UuqLSqndAFYBeFAp9UxN9H4ANymlngdwU60tCMLLhMOu/FrrDwH4kKH/OQDnzB8hCMLLAXm9VxB8iii/IPgUUX5B8Cmi/ILgUzr+kk8YAXi1e1C46V4UCfCpOSQZZCrNX9TIV7jb48LLzqeyjadyt92Wf/6RsX98D48EHOnjL+T09XTTtuNw11zR4m7yXPNxF4sWH5vL3XkTk/zlIBjqxc1S8czRhdkM39f0TOsxTza13QCP4Axa3Kn7J8zu4BHLi1JI8oSgaUutvqJnqQEZ4Mk1Q0nzS1ZugLcDgfkv+Zj6mpGVXxB8iii/IPgUUX5B8Cmi/ILgU0T5BcGniPILgk/puKsvWkkgUKm6gWKVRpLK5Ut5/Prj7gFj/xR4VNmKjTwO/fxLTqWyk09ZQWVDSfPp+8l9P6NjUtPcHZnLds1pN1xbk+M8YtGxxIZXwub7e7rIo8oyDneLDhA3KwDEwBOhusQdOW2J3nTm1LpzmrYRifIox0KJz3+qYHYtRiyJRPMhnt8gDx7P74C7MXNlfh2EesxuzGRXnLZdQ1KeaJjnjgBk5RcE3yLKLwg+RZRfEHyKKL8g+BRRfkHwKR239ucz5XrZoVyqYaUNxnigRZHEWaxYu5qOef2151LZiWoJlUUT3Aq88UKzl6BsOatbvvp9Ktv64h9oO1DkG3XL3KqMqDmAZNJitR8csOQLTHALcj7Fg1zSJDtu1hJfFAq1HnM51DiWYpkPnCnwIKhc0Hw+fr+Hp5/cNc73lbYEQXmWtPhFWMq2Lekz9nd3JWl7MjPf6+BavA2ArPyC4FtE+QXBp4jyC4JPEeUXBJ8iyi8IPkWUXxB8SsddffunDiGXn8bZAHZPNMpfPfLUI3TM0vVmV8g1730rHXPCqdydFwjznHvFoiVwwzEHsrxi8yl0zM4nXqSyB+//eUt7W7aRLy/q8HJSpSIPqPEq5oCavjh3Na0eMRezBABY8sJlHO4+ZAE100VLLr457QP5xpwjET6PdITPI9KfNPaP7ebFOPen+faWrOEBY3t3c/dhucRz+AUDZndqaqrJlTrc2i6U588xGLH4UdGm8iulbgVwNapltTdprZ+u9e8AUKj9A4CPaq1/2s42BUHoLO2u/N8B8A8AfmWQvW32ZiAIwsuHtpRfa70FAJRSx3Y2giAcNwIVyyuIc6k95r9pzmP/DIAAgC0APq61nm5zc6MAtre9c0EQjpZ1AHbM7Vyowe8irfWYUioG4AsAbgNw3ZFs4Kc//3vk8tO46opP49s//FS9/0f3cYNfKWB2UrxUDH7JsNkgCQA//OoPqKzZ4PfTpw/gT16xrN4+nga/0zedRGXdXbwgxuQhbjSbnDIbzdo1+N395ON41xmb6+1IhL+3ni7PUBkz+G2zGPz2pRbf4FcscIPfjR9/h7H/wivX1j+/Xv13/ET/33o7YzjmZKQfb9zwYbqfBbn6tNZjtb9FAF8GcMFCticIwvHjqFd+pVQXgLDWekYpFQDwdgBbj3Q7S9cuR7FUzZG2fP2qen+5m7spTj/rlcb+E1+5nI5xKzxnWsnlUWAOKXcFAAiZV89oNz+tayyraubbv2htRxvbD5f4z7NUlq9MUZLD7/STT6BjRtdx2UyWn8fsQf4EtT9nPo8HcnwFD4Van2h2NW0jFOYRhN3L+ap6wRvNpdkOfP/f6Zi9pb1U9uY/fx2V/fLnv6GyRx/eSWV7yBNDqbhmTrtxTQQM5b8ClnJhQPuuvi8CeCuA5QAeVEpNALgSwLeUUiEAIQDPAvhgO9sTBKHztGvt/xCADxlEZyzudARBOF7I672C4FNE+QXBp4jyC4JPEeUXBJ/S8ai+3qX9KFWqUUz9I4P1/hs+/G46Jpow37NKQe7+CVpKSQUtpyGR6KGySsW8zbLHXW8r1nJ35IZTTqLt3U/xF0YqLt9fKGLOduqEeZLOrS9yN9TBaf4Czf5D3A14aMbsuk0ZXFSzBEOtrsM9zlT9c3ecu2DPufQiKnvVG84x9v/md/xl09wLY1TW1c8Tml751ldT2fPPfJvKtj5mDpW55MrW62PyUKOE2/LRgXn/Px7m1y4gK78g+BZRfkHwKaL8guBTRPkFwaeI8guCTxHlFwSf0nFXX87JwvGqLqJsseGq6xrkrigPZjcPc70BQCDE73PlIo8sq1Rs90dzpJ1T4lGC/cu4++XKq99A29/Y/z06Ljdtq8lmdqVNBHnU5JJhno/AFDc+S9GSlDLcZY6jT4TM+QYAYHjpspb26tMa7XPOM9dJBIBzX7eZygL95u9zxbpBYz8AeB7PYfDCC9xFeOUVr6IypUao7PEntLF/9459tL32xBXz/n8sxHMlALLyC4JvEeUXBJ8iyi8IPkWUXxB8iii/IPiUjlv7XddB2a1ansvlhgXamn6MWPXDFmtz2ZKivGI5DZUKl5XKZqt+Jcit72VLKanVp43SdmJ5Lx038/s9VBYImy3Vq89ZR8f86TWXU9m+A/uo7OBBnrU9nTV7aMoBbu1fOdKacfnd//Vt9c9rLFlznTAP+pnKm7P0rlrLrf3hIM+c/Ifn+bnv+jN+HZx15olU9uQT24z9+axD225p/r7cw6Tll5VfEHyKKL8g+BRRfkHwKaL8guBTRPkFwaeI8guCT+m4qw8IIIBA/dMs5RJ314TDZpeeZ4lvyeW4i83mzgP4Rt2yeY6ROA8EcSy320R/63EFm9rdK/rpuP1Znruwr8/sIhxePz/nW33MaDeVxVespbITA1xWypsDiTIF/r14bqsbcPVJQ/XPwaAliKvCvzMW7LJk6ZCxHwB6enmQWTTC3YDJHh4g9cpX8bJtA99+2NjvlXg7EZt/DcdC3PUNtKH8SqkhAPcAWA/AAbANwPu01oeUUucCuBNAAtUSwNdprQ8ebpuCIHSedh77KwBu0VorrfUmAC8CuFkpFQRwL4AbtdYbAPwSwM3HbqqCICwmh1V+rfWk1vqhpq5HAawFsBlAQWu9pdZ/B4BrFn2GgiAcEwKVw7wC2ExttX8AwPcA7AHwX7TWVzTJcwBWaa0n29jcKACeCUEQhMViHao/y1s4UoPflwBkANwG4KqFzwl48sB9cNwMzlnxF/i3vV+t97vcnkMNftEIf5ApEOMccDiDH5e5ZbOxKhIPGPsBoGQz+FUahrbNS67F4+P319tf+z/fouPu/8oDVMYMfjfdfDUd8ydXX0plRYdnAArxw16wwe+S0b/AQzsa14clMROKFR4vECKZg3Y/eYCO+dRHeYGNU8/kMQaf/Aw/xy8+zdfHT3zsLmP/Nf/5NfXPn/3IP+Hjn393vf2may6Y9/9joW5sXvEOup+2XX1KqVsBnATgWq21B2AXqo//s/IlALw2V31BEDpMWyu/UuqzqP7Gv0JrPXurfhxAQil1Ye13//sBfPNIJ1AsVVAoV3965J3GT5CQ5dYeDZunXSY59QAgV+QrVr5gKfMVPPIcfl0h7ipzA3x7wWBrlGDRbbT7R7hrrhzirsVgxOzaGhzk2yu5fOV0SP5EAAiSJyEACLBxFpedU2r9zhyvUb4rUOGPGRXLdRANmctrdfdyV9/AEn5+R1bOz503i2uJBhxaw+e4Zr15LhU3QNvhwPzzETL0NdOOq28jgI8BeB7AI0opANiutb5KKXU9gDuVUnHUXH2H254gCC8NDqv8WutnABhvIVrrRwBsWuxJCYJw7JHXewXBp4jyC4JPEeUXBJ8iyi8IPqXjUX3FElCoeZYKTd6goCVErwSz265UsriaAhb3T8zs/gEAt8xdUZ5n3mbB4lYsOJbjav42lgGZTKre7Onj7sNQlEdvReIJY38sssTYDwDFnCUBadDyUk4xR2Vhj0RiWl7mqsyxM1daoj65OzKX5/MoBs3f9eRklo7JO3x7yS7z+QWA8Ule2qxc4gfeRaIBs1mXtnO5+a5UL8zPESArvyD4FlF+QfApovyC4FNE+QXBp4jyC4JPEeUXBJ/ScVdfruQiV3PbZJ2Ga6Jc4u6yMInbT6d5rbieLp6EcekQj+iqRCw1/kgilHzBEkGYy1OZG2p1sWWzDfeT63G3TTDKo7emm9yFzezcPkXHDIz0UFkokaGyissj/jxSRzFd4Oej4DS5FU8Axg815mxLQlOyJH8tk+9z1xivQTiTNp9DAAhackikMvxcBSvcvZwvmOe47YU9tD2Tmn/MTpSfB0BWfkHwLaL8guBTRPkFwaeI8guCTxHlFwSf0nFrfzabRcapWkXTTdbRaIRbQ2Nhc061aNScrw4AggF+qAGLzHEKVJbLmQM+SpagDUt6uXmi5nbJlpE2zu/h09Nmq/4Pf/QgHdM79EYqGz3Bkp/Qkt+vTPIC5vI8UCg9x1o+M91ol8v8fESilpyGnlm278AEHeNYgrvChjJZ7YxzHX7cZRLUtnfX3pb2zqb2xMR8z0J33F6uS1Z+QfApovyC4FNE+QXBp4jyC4JPEeUXBJ8iyi8IPqXjrr5YLAo3UHXrJZpy6cXj3NXHCnLGB8y5zwAgFrYEUuS5O29mmudhy5Nccd3d5uKYAFCxJK2b6zpsaVtu0119SSo74+wzjf07xrbRMV+9/R4qu/jVr6Kyk09bTWV9y8xu2EqFu6PCoThtB8DPY9nhbsBDM+bgrxde3EHH2M69a3HBuh4PuMpbCp4mus07jKRb1TUSb7SzhkKowYA9sKedcl1DAO4BsB6AA2AbgPdprQ8ppSoAngIw65i8Xmv91OG2KQhC52ln5a8AuEVr/RAAKKU+B+BmAO+pyc/XWvPYRUEQXpK0U6tvEsBDTV2PAvjAsZqQIAjHh4AtKcJclFJBAA8A+J7W+ou1x/7HUb2J/BjA3zaV8D4cowC2H9l0BUE4CtahWkW7hSM1+H0JQAbAbbX2Gq31mFKqF1W7wCcA/M2RbPD7z34NOSeFa0//S9y/9Qv1fqvBjxSpsBXmsBn8bJlfFtvg59iyzJQbmXuuO/fvcO+jn6y3X3iav3t+z50PUNmGDScb+20Gv2SUn6vFNvgVXW64KxQa68h7X/d3+MqDjfPhWsbZ3vsvGgxjAPCjf/kVHfPU73ZS2fU3XEVlp56+isryhiIb9bl885fG/r37Juuff/evT+GVlzUKZN9w49Xz/n9Psh/vvvwv6X7advUppW4FcBKAa7XWHgBorcdqf1MA7gJwQbvbEwShs7S18iulPgtgM4ArZh/rlVIDAApa67xSKgzgbQC2HukEInARqbltIk3um6DLXSHxkLlEUsUSMlexlP/yXMsTQ4zn/ouSFTKR6KJj0mluG3Xd1ieJQKDhKoon+TzK4CvderXW2L9h0zI65of3P0xl3/5/v6ayy7NmtyIAnPVa8zy8IL8E55a0ao6WDAT4ulWpcBfbwYPmJ6h0hrt7V69dQ2XpTJrK9h88RGVhy3H3DZllwchwS3vVmkY7k51fbiwE/gQHtOfq2wjgYwCeB/CIUgqo/la/BcCdtd/9EQCPoPrYLwjCy4B2rP3PAGC30tMWdzqCIBwv5PVeQfApovyC4FNE+QXBp4jyC4JP6XhUX9kpolxLklluSpZZdrj7LUwCwZJJswsQACKWhKAhi9vFlkiUvR1ZLPCXHD2Hv5wSdCO0XS7ycaUS39/klNm1dd6rT6FjzrnwLCp79OFnqGz7zt1UtnzM/JJPrJsnBO3rG2ztCDTOh2Mp55ZKzXd7zZLOmF/MOunU9XRMf/9yKusd4FGJ0zO8zFcoyMetOWmlsb+Qa12r15/SeKkq58w/ZltCW0BWfkHwLaL8guBTRPkFwaeI8guCTxHlFwSfIsovCD6l466+fKGMbKEa25xtinEulXm8c6lsvmc5Do/mSia469AWGw5LhFgoZD59rsWdV8rz48plWqPzslON9oE9PJ5/2dIlVDbQ12/el8U9uHbTUiqbKnBZNMzXkgzxepWCPCIxmmiV5ZsSc7pliys4xhOaLltpjrEfPYG7xRxLQlBLcCGcEnfnzaR4noiubrPLOhFvPebEYMMNHU7Or0EYivOahYCs/ILgW0T5BcGniPILgk8R5RcEnyLKLwg+RZRfEHxKx119qVQBqXweADA9k29rjEuSe+by3MUW8CzpnAt8v8ydBwCxuDmppi2aKpPjieH+8MIAAAZySURBVCJLc9xXhXIj6WjPYA8dd97Fm6lszeiIsT8Y4eejZ5AnID397FOpLBnlLrbeXnM68yIs535OtGVzO2BxK8YsEXMsx2vBsXwvlnTr8QSPJO3p4d9ZNMavkVDUfM05xVb3bLKr8T2Zthe1JJ8FZOUXBN8iyi8IPkWUXxB8iii/IPgUUX5B8Ckdt/Z7iMCrlRXymsoLRcKWoISgWZbJcsux6/BAlmyG53wLWazKA/1mq3IobLGyWqy88TnBGfH+hrV4ObEAA0DXEl4CLNFjnr/r8eMKe3yO4QH+vXTFuJcgEjbPv5Tn30vQDdD23FJezaTSPGimSK4Dm/cgbDn3FV4FDrG45TxG+HnM5sxzDAZjtJ1Jz/dWhD17wex2a/V9B9Uyvx6qVXpv0lpvVUptAHA3gCEAEwDeqbXm5V8FQXjJ0O5j/7u01q/UWp8B4FYAX6v13wHgdq31BgC3A7jzGMxREIRjQFvKr7Vufo7qA+AppYYBnAngvlr/fQDOVErxgG9BEF4yBFju+bkope4CcDmqRTtfDyAO4Ota641N/+dZANdprZ9oY5OjqFb7FQTh2LIOwI65nW0b/LTWNwCAUup6AJ/DIpXj/uoDn0cqP42PvPkz+Px3G5u0GfwiUbOskD9agx83mNkNfuYsOT29/LXOfJm/VhsKNY7rhov/Cnc9/Ll6O5fjx5ZO8/n39Jhfq3UtrzvDYsQqF7jwaAx+qTw3zoWbqrP8tzd/Cv/w3U/X27aiHRnL+Vhsg1/UYrjr6zefewBwXX4emcGveZn+xJ/djM9883/W24XCfINff9cg/uqtn6T7OWJXn9b6HgCXAtgNYKVSKgQAtb8rAIwd6TYFQTj+HHblV0p1AxjQWo/V2lcCmARwEMBWAO8AcG/t75Na60NHMoFSuQKnVL2nzf4FgLIlmCJP8uBls+ZSTAAQs5XrCvMVyxLXg0rA7Oorlrkbqmi545fmlFyayTfaFfBtxnr5JMsBc8CKU+Dbc4t8jsUsf4JyQnw1Zk9y45MH6ZjBgdYnq3yq8f16lp+r4/v4JVhwzHNcMsJLcrkBnsdxMjVFZTSKCEDQcmHt22vepue1bm/v7vH6Z9eb/316lusCaO+xvwvAN5VSXQBcVBX/Sq11RSn1fgB3K6U+CWAKwDvb2J4gCC8BDqv8WusDAM4lsucAnLPYkxIE4dgjr/cKgk8R5RcEnyLKLwg+pZOBPSEA6Ek0fKF9yYZ1NwBuYWVEwAMpbP7Yiset2zZrfxdJ4RSNc89C2FIdqDzHYtubaJyPSsVSVSjAZUFyfy9ZvAdumFupnQC36EdDlkAWVt3IEqDT19U7pz1Q/2yz9uf7eEquIvEiDXQP0jHBCE8LFg/z73ru/Fu2GeQXVoEEY8095sHeoYbMYO3v766fL+MBtP2G3zHgQgC/6tTOBcFHXARgy9zOTip/DMDZAPYBlmVIEISjJQRgBMBvAcx7QaOTyi8IQgcRg58g+BRRfkHwKaL8guBTRPkFwaeI8guCTxHlFwSfIsovCD5FlF8QfErHi3b4Pfe/UupWAFejmtB0k9b66Vq/L8+LUmoIwD0A1gNwAGwD8D6t9SGl1LmopodPoJqQ8jqtNU8F9EfCsaqb8VJY+f2e+/87AF4NYOecfr+elwqAW7TWSmu9CcCLAG5WSgVRTRd3Y+2c/BLAzR2c5/HkmNTN6KjyS+5/QGu9ZTY/4ix+Pi9a60mt9UNNXY8CWAtgM4CC1no2QOUOANcc5+l1hGNVN6PTK/9qAHu01i4A1P7urfX7GTkvAGqr/QcAfA/AGjQ9HWmtxwEElVI8FvePCKXUXUqpXQD+N4B3YRGukU4rvyDY+BKqv3Fv6/REOo3W+gat9RoAH0e1bsaC6bTyj0Fy/5vw/XmpGUJPAnCt1toDsAvVx/9Z+RIAntZ6skNT7AiLWTejo8pfs9TO5v4HjjL3/x8bfj8vSqnPovob/y1a69k49McBJJRSF9ba7wfwzU7M73iilOpWSq1uapvqZgBHcY10PJ5fKXUyqu6KAdRy/2utdUcndRxRSn0RwFsBLAcwDmBCa73Rr+dFKbURwNMAngcwW6Nsu9b6KqXU+ahatONouPoOdGSixwml1DIA30W1fsZs3Yz/obV+YqHXSMeVXxCEztDp3/yCIHQIUX5B8Cmi/ILgU0T5BcGniPILgk8R5RcEnyLKLwg+5f8DkYTM3Vtsf1sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "8\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n",
            "9\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "**************************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhMex-_KOMSl"
      },
      "source": [
        "# Approach -1 - Using simple DNN or FC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5BV8jl6COP3g"
      },
      "source": [
        "# Load the data\n",
        "(x_train,y_train),(x_test,y_test)=cifar10.load_data()\n",
        "\n",
        "# train set / data \n",
        "x_train = x_train.reshape(-1, 32*32*3)\n",
        "x_train = x_train.astype('float32') / 255\n",
        "\n",
        "\n",
        "\n",
        "# test set / data \n",
        "x_test = x_test.reshape(-1, 32*32*3)\n",
        "x_test = x_test.astype('float32') / 255\n",
        "\n",
        "# train set / target \n",
        "y_train = tf.keras.utils.to_categorical(y_train , num_classes=10)\n",
        "\n",
        "# test set / target \n",
        "y_test = tf.keras.utils.to_categorical(y_test , num_classes=10)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7eZQ1BQ3QzPz",
        "outputId": "f63d4bc4-50ee-4a69-cfda-e11481d0da91"
      },
      "source": [
        "x_train.shape,x_test.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((50000, 3072), (10000, 3072))"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdoz13C0Omds",
        "outputId": "d9f90027-7b9c-47fa-fc54-ad75c6ae3b4f"
      },
      "source": [
        "#Models\n",
        "model = Sequential()\n",
        "model.add(Dense(800, input_dim=3072, activation=\"relu\"))\n",
        "model.add(Dense(10, activation=\"softmax\"))\n",
        "\n",
        "# For using custom metrics\n",
        "# https://keras.io/api/metrics/#custom-metrics\n",
        "\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"SGD\", metrics=[\"accuracy\",tf.keras.metrics.AUC()])\n",
        "history = model.fit(x_train, y_train, validation_data=(x_test,y_test),\n",
        "                    batch_size=200, \n",
        "                    epochs=5,  \n",
        "                    verbose=1)\n",
        "# https://keras.io/api/models/model_training_apis/"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "250/250 [==============================] - 5s 10ms/step - loss: 1.9974 - accuracy: 0.2904 - auc: 0.7435 - val_loss: 1.8827 - val_accuracy: 0.3432 - val_auc: 0.7836\n",
            "Epoch 2/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.8419 - accuracy: 0.3583 - auc: 0.7937 - val_loss: 1.8038 - val_accuracy: 0.3726 - val_auc: 0.8035\n",
            "Epoch 3/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.7856 - accuracy: 0.3809 - auc: 0.8083 - val_loss: 1.7625 - val_accuracy: 0.3882 - val_auc: 0.8136\n",
            "Epoch 4/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.7467 - accuracy: 0.3959 - auc: 0.8180 - val_loss: 1.7416 - val_accuracy: 0.3880 - val_auc: 0.8167\n",
            "Epoch 5/5\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 1.7174 - accuracy: 0.4077 - auc: 0.8247 - val_loss: 1.7147 - val_accuracy: 0.4046 - val_auc: 0.8233\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f88OJHR6RGEz",
        "outputId": "532c65fa-09cb-4a46-ab06-54dbc08af73b"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 800)               2458400   \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                8010      \n",
            "=================================================================\n",
            "Total params: 2,466,410\n",
            "Trainable params: 2,466,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGNPCWwHPKU2",
        "outputId": "ff738c94-537e-4b48-efc6-159fcb0090af"
      },
      "source": [
        "history.history"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'accuracy': [0.2903600037097931,\n",
              "  0.35830000042915344,\n",
              "  0.3809199929237366,\n",
              "  0.3959200084209442,\n",
              "  0.4077399969100952],\n",
              " 'auc': [0.7435173392295837,\n",
              "  0.7936551570892334,\n",
              "  0.8082573413848877,\n",
              "  0.8180397152900696,\n",
              "  0.8247133493423462],\n",
              " 'loss': [1.9974480867385864,\n",
              "  1.8419305086135864,\n",
              "  1.7855725288391113,\n",
              "  1.7466938495635986,\n",
              "  1.7174471616744995],\n",
              " 'val_accuracy': [0.3431999981403351,\n",
              "  0.3725999891757965,\n",
              "  0.3882000148296356,\n",
              "  0.3880000114440918,\n",
              "  0.40459999442100525],\n",
              " 'val_auc': [0.7836068272590637,\n",
              "  0.8034802079200745,\n",
              "  0.8135594725608826,\n",
              "  0.8167243003845215,\n",
              "  0.8232808709144592],\n",
              " 'val_loss': [1.8827219009399414,\n",
              "  1.8038462400436401,\n",
              "  1.7624986171722412,\n",
              "  1.741645336151123,\n",
              "  1.7146626710891724]}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "XhZg7hTqOmVI",
        "outputId": "758f2463-a2d1-4eee-8cce-c7584f7dd810"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "7atA3EVuTG1I",
        "outputId": "68dcd07b-6bd8-43ea-bf9f-59537f177df0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['accuracy'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)\n",
        "plt.legend(['Training accuracy', 'Validation accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "Y-j_GIG_TVNT",
        "outputId": "c3cf1d95-2329-48ee-e88c-49650e9ed063"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['auc'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_auc'],'b',linewidth=3.0)\n",
        "plt.legend(['Training AUC', 'Validation AUC'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('AUC',fontsize=16)\n",
        "plt.title('AUC Curves',fontsize=16)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'AUC Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TdLCjMNAAl1U"
      },
      "source": [
        "# Approach -2 : Using CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aHl7atlQIQ1S"
      },
      "source": [
        "## Change the Datatype to Float and Normalize it"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9QWdrxZIQ1S"
      },
      "source": [
        "# Load the data\n",
        "(x_train,y_train),(x_test,y_test)=cifar10.load_data()\n",
        "\n",
        "#Normalize\n",
        "x_train=x_train.astype('float64')/255\n",
        "x_test=x_test.astype('float64')/255"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HfKVxkGNIQ1T",
        "outputId": "fa374b84-cff6-4815-9bd8-0a27692c7aa6"
      },
      "source": [
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(50000, 1)\n",
            "(10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIwm67YzIQ1U"
      },
      "source": [
        "## Do the one hot Encoding of the Dependent Variable using Keras.utils"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nxfOBs_WIQ1U"
      },
      "source": [
        "from keras.utils import np_utils\n",
        "from tensorflow.keras.utils import to_categorical"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nfq2PcoBIQ1V"
      },
      "source": [
        "num_classes= len(np.unique(y_train))"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUkpy3LzIQ1W"
      },
      "source": [
        "y_train= to_categorical(y_train,num_classes)\n",
        "y_test=to_categorical(y_test,num_classes)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIf4vb8QIQ1W"
      },
      "source": [
        "### Divide the Training Dataset into  validation, Training and Test."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkmQQQ3hIQ1W"
      },
      "source": [
        "(y_train, y_valid) = y_train[5000:], y_train[:5000]\n",
        "(x_train, x_valid) = x_train[5000:], x_train[:5000]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANfGVuusIQ1X",
        "outputId": "862a3f92-1d34-4f50-b304-5226ee2fbc1f"
      },
      "source": [
        "print(y_train.shape)\n",
        "print(x_train.shape)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(45000, 10)\n",
            "(45000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpBahc2IIQ1X",
        "outputId": "cb2aedb1-235d-4fca-823c-58db6328937f"
      },
      "source": [
        "print(y_valid.shape)\n",
        "print(x_valid.shape)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(5000, 10)\n",
            "(5000, 32, 32, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tDX2vNT2UeWH"
      },
      "source": [
        "#  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fSeDSbnCUiFE"
      },
      "source": [
        "## Build a CNN Architecture with KERAS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sor7CUOWdDdT"
      },
      "source": [
        "#### CNN using just the CONV layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EtQm-aOUdm0",
        "outputId": "51624a04-8791-496e-d8fa-76e0b54ea84b"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(Conv2D(filters=9,kernel_size=3,activation='relu',input_shape=(32, 32, 3)))\n",
        "model.add(Conv2D(filters=6,kernel_size=3,activation='relu'))\n",
        "model.add(Conv2D(filters=3,kernel_size=3,activation='relu'))\n",
        "model.summary()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 30, 30, 9)         252       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 6)         492       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 26, 26, 3)         165       \n",
            "=================================================================\n",
            "Total params: 909\n",
            "Trainable params: 909\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A5CrFezxaFCi",
        "outputId": "a2290f64-95a0-4237-aee9-497802df6da1"
      },
      "source": [
        "model.add(Flatten())\n",
        "model.add(Dense(units=256,activation='relu'))\n",
        "model.add(Dense(units=10,activation='softmax'))\n",
        "model.summary()\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 30, 30, 9)         252       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 6)         492       \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 26, 26, 3)         165       \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2028)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               519424    \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 522,903\n",
            "Trainable params: 522,903\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C30k3SRLbFQM",
        "outputId": "8cd75a34-e4b4-4c50-ffe0-d811fa7c8d1f"
      },
      "source": [
        "#Compile & Train the model\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"SGD\", metrics=[\"accuracy\"])\n",
        "history = model.fit(x_train, y_train, validation_data=(x_valid,y_valid),\n",
        "                    batch_size=200, \n",
        "                    epochs=5,  \n",
        "                    verbose=1)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "225/225 [==============================] - 30s 13ms/step - loss: 2.2600 - accuracy: 0.1299 - val_loss: 2.1764 - val_accuracy: 0.2036\n",
            "Epoch 2/5\n",
            "225/225 [==============================] - 3s 12ms/step - loss: 2.0869 - accuracy: 0.2484 - val_loss: 2.0018 - val_accuracy: 0.2784\n",
            "Epoch 3/5\n",
            "225/225 [==============================] - 3s 11ms/step - loss: 1.9300 - accuracy: 0.3041 - val_loss: 1.8633 - val_accuracy: 0.3244\n",
            "Epoch 4/5\n",
            "225/225 [==============================] - 3s 12ms/step - loss: 1.8194 - accuracy: 0.3484 - val_loss: 1.7468 - val_accuracy: 0.3624\n",
            "Epoch 5/5\n",
            "225/225 [==============================] - 3s 12ms/step - loss: 1.7392 - accuracy: 0.3811 - val_loss: 1.6866 - val_accuracy: 0.4018\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "oDo6CUVic0yN",
        "outputId": "1938263d-efc6-47d4-8491-4aec74ac8713"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['accuracy'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)\n",
        "plt.legend(['Training accuracy', 'Validation accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "aS7jEsnBc2rA",
        "outputId": "715f4fa9-dd07-4cb6-8e94-47cdf64df9a7"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1rvVBpLivryy+vlWZN28Bs2bNYcCAQaSlpfHRRzMYMqQ/Bw/ut5z36KNP8O23yxk3bjyhoTVZs2YVo0c/wKefzrJi9GUjd/TlxKVaILVeepmzn8ziytEjACT9+jOGi4kEPfKYZSldIUTJ+N/brcLvnItiy3uf//jjeoKDQ3jvvVkFkvW2bX9ZMaprCwoKYdeu7YWmAwwGA7GxZ/H29inVdXNHE06ePE5oaM0Cx06dOgGYRkVyOTk50apVJK1amaYXjh07ysMPD+frr+fz7rszLeeFhtZk4MDBDBw4mIyMDJ577ikWL17AkCHD7XI4X+7oy5GTtzeh45/HO7KNpS1l9y7OvP8O2aUcmhJCiKvp9U6ArsBds8FgYNGir6wW0/XccUd7srOzWbLk2wLtq1evICUlpdTXbdfuTnQ6HYsXL8RgMFjaL1y4wLp1awgKCqZBAwVAUhE7kNauHY6bmzuXL18GICUlpcB1ANzc3KhdOxyA5OTLpY7VmuSOvpzpXVwJfnQMF/z8ufjzjwCkHz9G1LQphD4zHtfA6laOUAhh7zp27MzcuR/z/PPj6NChE6mpqfz88482u6hNr159Wb16OfPmzSYm5oylvO63336hZs1aZGeXbhvWsLBwhgwZweLFC3jyydF07tzFUl535Uoar7/+lmVqYfr0ycTHn6dNm1sJCgomIyOD3377mbS0VLp27QHA7t07eeedKXTseBdhYbXx8PBE0/5l7dpVNGkSQVhYeHl9SyqVbf5U2DmdXk/g/UNw9vcn/ofvICeHrHNxRE+dLFvdCiHKbOjQEUAOa9euYubM9/H3D6Bz5y50796b4cPvs3Z4hbi6uvLhh7P59NOZ/PHH7/z22880aRLBzJmfMm3aZDLK8LDlE0+Mo2bNWqxYsYQ5cz7G2dmFJk2a8vrrkwssKdy1a3fWr1/Dxo3rSEq6iJeXF+HhdZk8eTodO3YGoH79BnTo0Ik9e3bx008bMRqzqVEjiBEjRjF48PAyfx+sRWdrD0yUg3DgZEJCCkZj+fQtMNCH+PjSDb0n79xB3OdzyTEPB+lcXQl+/Em8m7col9hKqix9sTXSF9tTln7ExZ0mKKh2OUdUerY8R19SttqX7OxsevS4myZNIpgx46Mbnm+r/SiNkvTlRv829HodAQHeAHWAU4WOly5EUVw+kW2o+dwL6L1MK0TlZGbKVrdCiJtOUXftK1cuIyUlmTZtZN2RiiRD95XAo0FDwl56mTMzZ2C4cCHfVrcJBPTpL7X2QgiHN336FDIzM4iIaI6LiyuHDh3g5583mteR72ft8ByaJPpK4hocQtiEV67a6nYNhoREajwgW90KIRxbmza3snz5EnbunM+VK2n4+wfQs2dfRo9+vMQ78omSkexSiUxb3U7g7JxP8ra6/Xsrhkuy1a0QwrF169bTbjeFsXcyR1/J9O7uhI59mirt7rS0mba6nYoh6aIVIxNCCOGIJNFbgWmr21EE5JuXyoiOJmrqW2TExFgxMiGEEI5GEr2V6HQ6Anr1ocaDD4N5QQdDYiLR06eQpv1n5eiEEEI4Ckn0Vubbrj2hTz1j2bQjd6vby9u3WTkyIYQQjkASvQ3wimhGrRcn4ORr2h4yx2Ag7rM5JP64weZ2gBJCCGFfJNHbCPew2oRNfBXX4BBL24Ul3xP/7Tey1a0QQohSk0RvQ1wCqlHrxYl4NGhoaUv67RdiZ3+CMTPTipEJIYSwV5VaR6+UCgAWAvWATOAo8JimafFXnfcJ0BnIAFKApzVN21mZsVpL7la3cfM/J2XndgBS9pi2ug0d+zROPqXbt1kIIcTNqbLv6HOAdzRNU5qmNQOOA9OKOG8D0EzTtBbA28D3lRij1Zm2un0cv3u6WtpMW91OJjP+vBUjE0LYstjYs7RrF8n8+XMLtN92WyumTJlUrGvMnz+Xdu0iiY09W+7xrV+/hnbtItm9+6a4b7MZlZroNU1L1DRtc76mbUChLXk0TVuraVqW+cu/gZpKqZtqmkGn1xM4aDCBg4eCeS38rHPniJ46mfSTJ6wcnRCitF555UXatYvk6FHtmufk5ORw33296dq1Y5m2cLWG3bt3Mn/+XJKTbXM3xtwPQzNmTLd2KJXGasnTnLjHAKtvcOpYYJ2maTflE2l+d99D8ONPWNbCz06+TPS700jZv9fKkQkhSqNnzz4ArFu35prn7N69k9jYs3TufA9u5tLbsvj997958cVXynyd4tizZxdffjmPlJTCif7ee7vz669badmyVaXEIkysudb9R5jm3z++1glKqcHAUODOa51zLea9ectNYKD15sYDu95Ftdoh/DvlbQzJKeatbmdR7/HRBN17T8mvZ8W+lDfpi+0pbT/On9fj7GxbA3cVEc/tt99OjRpB/PzzRp5++llcXFwKnbNhg+lDQJ8+/UoUg5OT6Vy9Xlfgdc7ObsW+hl6vs1yrNP2/3uudnfW4uRXub0mU9e/kWt8jayju++v1+jL9/2CVRK+Ueg9oAPS61p26UqofMAXorGnauZK+R0JCCkZj+dSgBwb6EB9v5WGoaqHUfPFlznz4vmWr2+OfzuVi1NkSbXVrE30pJ9IX21OWfhiNRgwG2xm4c3bWV1g83br15KuvPmfz5k106nR3gWOpqSls2vQrdevWo2HDxly+nMyiRV+zY8c/nD17hrS0NKpXr0HHjp0ZNWo07u55d/zZ2aZ4jcacArG3axdJt249efnlSZY2o9HIN998zerVK0hIuEBoaE1GjBhl+X8zOzvv7+P06VMsWfIde/fu5ty5OIzGbGrXrkO/fgPp1auv5ZpTpkxiw4a1APTvn7eBzahRo3n44cdYv34NU6e+waxZc2jVKtJyPCkpifnz57J16xYSExPw9w/gjjvu5JFHHsPXt6rlvI0b1zJ58iRmzpyNpv3HypVLiY8/T40awTzwwEPF2jTnWt+johw7dpT58+eyb98e0tOvEBISSrduPRk8eDhO5hVNAc6di2P+/Lns2rWDxMQEvLy8zdvv9rfEZDQaWbr0O9atW83Zs2dNq6MGBNC8eUv+97+JOF9nB1Oj0Xjdf1d6ve66N7eVnuiVUlOB1kAPTdMyrnFOT2AG0EXTtFOVGJ5Ncw0KNm11O+tDMk6fAmSrWyHsUffuvfj66/msX7+mUKL/5ZefyMjIsAzxx8fHs3btKjp0uIsuXbri5OTE3r27Wbx4AUePasyYcc1B0ev66KMPWLLkW1q2bMWgQUO5eDGRGTOmExISWujcPXt2sm/fbm6/vR0hISFcuZLOpk2/MH36ZJKSLjJixCgA+vTpT2pqKlu2bGLcuPGWJF2vXoNrxpGSksLjjz9ETEw0PXr0pmHDRhw9qrFy5VJ2797BvHlfF9rGdu7cT8jIyKBPn/64uLiycuVSpkyZRGhoTZo3b1mq78fV/vvvMGPHPoqzszP9+t1HQEAAW7f+wezZH3Hs2FFef30yAAaDgWeffZL4+Hj69RtIWFgYKSkpHD9+jH379lgS/YIFX/D553O444729OkzABcXJ86ciWHr1i1kZmZeN9GXVWWX1zUFJgBHgL+UUgAnNU3rp5TaC3TXNO0s8CWm8rul5nPAdGefUJnx2iJn36rU+t9LnJ3zKWkH9wPmrW6Tkgh+Qra6FcIehISE0qpVJNu3b+PChQtUq1bNcmz9+jW4uLhwzz3dLecuX76uQCIYMGAQ8+bN5uuv53P48EGaNIko0ftHRZ1i6dLvaN26DTNmfGy5O+3Q4S4eeWREofPvvbcHffsOLNB2//1DGTfucRYt+oohQ0bg7OxMRERz6tWrz5Ytm2jfviPB+RYAu5ZvvvmaM2eiGD/+Rfr3v8/SXr9+Qz744B2++WYBo0ePKfCarKxMPv98gWXao1Onzgwa1Idly34ot0Q/c+Z7ZGVlMWfOl9Svb/qgMmDA/bz22gR+/nkjPXr0JjKyLadOnSQq6jRjxjzFsGEPXPN6W7ZsIjy8DtOnfwDkjRiNGfNUucR7PZWa6DVNOwQUOcasaVrLfH8OrLSg7JDe3Z3Qp57m3KKvufzHFgDS/j1E9PSphD49Hhc/PytHKET52fhPFKu2niQjM9vaoVi4uTrR5446dL01rNTX6NGjD7t27WDjxrUMH/4gYBoiP3ToAB07dqZqVdPdcP45fIPBQFpaGkajkcjItqVO9H/88Ts5OTncf/+wAkPQSjWiTZtb2X7VXhse+W4gMjIySE+/Qk4OtG17G3v37ub06VPUq1e/pN8CcyybqVrVj969+xVo79OnP19+OY8tWzYVSvT9+t1X4PsSGFidWrXCOHMmulQxXO3ixUQOHNjPnXd2siR5MG1GNnLkQ2za9AtbtmwiMrIt3t6mIfPdu3fRvXsv/Pz8i7yml5c3MTH/sW/fXlq0KJ8PI8UlY712SufkRI2Ro3DxDyBh1QoAMs9EE/32W4Q+/RxuoYWH34SwRz/uiLKpJA+QkZnNjzuiypToO3TohLe3D+vXr7Ek+nXrVgHQo0fvAucuX76EVauWcfLkCYxXLYldmjK2s2dN22HXrh1e6Fh4eJ1CiT4tLY0vvviM3377mfPnCz8ylZx8ucQx5MVylkaNGhcaunZ2dqZWrTCOHCm8m2dR0wtVqvhy7lxcqeO4OiaAOnXqFjoWHl4HvV5v+R4GBQUzcuRDLFr0FX36dKV+/YZERrahU6e7ady4qeV1jz32JBMmPM+TTz5CtWqBtGoVyW233UGnTp2LfCCzPEmit2O5W906+/tzbsFXkJ1t2up22mRCnhyHZ6PG1g5RiDK7t02YTd7R39um9EkewM3NjS5durJixRIOHNhHkyYR/PjjeqpXr8Gtt/6f5bzvvlvExx9/SNu2tzFw4GCqVauGs7MLFy7EM2XKpEKJvyK88cbL/PXXn/Tu3Y8WLVrh6+uLXq9n27atfP/94kqJIT+9vuin1a21Cdijjz5Bjx69+fvvP9m3by9r1qxi8eKFDB06kieeGAdARERzvv9+Jdu3/83u3TvZs2cXP/20gQUL5vPpp59TpYpvhcUnid4B+N7RHueqfpz99GNyMtIxXrlCzIfvU+OhR6jS9jZrhydEmXS9NaxMd86lVZFP3efq2bMPK1YsYf36NVy+fJmEhARGjnyoQCL78cf1BAeH8N57swq0b9v2V6nfN/eO+PTpU4SG1ixw7NSpkwW+Tk5O5q+//uTee7vzv/9NLHBsp3mZ7vyKWwGUP5aoqNMYDIYCd/UGg4Ho6Kgi794rWkiI6dmCk0UsTnb69CmMRmOhuEJDazJw4GAGDhxMRkYGzz33FIsXL2DIkOGW4XxPT086duxMx46dcXbW88MP3zNjxnTWrl3F0KEjK6w/tlW0KkrNq2mEeatb07yeZavbjetlq1shbJRSjWjQoCG//vozy5cvQafTFRq21+udAF2Bf8cGg4FFi74q9fu2a9cBnU7H999/Q3Z23kiJpv1XKHnn1p1f/f/IhQsXWLt2ZaFre3h4AnD5cvGG89u370BS0sVC11qzZiVJSRe5885OxbpOefLz86dZs+Zs3bqFEyeOWdpzcnJYuPBLAEtcKSkpGAyGAq93c3OzTIvkTmskJSUVep+GDRsBxf9elZbc0TsQ01a3rxDz4QwyzetUX1j6A4bEBAIHD0N3jeEuIYT19OzZhw8+eJd//vmLW25pXegOu2PHzsyd+zHPPz+ODh06kZqays8//1imcqzatcPp3/8+li37gaefHkOHDndx8WIiy5f/QP36DThyJG95Xk9PL9q0uY2fftqAm5sbjRs3JS4ullWrlhMcHMqlS5cKXLtpU9ODgbNnz+Kee7rh6upK3br1qFu36If1hg17gE2bfmXGjHfQNI2GDRVHjmisW7eKsLDaDBtWMXe6//33L1999XmhdicnZ0aMeJCnn36esWMf5YknRtO/f2553Z9s3/43Xbp0JTKyLWBaxfCdd6bQseNdhIXVxsPDE037l7VrV9GkSQRhYeEADB8+kCZNmtGkSVOqVQvk4sUEVq5cjouLC507l3zhs5KQRH8DmVnZHIm6iK+7E/oSDklZg0tANWq99DJnP5nFFfM/1qTffiXr4kWCRz9u5eiEEFfr0qUbn3wyi8zMjEJ38wBDh44Acli7dhUzZ7UTyL4AACAASURBVL6Pv38AnTt3oXv33gwffl/hCxbT008/j79/AKtXr+DTT2dSs2Ytxo9/kejoqAKJHuC1195izpyP2Lr1DzZuXEfNmrV49NEncHZ2ZurUNwqc27x5S8aMeYqVK5czffpksrOzGTVq9DUTvbe3N7Nnz7csmLN+/Wr8/QPo02cADz/8WKEa+vJy+PBBDh8+WKjd1dWVESMepFGjJsye/QVffDGXFSuWWhbMGTPmKQYPHm45v379BnTo0Mk8574RozGbGjWCGDFiVIHz7r9/ONu2bWXp0u9JTU3Bz8+fJk0iGDFiFA3ybU1eEXQOOKwbDpwsj5XxcnJyeOXzf4hNSKNeSBXGDWyOj6druQRZ0YxZmQW2ugVwr1ef5pNeJinD9j+wFIejrCYHjtOXsvQjLu40QUGF9riymsqYo68sjtIXR+kHlKwvN/q3kW9lvDrAqULHSxfizSEzy0jiZdPifcfPXmbqwl2cT7pi5aiK51pb3e5/caJsdSuEEDcRSfTX4ebqxODO9XN3ieXcxStMXbCTU3EV++BEecnb6naYZavb9LOxstWtEELcRCTR30CHlqG8OLINzuYnTy+nZTH9mz0cPGE/q/H63d2F4MefRGdelMGy1e0+2epWCCEcnST6YrijeQjPD26Jp5vp2cWMrGxmLt3P1gOxVo6s+HxaR1Jz/As4+5iWazRtdTuTpN83WTkyIYQQFUkSfTE1rFWVCSNa41/FtK9ztjGH+ev+Ze1fp+ymTt2jQQOaTZuKc+4GGjk5nF/4NRdWLLObPgghhCgZSfQlEFrNi5dHRFIzMG/f3+VbTrDopyNlfsK/snjWDCVswiu45VvjOnHdGs598Tk5Vy36IIQQwv5Joi8hPx83XhrWikZhVS1tm/bE8MmKA2Rm2c5a3NeTu9WtV7PmlrbLf28lZuYHZKelWTEyIYQQ5U0SfSl4ujvz7KCW3NqkhqVtz9ELvPvdHlKuZFkxsuLTu7sTMvZpfO/sYGlL+/cQ0e+8TdbFi1aMTNzMZApJiILK49+EJPpScnHWM7pXE7q2zdts43iMqdb+gp3U2uucnKg+4kEC+va3tGWeiSZ66ltkxJyxYmTiZqTXOxVYd10IAUZjtnm/g9KTRF8Gep2OQXfVZ0jnBuSuNReXmMaUhbs4HWcfq5zpdDoCevamxqiHwcn0w2S4mEj0tCmk/fevlaMTNxM3Nw/S01OtHYYQNiU9/QouLmVbkVUSfTno0qYWj/eNwNnJlO4vpWYybfFuDp1MtHJkxed7R3tCxz2L3t0dwLLV7eV/tlk5MnGz8PKqQlpaMikplzAYDDKML25qOTk5ZGamk5p6CW/vqjd+wXXIpjblpE2j6lTxdOGjZQdIyzCQkZnNh0v2Map7I26PCLZ2eMXi1TSCmi9MMD2UdynJtNXtvDkYLibid2+3Eu8zLURJODu74O9fg9TUyyQmxmE0WncYX6/XYzQ6xrrqjtIXR+kHFK8vzs4u+Pj4lfmOXja1KYaSbNQRE5/CjB/2cTE5w9I2oENdut9W2yYSZXH6kpWQQMzM98k8e9bS5tupM9WH2NZWt46yEQw4Tl8cpR8gfbFFjtIPKN++yKY2lSw00JuXR7QmNDBva8Vlv5/gm5/tp9beJSCAWi++jEdDZWm7tOlXzn76EcaMjOu8UgghhK2RRF8B/Ku4M+GqWvvfdsfw6cqDdlNr7+TlReizz+PTpq2lLXXvHs68/w7ZyY7xiVoIIW4GkugriKe7C88OaknbxtUtbbuPxPPed3vtp9bexYWg0Y/jd2++rW5PHCdq2mQyz8tWt0IIYQ8k0VcgF2c9j/Zuyj1talnajsVc4u1Fu7hwyU5q7fV6Au8bTOCQvK1us86dI/rtt2SrWyGEsAOS6CuYXqdjcOcGDL6rvqUtNsFUax91zn6GwP06dyF4zNh8W90my1a3QghhByTRV5J72obxeJ+mebX2KZlM+2Y3h0/ZT629T6vW1HzuBfRepgcNZatbIYSwfZLoK1HbxjUYP6glHuZ97dMzs/ngh338fSjOypEVn0f9BoRNeAWXaoGmhtytbpcvlQVOhBDCBkmir2SNavsxYVgr/Hzy9rWft+YwG7adtptE6RoUTK0Jr+AWXsfSlrh+LXFfzJOtboUQwsZIoreCmtXNtfbV8mrtl2w+zuJfjtpNrb2zr69pq9vmLSxtyX//RczMGbLVrRBC2BBJ9FbiX8WdCcNb0bBWXq39r7vOMHvVQbIM9lFrr3dzI+TJcVdtdXtYtroVQggbIoneijzdXXju/hZENsqrtd+lxfO+HdXay1a3Qghh2yTRW5mLsxOP92lKl8i8WvsjZ0y19gmX0q0YWfHlbXX7iGx1K4QQNkYSvQ3Q63QMubsBgzpdXWu/k+jzKVaMrGR872hXaKvbMx+8x+V//rZyZEIIcfOSRG9Dut4axqO9m+CkN9XaJ6VkMu2bXfxrR7X2uVvdOvmanz3IziZu3lwSN6y3m6oCIYRwJJLobcxtTYIYP6gFHm6mIfArGdnM+GEf2w7bT629e1htwia+imtIiKXtwrIfOL94ETkOspe0EELYC0n0NqhxuD8vDWtNVW9XwFRr/9nqw2z8J8pu7oplq1shhLANkuhtVK3q3rw8IpKQfLX2P2w6xre/HsVoJ8nestVt21stbaatbqdjSL5sxciEEOLmIYnehgX4uvPSsFY0rOlraftl5xnmrDpkP7X2Li4EPfIYfvd2s7SlnzhB9NtTZKtbIYSoBJLobZy3hwvPDW5JpAq0tO387zzvf7+P1HQ7qbXX6wm8734Chw7P2+r2vGmr2ysnZKtbIYSoSJLo7YCp1j6Czq1rWtqORCcxbdFuEi/bR609gN9ddxfa6vbMe9NI2bvHypEJIYTjkkRvJ/R6HUPvbsB9nepZ2mIupDJl4S7O2FGtvU+r1tR8/kX03t6AeavbT2aRtPk3K0cmhBCOSRK9HdHpdHS7tTaP9sqrtb+YnMHb3+ziv9P2s7a8R736hL101Va3ixbIVrdCCFEBJNHboduaBvHsoBa4u+avtd/L9n/PWTmy4nMNCip6q9v5n8lWt0IIUY4qNdErpQKUUuuVUppS6oBSarlS+Z4yyztvuFJqv1LKoJQaW5kx2osm4f68NKwVvuZae0N2DnNWHeLH7VFWjqz4itzqdtvfstWtEEKUo8q+o88B3tE0TWma1gw4Dkwr4ry9wGBgcWUGZ2/Cavjw8ojWBAd4Wtq+/+0Y39lRrX3eVrcdLW1p/x4mevpUshLtZ+lfIYSwVZWa6DVNS9Q0bXO+pm1A7SLOO6hp2mFA1ku9gWq+HkwY3pr6+Wrtf9oRzWerD5FlsI9vn2mr2wcI6DfA0pYZc4botyeTcSbaipEJIYT9s9ocvVJKD4wBVlsrBkfh7eHC8/e3pHXDvFmQ7f+e54Mf9pJmL7X2Oh0BPXoR9NDoglvdTp9K2r+HrRydEELYL521nnJWSn0ChAL9NU0r8tZTKfUVsFPTtI9LcOlw4GSZA7RD2cYc5q08wLqted2vHeTDpNH/R7WqHlaMrGSS9u7jv2nvkn3lCgA6Z2cajBtLYIf2Vo5MCCFsWh3g1NWNVkn0Sqn3gOZAL03TrrnDSVkSfUJCCkZj+fQtMNCH+PjkcrlWRcvJyWHDP1Es3Xzc0ubn48b4QS0IDfS2m75kREdxZuYMspOSLG3VBtyHX9fu6Myr69lLX4rDUfriKP0A6YstcpR+QPn2Ra/XERDgDddI9JU+dK+Umgq0BvpeL8mL0tHpdHS/rTaP9GxcsNZ+0W60KPuptXerFWbe6jbU0nZh2RLOL14oW90KIUQJVHZ5XVNgAhAC/KWU2quUWmE+tlcpFWL+8xCl1BngPuAtpdQZpVSTyozV3t0eEcwz97XAzVxrn5Zh4P3v9/LnvhgrR1Z8Lv4B1HppIh6qkaXt0qbfZKtbIYQoAavN0VegcG7iofurnY5L5sMl+7iUmgmY9pQZfFcDurSpZeXIis+YlcW5Lz8nefs/ljb3unVpPukVkjIdY80ne/4Zy89R+gHSF1vkKP0ABx+6F5WrdpCp1j7I31Rrn5MD3/56lB9+O2Y/tfa5W9127W5pSz9xgv0vTCTjrP2MUAghhDVIor8JVKvqwcQRrakXWsXStnF7lH3V2uv1BA4cRPV8W92mx8URNeUtknfvsnJ0QghhuyTR3yS8PVz43+BbuC0iyNKWV2tvP2vLV73rbkKeGIvO1bT0b05GOrGffsSFlcvkIT0hhCiCJPqbiKuLEy890JZOrfKeZP8vKolp3+zmYrL9PNzmfUtrwia8intQDUtb4to1nP3oQ7LTUq0YmRBC2B5J9DcZJ72O4V0aMqBDXUvbmfgUpizcScwF+0mSbrVq0fy96Xg2jbC0pR7YT9SUN2XeXggh8pFEfxPS6XT0+L9wHu6RV2ufeDmDtxfu4kh00g1ebTtcfHwIfXp8gYf0ss6dM83b79ppxciEEMJ2SKK/id3RLJin72teoNb+ve/2svO/81aOrPhyH9ILfuyJgvP2sz/mwgqZtxdCCEn0N7mIOgG8NLQVVbxy97U3MnvlQX7ZaV+7xvm0aUvYxFdxCczb2CdxnczbCyGEJHphqbWvkVtrDyz+5ShLNtlPrT2AW81ahL38euF5+8lvkhEj8/ZCiJuTJHoBQGBVDyYOb0W9kLxa+w3/RPH52sMYsu1n+NvJ29s0b9+th6Ut6/w5oqbKvL0Q4uYkiV5Y+Hi68vyQW2hZv5qlbduhc3zwwz6uZNhPrb1OrydwwH0EPy7z9kIIIYleFODm4sST/SPo2DLE0vbv6Yt2V2sP4BMp8/ZCCCGJXhTipNcz4l5Fvzvzau2jz6cwdeFOztpRrT3IvL0QQkiiF0XS6XT0uj2ch7o3Rm9eWz7hcgZvL9rF0TP2U2sPMm8vhLi5SaIX19WuubnW3sVUa5+abqq136XFWzmykpF5eyHEzUoSvbihZnUDeGHoLVTxdAEgy2Dk0xUH+HXXGStHVnIyby+EuNlIohfFUie4ChNHRlLDzwMw1dp/8/MRlm4+To4d1dqDzNsLIW4ukuhFsVWv6sGEEa2pE5xXa79+22k+X/uvXdXag8zbCyFuHpLoRYlU8XTlhSG30KJegKXt70NxzFxiX7X2IPP2QoibgyR6UWJurk6MHdCMO1vk1dofOnWR6Yt3k5RiX7X2IPP2QgjHJolelIqTXs8DXRV929extEWdS2HKgl3EJthfcpR5eyGEo5JEL0pNp9PR+446PNitUb5a+3SmLtzFsTOXrBxdycm8vRDCEUmiF2V2Z4sQxg1shquL6ccpNd3Au9/tYfcR+6q1h6vm7d3cgHzz9suXyry9EMLuSKIX5aJ5vWq8OLQVPvlq7T9ZcYBNu+2v1h7yz9tXt7Qlrl9LzCyZtxdC2BdJ9KLc1AmuwssjWlO9qrnWPgcW/nSEZb/bX609gFtoTcJeKThvn3ZQ5u2FEPZFEr0oV9X9PJk4ojV1gn0sbev+Ps0X6+yv1h7AycuL0KfH49+9p6XNNG//Jsm7dlgxMiGEKB5J9KLcVfFy5YUhrWier9Z+68E4Zi3db3e19mCat6/WfyDBjz+Zb94+g9jZn8i8vRDC5kmiFxXCzdWJpwY0o33zYEvbwZOJvLN4D5fssNYewCeyjczbCyHsjiR6UWGc9Hoe7NaI3neEW9pOn0tmysJdxCWmWS+wMpB5eyGEvZFELyqUTqejb/u6BWrtL1wy1dofj7G/WnuQeXshhH2RRC8qxZ0tQnhqQF6tfcqVLN79dg97jtpfrT3IvL0Qwn5IoheVpkX9arwwpBXeHqZa+0yDkY+XH2DzHvsd8pZ5eyGErZNELypV3RBTrX1gVXfAVGu/4EeNFVtO2GWtPci8vRDCtkmiF5Wuhr8nE0dEEh6UV2u/5q9TfLn+P7ustQeZtxdC2C5J9MIqfL1ceWHoLTSrm1dr/+eBWGYt2096pv3V2oPM2wshbJMkemE17q7OPDWgGe2a5au1P5HI9MV7uJSaacXIykbm7YUQtkQSvbAqZyc9o7o3otft4Za203HJTF24k3N2WmsPMm8vhLAdkuiF1el0OvrdWZeRXRXmUnvik9KZsnAXJ85etm5wZSDz9kIIWyCJXtiMji1DGdu/Ga7OebX273y7m73HLlg5stKTeXshhLVJohc25ZYGgfxvyC15tfZZRj5atp/f99r3cPd15+1TZd5eCFFxJNELm1Mv1JeJI1pTzTev1v7rjRor/7DfWnvIN28f0czSZpq3f4PU01FWjEwI4cgk0QubFOTvycsjI6ldI6/WfvXWU3y14T+y7Xi428nLi9Bxzxact48/z/4XJsi8vRCiQjhX5psppQKAhUA9IBM4CjymaVr8Ved5Al8CrQED8LymaWsrM1Zhfbm19rNXHuTgyUQA/tgfy6XUTMb0ibjBq21X7ry9W1ht4r78nJyMDIzp6cTO/oSM7j0J6NsfnV4+gwshykdl/2+SA7yjaZrSNK0ZcByYVsR5zwOXNU2rD/QCPldKeVdinMJGeLg5M25gc+6ICLK07T+ewDvf7iYp2T73tc8l8/ZCiMpQqYle07RETdM252vaBtQu4tT7gbnm1xwFdgLdKjxAYZOcnfQ81KMxPW/P+1E5GZvM/z7aYtfld5A3b1+11S2Wttx5+4yYM1aMTAjhKKw2PqiU0gNjgNVFHA4DTuf7OgqoVRlxCduk0+nof2c9RtybV2sfl5DG1IW7WLP1JEaj/T6k5+TlRZNXJhSat4+a+pbM2wshykxXHk8xK6UCNE1LKOFrPgFCgf6aphmvOpYM1M2du1dKfQoc0zRtRjEuHQ6cLEkswr5sOxjLjMW7uZKRtyZ+43B/xg9tRVCAlxUjK7sLW//m6KyPMaanW9pqDuxP2NDB6JycrBiZEMIO1AFOXd1YokSvlBoNVNU07V3z182ADUAwsAfoqWlaXDGu8x7QHOilaVqhiVal1CHgAU3Tdpq/Xgt8rWnakmKEGQ6cTEhIKbe7vMBAH+Ljk8vlWtbmKH2JT7rCVxs1/j2VaGlzd3ViWJeG3B4RhC73tt9O5P97yYg5w9mPZ5EVf95y3DOiOcGjH8PJy7Y/yDjKzxdIX2yRo/QDyrcver2OgABvuEaiL+nQ/VPAlXxfzwCSgGcAX+DNG11AKTUV09P0fYtK8mZLgMfM5zcA2gAbSxircGCBVT14+4k76Ne+DnpzUk/PzGb+un+Zs+oQqelZVo6w9K5Xby/z9kKIkippoq8N/AeglPIFOgAvaJr2EfA6cO/1XqyUagpMAEKAv5RSe5VSK8zH9iqlQsynvgtUVUodA9YCj2qa5hgf40S5cXLS0+uOOkwc0Zrqfh6W9h3/nee1+dv59/RFK0ZXNteqt5d5eyFESZW0jl4P5M6nt8NULrfZ/HU0UL2I11homnYIKHJMVdO0lvn+nArcV8LYxE2qbkgVJo1qw3e/HmPLvrMAXEzO4L1v93DvrWH0a18XF2f7q0svqt4+d518qbcXQhRXSf+XOAr0MP95MPCXpmm5e4mGAIlFvkqICubu6syD3Roxtn8zyzr5OcDGf6KYsmAnMRfsty5d6u2FEGVR0kT/HvCMUuoCMBT4KN+xTsD+8gpMiNJo1TCQNx9uS0Qdf0tb1PkU3vxqB7/uOmO3a+XLvL0QorRKlOg1TVuMaV7+baCTpmnL8x0+R8HEL4RVVPV245lBLRhydwOcnUw/4lkGI9/8fIQPl+znUop9rqgn8/ZCiNIolzp6GxOOlNdd083WlzPxKXy2+jBn4lMsbd4eLjzUvTEtG1Sr6BCLraR/L8k7d1jm7XP528C8/c3282UvHKUvjtIPsOHyOqXU7Uqpnvm+DlBKfauUOqCUek8pJSt6CJtSM9CbVx+I5N62eQsrplzJYtay/SzY+B8ZmdlWjK70rj1v/4HM2wshCijpR/9pmGrgc70LdAeOYFrOdmI5xSVEuXFx1nP/XQ14fnBL/HzcLO2b955l0lc7OBlrn+vl583bN7e0pR08IPP2QogCSproG2PaYAallAswEHhW07QBwMuYHtATwiY1CffnjYfaEqkCLW3nEk3r5a/965Rdrpdvmrd/puh5+50yby+EKHmi9wZyb3/aAl6YFrQB2I1pMxohbJa3hwtj+kbwcI/GuLmaZpqyjTks33KC6Yt3cyHpyg2uYHty6+2DxzyJzs00YpGTkUHsnE+4sHwpOUbjDa4ghHBkJU30MUAL85+7AQc1TctdkNsPSCvyVULYEJ1Oxx3NgnnjobbUC61iaT965hKvf7mdvw/G2WUZnk/rNoRNfA2X6jUsbTJvL4QoaaL/FpiqlFoKjAcW5TvWCtOCOkLYhepVPXhpWCv6tstbL/9KRjbz1h5m7upDpNnhevluoaGEvfyazNsLISxKmugnAdMBN0wP5n2Q71gLTJvRCGE3nPR6ererw4ThraheNW+9/O3/nue1L7bznx2uly/z9kKI/KSOvhikdtM2lXdf0jMNfPvLUf7YH2tp0wFdbw2j3511LYvvVISK+ntJ3rWDuC8qr95efr5sk6P0xVH6AZVbR1/STW0AUEpFYFohzx/T+vabzRvWCGG33F2dGdW9Mc3rBfDVhv9ITTeQA2z4J4pDpxJ5tFdTQqrZ9n7wV/Np3QbXoBDOfjKLrPPnANO8fXrUaYJHP27z+9sLIcqupAvmOCulFgH7MC13+4b59/1KqYWyYI5wBK1Vdd58+FaahvtZ2qLOpfDGVzv4bbf9rZcv8/ZC3NxKOnb3OjAIeA3TEIGH+ffXgPvNvwth9/x83Hj2/pYM6VxwvfxFPx1h5tL9XErNtHKEJSPz9kLcvEqa6IcDkzVNm6Jp2mlN0zLMv08BJgMjyz9EIaxDr9PRpU0tXnsgkpqBeUPc+48n8Nr8f9h77IIVoys5qbcX4uZU0kQfAvx1jWN/mY8L4VBqVjetl39Pm7z18pPTspi1dD8LftTIyLKv9fKl3l6Im0tJE/1Z4I5rHLvdfFwIh+Pi7MTgzg147v6WVPV2tbRv3hPDG1/u4FScfa2XL/P2Qtw8SprovwFeVkq9qpSqq5TyUErVUUpNwLTW/cLyD1EI29G0jj9vPnwrrRvmrZcfl5jGlAW7WPe3fa2XL/P2QtwcSrNgzlJMT9sfBVKAY8AUTIvlvFmewQlhi7w9XHiiXwSjujcqsF7+st9P8M63e7hwyX7Wy5d5eyEcX4kSvaZpBk3ThgLNgLGYnrIfa/76K0wb2wjh8HQ6He2bh/DGqDbUC8lbL/9IdBKvf7GDbYfirBhdycm8vRCOq1RLY2madkjTtNnmp+9nmxfL8QWalm94Qti26n6evDS8FX0KrJdv4LM19rdevltoKGGvvIZXM5m3F8KRVNyankLcJJz0evq0q8NLw1sRWNXd0v7P4XO8/sV2tCj7WS/fydOLkKeewb9HL0tb3rz9ditGJoQoLUn0QpST+qG+TBrVlnbNgi1tCZczeGfxHpZuPo4h2z7mu3V6PdX6DSB4zNir5u0/JX7ZEpm3F8LOSKIXohx5uDnzUI/GPNE3Ai9301YSOcD6baeZsmAXsQn2M9/t0zqy0Lz9xQ3riJk5g+yUFCtGJoQoiRtuaqOUqlvMawWVMRYhHEZko+rUC/Vl/rrDHD5lGro/fS6ZN77cwf131afjLaHozHP6tix33j5u3lxSD+wHIO3QQaKmvEHIk+Nwq1nrBlcQQlhbce7oj2EqpbvRr48qKEYh7JKfjxvj72/J4Lvq4+xkSuqZBiMLfzrCrKX7uWwn6+UXPW8fT9Tbk2XeXgg7UJxtakdVeBRCOCi9Tsc9bcNoHO7PZ2sOERNvGrrfZ14vf1T3xrSoX83KUd5Y7ry9W1ht4r6YR05GhmXePr3baar1G1Ah+9sLIcruhole07SvKyMQIRxZrerevPZAJEs2H+eXnaZStctpWcxcup9Ot4TyxKCWVo6weHxaR+IaFFxgf/uLG9aRYd7fnkAfK0cohLiafAQXopK4ODsx9O6GjL+/Bb5eeevlb9oTw7Mf/M7puGQrRld8Rdbbm+ftU0+dtmJkQoiiSKIXopJF1AngzYfb0irfevlnzqcwecFO1m87bRfr5V9r3n7/ixO5+Nsv5BgMVoxOCJGfJHohrMDH05Un+0XwYLdGuLnkrZe/dPNx3v12DwmX0q0c4Y0VVW9vTE8nfvEiTk96lZT9e8nJsf0PLUI4Okn0QliJTqfjzhYhTBrVhoZhVS3tWnQSr32xnW2H7WO9fEu9fY28CtvMuFjOzvqQmA/eI+NMtBWjE0JIohfCymr4ezJ9bHt63xFObmn9lQwDn60+zGdrDpGWbvvD4G6hodSe9Ca1Rw5H7563DHDa4UOcfuM1zi34EsOlJCtGKMTNSxK9EDbA2UlP3/Z1mTCsNdV88xLltkOm9fKPRNt+ktS7uFJzQD/Cp76Db4dOWD615ORwacvvnJz4Eonr12LMtI/1A4RwFJLohbAh9Wv68sZDbbkjIm8YPOFyOtMX72bZ7/axXr5zlSrUGPEAtSdNxrNphKU9JyOdC8uXcuqVCVz+Z5vM3wtRSSTRC2FjPNycebhnE8bkXy8/B9b9fZopC+1nvXy30FBqPvs8oc+MxzUkxNJuSEwgbt4cot+ezJXjx6wYoRA3B0n0QtioNo2q88ZDbWlc28/SdjoumTe+2sHmPTF2c0fsFdGc2q+/RfVhI3HyzltQJ/3EcaLfnkzs3E/JuhBvxQiFcGyS6IWwYf5V3HlucEsGdcq3Xn6WkQU/any07ACX0+xjvlvn5ETVTncRPnU6fl27o3POW5Qzecd2Tr0ygfhlS8i+csWKUQrhmCTRC2Hj9DodXW8N45WRkYRU87K07z12gdfmb2f/8QtWjK5knDw9CRw4iPC33sY7sq2lPcdg4OKGdZya+AJJv28iJzvbilEK4Vgk0QthJ8Jq+PDaRVjtngAAIABJREFUA5Hc3bqmpe1yaiYfLtnPop80MrPsJzm6BAYS8vgT1HrxZdzC61jas5OTOb/wa06/+Tqphw5aMUIhHIckeiHsiKuLE0O7NGT8oILr5f+2O4Y3vtphN+vl5/Jo0ICwia8SNPoxnP39Le2ZMWeI+eA9znw4g4yzMVaMUAj7J4leCDsUUTeANx5uyy0N8ra4jU1IY/KCnWzYdhqjnTyoB6aldKvc+n+ET55GQN/+luV0AdIO7uf0pFc5t2gBhuTLVoxSCPtVnP3oy41S6j1gABAONNM0rdDYnFIqCJgL1AFcgCmapi2qzDiFsAdVPF0Z278ZW/ad5dtfj5KZZSTbmMOSzcc5cCKBR3o2wb+K+40vZCP0rq4E9OyNb/s7ubByOZf//MNUV2g0cmnzbyT/8zf+PXpRtXMX9C4u1g5XCLtR2Xf0K4E7gevtZTkD2KlpWnPzuVOVUrUqIzgh7I1Op6NDy1DeGNWWOsF5pWv/RSXx2vztbP/3nBWjKx1n36oEPfAQtV97A49GjS3txitXuLD0B06/OpHkndvtprxQCGur1ESvadqfmqbdaIeLFsBG8/nxwF5gUEXHJoQ9q+HvyYThrel5e956+WkZBuasOsS8NYftYr38q7nVCqPmcy8Q8tQzuATlrRSYdSGe2DmfEj19KldOnLBihELYB1uco98FDFZK6ZRSdYDbgdpWjkkIm+fspKf/nXV5cWirAuvl/30ozm7Wy7+aTqfDu0VLwidNJnDIMPReeeWF6ceOEj31TWLnzSUrMcGKUQph23TWGP5SSp0Cel5jjj4Q+ABoBkQBaUCMpmnji3n5cOBkuQQqhJ1KvZLFZysP8NvOvAE0vQ4Gdm7IkHsUzk62+Bn/xgwpKUT/sJTYdRvIMeSNUuhdXQnp25ua/fvi5OFhxQiFsKo6wKmrG20u0Rdx7npguaZpnxfz8uHAyYSEFIzG8ulbYKAP8fH2VbZ0LdIX21RRfdn+7zkWbNRIy8hLinWCfRjdqylB/p7l/n6V9XeSeS6OC0uXkLJnV4F2J19fqvXtT5U72qPTl+3DjPx82R5H6QeUb1/0eh0BAd5wjURvcx/rlVIBSiln85/vwnRnv9i6UQlhn9o2rsGbD7elUVhVS9vJ2GQmfbmdzXvtZ738q7nWCCLkyaeo+b+XcAvLm9nLvnSJc19/SdRbr5P272ErRiiE7ajURK+UmqWUOgPUBH5RSh0yt69XSkWaT2sL/KuU+g94E+ilaVpaZcYphCPxr+LO80Nu4b5O9XDS51svf6PGx8vtZ738oniqRoS98jpBD43GqWreh5mM6GjOvP8OMR99SGZcrBUjFML6rDJ0X8HCkaH7a5K+2KbK6kvUuWTmrj5EbELeZ2dfL1ce6tGYZnUDynx9a/6dGDMyuPjjBhI3ricnM9+HFycnqnboREDvvjh5exf7evLzZXscpR9wkw/dCyEqTlgNH15/8P/bu/P4qMp78eOfM5OZ7JmsJCQsgSQcdhJklR0RrVZt3ereVmvtvb29t7e3rXVD3KitvW1vt5+9WqtVqxdba61aVLbIooCQAAF8kgAJkJCQdSb7MjO/P2aYhBBgkCQzc/i+Xy9fknMeJs+Xk8l3zvM85/vM5LLpPfXy7S2d/GL1bl79oDik6uX3ZQoPJ+naL5H51E+Iu3Q+vucMnU4a16/l8IM/pOGD909ZxCfExUASvRAXGavFzO3Lx/Hdm6YR16te/rpdx3j8pU85Uh3ad0yWhATS7v4Gox5+lMhxuu+4q7WVmtWvUbbiIZoLdobs+gQhzpckeiEuUlOzknj8nlnkZvfUy6+sbeGJlz5lzbYjIVUvvz8RozMZ8YMfkf7t72AZluo73nWimsrf/ppjzzxNe3lZ4DooxBCRRC/ERSwuysp3bpjCXVfoWMM8vw6cLjerN5Tys9cKqHe0B7iHF0bTNGLyLiHz8adIuflWTFE9jxS2FSuOPPkYVS88R1dDQwB7KcTgkkQvxEVO0zQW52Xw6NdnkplmjHr5fWlhYSQsv4Ixq35K/GWXg9nsOeF249i6hbKH7qfu7bdwdXQEtqNCDAJJ9EIIAIYnRfPgnZdw9dzReJex+erlP//Ofto6Qn8RmzkmhmG33k7mY08SPS3Xd9zd2Und229R9vCPcGzdgtvlCmAvhRhYkuiFED5hZhM3LMri/tunk9Rri9utRZ56+SXHQq9efn+sacPJ+M53GfFfP8Q6omdzzO6GBqpeeI4jTz6GvWhfAHsoxMCRRC+EOM24kfE8dvcs5k7qWcRWa2/n6Vd38bePDtHtNMYdb9SEiYxe8RipX/06ZpvNd7zjSDlFD62g8re/prM69KcuxMVNEr0Qol9REWHce80k7rt2EpHhYQC43fCPrWX8+JVdVNcbo2ClZjJhW7CIMU89TeIXr0GzWHznmgt2UrbiQWr+7zWcLS0B7KUQn58keiHEWc2emMrjd89CH9m7Xr6DlX/cwUe7Kw3zPLopIpLkL91A5lNPEzt7bs8Jp5OGD9/3FNxZ96EU3BEhRxK9EOKckmwR/ODWPG5a3FMvv6PLyYv//IzfvLmXphCul9+XJTGJ4ffex9RnniYiO8d33NXSQs1rr1K28mGadxca5gOOMD5J9EIIv5hMGl+YM5qH75rB8KSe59ELSmpZ8YftFB2qC2DvBl7suBxG3v8gw7/1bSzJKb7jXVVVVP76l1T8/Bk6jh4JYA+F8I8keiHEeRmdFsuKr81kyfQM3zF7Syc/X72b/31rb0jXy+9L0zRiZ8xk9BNPkXzjzZgiI33nWg/sp/zxR6l66QW67cZ4GkEYkyR6IcR5C7eYuXO5zn/cOJW4qJ7Fa//YdIiHn9/GruIaQw1tmyxWEq+8isxVP8G2eGnPhjluN45NH3H4wR9R987buDqNM4UhjEMSvRDic5uWnczj98xmWlbPFre19nZ+8+ZefvHGbqoMsjL/pLDYOFLvuIvRK58kavJU33F3Rzt1b73pKbiz7WMpuCOCiiR6IcQFiYu28u83TuVrXxhPbK+7+6JD9Tzy/Db+svEg7Z3GWqkenpHBiO9+j4zv/hfW9J4pjO76eqqe+z1Hf/wkbSUlAeyhED0k0QshLpimaSycls6zP1rGkrwMXwldp8vNe5+U89Bz29h+oNpQw/kA0ZOnMPrRxxl251cxx/bsE9B++BBHf/IUlc/+jq6amgD2UAhJ9EKIARQXbeXOK3RWfG0mWRlxvuMNTR08+/d9PPNaAcdqmgPYw4Gnmc3EL1pC5qqfknDlVWhhYb5zzZ9up+yRB6j5y2qcrcaaxhChQxK9EGLAjU6L5YE7LuGeqycQF231Hf/sSCMrX9jBn9cW09reFcAeDjxzZCQpN95M5hM/JnbmLN9xd3c3DWveo+yh+2ncuB630zhPJYjQIIleCDEoTJrGvCnDWXXvHJbPHInJu1Ld5Xaz9tNjPPi/n7B5z3FcBhvOt6SkMPy+f2XkAw8TMXas77izqYkTr/yJ8sceoaVoTwB7KC42kuiFEIMqKiKMWy7L4bG7ZzJ+VE8ZXUdrFy+8d4Afv7KT8qqmAPZwcERmZTPygUdIu/dbhCX2PJXQWVlJxS9/zrFf/jcdFRUB7KG4WEiiF0IMiYyUGH5wax7fum4SCbHhvuMHKxw8/uIO/vS+ornNWMP5mqYRN3sOmU/+mOTrb0QL79n6t7VoL+UrH6b65ZfodjgC2EthdJLohRBDRtM0Zk1I5al7Z3P13NG+uvluYGNBBQ/8/mM2FFTgchlrON9ktZJ41RcZs+ppbAsXnVJwx56/gbIHf0j9P9/F1SUFd8TAk0QvhBhyEdYwbliUxRPfmM3ksYm+4y3t3bz8vuLxl3ZQWmEPYA8HR5gtntS7vs7oFY8TNWGS77irvZ3av75B2SMP0rRju+EeQxSBJYleCBEwaYlR/OdN0/jODVNItvUMax+pbmbVyzv5wzv7sbcY7y43fORIMr73fdL//btY04b7jnfX1nL897/j6NNP0XboYAB7KIwk7NxNhBBi8GiaRl5OCpMyE1mz7QjvflJOV7enhOyWoip2ldRw3fyxLJ2eQZjZOPcmmqYRMzWX6ImTsX+0kdq338LV7Kkx0H6wlKOrniB29hySr78JS1LSOV5NiDMzzrtGCBHSrBYz184fw1PfmM30cT3bwrZ1OHl9XQmPvbiDz8obAtjDwaGFhRG/dBljVv2EhOVXgtnsO9e07RPKHv4RtW/+BVd7WwB7KUKZJHohRFBJjo/k366fwvdunkZqYs++9xU1Lfz0tQKe/XsR9Y72APZwcJijokm5+RYyn/gxMZfM8B13d3VR/947HH7wfuwf5cuGOeK8SaIXQgSlyWOTeOKeWdy0OItwS89d7vYDJ3jouW28+3GZb4jfSKzDhpH+L//GiB8+QPjoTN9xp8NB9Z/+SPljK2jZvy9wHRQhRxK9ECJohZlNfGHOaFZ9cw6zJ6b6jnd0Oflr/iFW/GEbew/VBbCHgydqnM6oh1aQds+9hCUk+I53Vhyj4ufPUPGrX9B5vDKAPRShQhK9ECLoJcSGc9+1k7j/tjwyUqJ9x6sb2vjF6t38+q97qGk03hy2ZjIRN3cemU8+TdJ1X0az9uwb0LJnN2WPPsyJP7+Ms9lYGwWJgSWJXggRMvRRCaz8+kxuXZZDZHjPcH5BSS0PP7+NtzYdorPLeJvGmMLDSbrmOsas+glx8xb0FNxxuWhcv47DD/6Qhg/W4OoyVmVBMTAk0QshQorZZOLyGSNZ9c25zJ/S8wx6V7eLt7eU8fDz29hVXGPIojNh8Qmkff0eRj2ykkh9vO+4q7WVmtWvU77iIZp2fmrI2MXnJ4leCBGSbNFW7r56Ag/deQmjU2N9x2vt7fzmzb384o3dVNUbcw/4iFGjGfH9+0n/9r9jSe1Zu9BVc4Lj/+83HHvmadrLygLXQRFUJNELIUJaVoaNR746g7uu1ImO6KkBVnSonkee38YbG0tp7+wOYA8Hh6ZpxORNJ/Oxp0i55TZMUT1rF9qKFUeeXEnVH56jq74+cJ0UQcG8cuXKQPdhoMUD321r62SgRq+io8NpbTVGGU6JJTgZJZZAxaFpGplpcSyclk57p9O37a3bDaXH7GwtqiI+JpyM5Gi0k/Pb5xAq10QzmYgcm4VtwSLc3V20l5dz8pdfx7Gj2PM34GxrQ0tIwtzrw0AoCpVr4o+BjEXTNKKirAD/AzT2PS+J3g/ywxWcJJbgE+g4rBYz07KTyc1O5lhNMw1NHQC0dzrZqWpQRxrJHB5LXLT1HK8U+FjOl8lqJXryVGJnzqarvo6uqirPCaeTpgOf0bhuLe1lhzFFRmJJGeb3B55gEmrX5Gwk0V8YSfRnIbEEJ6PEEixxxMeEM3/qcFLiIzlY6aDDuxK/ztFOfmElze1dZKXHYQkzn/E1giWW82WOiSFu1hwic8bRcewoTkfPLoBd1dU0bfsEx9bNuDo7saamYoqIOMurBZdQvSb9kUR/YSTRn4XEEpyMEkswxaFpGqNSY1k4LZ1up4vDx5twA27gUKWDzXuOExNpZcSwmH7vboMpls/DkpKCbeEiIkaNwtzVQXtVte+cq62Nts8O0LDuQzoqKjDHxBCWlBz0d/mhfk16k0R/YSTRn4XEEpyMEkswxmEJMzF5bBKX6Ckcr2uh1u6pk9/R5aKgpJZ9ZfWMTo0lPib8lL8XjLGcL03TsA5PJ/Pq5ZinXIJmsdBZXYW70xuX201nZQWOrVto3rEdt8uFNS0Nk+XcUxuBYIRrcpIk+gsjif4sJJbgZJRYgjmOuGgrl05OIz05moOVDto7PcP5DU0dfFRYib25g6wMG1ZvXf1gjuV8RUeH06FZiJ40mfjLlmFNG063w053Q8+KfGdzM61Fe2lct5auEycIi48/pfRuMDDaNRmqRC/70QshLhqapjFrQipTs5J49+Ny1mw7gtPlxg1sLKxkx2cnuH5RFoumpQe6q4PGZLESN/dS4uZeSsfRozTmb8Dx8VbcHZ6RDndnJ44tm3Bs2UT46EziFy0hdvYcTOHh53hlEaw0A1ZQygQO19U143INTGwpKbHU1DQNyGsFmsQSnIwSS6jFUVXfyp/XFlN06NRnzUelxvCdm/NIirYEqGcD61zXxdXehmPbJ9g3rqfj6NHTzpsiI4mbOw/b4iWEp2cMZlfPKtR+vs5mIGMxmTSSkmIAxgBlfc8PaaLXdf1nwA14kvEUpVRRP22GAX8ERgIWYAPw70opfyteZCKJ/owkluBklFhCMQ63201haS2vrS3xzd+fNG9yGjcuycbmx+N4wczf6+J2u2k/dBD7xg007diGu/v0X7uR43Rsi5YQM/0STJah/SAUij9fZzKUiX6oh+7fwjOHsOksbR4EDiilrtZ13QJsBq4HVg9B/4QQFxlN08jLSWFSZiJrth3h3U/KffvcbymqYldJDdfNH8vS6RmEmY1dTFTTNCKzsonMyiblK7fi2LqZxvwNdFX3rNhvK1a0FSvMsbHEzV9I/MLFWFJSAthrcS5DmuiVUpsBdF0/WzM3EKvrugkIB6xAxeD3TghxMbNazFw7fwyXTk7j9fWl7CquAaCtw8nr60rYtKeS25eNY/zo4FqgNljMMTEkLL+S+GXLaVOf0bhxPc0Fu8Dl+RDkbGqi4Z/v0rDmPaImTSF+8RKip05DMxn7w1AoCsbFeE8AfwWOA9HAb5RSWwLbJSHExSI5PpJ/u34KR+va+N1fd1Pt3RinoqaFn75WwKwJw7h5STaJcaFTaOZCaCYTURMmEjVhIt2NDdg3fYT9o/yeFftuN61Fe2gt2kNYYiK2hYuxzV9IWHx8YDsufAKyGE/X9TLgi2eYo78PmAj8JxAL/BP4uVLqL36+fCZweEA6KoS4qHV1u3j7o4O8/qHyPY4HEG4185Vl4/jSoqyzVtczKrfTSf2nO6la8wGNBYX0fZZZM5tJnD2LtC9cgW3K5KAvxGMggV+Md9I5En0RcLdSarv36/uBUUqpb/v58pnIYrwzkliCk1FiMUoccGosDU0drN5Qyrb91ae0SU2I5LbLxzFlbFIguui3wbwunTUnsOdvxLF5E87m07+HJTWN+EVLiLt0HuaYmAv6Xkb9+bpQ51qMF4yTKYeBKwF0XbcCy4DTPhAIIcRQSYgN575rJ3H/bXlkpPTsAFfd0MYvVu/m13/dQ01jWwB7GDjWlGGk3HgzY575OWn33kdkzrhTzndVV1Gz+jUO/eA/qXrhOdoOHcSAj3UHtSGdo9d1/Vd4VtCnAWt1Xa9TSk3Sdf09YIVS6lPgu8Czuq7vBcx4Hq97bij7KYQQ/dFHJbDy6zNZv6uCtzYdpq3D8/hZQUktRYfr+cLsUVw1Z7Svut7FxGSxEDd7LnGz59JRcYzGjRto+ngLrnZvIZ6uLhxbt+DYuoXwUaOxLVpC3Ow5IbWpTqiSgjl+kOGi4CSxBB+jxAHnjsXR0slfNh5k897jpxxPtkVwy2U55OUEzyYxgbourvZ2HNs/wb5xAx1Hyk87b4qIIHbuPOIXLyE8Y8Q5X+9i+vk6H8H2HL0QQhhCXLSVu6+ewKLcdF75sJjyKs8v7Vp7O795cy+TxyRy67IchidFn+OVjMsUEUH8wsXYFiyi/fBh7BvXewrxdHUBng8C9g3rsG9YR2TOOGyLlxAzfcaQF+IxOtnUxg+ykUJwkliCj1HiAP9jSYyLYOHUdOJjwyk9ZvcV2znR2EZ+YSUdXU6y0uMCWmwn0NdF0zQsCQnE5E0nfvFSwmw2uupqcTU3+9p019fRvGsn9vyNOJubsSSnYI4+9UNSoOMYSLKpjRBChBCTSWNxbgYz9GH87aNDbCyowA04XW7++ckRPtlXzc1Lspk1YVjQDOcHijkmhoTLrzi9EI/T8/iis7mJhjXveQvxTCZ+8VJPIR7zxbfuYaBIohdCiAESE2nhzit0Fk5L59UPiymtsAOex/N+//Y+NhZUcPvycYxIubDHzIxA0zSixk8gavwEuhsbsW/2FuKpr/O1ad1XROu+IsISErEtXETcdVfh2QJFnA9ZjOcHWQASnCSW4GOUOODCY3G73Xy8r4rVGw7iaOkZojVpGksvyeBL88cQFTE0SStUrovb5aJlz27s+RtoKdp7WiEeTCZicvOwLV5K1PgJIV1uVxbjCSFEiNM0jUsnDyc3O4W3txxm7afHcLnduNxu1n56jO37q7lxcTaXTknDdJEP55+keRN5TG4eXTU12DflY9/0Ec4mh6eBy0Xzrp0079qJJTXVU2533oILLsRjdHJH74dQ+TTsD4klOBklFqPEAQMfS0VNM69+WMxnR05dK5WVHsfty8eRmRY3YN+rr1C+Lu7ubpp2fYp94wbaitVp57WwMGJmziJ+8VIixmaFzBoIw+5HP0QykUR/RhJLcDJKLEaJAwYnFrfbzY7PTvB/60tpaOrwHdeARbnpXL8oi5jIgR/ON8p1iW5r5PDf3sHx8RZcbadXIgwfOdJTiGfOXEwRkQHoof+GMtHL43V+kEc6gpPEEnyMEgcMTiyappGREsOi3HQADh93cPJ+pKyqiY92VxIZHsbo1NgBvTM1ynWJT09ByxpP/GWXY0lJobuxEae9Z4TE6XDQsmc3DevW0V1fhyUxiTCbLYA9PjN5vE4IIQwswhrGDYuymD9lOH9eW8LeQ56V5i3t3bz8viK/sII7lutkZwRnkgo0U3g4tgWLPIV4yg7TuHE9Tdu34e70JE53Rzv2/A3Y8zcQkZVN/OKlxMyYgcliDXDPA0Pu6P1glE/DILEEK6PEYpQ4YGhiiYm0MGdiKplpcRystNPqrZ1vb+lk057j1Da2kZVhI8J6Yc+QG+W69BdHWHwCMbnTiV+ylDBbAt11dafsotfdUE9zwU4a8zfgbHJ4CvEEweI9uaMXQoiLhKZp5OYkMzEzgTXbj/Dux+W+6npbiqrYVVLDdfPHsnR6RkCr6wU7c1Q0CcsuJ/6yZbQVK0+53V07fYV4XM3NNLy/hob31xA1cRK2xUuJmZZ7URTikUQvhBBBwGoxc+28MVw6KY3X15eyq7gGgLYOJ6+vK2HT7kpuu3wcE0YnBLinwU3TNKL08UTp40mx23Fs2URj/ga663oV4tm/j9b9+zDHx3umABYuxpJg3H9XGbr3g1GGvUBiCVZGicUocUDgYomKsDBrQipZGXEcPt5Ec5tnA5im1i62FlVxvK6FselxRIb7f59mlOtyvnGYIiKIzBlH/GWXEzFmLK62NrpOnPCdd7e301asaFz3Ie1HyjFHR2NJHppdB2XoXgghLnKTxyTx+D0JfPjpUd7eXEZHl2cIevuBExSW1nLNpZksnzkKS5gM55+LZjIRM3UaMVOn0VVXi/2jfOyb8nE6egrxtBTsoqVgF5aUYdgWeQvxxMYGtuMDRO7o/WCUT8MgsQQro8RilDggOGIxmTRyRsQzb8pwHC2dHKtpATyb5Rwob2DHgWpSE6NITYg66+sEQywDYSDiMEdFETVhIgmXXU54xgicLc1019b6zrtaW2jdv4/GdR/Sefw4YXE2whITB/wuX+7ohRBC+CTEhvPNayexKNezWc7JhF/d0MYvVu8mLyeZWy7LISU+uIvEBBMtLIzYmbOInTmLzuOVNOZvxLF1M67WVsBbkW/bxzRt+xhrxgjiFy8hds6lmCND799Y7uj9YJRPwyCxBCujxGKUOCA4Y0m2RbIwN53YSAulFQ66nZ7V+VX1reQXVuJ0uhibHoe5z+r8YIzl8xisOMyxsURPnkL80mVYhqXitNvpbmzwnXc2OWjZu4fG9evorq8lLCGBMFv8BX1PuaMXQgjRL7PJxLIZI5k1IZW/5B9k857jAHR1u3h7Sxlbi6q45bIc8nKGZlGZkZjCw7HNX4Bt/gLay8pozF9P07ZP+hTi2Yg9fyMRY7O8hXhmYrIGdyEeuaP3g1E+DYPEEqyMEotR4oDgjyXcaiYvJ4XJYxM5Ut2MvdnT19aObrYfOMGhSgeZw2OJjbIGfSz+Gso4wuLjicnNI37JZYQlJNBdV4uzqXchngaaC3bRuHE9TocDS8r5FeIZyjt6SfR+MMqbBCSWYGWUWIwSB4ROLImxESycmk5CbDgHKxx0eovtnGhsI7+wko4uJ5OzU+j0Vt0LZYG4JiaLhcgxY7EtXkrUhIm4u7rprDrOyQTj7uqi/dBBGtevpa2kGC3cinVYKprp7E9DyNC9EEIIv5lMGotyM7hEH8bfNh1iY0EFbrdndf4/PznChl0VTMxMJC8nmalZScRGBfdQczDSNI2ocTpR43RSHLfh2LIJe/5GumprfG1aD+yn9cB+zLZ4bAsWYlu4CEtiUgB77SHb1PrBKFs8gsQSrIwSi1HigNCOpbyqiVc/LKa0wn7aOU2DnAwbuTkp5OUkk5p49kfzgkmwXRO3y0Xr/iIaN26gZXchpw0jaxrR03KJX7yEqImTT7nLH8ptauWOXgghDGZ0WiwP3DGdrUVVvPNxOdX1rb5zbjcUH7NTfMzO6g2lDE+KIjcnmbycFMamx2GSBXx+00wmoidPJXryVLrq6rBv8hbisXs/YLndtBQW0FJYgCU5BduixcTNX0BYbNzQ9lPu6M8t2D5FXgiJJTgZJRajxAHGiqXdBeu3l1NYUsvBCjtn+s0YF2VhWnayd5OdRMItwbXhSyhcE3d3N82FBdjzN9B6YP9p57WwMGIumUHml6+hIzljQL7nue7oJdH7IRR+uPwlsQQno8RilDjAuLHYWzrZU1pLQUkt+8vqfYv3+rKGmXzz+tOyk4mLDvy8fqhdk86q455CPFs242ptOe182je/RdysORf8fWToXgghhI8t2sqCaeksmJZOR5eT/WX1FJbUsru0Fkdrl69dZ7eLwtJaCktr0YCsDBt5OZ67/eFJ0YELIIRY04Yz7Cu3kvzlG2jasR17/gbaDx30nW8/dGhno4W+AAAQdElEQVRAEv25SKIXQoiLVLjF8yx+Xk4KLpebQ8cdFJTUUFhSy/G6XvP6QGmFndIKO29sPEhqYhR53iH+7AwbJpPM65+NyWrFNm8+tnnzaT9SjmPLZqyak5irvzgk318SvRBCCEwmjewMG9kZNm5anE11fSsFJbUUltRQUmE/ZUF5dX0ra7YfYc32I8REWpiWnURudgqTxyQSbg2uef1gEzFqNBGjRg/pNIQkeiGEEKdJTYziytmjuHL2KBytnew9WEdBSS1Fh+vo7OqZ129u62LL3iq27K0izGxiYmaCb14/PiY8gBGIkyTRCyGEOKu4KCvzpgxn3pThdHU72V/W4Jm/L6nF3tJT3a3b6WLPwTr2HKwDFGPT48jNTiYvJ5n05GipvR8gkuiFEEL4zRJmZlq25479zivclB1v8szrl9ZSUXPqyvJDlQ4OVTp486NDDIuP9D6vn0z2CBvmc5SIFQNHEr0QQojPxaRpjE2PY2x6HDcsyuJEQyuFpXUUltRQfNSOq9fE/onGNj7YcZQPdhwlOiKMqVmepD9pTCKR4ZKKBpP86wohhBgQwxKiWD4ziuUzR9Lc1uWd169h7+F6OjqdvnYt7d18vK+Kj/dVEWbWGD86gbycFHKzk0mIlXn9gSaJXgghxICLibQwd3Iacyen0dXt4rMjDRSWeJ7Lb2jq8LXrdropOlRP0aF6Xn5fkZkW6yvJOyJF5vUHgiR6IYQQg8oSZmLK2CSmjE3ijuXjKK9uoqDYU53vWE3zKW3Lqpooq2rirU2HSbZF+Bbz5YyMD1DvQ58keiGEEENG0zQy0+LITIvjywvHUtvYRoF3BX/x0UacvUqX19rbWbvzGGt3HiMqPIyZE9OYMMrG5DFJREVI+vKX/EsJIYQImOT4SC6fMZLLZ4yktb2LPYfqKCypZe+hOto6eub1Wzu6yS84Rn7BMcwmjfGj4sn1zusn2SICGEHwk0QvhBAiKERFWJgzMY05E9PodrpQRxopLKmloLSGekfPvL7T5WZfWQP7yhp49cNiRqXGeIf4UxiVGiPz+n1IohdCCBF0wswmJo1JZNKYRG67PIejJ5oprnCwZXcl5dWnlo49Ut3Mkepm3t5SRmJcOLneOvzjRyUQZpbn9SXRCyGECGqapjEqNZZLJqezbHoG9Y52Cr1b7X5W3nDKvH69o4P1uypYv6uCCKuZKWOTyMtJZkpWEtERlgBGETiS6IUQQoSUxLgIlk4fwdLpI2jr6Gavd15/z8E6Wju6fe3aO53s+OwEOz47gdmkMW5kvG8Vf3J8ZAAjGFqS6IUQQoSsyPAwZk1IZdaEVLqdLkqONvpW8dfa233tnC43B8obOFDewGvrShiREk1uTgp5OcmMTovFZOB5/SFN9Lqu/wy4AcgEpiilivpp8ydgaq9DU4EvKaXeHpJOCiGECElhZhMTMhOZkJnIrZflUFHT4qvDf/j4qfP6x2paOFbTwjtby4iPsfpW8E8YnYAlzFjz+kN9R/8W8D/ApjM1UErddfLPuq5PA9YD7w9+14QQQhiFpmmMGBbDiGExXDNvDA1NHez2zusfKK+n29kzr9/Y3MnGggo2FlQQbjUzeUwieTnJTM1KJiYy9Of1hzTRK6U2A+i67u9fuQd4VSnVcc6WQgghxBkkxIazOC+DxXkZtHV0s+9wPQUltew5WEtLe8+8fkenk52qhp2qBpOmkTPC5tt1b1hCVAAj+PyCdo5e13UrcBuwLNB9EUIIYRyR4WHMGD+MGeOH4XS5KD1mp6DEM69/orHN187ldqOONqKONvJ/60tJT44mL8fz6N6Y4XEhM6+vuXttIzhUdF0vA77Y3xx9rzY3Az9SSk0/z5fPBA5/7s4JIYS4KLndbo5WN7FtXxXb9lWhyhvO2DYhNpxZk9KYNSmNaTkphFvMQ9jTMxoDlPU9GLR39MDdwAuf9y/X1TXjcg3Mh5iUlFhqaprO3TAESCzBySixGCUOkFiC0VDEEWnWWDx1OIunDsfe3MHug3UUFNewv7yBrm6Xr11DUwfvf1LO+5+UY7WYmJSZSF5OClOzk4iLsp7z+wxkLCaTRlJSzBnPB2Wi13V9BLAAuDXQfRFCCHFxssWEs3BaOgunpdPR6WRfWb1vq93mti5fu84uFwUlnoV+mgbZGTbfVrtpiYGf1x/qx+t+BVwPpAFrdV2vU0pN0nX9PWCFUupTb9OvAv9QSp153EQIIYQYIuFWM9PHpTB9XAoul5vSCruvOl91fauvndsNJcfslByz88aGg6QlRpHnTfpj0+MwmYZ+Xj8gc/SDLBM4LEP3/ZNYgpNRYjFKHCCxBKNgjeN4XYt3851aDh6zc6bMExtlYVp2MnnZySycOYome9sZWp6fXkP3ITdHL4QQQgS94UnRDE+K5gtzRuNo6WT3Qc8K/n2H6+nsNa/f1NrF5j3H2bznOC+u+YzvXD+V7BG2Qe+fJHohhBBigMRFW1kwNZ0FU9Pp7HKyv6yBwtIaCkvrcLR0+to1tXaxbX+1JHohhBAiVFktZnK9z9273G4OVzooKKll98FaTCYTC3PTh6QfkuiFEEKIQWbSNLIybGRl2LhxcdaQrjcwVuV+IYQQQpxCEr0QQghhYJLohRBCCAOTRC+EEEIYmCR6IYQQwsAk0QshhBAGJoleCCGEMDBJ9EIIIYSBSaIXQgghDEwSvRBCCGFgkuiFEEIIAzNirXszePbnHUgD/XqBJLEEJ6PEYpQ4QGIJRkaJAwYull6vY+7vvOZ2uwfkGwWR+cCmQHdCCCGEGGILgM19Dxox0YcDM4HjgDPAfRFCCCEGmxkYDuwAOvqeNGKiF0IIIYSXLMYTQgghDEwSvRBCCGFgkuiFEEIIA5NEL4QQQhiYJHohhBDCwCTRCyGEEAYmiV4IIYQwMCOWwP1cdF0fB7wEJAF1wF1KqZI+bczAr4ArATfwtFLq+aHu67n4GctK4F+BSu+hLUqpbw9lP89F1/WfATcAmcAUpVRRP21C5Zr4E8tKgv+aJAEvA1lAJ1AC3KeUqunTLgr4I3AJ0A18Xyn1zhB396zOI5YXgWVArffQG0qpp4awq37Rdf0tYAzgApqB7yilCvu0CZX3iz+xrCTI3y8n6br+KLCSft77Q/FekTv6Hs8Cv1VKjQN+C/y+nza3A9lADjAXWKnreuaQ9dB//sQC8CelVK73v2B8g7wFLATKz9ImVK6JP7FA8F8TN/BTpZSulJoCHASe7qfd9wGHUiobuAZ4Xtf1mCHspz/8jQU8CfHkdQm6JO/1VaXUNKVUHvAz4IV+2oTK+8WfWCD43y/ouj4dmMOZ3/uD/l6RRA/ouj4MmA685j30GjBd1/WUPk2/AjynlHJ5P/W/Bdw0dD09t/OIJegppTYrpY6eo1nQXxPwO5agp5SqV0pt7HXoE2B0P02/gvcDpnc06VPgC4PewfNwHrGEBKWUvdeXNjx3w32FyvvFn1iCnq7r4Xhutv7lLM0G/b0iQ/ceI4EKpZQTQCnl1HW90nu89zDeKE79VHbE2yaY+BsLwC26ri8HqoBHlVIfD21XB0QoXJPzETLXRNd1E55fYG/3czqkrss5YgH4nq7r9+G5639AKXVgyDp3HnRdfx5YDmh4huf7Cpnr4kcsEPzvl8eBV5RSZbqun6nNoF8TuaO/eD0LjFFKTQWeAf7unbMUgRNq1+TXeOZPfxPojgyAs8XyEJDtHd5/E1jjnesOOkqpbyilRgEP4vkZCll+xBLU7xdd1+cCM4DfBbovkug9jgIZJ9+83v+ne4/3doRTh/ZG9dMm0PyKRSlVpZTq8v75Q+/5yUPc14EQCtfEL6F0TbyLC3OAryil+htWDZnrcq5YlFIVJ48rpf4ExAAjhraX50cp9TKwpJ/EFzLX5aQzxRIC75dFwATgsK7rZXh+Zt73jkD0NujXRBI9oJQ6ARQCt3oP3QoU9F19C7wB3Kvrusk75/0l4C9D19Nz8zcWXdczev05F89qcDVE3RxIQX9N/BUq10TX9VV4Vgh/SSl12paYXm8A93nb5+DZOnrN0PTQf/7E0ue6XIFn++uKoemhf3Rdj9F1fWSvr68B6r3/9Rb07xd/Ywn294tS6mmlVLpSKlMplQkcA65QSn3Qp+mgv1dkjr7Ht4CXdF1fATQAdwHouv4esEIp9SmeR3Fm43kMB+BxpdThQHT2HPyJZZWu65fg+aXVCdyplKoKVIf7o+v6r4DrgTRgra7rdUqpSaF4TfyMJRSuySTgAaAY2OqddzyslPqyruuFwFVKqUo8Q6kv6rpeiieebyqlmgLV7/6cRywv6bqeimdBmAO4VinVHah+n0E08Iau69F4/r3rgWuUUu4QfL/4G0vQv1/OZKjfK7IfvRBCCGFgMnQvhBBCGJgkeiGEEMLAJNELIYQQBiaJXgghhDAwSfRCCCGEgcnjdUIYkK7rX8OzI1Z/7Eqp+CHszilO7ganlArqojNCGIUkeiGM7SY8hTp6C7ZnwIUQg0gSvRDGVqiUKg10J4QQgSOJXoiLWK8h/kXA94BlQAfwOvB9pVRbr7bDgZ8AVwGxeMqN/lQp9Uqf1xyDZ9euy4EEPOVi/6GU+o8+7fKAX+EpQVsB/LdS6tle59O83+9yIAlPhbSdwN3eUs9CCD9IohfC2My6rvd9n7v62bzlFWA1np22ZgEr8JQi/RqAtxxpPp7E/SCeTTfuAF7WdT1KKfW/3nZjgO1Aq/c1SvBs0tF3I4844M/AL/F8KPg68P90XVdKqQ3eNi/j2ezjB97vlwpcBkR9nn8IIS5WkuiFMLbP+jn2LvDFPsfeU0p93/vnD3RddwOP67q+SilVjCcR5wBLlFIbve3+6a0B/6Su639QSjmBx4BIYJq3jvdJL/X5frHAv55M6rqufwRcgWcTppOJfi7woFLq1V5/7w2/ohZC+EiiF8LYvszpi/Ea+2m3us/XrwNP4rm7LwYWAhW9kvxJr+AZ+p8I7MVz5/5OnyTfn9Zed+4opTp0XS/Gc/d/0g7gB7qua8B6oEgpJZtzCHGeJNELYWxFfi7Gqz7D1ye3Ak0Ejvfz96p6nQfPXHrfDxb9aejnWAcQ0evrrwCPAj/EM8R/XNf1Z4En+9s3XgjRPymYI4QAz/x3f1+f3He9Hs8Wu32l9ToPUEvPh4MLopQ6oZT6tlIqAxgPvIhnauC+gXh9IS4WkuiFEAA39/n6Fjz7r2/zfp0PjNB1fV6fdrcBJ4D93q8/AL7oXaE/YJTHg3hGAiYP5GsLYXQydC+EseXqup7cz/FPlVK9C+dcpev6M3gS9Sw8Q+Z/UkqVeM+/CPwH8Kau6w/hGZ6/Hc+jb/d5F+Lh/XtXAVt1XV8FlOK5w79SKXWHv53Wdd0GrAVexbOgsAu4Ds+q/w/8fR0hhCR6IYzuTKvUU/AMs590B/BfwL8AncBzwMlV+CilWnRdXwT8FHianufo7+z9HL1SqkzX9Tl4FvL9GIjBM/z/9/PsdzuwC7gXzyN2Lu/3u10pdb6vJcRFTXO7ZRGrEBerXgVzcqSCnhDGJHP0QgghhIFJohdCCCEMTIbuhRBCCAOTO3ohhBDCwCTRCyGEEAYmiV4IIYQwMEn0QgghhIFJohdCCCEMTBK9EEIIYWD/H5zBA3lAW/XXAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WkN168nwdK0a"
      },
      "source": [
        "#### CNN using CONV+ POOLING Layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RPKgVzWMdOlr",
        "outputId": "eeec7e83-0b4c-45ed-c20a-d164eee432bd"
      },
      "source": [
        "model=Sequential()\n",
        "model.add(Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=(32, 32, 3)))\n",
        "model.add(MaxPool2D(pool_size=2))\n",
        "model.add(Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2))\n",
        "model.add(Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2))\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=512,activation='relu'))\n",
        "model.add(Dense(units=512,activation='relu'))\n",
        "model.add(Dense(units=10,activation='softmax'))\n",
        "model.summary()\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_3 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 512)               33280     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 306,154\n",
            "Trainable params: 306,154\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v9zFhOCxdOdH",
        "outputId": "7ce5cae4-b1c4-478b-efd1-b9b84bb4e2fe"
      },
      "source": [
        "#Compile & Train the model\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"SGD\", metrics=[\"accuracy\"])\n",
        "history = model.fit(x_train, y_train, validation_data=(x_valid,y_valid),\n",
        "                    batch_size=200, \n",
        "                    epochs=5,  \n",
        "                    verbose=1)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "225/225 [==============================] - 3s 10ms/step - loss: 2.3021 - accuracy: 0.1091 - val_loss: 2.3007 - val_accuracy: 0.1246\n",
            "Epoch 2/5\n",
            "225/225 [==============================] - 2s 8ms/step - loss: 2.2995 - accuracy: 0.1472 - val_loss: 2.2975 - val_accuracy: 0.1714\n",
            "Epoch 3/5\n",
            "225/225 [==============================] - 2s 9ms/step - loss: 2.2951 - accuracy: 0.1765 - val_loss: 2.2913 - val_accuracy: 0.1954\n",
            "Epoch 4/5\n",
            "225/225 [==============================] - 2s 9ms/step - loss: 2.2860 - accuracy: 0.1817 - val_loss: 2.2771 - val_accuracy: 0.1970\n",
            "Epoch 5/5\n",
            "225/225 [==============================] - 2s 9ms/step - loss: 2.2618 - accuracy: 0.1840 - val_loss: 2.2345 - val_accuracy: 0.1820\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "JK24Pkk2ebuq",
        "outputId": "623a2dad-18e9-4812-d816-9cb5930801a0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['loss'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_loss'],'b',linewidth=3.0)\n",
        "plt.legend(['Training loss', 'Validation Loss'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('Loss',fontsize=16)\n",
        "plt.title('Loss Curves',fontsize=16)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loss Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "aTR4tGoHeg_3",
        "outputId": "24349125-f937-465c-975e-2f314bfbeddd"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=[8,6])\n",
        "plt.plot(history.history['accuracy'],'r',linewidth=3.0)\n",
        "plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)\n",
        "plt.legend(['Training accuracy', 'Validation accuracy'],fontsize=18)\n",
        "plt.xlabel('Epochs ',fontsize=16)\n",
        "plt.ylabel('accuracy',fontsize=16)\n",
        "plt.title('Accuracy Curves',fontsize=16)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Curves')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKi7ba9QIQ1Y"
      },
      "source": [
        "## Build a CNN Architecture with Tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4B_E1oGjCORZ"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aPCcsEepIQ1Y",
        "outputId": "7e297bff-e4ed-4c5b-f24f-247160d3eeea"
      },
      "source": [
        "del model\n",
        "model= tf.keras.Sequential()\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=(32,32,3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "model.compile(optimizer='adam',loss=\"categorical_crossentropy\",metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 5,738\n",
            "Trainable params: 5,738\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qIuB3NXgC4-M",
        "outputId": "c17e596e-d373-49e1-9def-bf3143e808a4"
      },
      "source": [
        "x_train.shape,y_train.shape"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((45000, 32, 32, 3), (45000, 10))"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8Uz0oGZBzKM",
        "outputId": "d4b45a96-fd3b-442a-af36-2e6b43bedb91"
      },
      "source": [
        "answers=model.fit(x_train,y_train,\n",
        "                  validation_data=(x_valid,y_valid),\n",
        "                  shuffle=True,epochs=2,verbose=2,batch_size=200)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "225/225 - 2s - loss: 2.0232 - accuracy: 0.2423 - val_loss: 1.7990 - val_accuracy: 0.3332\n",
            "Epoch 2/2\n",
            "225/225 - 1s - loss: 1.7363 - accuracy: 0.3615 - val_loss: 1.6479 - val_accuracy: 0.4082\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LOzbnSYIIQ1h"
      },
      "source": [
        "### Stack an ANN on top of CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5xx53QCzEgGb",
        "outputId": "92ac48e1-fb92-4428-d1d9-46c7a60a0408"
      },
      "source": [
        "model= tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu',input_shape=(32,32,3)))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Conv2D(filters=16,kernel_size=3,activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D(pool_size=2))\n",
        "\n",
        "model.add(tf.keras.layers.Flatten())\n",
        "model.summary()\n",
        "\n",
        "### Stack an ANN on top of CNN\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "model.add(tf.keras.layers.Dense(units=512,activation='relu',kernel_initializer='uniform'))\n",
        "\n",
        "model.add(tf.keras.layers.Dense(units=10,activation='softmax',kernel_initializer='uniform'))\n",
        "\n",
        "model.compile(optimizer='adam',loss=\"categorical_crossentropy\",metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 64)                0         \n",
            "=================================================================\n",
            "Total params: 5,088\n",
            "Trainable params: 5,088\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 15, 15, 16)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 13, 13, 16)        2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 4, 4, 16)          2320      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 2, 2, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 512)               33280     \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 1,356,778\n",
            "Trainable params: 1,356,778\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2imNDJGJIQ1i"
      },
      "source": [
        "### Fit the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jXI9_aPIQ1i",
        "outputId": "fbcde101-db3c-4e69-fc19-3d1e2acf9fbd"
      },
      "source": [
        "answers=model.fit(x_train,y_train,validation_data=(x_valid,y_valid),shuffle=True,epochs=2,verbose=2,batch_size=500)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "90/90 - 3s - loss: 2.0679 - accuracy: 0.1881 - val_loss: 1.8366 - val_accuracy: 0.2884\n",
            "Epoch 2/2\n",
            "90/90 - 1s - loss: 1.7681 - accuracy: 0.3226 - val_loss: 1.7111 - val_accuracy: 0.3486\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiRWlQqDIQ1j"
      },
      "source": [
        "### Evaluate the Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LhuJ29CiIQ1j",
        "outputId": "4d3a39b9-dff4-4cb1-ee32-bcb60c00315c"
      },
      "source": [
        "results=model.evaluate(x_test,y_test)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 2s 5ms/step - loss: 1.7198 - accuracy: 0.3396\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mcqb_EtKIQ1j",
        "outputId": "d35b01ac-2aff-4f50-d23f-1394d905201a"
      },
      "source": [
        "answers.history.keys()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "_rVwanJ-IQ1k",
        "outputId": "33cebe57-2b31-4ac1-c59a-a936165d69cf"
      },
      "source": [
        "plt.plot(answers.history['val_loss'],label='Validation Loss');\n",
        "plt.plot(answers.history['loss'],label='Training Loss');\n",
        "plt.legend();"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "3aNT7bD2IQ1k",
        "outputId": "acf0cd1a-a6fd-4440-f3c5-2ecd1a3a6593"
      },
      "source": [
        "plt.plot(answers.history['val_accuracy'],label='Validation Accuracy');\n",
        "plt.plot(answers.history['accuracy'],label='Training Accuracy');\n",
        "plt.legend;"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSMxpL1hg2f_"
      },
      "source": [
        "# Approch -3 CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0QS6JJGg55I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "006e4189-f6a8-4b40-ccd2-9b3f2fa74fde"
      },
      "source": [
        "from keras.layers import Conv2D, Flatten\n",
        "simple_cnn_model = Sequential()\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), input_shape=(32,32,3), activation='relu'))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(Flatten())\n",
        "simple_cnn_model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "simple_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "simple_cnn_model_history = simple_cnn_model.fit(x_train,y_train,validation_data=(x_valid,y_valid), batch_size=100, epochs=8)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "450/450 [==============================] - 7s 14ms/step - loss: 1.5129 - accuracy: 0.4628 - val_loss: 1.2587 - val_accuracy: 0.5552\n",
            "Epoch 2/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 1.1430 - accuracy: 0.5991 - val_loss: 1.0905 - val_accuracy: 0.6188\n",
            "Epoch 3/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.9728 - accuracy: 0.6629 - val_loss: 1.0174 - val_accuracy: 0.6484\n",
            "Epoch 4/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.8514 - accuracy: 0.7045 - val_loss: 1.0003 - val_accuracy: 0.6560\n",
            "Epoch 5/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.7596 - accuracy: 0.7375 - val_loss: 1.0021 - val_accuracy: 0.6580\n",
            "Epoch 6/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.6682 - accuracy: 0.7703 - val_loss: 1.0266 - val_accuracy: 0.6504\n",
            "Epoch 7/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.5835 - accuracy: 0.8015 - val_loss: 1.0828 - val_accuracy: 0.6514\n",
            "Epoch 8/8\n",
            "450/450 [==============================] - 6s 14ms/step - loss: 0.5018 - accuracy: 0.8280 - val_loss: 1.1658 - val_accuracy: 0.6534\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQYO6gFHiPBx"
      },
      "source": [
        "# CNN - Experiment - 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lr54kkR0hkjQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f9f3e73-bf32-483a-f7ea-099754d88e25"
      },
      "source": [
        "from keras.layers import Conv2D, Flatten\n",
        "simple_cnn_model = Sequential()\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), input_shape=(32,32,3), activation='relu'))\n",
        "simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "simple_cnn_model.add(Flatten())\n",
        "simple_cnn_model.add(Dense(10, activation='softmax'))\n",
        "\n",
        "simple_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "simple_cnn_model_history = simple_cnn_model.fit(x_train,y_train,validation_data=(x_valid,y_valid), batch_size=100, epochs=8)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "450/450 [==============================] - 5s 9ms/step - loss: 1.7349 - accuracy: 0.3669 - val_loss: 1.4703 - val_accuracy: 0.4802\n",
            "Epoch 2/8\n",
            "450/450 [==============================] - 4s 9ms/step - loss: 1.3857 - accuracy: 0.5042 - val_loss: 1.3257 - val_accuracy: 0.5392\n",
            "Epoch 3/8\n",
            "450/450 [==============================] - 5s 10ms/step - loss: 1.2517 - accuracy: 0.5576 - val_loss: 1.1804 - val_accuracy: 0.5860\n",
            "Epoch 4/8\n",
            "450/450 [==============================] - 5s 10ms/step - loss: 1.1704 - accuracy: 0.5889 - val_loss: 1.1412 - val_accuracy: 0.6046\n",
            "Epoch 5/8\n",
            "450/450 [==============================] - 5s 10ms/step - loss: 1.1114 - accuracy: 0.6082 - val_loss: 1.1074 - val_accuracy: 0.6132\n",
            "Epoch 6/8\n",
            "450/450 [==============================] - 5s 10ms/step - loss: 1.0627 - accuracy: 0.6279 - val_loss: 1.0628 - val_accuracy: 0.6348\n",
            "Epoch 7/8\n",
            "450/450 [==============================] - 5s 10ms/step - loss: 1.0302 - accuracy: 0.6405 - val_loss: 1.0204 - val_accuracy: 0.6522\n",
            "Epoch 8/8\n",
            "450/450 [==============================] - 5s 11ms/step - loss: 0.9917 - accuracy: 0.6546 - val_loss: 1.0281 - val_accuracy: 0.6462\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DUY2Pfq2ExAx"
      },
      "source": [
        "# Alternative Approach\n",
        "https://www.kaggle.com/aayushkubba/cifar-10-analysis-with-a-neural-network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjKiDfx1RHaW"
      },
      "source": [
        "## Data Augmentation \n",
        "To get a more generalised model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sH_vWoqKRHJv"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNOTqkxnT5Nz"
      },
      "source": [
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    horizontal_flip=True, vertical_flip=True,\n",
        "    validation_split=0.2)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9OonWCnUmhg",
        "outputId": "25340b64-4023-4fc8-c879-85fbc224688d"
      },
      "source": [
        "type(datagen)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "keras.preprocessing.image.ImageDataGenerator"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pX0qaJYMT8h-"
      },
      "source": [
        "from keras.layers import Conv2D, Flatten\n",
        "augmented_simple_cnn_model = Sequential()\n",
        "augmented_simple_cnn_model.add(Conv2D(32, (3,3), input_shape=(32,32,3), activation='relu'))\n",
        "augmented_simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "augmented_simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "augmented_simple_cnn_model.add(MaxPool2D(pool_size=2))\n",
        "augmented_simple_cnn_model.add(Conv2D(32, (3,3), activation='relu'))\n",
        "augmented_simple_cnn_model.add(Flatten())\n",
        "augmented_simple_cnn_model.add(Dense(10, activation='softmax'))\n"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRyeNo5kT8YR",
        "outputId": "b40bd90b-cc61-487f-8cc7-b52b17376e7d"
      },
      "source": [
        "augmented_simple_cnn_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "augmented_simple_cnn_model.summary()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_18 (Conv2D)           (None, 30, 30, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 15, 15, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 13, 13, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_12 (MaxPooling (None, 6, 6, 32)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 4, 4, 32)          9248      \n",
            "_________________________________________________________________\n",
            "flatten_6 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 10)                5130      \n",
            "=================================================================\n",
            "Total params: 24,522\n",
            "Trainable params: 24,522\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p1t9_xnOVMNe"
      },
      "source": [
        ""
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TrpgpQuPUcj1",
        "outputId": "d78c4820-2cd0-4161-93ec-f71ec4693b8f"
      },
      "source": [
        "augmented_simple_cnn_model_history=augmented_simple_cnn_model.fit_generator(datagen.flow(x_train,y_train,batch_size=256),\n",
        "                                                                            validation_data=(x_valid,y_valid),\n",
        "                                                                            epochs=8)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "176/176 [==============================] - 29s 163ms/step - loss: 2.0051 - accuracy: 0.2455 - val_loss: 1.8103 - val_accuracy: 0.3188\n",
            "Epoch 2/8\n",
            "176/176 [==============================] - 28s 160ms/step - loss: 1.7949 - accuracy: 0.3300 - val_loss: 1.6906 - val_accuracy: 0.3652\n",
            "Epoch 3/8\n",
            "176/176 [==============================] - 28s 161ms/step - loss: 1.7295 - accuracy: 0.3566 - val_loss: 1.7348 - val_accuracy: 0.3644\n",
            "Epoch 4/8\n",
            "176/176 [==============================] - 28s 162ms/step - loss: 1.6892 - accuracy: 0.3732 - val_loss: 1.6755 - val_accuracy: 0.3856\n",
            "Epoch 5/8\n",
            "176/176 [==============================] - 29s 162ms/step - loss: 1.6600 - accuracy: 0.3874 - val_loss: 1.6162 - val_accuracy: 0.4008\n",
            "Epoch 6/8\n",
            "176/176 [==============================] - 29s 165ms/step - loss: 1.6308 - accuracy: 0.4021 - val_loss: 1.5758 - val_accuracy: 0.4102\n",
            "Epoch 7/8\n",
            "176/176 [==============================] - 29s 164ms/step - loss: 1.6019 - accuracy: 0.4112 - val_loss: 1.5338 - val_accuracy: 0.4398\n",
            "Epoch 8/8\n",
            "176/176 [==============================] - 29s 164ms/step - loss: 1.5862 - accuracy: 0.4200 - val_loss: 1.4574 - val_accuracy: 0.4640\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9Fx0rvdf1vy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "6abad509-e32a-436f-b92c-09b27bf890e0"
      },
      "source": [
        "plt.plot(augmented_simple_cnn_model_history.history['val_loss'],label='Validation Loss');\n",
        "plt.plot(augmented_simple_cnn_model_history.history['loss'],label='Training Loss');\n",
        "plt.legend();"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "xt6eM7lTWNtm",
        "outputId": "7664ae10-715f-4aff-866b-97bbb35260de"
      },
      "source": [
        "plt.plot(augmented_simple_cnn_model_history.history['val_accuracy'],label='Validation Accuracy');\n",
        "plt.plot(augmented_simple_cnn_model_history.history['accuracy'],label='Training Accuracy');\n",
        "plt.legend;"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9Q5v7QVW0Mc"
      },
      "source": [
        "# Transfer Learning\n",
        "\n",
        "## RESNET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDGxeAO1WSkP"
      },
      "source": [
        "from keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from keras.layers import GlobalAveragePooling2D, Dropout"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ofb6UpmMXVaW",
        "outputId": "6205bc29-53ca-42e3-a893-bf518118d6b0"
      },
      "source": [
        "# RESNET as the base model\n",
        "base_model=tf.keras.applications.ResNet50(weights='imagenet',include_top=False)\n",
        "\n",
        "# We will only use resnet for feature extarction, freeze all the layers that we dont need\n",
        "for layer in base_model.layers:\n",
        "  layer.trainable=False\n",
        "\n",
        "# Take the output output of the base layers\n",
        "base_model_output=base_model.output\n",
        "\n",
        "# Add our layers\n",
        "## Pooling\n",
        "x=GlobalAveragePooling2D()(base_model_output)\n",
        "## FC\n",
        "x=Dense(2056,activation='relu')(x)\n",
        "## Dropout\n",
        "x=Dropout(0.5)(x)\n",
        "## Output layer\n",
        "x=Dense(10,activation='softmax')(x)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 1s 0us/step\n",
            "94781440/94765736 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MWd7ZdXLYf0z"
      },
      "source": [
        "resnet_model=Model(inputs=base_model.input,outputs=x)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ymI24TLPZqFk",
        "outputId": "e67b8303-fb8d-464a-88f2-3297794ce775"
      },
      "source": [
        "resnet_model.summary()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, None, None, 3 0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, None, None, 6 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, None, None, 6 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, None, None, 6 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, None, None, 6 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, None, None, 6 0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, None, None, 6 4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, None, None, 6 256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, None, None, 6 0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, None, None, 6 256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, None, None, 6 0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, None, None, 2 16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, None, None, 2 0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, None, None, 2 0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, None, None, 6 256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, None, None, 6 0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, None, None, 6 256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, None, None, 6 0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, None, None, 2 0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, None, None, 2 0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, None, None, 6 16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, None, None, 6 256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, None, None, 6 0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, None, None, 6 36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, None, None, 6 256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, None, None, 6 0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, None, None, 2 16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, None, None, 2 1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, None, None, 2 0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, None, None, 2 0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, None, None, 1 32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, None, None, 1 512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, None, None, 1 0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, None, None, 1 512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, None, None, 1 0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, None, None, 5 131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, None, None, 5 0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, None, None, 5 0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, None, None, 1 512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, None, None, 1 0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, None, None, 1 512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, None, None, 1 0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, None, None, 5 0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, None, None, 5 0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, None, None, 1 512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, None, None, 1 0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, None, None, 1 512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, None, None, 1 0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, None, None, 5 0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, None, None, 5 0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, None, None, 1 65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, None, None, 1 512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, None, None, 1 0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, None, None, 1 147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, None, None, 1 512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, None, None, 1 0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, None, None, 5 66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, None, None, 5 2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, None, None, 5 0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, None, None, 5 0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, None, None, 2 131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, None, None, 2 0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, None, None, 2 0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, None, None, 1 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, None, None, 1 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, None, None, 1 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, None, None, 2 0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, None, None, 2 0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, None, None, 1 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, None, None, 1 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, None, None, 2 0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, None, None, 2 0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, None, None, 1 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, None, None, 1 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, None, None, 2 0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, None, None, 2 0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, None, None, 1 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, None, None, 1 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, None, None, 2 0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, None, None, 2 0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, None, None, 1 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, None, None, 1 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, None, None, 2 262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, None, None, 2 0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, None, None, 2 590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, None, None, 2 1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, None, None, 2 0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, None, None, 1 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, None, None, 1 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, None, None, 1 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, None, None, 1 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, None, None, 5 524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, None, None, 5 0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, None, None, 5 0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, None, None, 2 2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, None, None, 2 0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, None, None, 2 0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, None, None, 5 0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, None, None, 5 0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, None, None, 2 0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, None, None, 2 0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, None, None, 5 1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, None, None, 5 0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, None, None, 5 2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, None, None, 5 2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, None, None, 5 0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, None, None, 2 1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, None, None, 2 8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, None, None, 2 0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, None, None, 2 0           conv5_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d (Globa (None, 2048)         0           conv5_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_18 (Dense)                (None, 2056)         4212744     global_average_pooling2d[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 2056)         0           dense_18[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dense_19 (Dense)                (None, 10)           20570       dropout[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 27,821,026\n",
            "Trainable params: 4,233,314\n",
            "Non-trainable params: 23,587,712\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlTnOU_BaXUF"
      },
      "source": [
        "resnet_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWrUEnk4bAF9"
      },
      "source": [
        "For reference\n",
        "\n",
        "Without freezing\n",
        "\n",
        "Epoch 1/8\n",
        "176/176 [==============================] - 50s 238ms/step - loss: 1.4530 - accuracy: 0.5499 - val_loss: 4.2816 - val_accuracy: 0.1038\n",
        "Epoch 2/8"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiE8NFZ-ZvJd",
        "outputId": "e40ccbb7-5a86-4051-ef79-48a2e268c945"
      },
      "source": [
        "resnet_model_history=resnet_model.fit(x_train,y_train,batch_size=256,\n",
        "                                      validation_data=(x_valid,y_valid),epochs=4,shuffle=True)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "176/176 [==============================] - 22s 96ms/step - loss: 2.3252 - accuracy: 0.1971 - val_loss: 2.0100 - val_accuracy: 0.2700\n",
            "Epoch 2/4\n",
            "176/176 [==============================] - 13s 76ms/step - loss: 2.0098 - accuracy: 0.2628 - val_loss: 1.9381 - val_accuracy: 0.2896\n",
            "Epoch 3/4\n",
            "176/176 [==============================] - 13s 76ms/step - loss: 1.9690 - accuracy: 0.2762 - val_loss: 1.8977 - val_accuracy: 0.3228\n",
            "Epoch 4/4\n",
            "176/176 [==============================] - 13s 77ms/step - loss: 1.9488 - accuracy: 0.2876 - val_loss: 1.8994 - val_accuracy: 0.3206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mubk7CZ-LNDq"
      },
      "source": [
        "# VGGNET"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1aA37086LVSv"
      },
      "source": [
        "from keras.models import Model, load_model\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input\n",
        "from keras.layers import GlobalAveragePooling2D, Dropout, BatchNormalization"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oknJhnLbaRxC",
        "outputId": "51cab5d2-39d4-4c70-c60d-054b7c4048a8"
      },
      "source": [
        "\n",
        "\n",
        "# RESNET as the base model\n",
        "base_model=tf.keras.applications.VGG16(weights='imagenet',include_top=False)\n",
        "\n",
        "# We will only use resnet for feature extarction, freeze all the layers that we dont need\n",
        "for layer in base_model.layers:\n",
        "  layer.trainable=False\n",
        "\n",
        "# Take the output output of the base layers\n",
        "base_model_output=base_model.output\n",
        "\n",
        "# Add our layers\n",
        "## Pooling\n",
        "x=GlobalAveragePooling2D()(base_model_output)\n",
        "## FC\n",
        "x=Dense(2056,activation='relu')(x)\n",
        "## BAtch Normalisation - For Speed\n",
        "x=  BatchNormalization()(x)\n",
        "## Dropout\n",
        "x=Dropout(0.5)(x)\n",
        "## Output layer\n",
        "x=Dense(10,activation='softmax')(x)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n",
            "58900480/58889256 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tkek21GiM3TR"
      },
      "source": [
        "vgg_model=Model(inputs=base_model.input,outputs=x)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDilnNBgM_MN"
      },
      "source": [
        "vgg_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJXc2uVYNDSU",
        "outputId": "ec1e9bbf-fd1d-4559-d127-8005ce87d800",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "vgg_model_history=vgg_model.fit(x_train,y_train,batch_size=256,\n",
        "                                      validation_data=(x_valid,y_valid),epochs=40,shuffle=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "176/176 [==============================] - 12s 71ms/step - loss: 1.1518 - accuracy: 0.5968 - val_loss: 1.1623 - val_accuracy: 0.5992\n",
            "Epoch 2/40\n",
            "176/176 [==============================] - 12s 71ms/step - loss: 1.1361 - accuracy: 0.6003 - val_loss: 1.1809 - val_accuracy: 0.5882\n",
            "Epoch 3/40\n",
            "176/176 [==============================] - 13s 71ms/step - loss: 1.1169 - accuracy: 0.6064 - val_loss: 1.1435 - val_accuracy: 0.5986\n",
            "Epoch 4/40\n",
            "176/176 [==============================] - 12s 71ms/step - loss: 1.1011 - accuracy: 0.6147 - val_loss: 1.1503 - val_accuracy: 0.6046\n",
            "Epoch 5/40\n",
            "176/176 [==============================] - 12s 70ms/step - loss: 1.0844 - accuracy: 0.6202 - val_loss: 1.1538 - val_accuracy: 0.5988\n",
            "Epoch 6/40\n",
            "123/176 [===================>..........] - ETA: 3s - loss: 1.0666 - accuracy: 0.6253"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_utBU3CTNG0o"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}